From 4e0dc89c20e1b10c0bc8bb0bf0ab277197d7a903 Mon Sep 17 00:00:00 2001 From: campbelle1 Date: Tue, 22 Aug 2023 18:17:44 -0500 Subject: [PATCH] updates - 3.3, 4.4, 9, 15 --- textbook/03/3/DataTypes.ipynb | 175 ++- textbook/04/3/Arrays-Intro.ipynb | 653 ++++++++- textbook/04/3/img/flowers_array3.png | Bin 0 -> 26297 bytes textbook/04/4/Arrays-Slicing.ipynb | 267 ---- ...raries.ipynb => Intro-to-Matplotlib.ipynb} | 33 +- textbook/09/2/Categorical_Data.ipynb | 920 ------------ textbook/09/2/Numerical_Data.ipynb | 1234 +++++++++++++++++ textbook/09/2/img/boxandwhisker.png | Bin 68181 -> 0 bytes textbook/09/3/Categorical_Data.ipynb | 988 +++++++++++++ textbook/09/3/Numerical_Data.ipynb | 779 ----------- textbook/09/4/img/BoxandWhisker.png | Bin 0 -> 52323 bytes textbook/09/4/img/median(even).png | Bin 0 -> 46171 bytes textbook/09/4/img/median(odd).png | Bin 0 -> 55118 bytes textbook/09/4/other-viz.ipynb | 1021 ++++++++++++++ textbook/09/data-visualization.md | 2 +- textbook/15/1/ethics-and-law.ipynb | 4 +- textbook/15/{5 => 2}/img/Dresseletal.png | Bin .../15/{5 => 2}/img/indiana-eugenics.jpeg | Bin textbook/15/2/img/lifecycleofdata.png | Bin 453059 -> 0 bytes .../15/{5 => 2}/img/virginia-eugenics.jpeg | Bin textbook/15/2/pillar1.ipynb | 92 +- textbook/15/5/img/lifecycleofdata.png | Bin 0 -> 507162 bytes textbook/15/{2 => 5}/img/trust.png | Bin textbook/15/5/pillar4.ipynb | 109 +- textbook/_toc.yml | 22 +- 25 files changed, 4161 insertions(+), 2138 deletions(-) create mode 100644 textbook/04/3/img/flowers_array3.png delete mode 100644 textbook/04/4/Arrays-Slicing.ipynb rename textbook/09/1/{Libraries.ipynb => Intro-to-Matplotlib.ipynb} (91%) delete mode 100644 textbook/09/2/Categorical_Data.ipynb create mode 100644 textbook/09/2/Numerical_Data.ipynb delete mode 100644 textbook/09/2/img/boxandwhisker.png create mode 100644 textbook/09/3/Categorical_Data.ipynb delete mode 100644 textbook/09/3/Numerical_Data.ipynb create mode 100644 textbook/09/4/img/BoxandWhisker.png create mode 100644 textbook/09/4/img/median(even).png create mode 100644 textbook/09/4/img/median(odd).png create mode 100644 textbook/09/4/other-viz.ipynb rename textbook/15/{5 => 2}/img/Dresseletal.png (100%) rename textbook/15/{5 => 2}/img/indiana-eugenics.jpeg (100%) delete mode 100644 textbook/15/2/img/lifecycleofdata.png rename textbook/15/{5 => 2}/img/virginia-eugenics.jpeg (100%) create mode 100644 textbook/15/5/img/lifecycleofdata.png rename textbook/15/{2 => 5}/img/trust.png (100%) diff --git a/textbook/03/3/DataTypes.ipynb b/textbook/03/3/DataTypes.ipynb index c6ff0c73..e385748f 100644 --- a/textbook/03/3/DataTypes.ipynb +++ b/textbook/03/3/DataTypes.ipynb @@ -8,7 +8,7 @@ "# Data Types\n", "*Evelyn Campbell, Ph.D.*\n", "\n", - "Python offers a number of different data types that can be manipulated and used by various functions. Some important built-in Python data types include booleans, strings, integers, and floats. These data types can be used to build various data structures, such as lists, dictionaries, arrays, and dataframes, which will be covered in Chapters 4 and 6. Here we will explore each data type and corresponding functions that are useful when working with these data types." + "Python offers a number of different data types that can be manipulated and used by various functions. Some important built-in Python data types include **booleans**, **strings**, **integers**, and **floats**. These data types can be used to build various data structures, such as lists, dictionaries, arrays, and dataframes, which will be covered in Chapters [4](../4/DataStructures.ipynb) and [6](../6/DataFrames.ipynb). Here we will explore each data type and corresponding functions that are useful when working with these data types." ] }, { @@ -18,12 +18,12 @@ "source": [ "## Booleans\n", "\n", - "Booleans are a data type that consists of two possible outcomes: `True` or `False`. Under the hood, these values take on a binary value, where `True` is equal to 1 and `False` is equal to 0. Booleans are very commonly used with comparison operators, and because they also can have a numeric meaning, they can be used in calculations as well. Let's start with a simple example of a Boolean." + "Booleans are a data type that consist of two possible outcomes: `True` or `False`. Under the hood, these values take on a binary value, where `True` is equal to 1 and `False` is equal to 0. Booleans are very commonly used with comparison operators ([discussed more in section 3.4](../3/4/Comparisons.ipynb)), and because they also can have a numeric meaning, they can be used in calculations as well. Let's start with a simple example of a Boolean." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "id": "0856f3c4", "metadata": {}, "outputs": [ @@ -33,19 +33,29 @@ "False" ] }, - "execution_count": 1, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "boolval = 2 + 5 < 3 + 1\n", + "boolval = 5 < 3\n", "boolval" ] }, + { + "cell_type": "markdown", + "id": "061f80a6", + "metadata": {}, + "source": [ + "Above, the variable `boolval` is equated to the expression `5 < 3`, which reads \"5 is less than 3.\" Because 5 is not in fact less than 3, the entire statement is `False`, and this Boolean value is assigned to `boolval`.\n", + "\n", + "Below, we add 5 to the value of `boolval`. Recall that `False` has a numerical value of 0, so essentially, `boolval + 5` is the same as 0 + 5:" + ] + }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "id": "9474bdc6", "metadata": {}, "outputs": [ @@ -55,7 +65,7 @@ "5" ] }, - "execution_count": 2, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -65,6 +75,14 @@ "boolval" ] }, + { + "cell_type": "markdown", + "id": "071ba5da", + "metadata": {}, + "source": [ + "Using the variable directly in a comparison expression, we can see that the value of `boolval` is less than 10, and thus returns another Boolean value of `True`:" + ] + }, { "cell_type": "code", "execution_count": 3, @@ -91,7 +109,9 @@ "id": "37df7040", "metadata": {}, "source": [ - "The `bool()` function converts an input (i.e. a numeric value, string, or even data structures) to a boolean value." + "Python has built-in **functions** that use values and variables as input to perform a task and produce an output. We have already used some basic functions, such as the `print()` function, and we will learn about a few more that are associated with datatypes. Built-in functions will be further discussed in section [section 3.4](../3/5/IntroFunctions.ipynb). \n", + "\n", + "For now, we will use a few basic functions associated with data types. The `bool()` function converts an input (i.e. a numeric value, string, or even data structures) to a boolean value." ] }, { @@ -127,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "id": "96368899", "metadata": {}, "outputs": [ @@ -142,7 +162,7 @@ ], "source": [ "something = 6542\n", - "nothing = 0 # an empty list\n", + "nothing = 0\n", "print(bool(something))\n", "print(bool(nothing))" ] @@ -154,12 +174,14 @@ "source": [ "## Strings\n", "\n", - "Strings are a data type that can consist of concatenated alphanumeric and punctuation characters. Strings are recognized by Python through the use of single (' ') or double (\" \") quotation marks. " + "A **string** a data type that can consist of **concatenated** alphanumeric and punctuation characters. According to the Merriam-Webster dictionary, to concatenate means *to link together in a series or chain*.\n", + "\n", + "Strings are recognized by Python through the use of single (' '), double (\" \"), or triple (''' ''') quotation marks. " ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "9868b82e", "metadata": {}, "outputs": [ @@ -186,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "4e147421", "metadata": {}, "outputs": [ @@ -204,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "5b4ec625", "metadata": { "tags": [ @@ -214,10 +236,10 @@ "outputs": [ { "ename": "SyntaxError", - "evalue": "unterminated string literal (detected at line 1) (3546504085.py, line 1)", + "evalue": "invalid syntax (3546504085.py, line 1)", "output_type": "error", "traceback": [ - "\u001b[0;36m Cell \u001b[0;32mIn [8], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m print('This isn't easy.')\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unterminated string literal (detected at line 1)\n" + "\u001b[0;36m Input \u001b[0;32mIn [9]\u001b[0;36m\u001b[0m\n\u001b[0;31m print('This isn't easy.')\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], @@ -225,17 +247,75 @@ "print('This isn't easy.')" ] }, + { + "cell_type": "markdown", + "id": "101ed527", + "metadata": {}, + "source": [ + "The above error can be fixed by an **escape sequence**. Escape sequences are string modifiers that allow for the use of certain characters that would otherwise be misinterpreted by Python. Because strings are created by the use of quotes, the escape sequences `\\'` and `\\\"` allow for the use of quotes as part of a string:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "18da1844", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This isn't easy.\n" + ] + } + ], + "source": [ + "print('This isn\\'t easy.')" + ] + }, + { + "cell_type": "markdown", + "id": "2dae3eff", + "metadata": {}, + "source": [ + "Other useful escape sequences include `\\n` and `\\t`. These allow for a new line and tab spacing to be added to a string, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "241848e8", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is the first sentence \n", + "This is the second sentence! \tThis is the third sentence?\n" + ] + } + ], + "source": [ + "sentences = '''This is the first sentence \\nThis is the second sentence! \\tThis is the third sentence?'''\n", + "print(sentences)" + ] + }, { "cell_type": "markdown", "id": "a46d5107", "metadata": {}, "source": [ - "Strings can be used in simple additive mathematical operations, like addition and multiplication." + "Strings can be used in simple additive mathematical operations, like addition and multiplication, resulting in concatenation of the strings:" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "3b40b622", "metadata": {}, "outputs": [ @@ -265,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "c57fbd1b", "metadata": {}, "outputs": [ @@ -283,12 +363,45 @@ "print(words, words)" ] }, + { + "cell_type": "markdown", + "id": "ca3e1bc1", + "metadata": {}, + "source": [ + "Escape sequences also can be used in the `print()` function as an argument or through concatenation:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "74b515d1", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is a sentence. \t This isn't easy.\n", + "\n", + "\n", + "This isn't easy.\tThis is a sentence.\n" + ] + } + ], + "source": [ + "print(words, '\\t', 'This isn\\'t easy.') # Escape sequence used as an argument in the print function\n", + "print('\\n') # Escape sequence used to print a blank line\n", + "print('This isn\\'t easy.' + '\\t' + words) # Escape sequence concatenated to strings in the print function" + ] + }, { "cell_type": "markdown", "id": "396964f1", "metadata": {}, "source": [ - "When manipulating string variables, data scientist will often used what are called *methods*. A method is piece of code that is associated with a defined variable, as opposed to a *function* which uses defined variables as input parameters. Functions will be further discussed in the upcoming section.\n", + "When manipulating string variables, data scientists will often use what are called **methods**. A method is piece of code that is associated with a defined variable, as opposed to a **function** which uses defined variables as input arguments for parameters. Functions will be further discussed in the upcoming section.\n", "\n", "\n", "Some methods can be used on strings to quickly and efficiently alter them. A few include the `.upper()`, `.lower()`, `.capitalize()`, `.title()`, and `.swapcase()` methods. There are many others, but these few are great to start exploring the different ways string variables can be manipulated:" @@ -382,7 +495,7 @@ "id": "01d68864", "metadata": {}, "source": [ - "We can confirm that these are indeed strings by calling these variables into the `type()` function, which can be used on any variable to check its data type." + "We can confirm that these are indeed strings by calling the `type()` function on these variables, which can be used on any variable to check its data type." ] }, { @@ -412,7 +525,7 @@ "id": "c8160dd5", "metadata": {}, "source": [ - "Keep in mind that when a numerical value is converted to a string, it can no longer be used to perform advanced mathematical calculations, such as division, subtraction, or exponentiation." + "Keep in mind that when a numerical value is converted to a string, it can no longer be used to perform certain mathematical calculations, such as division, subtraction, or exponentiation." ] }, { @@ -485,7 +598,7 @@ "source": [ "## Integers & Floats\n", "\n", - "Integers and floats are numerical data types that are often used to perform mathematical operations. Integers consists of whole numbers, while floats consists of whole numbers with floating decimal places. Floats can hold up to 15 significant figures following the decimal point and can be used to obtain more accurate calculations. However, it is easier and faster for a computer to do calculations using integers. Thus, one must weigh the pros and cons of using these data types when doing calculations and writing functions to obtain outcomes that are most aligned with their end goals. Let's take a look at these data types in use.\n" + "Integers and floats are numerical data types that are often used to perform mathematical operations. Integers consist of whole numbers, while floats consist of whole numbers with floating decimal places. Floats can hold up to 15 significant figures following the decimal point and can be used to obtain more accurate calculations. However, it is easier and faster for a computer to do calculations using integers. Thus, one must weigh the pros and cons of using these data types when doing calculations and writing functions to obtain outcomes that are most aligned with their end goals. Let's take a look at these data types in use.\n" ] }, { @@ -596,7 +709,7 @@ "id": "0b7c63c9", "metadata": {}, "source": [ - "We can see that the conversion of an integer to a float simply adds one significant figure after the decimal place. Moreover, converting a float to an integer rounds the number down to the nearest whole number. We can also convert numerical values in strings and boolean data types to integers and floats" + "We can see that the conversion of an integer to a float simply adds one significant figure after the decimal place. Moreover, converting a float to an integer rounds the number *down* to the nearest whole number. We can also convert numerical values in strings and boolean data types to integers and floats" ] }, { @@ -662,10 +775,16 @@ "id": "e37e3887", "metadata": {}, "source": [ - "## Conclusions\n", - "\n", - "In this section, we learned about various different data types. These include the `boolean`, `string`, `int`, and `float` data types. As you become more acquainted with Python, you will see the ubiquity of these data types in many data structures, which we will discuss in upcoming chapters. For now, explore these data types and relevant functions to learn how and when these data types can be used. Happy coding!\n" + "By understanding data types, we can begin to use them in other analyses and functionalities in Python. Next, we will learn how to use data types in comparisons, which can help further down the line in functions ([Chapter 3.5](../3/5/IntroFunctions.ipynb)), for loops ([Chapter 5.3](../../05/3/Control_Statements_Iteration.ipynb)), and subsetting data from DataFrames ([Chapter 5.3](../../06/6/Select_Condition.ipynb))." ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23d5baaa", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -684,7 +803,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/textbook/04/3/Arrays-Intro.ipynb b/textbook/04/3/Arrays-Intro.ipynb index b9da0849..a07dea81 100644 --- a/textbook/04/3/Arrays-Intro.ipynb +++ b/textbook/04/3/Arrays-Intro.ipynb @@ -14,7 +14,7 @@ "id": "e72d9bce", "metadata": {}, "source": [ - "An *array* is a data structure that consists of a collection of elements organized into a grid-like shape. In Python, arrays can be one-dimensional, akin to a list, or multidimensional (2D, 3D, etc.). However, unlike a list, an array consists of elements that are all of the same data type. This makes arrays ideal for convienent for storage and fast manipulation of data elements. Arrays are offered through the `NumPy` library, and are often used in conjunction with other Python libraries, such as `pandas`, `SciPy`, and `Scikit-learn`. We will explore arrays in this section, along with commonly used functions used with arrays." + "An *array* is a data structure that consists of a collection of elements organized into a grid-like shape. In Python, arrays can be one-dimensional, akin to a list, or multidimensional (2D, 3D, etc.). However, unlike a list, an array consists of elements that are all of the same data type. This makes arrays ideal for convenient storage of data elements. Arrays are offered through the `numpy` library, and are often used in conjunction with other Python libraries, such as `pandas`, `scipy`, and `scikit-learn` (linked below). We will explore arrays in this section, along with commonly used functions used with arrays." ] }, { @@ -22,12 +22,14 @@ "id": "77369985", "metadata": {}, "source": [ - "To make an array, we first need to import `NumPy`. We can then build an array from a list using the `np.array()` function." + "## Constructing arrays\n", + "\n", + "To make an array, we first need to import `numpy`. We can then build an array from a list using the `np.array()` function." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 56, "id": "ab8b729c", "metadata": {}, "outputs": [ @@ -37,7 +39,7 @@ "array([30, 50, 70, 90])" ] }, - "execution_count": 2, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -55,12 +57,12 @@ "id": "0b31777b", "metadata": {}, "source": [ - "Another way an array can be made is by using the `np.arange()` function. With this function, one can build an array with a given inclusive start value and exclusive stop value." + "Another way an array can be made is by using the `np.arange()` function. With this function, one can build an array with a given inclusive start value and exclusive stop value as well as a step, which by default is 1." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 57, "id": "9f09fedf", "metadata": {}, "outputs": [ @@ -70,7 +72,7 @@ "array([ 4, 6, 8, 10])" ] }, - "execution_count": 3, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -85,7 +87,7 @@ "id": "ed9c9a2b", "metadata": {}, "source": [ - "Above, we made a one-dimensional array with four elements. We started the array at 4 and stopped it at 11. Because we specified the spacing as 2, our array gave us the values 4, 6, 8, and 10 because 11 is exclusive. Therefore, the `np.arange()` function will evenly space out the elements of our array just before the stop value." + "Above, we made a one-dimensional array with four elements. We started the array at 4 and stopped it at 11. Because we specified the step as 2 (default is 1), our array gave us the values 4, 6, 8, and 10 because 11 is exclusive. Therefore, the `np.arange()` function will evenly space out the elements of our array just before the stop value." ] }, { @@ -93,12 +95,14 @@ "id": "7e71b117", "metadata": {}, "source": [ - "As previously stated, arrays are a data structure that allow for fast calculations. For example, we can easily multiply our two arrays to obtain a new array of values. " + "## Mathematical operations with arrays\n", + "\n", + "Arrays also allow for convenient elementwise calculations. For example, we can easily multiply our two arrays to obtain a new array of values. " ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 58, "id": "a8c411f9", "metadata": {}, "outputs": [ @@ -108,7 +112,7 @@ "array([120, 300, 560, 900])" ] }, - "execution_count": 4, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -123,7 +127,9 @@ "id": "2c972b46", "metadata": {}, "source": [ - "The resulting array consists of the products of element-by-element multiplication of the first two arrays. Keep in mind that when performing calculations with multiple arrays, the dimensions of the arrays must be compatible. A discussion on array shape compatibility in mathematical operations can be found in the referenced documentation on *Array Broadcasting in Numpy* below." + "The resulting array consists of the products of element-by-element multiplication of the first two arrays. Keep in mind that when performing calculations with multiple arrays, the dimensions of the arrays must be *compatible*. \n", + "\n", + "Performing elementwise operations on arrays of different shapes is called **broadcasting**, and a discussion on array shape compatibility in mathematical operations can be found in the referenced documentation on *Array broadcasting in numpy* below. " ] }, { @@ -131,12 +137,12 @@ "id": "e7cefaca", "metadata": {}, "source": [ - "More simply, we can also perform a desired calculation on all elements of an array" + "More simply, we can also perform a desired calculation on all elements of an array using scalar values:" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 59, "id": "1fa0c046", "metadata": {}, "outputs": [ @@ -146,7 +152,7 @@ "array([13., 22., 35., 52.])" ] }, - "execution_count": 5, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -160,12 +166,14 @@ "id": "5e772d89", "metadata": {}, "source": [ + "## Reshaping and combining arrays\n", + "\n", "Arrays can also be reshaped and combined. We can use the `np.reshape()` function to change the first two arrays from a 1-dimensional 1x4 array to a 2-dimensional 2x2 array." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 60, "id": "63529f0f", "metadata": {}, "outputs": [], @@ -277,6 +285,8 @@ "id": "3b6bc94d", "metadata": {}, "source": [ + "## Array functions\n", + "\n", "Construction and reshaping of arrays is an important consideration if you wish to perform aggregate functions on them. Some useful aggregate functions that can be performed on arrays include `np.min()`, `np.max()`, `np.sum()`, and `np.average()`. These functions can be applied to the entire array or across rows and columns." ] }, @@ -383,10 +393,608 @@ }, { "cell_type": "markdown", - "id": "6655852a", + "id": "ec7f35b1", + "metadata": {}, + "source": [ + "## Indexing and Slicing\n", + "\n", + "1D arrays can be indexed similarly to how lists are indexed, as mentioned in the [section 4.1](../../1/Lists).\n", + "\n", + "\n", + "To begin in demonstrating this, let's make a new array of string elements called `flowers`:" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "c1f4ad88", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['orchid', 'rose', 'lilac', 'peony', 'tulip', 'dahlia', 'lily',\n", + " 'violet'], dtype='\n", + "\n", + "A single colon (:) can be used to slice a range of elements in an array. The format for simple slicing an array is as follows:\n", + "\n", + "```\n", + "array[start:end]\n", + "```\n", + "\n", + "\n", + "\n", + "If used between the indices *j* and *k*, slicing the elements of an array will return all elements between *j* and *k*, excluding k.\n", + "\n", + "In this case, we use 2:6 to slice from the third to the sixth element because we want to include the sixth element (which is located at index 5):\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "9935ef70", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['lilac', 'peony', 'tulip', 'dahlia'], dtype='Mathematical aggregate functions by NumPy\n", - "- Array Broadcasting in Numpy" + "- Mathematical aggregate functions by numpy\n", + "- Array broadcasting in numpy\n", + "\n", + "- Scipy documentation\n", + "- scikit-learn documentation" ] }, { "cell_type": "code", "execution_count": null, - "id": "0e5e643e", + "id": "e3b13b05", 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-1,267 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "2eeb5048", - "metadata": {}, - "source": [ - "# Indexing and Slicing" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "89700eb9", - "metadata": {}, - "source": [ - "In this section, we will discuss common methods used to access data from arrays. Let's start by making an array of string elements called `flowers`:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "ac3fad2b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['orchid', 'rose', 'lilac', 'peony', 'tulip', 'dahlia', 'lily',\n", - " 'violet'], dtype='here.\n", "\n", - "A link to documentation for the visualization libraries used can be found at the end of this section.\n", + "Other visualization libraries, such as `seaborn` can be used as well and will be used later in the chapter.\n", "\n", - "Let's start by importing our libraries:" + "For now, let's start by importing our libraries:" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 11, "id": "85ee9e74-01e3-416f-8a12-bda151809cfd", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", - "import seaborn as sns\n", "\n", + "# Imports the pyplot module from matplotlib\n", "from matplotlib import pyplot as plt\n", "\n", - "sns.set_style('whitegrid')" + "# Sets the style for visualizations\n", + "plt.style.use('fast')" ] }, { @@ -45,12 +46,12 @@ "source": [ "To practice making these visualizations, we will be working with data from the World Bank. This data examines the military spending in each country in North America 1960-2020.\n", "\n", - "Let's again first take a look at this data in its comma-separated values format. (Then we'll load it as a file.)" + "Let's again first take a look at this data in its comma-separated values format." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 12, "id": "27a2775a-747a-41dd-a923-ef51efb68c31", "metadata": { "tags": [ @@ -139,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 13, "id": "3fb13d36", "metadata": {}, "outputs": [ @@ -318,7 +319,7 @@ "[61 rows x 6 columns]" ] }, - "execution_count": 3, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -370,14 +371,10 @@ "## Resources\n", "\n", "- Matplotlib documentation\n", + "- Matplotlib visualization gallery\n", "- Pyplot documentation\n", - "- Seaborn documentation\n", - "- List of styles for plots in matplotlib\n", "- World Bank Data on Military Expenditure (in USD - MS.MIL.XPND.CD)\n", - "- World Bank Data on Military Expenditure (% of GDP - MS.MIL.XPND.GD.ZS)\n", - "\n", - "\n", - "\n" + "- World Bank Data on Military Expenditure (% of GDP - MS.MIL.XPND.GD.ZS)" ] } ], diff --git a/textbook/09/2/Categorical_Data.ipynb b/textbook/09/2/Categorical_Data.ipynb deleted file mode 100644 index 484b4d0d..00000000 --- a/textbook/09/2/Categorical_Data.ipynb +++ /dev/null @@ -1,920 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7ea83cf0", - "metadata": {}, - "source": [ - "# Categorical Data\n", - "\n", - "Categorical data consider numerical values (_i.e._ a quantitative variable) in the context of a category (_i.e._ a qualitative variable).\n", - "\n", - "Surveys, like the ones we see on the television show Family Feud or the frequency of people with various eye colors, are examples of categorical data. \n", - "\n", - "In this chapter, there are two types of categorical data that we consider: ordinal data and nominal data. \n", - "\n", - "**Ordinal data** consists of data that can be described as having a meaningful order, ranking, or relationship between categories. An inventory that quantifies the number of small, medium, and large shirts in stock is an example of ordinal data because there is a ranked relationship between shirt sizes.\n", - "\n", - "**Nominal data** can be described as named categories that have no meaningful relationship to one another. Counting the number of people with black, brunette, red, and blonde hair colors in a room is an example of nominal data because hair color has no inherit meaning amongst each other - one hair color is not greater than or less than the others.\n", - "\n", - "\n", - "Categorical data can be visualized using bar graphs, box and whisker plots, and pie charts, and in this section, we will practice making such visualizations.\n", - "\n", - "Let's load the necessary libraries and read in the data." - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "45d41791-b19e-48cf-917d-c411750c6122", - "metadata": { - "tags": [ - "hide_cell" - ] - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "\n", - "from matplotlib import pyplot as plt\n", - "\n", - "sns.set_style('whitegrid')\n", - "\n", - "NorthAmerica_Military_USD_PercentGDP_Combined_csv = '''\\\n", - "Year,CAN-PercentGDP,MEX-PercentGDP,USA-PercentGDP,CAN-USD,MEX-USD,USA-USD\n", - "1960,4.18525654,0.673508659,8.993124587,1.702442711,0.084,47.34655267\n", - "1961,4.128312243,0.651780326,9.1560315,1.677820881,0.0864,49.87977061\n", - "1962,3.999216389,0.689655172,9.331672945,1.671313753,0.0992,54.65094261\n", - "1963,3.620650112,0.718685832,8.831891186,1.610091701,0.112,54.56121578\n", - "1964,3.402062837,0.677506775,8.051281106,1.657457283,0.12,53.43232706\n", - "1965,2.930260659,0.591269841,7.587247177,1.57470454,0.1192,54.56179126\n", - "1966,2.683282422,0.576379066,8.435300286,1.614422827,0.1304,66.44275153\n", - "1967,2.74792677,0.545217107,9.417795933,1.775500366,0.1336,78.39844224\n", - "1968,2.54364188,0.548510764,9.268454275,1.797265817,0.1488,84.32903122\n", - "1969,2.27378467,0.600160043,8.633263795,1.770108751,0.18,84.99016543\n", - "1970,2.188979696,0.497411659,8.032743584,1.889157918,0.1768,83.407993\n", - "1971,2.131485639,0.48765558,6.943069609,2.077659711,0.1912,78.23797989\n", - "1972,2.011818438,0.536568089,6.519756924,2.233737031,0.2424,80.70807097\n", - "1973,1.832601818,0.544217687,5.893870591,2.363060955,0.3008,81.46979441\n", - "1974,1.783813085,0.565744137,5.954111197,2.809465529,0.4072,89.27892034\n", - "1975,1.863541853,0.57358422,5.622679096,3.18091549,0.5048,92.08092875\n", - "1976,1.765927978,0.598103574,5.191071429,3.581805735,0.531576968,94.71525108\n", - "1977,1.8057636,0.534256205,5.155617351,3.752174526,0.437692986,104.665219\n", - "1978,1.848887401,0.504834431,4.943087248,3.969158477,0.518287193,113.3820637\n", - "1979,1.711245918,0.505297474,4.951991535,4.084145738,0.679663588,126.8799271\n", - "1980,1.764448615,0.416107383,5.153537467,4.744402251,0.810422204,143.6883549\n", - "1981,1.709915638,0.513301014,5.646541256,5.141128191,1.284948561,176.5588753\n", - "1982,1.954343585,0.495419418,6.814057094,6.017321456,0.858130163,221.6735426\n", - "1983,2.081196249,0.522866314,6.32114426,6.947104072,0.778556797,223.427165\n", - "1984,2.117188855,0.65981906,6.23641653,7.349795764,1.155945373,245.1491683\n", - "1985,2.097376234,0.676313139,6.453219205,7.460563318,1.241863652,272.1632293\n", - "1986,2.109197118,0.634622463,6.626522658,7.78013674,0.817296612,295.5462238\n", - "1987,2.062576371,0.580341889,6.420274023,8.694447168,0.813391574,304.0866487\n", - "1988,1.986767119,0.536145374,6.071277702,9.897335684,0.981914646,309.6612693\n", - "1989,1.934614309,0.517255829,5.871206008,10.74713469,1.153375828,321.8665588\n", - "1990,1.958793742,0.433081035,5.605175294,11.41463185,1.210872502,325.129314\n", - "1991,1.895444339,0.435402301,4.883429398,11.3385033,1.459136041,299.3727791\n", - "1992,1.8616877,0.469454656,4.970466808,10.78880312,1.824550066,325.033736\n", - "1993,1.821753504,0.442785494,4.604350295,10.26882262,2.122980338,316.7194437\n", - "1994,1.696680257,0.518830327,4.215264675,9.57737764,2.635284079,308.084\n", - "1995,1.554090071,0.450891531,3.860245792,9.176903908,1.562615372,295.8530977\n", - "1996,1.403752581,0.476484778,3.554982206,8.615884471,1.882873103,287.9606687\n", - "1997,1.246243202,0.458095854,3.405562244,7.945140183,2.184061042,293.1678258\n", - "1998,1.256293902,0.450450487,3.201558499,7.748607984,2.263223453,290.9960551\n", - "1999,1.241703064,0.460988776,3.085676783,8.21077854,2.652912012,298.0948913\n", - "2000,1.11808088,0.44604782,3.112242147,8.299385231,3.031454509,320.0863242\n", - "2001,1.137368973,0.442657004,3.123809803,8.375571425,3.229469276,331.8056106\n", - "2002,1.120852292,0.421606002,3.447618099,8.495399281,3.172268734,378.4631388\n", - "2003,1.115878799,0.405916547,3.827161045,9.958245602,2.960496802,440.5320696\n", - "2004,1.107966027,0.364898723,4.016312736,11.33648983,2.854385965,492.9993762\n", - "2005,1.110669655,0.355958931,4.090034876,12.98813296,3.123454978,533.203\n", - "2006,1.125832408,0.311171936,4.041627237,14.8098928,3.035131019,558.335\n", - "2007,1.188901783,0.401163918,4.079655081,17.41713993,4.223037646,589.586\n", - "2008,1.248621382,0.390513227,4.463827356,19.3420584,4.334654124,656.756\n", - "2009,1.377555631,0.501556275,4.88559968,18.93622605,4.514233914,705.917\n", - "2010,1.194338338,0.452734493,4.922641677,19.31568883,4.789031339,738.005\n", - "2011,1.193291895,0.465777803,4.840173995,21.39372086,5.498458542,752.288\n", - "2012,1.118404598,0.475987281,4.477401219,20.45210711,5.717035575,725.205\n", - "2013,1.0023672,0.507919455,4.046678879,18.51573121,6.473144378,679.229\n", - "2014,0.989925299,0.513829957,3.69589465,17.85364048,6.758693845,647.789\n", - "2015,1.152709374,0.466676122,3.477845166,17.93764189,5.468837812,633.829639\n", - "2016,1.164161567,0.495064414,3.418942337,17.78277554,5.33687574,639.856443\n", - "2017,1.351602232,0.436510296,3.313381294,22.26969632,5.062076646,646.752927\n", - "2018,1.324681094,0.477517407,3.316248808,22.72932758,5.839521271,682.4914\n", - "2019,1.27894142,0.52348249,3.427080181,22.20440844,6.650808254,734.3441\n", - "2020,1.415055841,0.573651659,3.741160091,22.75484713,6.116376582,778.2322\n", - "'''\n", - "\n", - "from io import StringIO\n", - "\n", - "NorthAmerica_Military_USD_PercentGDP_Combined_file = StringIO(NorthAmerica_Military_USD_PercentGDP_Combined_csv)\n", - "\n", - "military = pd.read_csv(NorthAmerica_Military_USD_PercentGDP_Combined_file, index_col='Year')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore')" - ] - }, - { - "cell_type": "markdown", - "id": "f26e1513", - "metadata": {}, - "source": [ - "## Bar graphs\n", - "\n", - "Bar graphs are a popular method to visualize categorical data. They're simple, concise, and can condense\n", - "large and complex datasets into a visual summary. Most bar graphs depict a categorical element as an\n", - "independent variable on the x-axis while the height of the bar corresponds to a numerical variable on the y-axis.\n", - "\n", - "We will practice making bar graphs using our military dataset in the context of an ordinal variable.\n", - "\n", - "First, let's create a graph to examine the percent of the GDP (Gross Domestic Product) spent on the military in Canada. We will look at the years 2018, 2019 and 2020.\n", - "\n", - "To do this, we must extract the data for the years of interest from the column containing the data pertaining to GDP percentage of military spending in Canada. We will call this `can_gdp`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "af3c0e6d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
CAN-PercentGDP
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" - ], - "text/plain": [ - " CAN-PercentGDP\n", - "Year \n", - "2018 1.324681\n", - "2019 1.278941\n", - "2020 1.415056" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "can_gdp = military.loc[[2018, 2019, 2020], ['CAN-PercentGDP']]\n", - "\n", - "can_gdp" - ] - }, - { - "cell_type": "markdown", - "id": "62a27826-df76-4802-9414-f1a2b5931a96", - "metadata": {}, - "source": [ - "Next, we call `plt.bar()` to create a bar chart using this data. The `plt.bar()` function needs two arguments. The first argument, `x`, is an array of values to be plotted on the x-axis. \n", - "\n", - "The second argument, `height`, determines the height of the bars (the y-values). \n", - "\n", - "We will create a list of our years of interest and call it `year_labels` to input as the first argument and use the \"CAN-PercentGDP\" column of `can_gdp` as our second argument." - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "b2be1224", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "year_labels = ['2018', '2019', '2020']\n", - "plt.bar(year_labels, can_gdp[\"CAN-PercentGDP\"])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "5748baf9", - "metadata": {}, - "source": [ - "The above code produced a plot, but this plot needs more descriptive labeling to help others understand the data. \n", - "\n", - "We need to add axis labels and a title to communicate what is being measured. Aesthetically, we can also reduce the width of each bar to give more room on the graph and more rest for our eyes." - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "b66c30a8-e66a-4ad1-b6c5-cbe53ee5eef9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.bar(year_labels, can_gdp[\"CAN-PercentGDP\"], width=0.25)\n", - "\n", - "\n", - "plt.title('Military Spending in Canada')\n", - "\n", - "plt.ylabel('Percentage of GDP')\n", - "plt.xlabel('Year')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "18931cc9", - "metadata": {}, - "source": [ - "This plot looks better and is a lot more descriptive.\n", - "\n", - "Let's add the data from Mexico and the United States.\n", - "\n", - "To do this, we use the `plt.subplots()` function. This function creates a `figure` object and an `axis` object, which we will name `fig` and `ax`, respectively. More information on the workings of `plt.subplots()` is linked at the end of this section.\n", - "\n", - "Using this function, we can add data for Canada, Mexico and the United States to the same plot, within the boundaries of the same axes. We do this by calling `ax.bar()`. Note that we want to group our bars by year. To do this we'll set precise positions on the x-axis.\n", - "\n", - "We will also use `plt.tight_layout()` to automatically adjust the subplot dimensions to give appropriate spacing between the bars and the axes boundaries." - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "6833fd5b", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "can_gdp = military.loc[[2018, 2019, 2020], ['CAN-PercentGDP']]\n", - "mex_gdp = military.loc[[2018, 2019, 2020], ['MEX-PercentGDP']]\n", - "usa_gdp = military.loc[[2018, 2019, 2020], ['USA-PercentGDP']]\n", - "\n", - "index = np.arange(len(year_labels))\n", - "\n", - "(fig, ax) = plt.subplots()\n", - "\n", - "ax.bar(index - 0.25, can_gdp[\"CAN-PercentGDP\"], width=0.25)\n", - "\n", - "ax.bar(index, mex_gdp[\"MEX-PercentGDP\"], width=0.25)\n", - "\n", - "ax.bar(index + 0.25, usa_gdp[\"USA-PercentGDP\"], width=0.25)\n", - "\n", - "plt.tight_layout()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "21c5b1b1", - "metadata": {}, - "source": [ - "We were able to create a bar plot with all three data sets together. Now, let's add the appropriate titles, axis labels, and other details.\n", - "\n", - "To add a title to the entire graph, we can call the `plt.title()` function, just as we did before.\n", - "\n", - "In a subplot, to add axis labels, we have to use the `set_xlabel()` and `set_ylabel()` methods on the axis object, here `ax`.\n", - "\n", - "We can also label each individual bar with the associated numerical value by calling the `bar_label()` method on `ax`. In order to label the bars, the label must be specified when creating each each bar.\n", - "\n", - "The location of the legend within the subplot will be set using `ax.legend()`. The y-axis limits are set using `plt.ylim()`." - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "61bdf283", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "(fig, ax) = plt.subplots()\n", - "\n", - "# round(decimals=2): rounds to 2 places after decimal\n", - "\n", - "can_bar = ax.bar(index - 0.25, can_gdp[\"CAN-PercentGDP\"].round(decimals=2), width=0.25, label='Canada') \n", - "mex_bar = ax.bar(index, mex_gdp[\"MEX-PercentGDP\"].round(decimals=2), width=0.25, label='Mexico')\n", - "usa_bar = ax.bar(index + 0.25, usa_gdp[\"USA-PercentGDP\"].round(decimals=2), width=0.25, label='USA')\n", - "\n", - "# Add labels, title, custom x-axis tick labels, etc.\n", - "\n", - "plt.title(\"Military Spending in North America\", pad=10)\n", - "\n", - "ax.set_ylabel('Percentage of GDP')\n", - "ax.set_xlabel('Year')\n", - "\n", - "ax.set_xticks(index, year_labels)\n", - "\n", - "ax.bar_label(can_bar, label_type=\"edge\", padding=4)\n", - "ax.bar_label(mex_bar, label_type=\"edge\", padding=4)\n", - "ax.bar_label(usa_bar, label_type=\"edge\", padding=4)\n", - "\n", - "ax.legend(loc=4, bbox_to_anchor=(1.3, 0.5))\n", - "\n", - "plt.ylim(0, 5)\n", - "\n", - "plt.tight_layout()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c0a52a3c", - "metadata": {}, - "source": [ - "Great! Now we have a well annotated, visually appealing graph that depicts an important message about the data: the percentage of the GDP spent on the military for each country for the years 2018-2020.\n", - "\n", - "From this graph, we can easily see that during this time period, Canada and Mexico contribute a smaller proportion of their GDP to military spending than the United States. This may not have been easily discernable by just looking at our large data table." - ] - }, - { - "cell_type": "markdown", - "id": "fbb08191", - "metadata": {}, - "source": [ - "## Box and whisker plots\n", - "\n", - "Box and whisker plots are another visualization method for displaying categorical data that is often used in the social sciences and biological sciences.\n", - "\n", - "Box and whisker plots are a useful data visualization method because they intrinsically display multiple summary statistics simultaneously. The central line of each box within a box and whisker plot is the *median*. The median (also known as the second quartile, $Q_2$) is a value within a dataset that lies within the middle, separating the higher half and the lower half of the dataset. The median is a valuable measure of center for a distribution because it is not greatly affected by outliers, as opposed to the *mean*.\n", - "\n", - "Box and whisker plots also show the *lower quartile*, *the upper quartile*, the *interquartile range*, *outliers*, the *minimum*, and the *maximum*. The lower quartile $\\left( Q_1\\right)$ is the value where the lowest 25% of the data points within a distribution lie. It is represented by the lower end of the box. \n", - "\n", - "On the other side, the upper quartile $\\left( Q_3\\right)$ is the value in which the highest 25% of the data resides. It is represented by the higher end of a box.\n", - "\n", - "\n", - "The interquartile range (IQR), is the difference between the upper quartile and the lower quartile. The IQR is used to make the length of a box and is represented by the equation:\n", - "\n", - " > $IQR=Q_3-Q_1$\n", - " \n", - "\n", - "Outliers are data points that are less than $Q_1-1.5×IQR$ or greater than $Q_3+1.5×IQR$. These data points are shown beyond the extremity of the whiskers.\n", - "\n", - "Lastly, the minimum and maximum values are represented by the lowest and highest values, respectively, that are within the range $Q_1-1.5×IQR$ and $Q_3+1.5×IQR$. Essentially, they are the lowest and highest values that do not qualify as outliers. The lower whisker represents the minimum value, while the upper whisker represents the maximum value.\n", - "\n", - "Below is a pictorial summary of the major components of a box and whisker plot with an accompanying set of numbers, *A*:\n", - "\n", - "![](./img/boxandwhisker.png)" - ] - }, - { - "cell_type": "markdown", - "id": "d4da5c77", - "metadata": {}, - "source": [ - "We will use a box and whisker plot to examine the percentage GDP spending on the military for each country in the '60s as a way to examine nominal data.\n", - "\n", - "First, we extract the data of interest:" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "76dfd2bc", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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19614.1283120.6517809.156031
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19682.5436420.5485119.268454
19692.2737850.6001608.633264
\n", - "
" - ], - "text/plain": [ - " CAN-PercentGDP MEX-PercentGDP USA-PercentGDP\n", - "Year \n", - "1960 4.185257 0.673509 8.993125\n", - "1961 4.128312 0.651780 9.156031\n", - "1962 3.999216 0.689655 9.331673\n", - "1963 3.620650 0.718686 8.831891\n", - "1964 3.402063 0.677507 8.051281\n", - "1965 2.930261 0.591270 7.587247\n", - "1966 2.683282 0.576379 8.435300\n", - "1967 2.747927 0.545217 9.417796\n", - "1968 2.543642 0.548511 9.268454\n", - "1969 2.273785 0.600160 8.633264" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "the60s = military.loc[1960:1969, ['CAN-PercentGDP', 'MEX-PercentGDP', 'USA-PercentGDP']]\n", - "\n", - "the60s" - ] - }, - { - "cell_type": "markdown", - "id": "c3849183", - "metadata": {}, - "source": [ - "Next, let's plot the data:" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "2f74054c", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.boxplot(the60s);" - ] - }, - { - "cell_type": "markdown", - "id": "1bf41ad3", - "metadata": {}, - "source": [ - "If we plot this dataframe, the bare minimum graph plots the data within the columns of interest, but doesn't automatically use the column names as categorical indicators on the x-axis. This doesn't allow for easy interpretation of the data. \n", - "\n", - "Furthermore, while the current plot does the job of displaying the median and interquartile range, adding individual data points will allow for viewers to more easily see the spread of the data. The addition of axis labels, a title, and some color will also enhance this plot and make it more aesthetically pleasing. \n", - "\n", - "We can accomplish this using a combination of functions from `matplotlib` and `seaborn`:" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "948bde16", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ax = sns.boxplot(data=the60s, palette=\"Set2\", linewidth=1)\n", - "ax = sns.swarmplot(data=the60s, palette=\"Set2\", linewidth=0.5, edgecolor = \"black\")\n", - "plt.xticks(ticks = [0,1,2], labels = ['Canada', 'Mexico', 'United States'])\n", - "plt.ylabel(\"Percent of GDP\")\n", - "plt.title(\"% GDP spent on the military in North America from 1960-1969\")\n", - "\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "a21d621d", - "metadata": {}, - "source": [ - "Now that we have proper labeling, we can see the median, upper quartile, and lower quartile of the percentage of the each country's GDP spent on the military. A noticeable observation this plot shows is that Mexico not only spent a small percentage of their GDP on the military (less than 2%), but the percentage of spending during this decade had very little variability. This makes it hard to see what the median, upper quartile, and lower quartile are for Mexico. The issue of being able to visually resolve displays of data is a common one that data scientists encounter. In the next section, we will offer one potential solution for this." - ] - }, - { - "cell_type": "markdown", - "id": "d28b66ff", - "metadata": {}, - "source": [ - "## Pie charts\n", - "\n", - "Pie charts are a commonly used visualization method to represent proportions in datasets. Pie charts use *wedges* to represent the numerical value of a proportion corresponding to a categorial variable.\n", - "\n", - "While pie charts are very common and can be easily interpreted by a layperson audience, they may not be the best way to represent data in certain cases. Firstly, because pie charts use the area of a circle to represent the proportion of a categorical variable, it can be difficult to gauge the numerical value that a wedge represents if the area doesn't appear as an easily discernible fraction (_e.g._ ½, ⅓, ¼). This can be aided with the help of labels and legends that explicitly show the numerical values associated with the wedges of the pie chart. Secondly, if you want to visualize many categorical variables or variables that make up a significantly small proportion of the dataset, it may be difficult to see the variable on a pie chart. Overall, pie charts can be a simple and effective way to communicate proportional categorical data, but before using them, consider what attributes of the data need to be highlighted to help decide if a pie chart is the most appropriate visualization method. \n", - "\n", - "Now, let's look at the total amount of money spent on the military in the entire North American continent for the year of 2000 and determine what proportion of this total amount came from each country. Because we are looking at a single year (2000), a pie chart is a good way to visualize the proportions of military spending contributed by each country. To do this, we will extract data for the year 2000; we'll call it `usd_2000`. Then, we will make a pie chart using the `plt.pie()` function. We will use `usd_2000` to determine the wedge sizes and the argument `normalize=True` to normalize the data to 1. We'll also set the figure size, in inches, using `plt.figure()`." - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "90158219", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "CAN-USD 8.299385\n", - "MEX-USD 3.031455\n", - "USA-USD 320.086324\n", - "Name: 2000, dtype: float64" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "usd_2000 = military.loc[2000, ['CAN-USD', 'MEX-USD', 'USA-USD']]\n", - "\n", - "usd_2000" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "cdde5f79-2c19-49e8-b883-0e3121fccb57", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10, 6))\n", - "\n", - "plt.pie(usd_2000, normalize=True);" - ] - }, - { - "cell_type": "markdown", - "id": "7e7cc03f", - "metadata": {}, - "source": [ - "Now that we have a pie chart, let's add some more detail to it to make it more descriptive.\n", - "\n", - "We can label the sectors of the chart so that we know which country corresponds to which color. Likewise, we can label the percentage of each sector to know the definitive proportion of each country's contribution to the total amount of money spent on the military in North America.\n", - "\n", - "To do this, we will create a list called `countries`, containing the strings \"Canada\", \"Mexico\", and \"USA\". We then assign the `labels` argument within `plt.pie()` to `countries` and add the `autopct` argument, which labels the wedges using the printf style format. More information on that format is linked at the end of this section." - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "a4f10620", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10, 6))\n", - "\n", - "countries = ['Canada', 'Mexico', 'USA']\n", - "\n", - "plt.pie(usd_2000, normalize=True, labels=countries, autopct='%.1f')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "0d8962d3", - "metadata": {}, - "source": [ - "This plot is okay, but it can be better.\n", - "\n", - "Because the sectors of Mexico and Canada are a lot smaller than the sector for the United States, overlaying the percentages on top of the sector creates spatial issues that can be visually displeasing. Instead, let's add the percentages into a legend along with the labels of each sector. Let's also add a title so others can know what they are looking at when they view this chart." - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "d09a15ca", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10, 6))\n", - "\n", - "\n", - "patches, text = plt.pie(usd_2000, normalize = True)\n", - "labels = ['Canada (2.8 %)', 'Mexico (0.9 %)', 'USA (96.4 %)']\n", - "total = usd_2000.sum().round(decimals=1) #finds the sum of usd_2000 and rounds it to 1 position after the decimal\n", - "\n", - "plt.legend(patches, labels, loc=4, bbox_to_anchor=(0.8, -0.2), fontsize=15)\n", - "plt.title(\"Military Spending in North America in 2000\" + \" (\" + str(total) + \" Billion USD)\", loc = 'center',\n", - " fontsize = 15)\n", - "\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "9c5fa589", - "metadata": {}, - "source": [ - "Above, we used `plt.pie()` in a way that we had not used it before.\n", - "\n", - "Under the hood, the `plt.pie()` function returns two default outputs, which we named: `patches` and`text`. `patches` is an object that dictates the size of each wedge. `text` consists of a list of labels for our data. Here, we needed to specifically assign `patches` and `text` objects so we could use `patches` as an argument for the `plt.legend()` function. \n", - "\n", - "The `plt.legend()` function has two required arguments. The first argument dictates **what** is being labeled. In our case, the wedges of the pie chart (*i.e.* the `patches` object) are being labeled. The second argument dictates **how** things are labeled. Here, we simply created a variable called labels, which consists of the three strings:" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "d430985c-cfee-49e0-ae4d-4fc69e193f36", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Canada (2.8 %)', 'Mexico (0.9 %)', 'USA (96.4 %)']" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "labels" - ] - }, - { - "cell_type": "markdown", - "id": "ee88547d-89be-493c-b49d-64fbfb03bb72", - "metadata": {}, - "source": [ - "The other arguments, `bbox_to_anchor` and `fontsize`, are optional when using the `plt.legend()` function.\n", - "\n", - "The argument `bbox_to_anchor` designates the position in the plotting area where the legend will be, while the `fontsize` argument dictates the font size, in points, of the legend text." - ] - }, - { - "cell_type": "markdown", - "id": "e64c6b88", - "metadata": {}, - "source": [ - "## Conclusions\n", - "\n", - "In this section, we were introduced to the `plt.bar()` and `plt.pie()` functions to construct bar plots and pie charts, respectively.\n", - "\n", - "The `plt.bar()` function requires `x` and `height` arguments, which can be an array of number values, but other parameters can be included.\n", - "\n", - "The use of the `plt.boxplot()` function allows us to make a basic box and whisker plot, but does not offer preferred labeling and coloring by default. The `sns.boxplot()` and `sns.swarmplot()` functions are good alternatives that have more customization and flexibility.\n", - "\n", - "The `plt.pie()` function only requires an `x` argument as an array of values and has other arguments that can be included as well.\n", - "\n", - "Both of these types of visualizations are used for depicting categorical data.\n", - "\n", - "As a reminder, when deciding on whether to use a pie chart, consider certain attributes of the data, such as the number of categorical variables or the size of the proportions to be plotted. Below is a list of functions with linked documentation for your reference and further reading:\n", - "\n", - "- plt.bar( )\n", - "- plt.pie( )\n", - "- plt.boxplot( ) \n", - "- plt.subplots( )\n", - "- plt.title( )\n", - "- plt.ylim( )\n", - "- plt.xlim( )\n", - "- plt.ylabel( )\n", - "- plt.xlabel( )\n", - "- plt.xticks( )\n", - "- ax.bar( )\n", - "- ax.legend( )\n", - "- plt.tight_layout( )\n", - "- plt.figure( )\n", - "- plt.show( )\n", - "- sns.boxplot( )\n", - "- sns.swarmplot( )\n", - "- Formatted String Literals" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "253ed247", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/textbook/09/2/Numerical_Data.ipynb b/textbook/09/2/Numerical_Data.ipynb new file mode 100644 index 00000000..f7e3fa47 --- /dev/null +++ b/textbook/09/2/Numerical_Data.ipynb @@ -0,0 +1,1234 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0fb30d10", + "metadata": {}, + "source": [ + "# Numerical Data\n", + "\n", + "Numerical data consists of *discrete* and *continuous* number values. Discrete data values can be integers or rational numbers, such as the number of marbles in a jar or shoe sizes. Continuous data values can be rational and irrational numbers, such as height recordings or temperature collections. In this section, we will practice making histograms, scatter plots, and line graphs to represent numerical data.\n", + "\n", + "Let's load the necessary libraries and read in the data." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3e479dc7-ce8e-4ca7-ae35-79038cf4bf99", + "metadata": { + "tags": [ + "hide_cell" + ] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "\n", + "from matplotlib import pyplot as plt\n", + "plt.style.use('fivethirtyeight')\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "NorthAmerica_Military_USD_PercentGDP_Combined_csv = '''\\\n", + "Year,CAN-PercentGDP,MEX-PercentGDP,USA-PercentGDP,CAN-USD,MEX-USD,USA-USD\n", + "1960,4.18525654,0.673508659,8.993124587,1.702442711,0.084,47.34655267\n", + "1961,4.128312243,0.651780326,9.1560315,1.677820881,0.0864,49.87977061\n", + "1962,3.999216389,0.689655172,9.331672945,1.671313753,0.0992,54.65094261\n", + "1963,3.620650112,0.718685832,8.831891186,1.610091701,0.112,54.56121578\n", + "1964,3.402062837,0.677506775,8.051281106,1.657457283,0.12,53.43232706\n", + "1965,2.930260659,0.591269841,7.587247177,1.57470454,0.1192,54.56179126\n", + "1966,2.683282422,0.576379066,8.435300286,1.614422827,0.1304,66.44275153\n", + "1967,2.74792677,0.545217107,9.417795933,1.775500366,0.1336,78.39844224\n", + "1968,2.54364188,0.548510764,9.268454275,1.797265817,0.1488,84.32903122\n", + "1969,2.27378467,0.600160043,8.633263795,1.770108751,0.18,84.99016543\n", + "1970,2.188979696,0.497411659,8.032743584,1.889157918,0.1768,83.407993\n", + "1971,2.131485639,0.48765558,6.943069609,2.077659711,0.1912,78.23797989\n", + "1972,2.011818438,0.536568089,6.519756924,2.233737031,0.2424,80.70807097\n", + "1973,1.832601818,0.544217687,5.893870591,2.363060955,0.3008,81.46979441\n", + "1974,1.783813085,0.565744137,5.954111197,2.809465529,0.4072,89.27892034\n", + "1975,1.863541853,0.57358422,5.622679096,3.18091549,0.5048,92.08092875\n", + "1976,1.765927978,0.598103574,5.191071429,3.581805735,0.531576968,94.71525108\n", + "1977,1.8057636,0.534256205,5.155617351,3.752174526,0.437692986,104.665219\n", + "1978,1.848887401,0.504834431,4.943087248,3.969158477,0.518287193,113.3820637\n", + "1979,1.711245918,0.505297474,4.951991535,4.084145738,0.679663588,126.8799271\n", + "1980,1.764448615,0.416107383,5.153537467,4.744402251,0.810422204,143.6883549\n", + "1981,1.709915638,0.513301014,5.646541256,5.141128191,1.284948561,176.5588753\n", + "1982,1.954343585,0.495419418,6.814057094,6.017321456,0.858130163,221.6735426\n", + "1983,2.081196249,0.522866314,6.32114426,6.947104072,0.778556797,223.427165\n", + "1984,2.117188855,0.65981906,6.23641653,7.349795764,1.155945373,245.1491683\n", + "1985,2.097376234,0.676313139,6.453219205,7.460563318,1.241863652,272.1632293\n", + "1986,2.109197118,0.634622463,6.626522658,7.78013674,0.817296612,295.5462238\n", + "1987,2.062576371,0.580341889,6.420274023,8.694447168,0.813391574,304.0866487\n", + "1988,1.986767119,0.536145374,6.071277702,9.897335684,0.981914646,309.6612693\n", + "1989,1.934614309,0.517255829,5.871206008,10.74713469,1.153375828,321.8665588\n", + "1990,1.958793742,0.433081035,5.605175294,11.41463185,1.210872502,325.129314\n", + "1991,1.895444339,0.435402301,4.883429398,11.3385033,1.459136041,299.3727791\n", + "1992,1.8616877,0.469454656,4.970466808,10.78880312,1.824550066,325.033736\n", + "1993,1.821753504,0.442785494,4.604350295,10.26882262,2.122980338,316.7194437\n", + "1994,1.696680257,0.518830327,4.215264675,9.57737764,2.635284079,308.084\n", + "1995,1.554090071,0.450891531,3.860245792,9.176903908,1.562615372,295.8530977\n", + "1996,1.403752581,0.476484778,3.554982206,8.615884471,1.882873103,287.9606687\n", + "1997,1.246243202,0.458095854,3.405562244,7.945140183,2.184061042,293.1678258\n", + "1998,1.256293902,0.450450487,3.201558499,7.748607984,2.263223453,290.9960551\n", + "1999,1.241703064,0.460988776,3.085676783,8.21077854,2.652912012,298.0948913\n", + "2000,1.11808088,0.44604782,3.112242147,8.299385231,3.031454509,320.0863242\n", + "2001,1.137368973,0.442657004,3.123809803,8.375571425,3.229469276,331.8056106\n", + "2002,1.120852292,0.421606002,3.447618099,8.495399281,3.172268734,378.4631388\n", + "2003,1.115878799,0.405916547,3.827161045,9.958245602,2.960496802,440.5320696\n", + "2004,1.107966027,0.364898723,4.016312736,11.33648983,2.854385965,492.9993762\n", + "2005,1.110669655,0.355958931,4.090034876,12.98813296,3.123454978,533.203\n", + "2006,1.125832408,0.311171936,4.041627237,14.8098928,3.035131019,558.335\n", + "2007,1.188901783,0.401163918,4.079655081,17.41713993,4.223037646,589.586\n", + "2008,1.248621382,0.390513227,4.463827356,19.3420584,4.334654124,656.756\n", + "2009,1.377555631,0.501556275,4.88559968,18.93622605,4.514233914,705.917\n", + "2010,1.194338338,0.452734493,4.922641677,19.31568883,4.789031339,738.005\n", + "2011,1.193291895,0.465777803,4.840173995,21.39372086,5.498458542,752.288\n", + "2012,1.118404598,0.475987281,4.477401219,20.45210711,5.717035575,725.205\n", + "2013,1.0023672,0.507919455,4.046678879,18.51573121,6.473144378,679.229\n", + "2014,0.989925299,0.513829957,3.69589465,17.85364048,6.758693845,647.789\n", + "2015,1.152709374,0.466676122,3.477845166,17.93764189,5.468837812,633.829639\n", + "2016,1.164161567,0.495064414,3.418942337,17.78277554,5.33687574,639.856443\n", + "2017,1.351602232,0.436510296,3.313381294,22.26969632,5.062076646,646.752927\n", + "2018,1.324681094,0.477517407,3.316248808,22.72932758,5.839521271,682.4914\n", + "2019,1.27894142,0.52348249,3.427080181,22.20440844,6.650808254,734.3441\n", + "2020,1.415055841,0.573651659,3.741160091,22.75484713,6.116376582,778.2322\n", + "'''\n", + "\n", + "from io import StringIO\n", + "\n", + "NorthAmerica_Military_USD_PercentGDP_Combined_file = StringIO(NorthAmerica_Military_USD_PercentGDP_Combined_csv)\n", + "\n", + "military = pd.read_csv(NorthAmerica_Military_USD_PercentGDP_Combined_file, index_col='Year')" + ] + }, + { + "cell_type": "markdown", + "id": "1af7e1d3", + "metadata": {}, + "source": [ + "## Scatter plots\n", + "\n", + "Scatter plots can be used to visualize the relationship between two numerical variables. They are most commonly used to visualize two continous numerical variables against each other (in other words, the data takes on values that are between whole number integers). These plots can also be used when data takes on a large number of different discrete integers. We will use a scatter plot to visualize the percentage of the GDP (Gross Domestic Product) of Mexico spent on the military versus the absolute dollar amount (in USD) over 1960-2020.\n", + "\n", + "We'll simply extract the columns for this data and assign them to `mex_gdp` and `mex_usd`, respectively. Then, we can plot this data using the `plt.scatter()` function and use `plt.show()` to display the plot." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "deb40aae", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "mex_gdp = military[['MEX-USD']]\n", + "\n", + "mex_usd = military[['MEX-PercentGDP']]\n", + "\n", + "plt.scatter(mex_gdp, mex_usd) # mex_gdp on the x-axis, mex_usd on the y-axis\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4fba0f1f", + "metadata": {}, + "source": [ + "Looking at this scatter plot out of context, it would be hard to understand what the data means. Let's add some important details to make it clear.\n", + "\n", + "Firstly, we can add a title using the `plt.title()` function. This function accepts a string argument to be used as the text for the title. It also has an optional `pad` parameter, which dictates the space between the title and the plotting area.\n", + "\n", + "We can also use `plt.ylabel()` and `plt.xlabel()` to label the y- and x-axes, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "eccd1b1a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(mex_gdp, mex_usd)\n", + "\n", + "plt.title(\"% GDP vs. Absolute Spending on Military in Mexico 1960 - 2020\", pad=10)\n", + "\n", + "plt.ylabel('Spending in USD (Billions)')\n", + "plt.xlabel('Percentage of GDP')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cd5d171b", + "metadata": {}, + "source": [ + "Now we have a better understanding of the data. \n", + "\n", + "In addition to this information, we can add a color scheme that will color each data point based on the year of collection. This adds another dimension of analysis, using year as a feature; the context of the spending relationship can be examined over time.\n", + "\n", + "The `plt.scatter()` function minimally needs two arguments - *x* and *y* - which are array-like variables. Other optional arguments include `c`, which determines how to color the data points; `alpha`, which sets the opacity of the data points; and `cmap` which sets the Colormap used to color the data points. \n", + "\n", + "The `plt.colorbar()` function displays a scale of the Colormap based on the feature used to color the data, which in our case is the year of collection." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "23a45386", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "mex_years = mex_gdp.index\n", + "\n", + "plt.scatter(mex_gdp, mex_usd, c=mex_years, alpha=0.4, cmap='winter')\n", + "\n", + "plt.title(\"% GDP vs. Absolute Spending on Military in Mexico 1960 - 2020\", pad=10)\n", + "\n", + "plt.ylabel('Spending in USD (Billions)')\n", + "plt.xlabel('Percentage of GDP')\n", + "\n", + "plt.colorbar()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "20146a6e", + "metadata": {}, + "source": [ + "We used the years of the dataset (which we defined as the index earlier in this chapter) as our `c` argument to color the data points based on the year of collection. We used the *winter* Colormap as our `cmap` argument, but many other Colormaps are available for your choosing. A list of other possible Colormaps to explore can be found here." + ] + }, + { + "cell_type": "markdown", + "id": "30ba35e7", + "metadata": {}, + "source": [ + "## Line graphs\n", + "\n", + "Next, we'll examine the use of a line graph as another visualization tool for numerical data. Line graphs are used to visualize sequential numerical data. By using line graphs, we can easily see trends within data over time.\n", + "\n", + "Let's examine the spending (in USD) on the military in Canada in the 21st century (2000-2020). We can extract this data and call it `can_usd`.\n", + "\n", + "In Python, visualizations can be made using dataframe methods or by directly calling functions from the `pyplot` library in `matplotlib`. We can quickly create a line graph using `plot()` method on the `can_usd` dataframe:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b26501e3-def6-48f3-80e5-d238e8220f9f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " CAN-USD\n", + "Year \n", + "2000 8.299385\n", + "2001 8.375571\n", + "2002 8.495399\n", + "2003 9.958246\n", + "2004 11.336490" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "can_usd = military[['CAN-USD']].loc[2000:2020]\n", + "\n", + "can_usd.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e9499c29", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "can_usd.plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "9d2ccd60", + "metadata": {}, + "source": [ + "The same plot can be made using the `pyplot` function `plt.plot()`:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d5f4f198", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(can_usd)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e199c81c", + "metadata": {}, + "source": [ + "Notice how the `plot()` method automatically uses the `Year` column to label the x-axis, while the `plt.plot()` function does not. This can simply be remedied using the `plt.xlabel()` function. We can add a y-label as well using `plt.ylabel()`.\n", + "\n", + "Also notice the increments of the x-axis for both plots. To change these increments to integers, we can create an array consisting of years of the desired increments and then use it as an argument for the `plt.xticks()` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ab3bb392-3509-4adc-b1ae-4460db515862", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2000, 2005, 2010, 2015, 2020])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "years = np.arange(2000, 2021, 5)\n", + "\n", + "years" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "88bcffa6-027e-4208-aeab-9c61649b913c", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(can_usd)\n", + "plt.xlabel('Year')\n", + "plt.ylabel('USD (Billions)')\n", + "plt.xticks(years)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d90f500d", + "metadata": {}, + "source": [ + "We can see from the graph that Canada's spending on the military has increased overall since 2000. The country had a period of time (around 2011 to 2017) where military spending was decreasing consistently.\n", + "\n", + "### Visualizing multiple trends using line graphs\n", + "\n", + "We can view trends for multiple variables at once. Let's add the data for Mexico as well to see the country's spending in the 21st century." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3eff4b2a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "mex_usd = military[['MEX-USD']].loc[2000:2020]\n", + "\n", + "plt.plot(can_usd)\n", + "plt.plot(mex_usd)\n", + "\n", + "plt.xlabel('Year')\n", + "plt.ylabel('USD (Billions)')\n", + "plt.xticks(years)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "81e3e39f", + "metadata": {}, + "source": [ + "We can now see that the military spending for both Mexico and Canada is vastly different. However, just looking at this graph out of context, we wouldn't be able to tell which line corresponds to which country. Let's add some descriptive details, such as a legend and a title:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "50c364ea", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(can_usd, label='Canada')\n", + "plt.plot(mex_usd, label='Mexico')\n", + "\n", + "plt.xlabel('Year')\n", + "plt.ylabel('USD (Billions)')\n", + "plt.xticks(years)\n", + "\n", + "plt.legend(loc=\"best\") # Adds a legend to the figure\n", + "plt.title(\"Military Spending in Mexico and Canada in the 21st Century\", pad=10)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3ab35d3c", + "metadata": {}, + "source": [ + "We can see that the overall trend of military spending in Mexico also increased from 2000 to 2020. However, this increase was a lot less drastic than observed in Canada. Mexico's military spending was a steady rise from about \\$3 billion to \\$6 billion over the course of 20 years, while Canada's spending rose from \\$8 billion to about \\$23 billion over the same period of time.\n", + "\n", + "Let's add data from the United States to see the trends in all North American countries." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "18c12059", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "usa_usd = military[['USA-USD']].loc[2000:2020]\n", + "\n", + "plt.plot(can_usd, label='Canada')\n", + "plt.plot(mex_usd, label='Mexico')\n", + "plt.plot(usa_usd, label='United States')\n", + "\n", + "plt.xlabel('Year')\n", + "plt.ylabel('USD (Billions)')\n", + "plt.xticks(years)\n", + "\n", + "plt.legend(loc=\"best\")\n", + "plt.title(\"Military Spending in North America in the 21st Century\", pad=10)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ae241382", + "metadata": {}, + "source": [ + "With the addition of the data from the United States, it's difficult to discern the data from Canada and Mexico. Because the spending on the military in the United States was a lot higher, plotting all three datasets on the same graph with the same axis does not allow us to clearly see trends in the other countries.\n", + "\n", + "To address this, we can graph the data for each country separately with axis limits that are tailored to each country. If we graph this data side by side, we can see the trends in each country while acknowledging that the axis intervals for each country provides a numerical context for cross-comparisons.\n", + "\n", + "To do this, we use the `plt.subplots()` function. This function creates a `figure` object and `axis` objects, which we will name `fig` and `ax`, respectively. More information on the workings of `plt.subplots()` is linked at the end of this section.\n", + "\n", + "By using `plt.subplots()`, we can add data for Canada, Mexico and the United States to the same figure by specifying the data assigned to each `ax` object. Here, we will define three `ax` objects: `ax1`, `ax2`, and `ax3`. This will allow us to create three separate plotting areas, bounded by three different axes, that are contained within one figure.\n", + "\n", + "Once the figure and axes are defined, we can create a title for the entire figure using `fig.suptitle()`. Using the `.plot()` method for each `ax` object, we can specify which data to plot in each axis. We can also specify other plotting features about each axis by using various methods on each axis object, such as the `set_title()`, `set_xlabel()`, `set_ylim()`, and `set_xticks()` methods:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "af08cad2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Defines a figure object and three axis objects\n", + "# Sets the dimensions of the figure (1 x 3 axes)\n", + "# Sets the figure size (15in x 3in)\n", + "(fig, (ax1, ax2, ax3)) = plt.subplots(1, 3, figsize=(15, 3))\n", + "\n", + "\n", + "# Sets title for entire figure\n", + "fig.suptitle('Military Spending in North America in the 21st Century', y=1.1, fontsize=15)\n", + "\n", + "\n", + "# Makes plots for each axis\n", + "ax1.plot(can_usd, color='blue') # Plots Canada data to ax1 \n", + "ax1.set_title('Canada') # Sets title for ax1\n", + "ax1.set_ylim([8, 24]) # Sets y-axis limits for ax1\n", + "ax1.set_xlabel('Years') # Sets x-axis labels for ax1\n", + "ax1.set_ylabel('USD (Billions)') # Sets y-axis labels for ax1\n", + "ax1.set_xticks(years) # Sets x-ticks for ax1\n", + "\n", + "ax2.plot(mex_usd, color='red') # Plots Mexico data to ax2\n", + "ax2.set_title('Mexico') # Sets title for ax2\n", + "ax2.set_ylim([2.5, 7]) # Sets y-axis limits for ax2\n", + "ax2.set_xlabel('Years') # Sets x-axis labels for ax2\n", + "ax2.set_ylabel('USD (Billions)') # Sets y-axis labels for ax2\n", + "ax2.set_xticks(years) # Sets x-ticks for ax2\n", + "\n", + "ax3.plot(usa_usd, color='orange') # Plots U.S. data to ax3\n", + "ax3.set_title('United States') # Sets title for ax3\n", + "ax3.set_ylim([300, 800]) # Sets y-axis limits for ax3\n", + "ax3.set_xlabel('Years') # Sets x-axis labels for ax3\n", + "ax3.set_ylabel('USD (Billions)') # Sets y-axis labels for ax3\n", + "ax3.set_xticks(years) # Sets x-ticks for ax3\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "efee0c99", + "metadata": {}, + "source": [ + "Now that we've created separate subplots, we can see the trends for all three countries over the last 20 years. All three countries seem to have decreased spending around 2011 and 2018. By observing the difference in scale, we can also see that while the trends are similar, the magnitude of spending was very different between Canada, Mexico, and the United States." + ] + }, + { + "cell_type": "markdown", + "id": "5190be46", + "metadata": {}, + "source": [ + "## Histograms\n", + "\n", + "Histograms are a great way to view a **distribution** of numerical data. A distribution of a dataset is a visual display of all the values within the dataset when plotted on a graph, showing the frequency of occurence of said values.\n", + "\n", + "\n", + "In histogram plots, a numerical component of data is divided into what are called **bins**. As data points are assigned to their respective bins, the total number of data points in each bin is quantified and plotted, visualizing a distribution of frequencies. In the upcoming exercise, we will explore how to visualize distributions of values in our dataset.\n", + "\n", + "\n", + "Let's examine military spending in the United States from 1960 to 2020. We can look at multiple ranges of dollar amounts spent on the military as our independent variable and organize them into bins. After, we can determine how many fiscal years fall into each of these bins and visualize the distribution.\n", + "\n", + "First, we will need to extract the data pertaining to the military spending in the United States. We will call it `hist_data`. Then, we will need to determine the minimum and maximum values of this subset of data so that we can determine the range of values." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "a45eb875", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "min: 47.34655267\n", + "max: 778.2322\n" + ] + } + ], + "source": [ + "hist_data = military[\"USA-USD\"]\n", + "\n", + "print('min:', hist_data.min())\n", + "print('max:', hist_data.max())" + ] + }, + { + "cell_type": "markdown", + "id": "b21d2e3e", + "metadata": {}, + "source": [ + "We see that the minimum amount the United States spent on the military between the years of 1960 and 2020 was about \\$47 billion, while the maximum amount was about \\$780 billion.\n", + "\n", + "With this information, we will create a range for our bins, named `binnum`, with integers between 0 and 801, so that it is inclusive of all the data values. We make the interval of the range 100, giving us eight evenly spaced bins." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ccfb3ade", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 100, 200, 300, 400, 500, 600, 700, 800]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "binnum = np.arange(0, 801, 100)\n", + "\n", + "list(binnum)" + ] + }, + { + "cell_type": "markdown", + "id": "55e62707", + "metadata": {}, + "source": [ + "To graph the distribution of military spending, a histogram can be made by using the `hist()` dataframe method. We can specify the bins so that they are evenly distributed on the x-axis. We can do this by inputting `binnum` as our `bins` argument. If we do not specify the `bin` argument, the data will be divided into 10 bins by default." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "90028e2d", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hist_data.hist(bins=binnum)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "43e31df5", + "metadata": {}, + "source": [ + "We can also use the `plt.hist()` funtion to make the same graph:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "daae9915", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(hist_data, bins=binnum)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d7d71458", + "metadata": {}, + "source": [ + "When determining the bins for a histogram, the bin size controls the number of bins that will show. A smaller bin size will result in more bins, which will show more granularity of the data, but could make it difficult to see patterns in the data. A larger bin size decreases the visible detail of the data, but could also make it hard to discern useful take aways from the data. While exploring data, it's important to try different bins sizes out to see which display provides the most useful information for your analysis needs.\n", + "\n", + "Consider the different bin sizes below:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "a2713731", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(hist_data, bins=range(0, 801, 200))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "5844a254", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(hist_data, bins=range(0, 801, 50))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "73ece7f2", + "metadata": {}, + "source": [ + "Both histograms show the same data in different ways. The top histogram has a larger bin size and from it, we can see that it shows most of the values in the data fall within the range of 0-200. The second one has a smaller bin size, and we can see that most of the values of the data fall within the range of 50-100. The latter gives us a more specific range of where most of the data lie, which can be useful down the line.\n", + "\n", + "For now, let's stick with the bin size in the latter graph. Now that we have our plot, let's add additional details to make it more informative:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "942043f1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(hist_data, bins=range(0, 801, 50))\n", + "plt.title(\"Distribution of Military Spending in the United States from 1960 to 2020\")\n", + "plt.ylabel('Counts of Fiscal Years')\n", + "plt.xlabel(\"Dollar Amount (USD)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5e52c962", + "metadata": {}, + "source": [ + "Awesome! From this plot, we can see that the United States had the highest frequency of fiscal years where \\$50 - \\$100 billion was spent on the military, while the \\$400 - \\$550 billion and \\$150 - \\$200 bins had the lowest frequencies with only 1 year spending those ranges of money." + ] + }, + { + "cell_type": "markdown", + "id": "778960f9", + "metadata": {}, + "source": [ + "### Visualizing multiple distributions using histograms\n", + "\n", + "We can also view multiple distributions on one plot on using multiple plots. Let's look at the distributions of the percentage of GDP spent on the military in Canada and the United States from 1960 to 2020." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "ea5e0162", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CAN-PercentGDPUSA-PercentGDP
Year
19604.1852578.993125
19614.1283129.156031
19623.9992169.331673
19633.6206508.831891
19643.4020638.051281
.........
20161.1641623.418942
20171.3516023.313381
20181.3246813.316249
20191.2789413.427080
20201.4150563.741160
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\n", + "
" + ], + "text/plain": [ + " CAN-PercentGDP USA-PercentGDP\n", + "Year \n", + "1960 4.185257 8.993125\n", + "1961 4.128312 9.156031\n", + "1962 3.999216 9.331673\n", + "1963 3.620650 8.831891\n", + "1964 3.402063 8.051281\n", + "... ... ...\n", + "2016 1.164162 3.418942\n", + "2017 1.351602 3.313381\n", + "2018 1.324681 3.316249\n", + "2019 1.278941 3.427080\n", + "2020 1.415056 3.741160\n", + "\n", + "[61 rows x 2 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "perc_gdp = military[['CAN-PercentGDP', 'USA-PercentGDP']]\n", + "perc_gdp" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "fb06b325", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CAN-PercentGDP 0.989925\n", + "USA-PercentGDP 3.085677\n", + "dtype: float64\n", + "CAN-PercentGDP 4.185257\n", + "USA-PercentGDP 9.417796\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "print(perc_gdp.min())\n", + "print(perc_gdp.max())" + ] + }, + { + "cell_type": "markdown", + "id": "5aab7d8f", + "metadata": {}, + "source": [ + "We see that the minimum values for these two countries is about 0.98%, while the maximum value is about 9.4%. To plot both of these distributions on a single plot, we can create another array called `binnum2` that can include all of the values.\n", + "\n", + "\n", + "We can then make histograms for each country, specifying the labeling and colors for each. We'll also add a legend to show which color corresponds to which country, as well as proper titles and labeling:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "64136357", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "binnum2 = np.arange(1,11, step = 0.5)\n", + "\n", + "# plotting histograms\n", + "\n", + "plt.hist(perc_gdp['CAN-PercentGDP'], label='Canada', alpha=0.6, color = 'blue', bins=binnum2)\n", + "plt.hist(perc_gdp['USA-PercentGDP'], label='United States', alpha=0.6, color = 'orange', bins=binnum2)\n", + "\n", + "# labeling\n", + "plt.legend(bbox_to_anchor=(1, 1))\n", + "plt.title('Distribution of the Percentage of Military Spending from 1960 to 2020')\n", + "plt.xlabel('% of GDP')\n", + "plt.ylabel('Counts of Fiscal Years')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8b901d2d", + "metadata": {}, + "source": [ + "Additionally, we can use the `plt.subplots()` function to create two separate plots in one figure. By specifying `ax1` and `ax2`, we can use the `hist()` method to create a histogram and set titles and axis labels for each axis." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "3b1fef90", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "(fig, (ax1, ax2)) = plt.subplots(1, 2, figsize=(12, 3))\n", + "\n", + "plt.suptitle('Distribution of the Percentage of Military Spending from 1960 to 2020', y=1.1)\n", + "\n", + "ax1.hist(perc_gdp['CAN-PercentGDP'], color = 'blue')\n", + "ax1.set_title('Canada')\n", + "ax1.set_xlabel('Dollar Amount (USD)')\n", + "ax1.set_ylabel('Counts of Fiscal Years')\n", + "\n", + "ax2.hist(perc_gdp['USA-PercentGDP'], color = 'orange')\n", + "ax2.set_title('United States')\n", + "ax2.set_xlabel('Dollar Amount (USD)')\n", + "ax2.set_ylabel('Counts of Fiscal Years')\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6e35bfce", + "metadata": {}, + "source": [ + "Notice with the above use of the `hist()` method, we did not specify the bins for each axis. Thus, each subplot created 10 bins by default to fit the range of the data." + ] + }, + { + "cell_type": "markdown", + "id": "886787a1", + "metadata": {}, + "source": [ + "## Conclusions\n", + "\n", + "In this section, we learned functions and methods to create histograms, scatter plots, and line graphs as a means of visualizing numerical data.\n", + "\n", + "The `plt.scatter()` and `plt.plot()` functions require numerical arrays that serve as `x` and `y` arugments. The `plt.hist()` function requires one numerical array of values for plotting distributions of data.\n", + "\n", + "The `hist()` and `plot()` methods can also be used directly on dataframes to create histograms and line plots, respectively.\n", + "\n", + "We can also create subplots within a figure using `plt.subplots()`.\n", + "\n", + "Lastly, we learned about a number of other functions that can be used to enhance and annotate our plots. Documentation for the functions used in this section, and related functions, are listed below:\n", + "\n", + "- plt.hist( )\n", + "\n", + "- DataFrame hist method\n", + "- plt.scatter( )\n", + "- plt.plot( )\n", + "- DataFrame plot method\n", + "- plt.subplots( )\n", + "- fig.suptitle( )\n", + "- ax.set_title( )\n", + "- ax.set_ylim( )\n", + "- ax.set_xlim( )\n", + "- ax.set_ylabel( )\n", + "- ax.set_xlabel( )\n", + "- plt.colorbar( )" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/textbook/09/2/img/boxandwhisker.png b/textbook/09/2/img/boxandwhisker.png deleted file mode 100644 index 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z_bCluHaFiU(Tjc)?>+AK90MhLtm#hOy192qL%Cd`o9+KBOqs%)raE#T#%rfUK$UgG zF~h0#TTty_@C34K=5=r2k^tddW%qPmesnGe`abj($w$q80x9d2sEL4Z02{?l}GS7c2a; zK^a_|@Vd5M{pzjEI@zG!Q^aO#Pp1&0VO#8PYzIpNAiuhX*e>@-le-s+) b7TD?ZJ)e?$x!^la-<>^ecZzn>EBb!{>vINF diff --git a/textbook/09/3/Categorical_Data.ipynb b/textbook/09/3/Categorical_Data.ipynb new file mode 100644 index 00000000..54bcb069 --- /dev/null +++ b/textbook/09/3/Categorical_Data.ipynb @@ -0,0 +1,988 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7ea83cf0", + "metadata": {}, + "source": [ + "# Categorical Data\n", + "\n", + "Categorical data considers numerical quantities in the context of categorical variables. Surveys, like the ones we see on the television show Family Feud or the frequency of people with various eye colors, are examples of categorical data. \n", + "\n", + "\n", + "In this chapter, there are two types of categorical data that we consider: ordinal data and nominal data. \n", + "\n", + "**Ordinal data** consists of data that can be described as having a meaningful order, ranking, or relationship between categories. An inventory that quantifies the number of small, medium, and large shirts in stock is an example of ordinal data because there is a ranked relationship between shirt sizes.\n", + "\n", + "**Nominal data** can be described as named categories that have no meaningful relationship to one another. Counting the number of people with black, brunette, red, and blonde hair colors in a room is an example of nominal data because hair color has no inherit meaning amongst each other - one hair color is not greater than or less than the others.\n", + "\n", + "\n", + "Categorical data can be visualized using bar graphs and pie charts, and in this section, we will practice making such visualizations.\n", + "\n", + "Again, we'll load the necessary libraries and data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "45d41791-b19e-48cf-917d-c411750c6122", + "metadata": { + "tags": [ + "hide_cell" + ] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "plt.style.use('fast')\n", + "\n", + "\n", + "NorthAmerica_Military_USD_PercentGDP_Combined_csv = '''\\\n", + "Year,CAN-PercentGDP,MEX-PercentGDP,USA-PercentGDP,CAN-USD,MEX-USD,USA-USD\n", + "1960,4.18525654,0.673508659,8.993124587,1.702442711,0.084,47.34655267\n", + "1961,4.128312243,0.651780326,9.1560315,1.677820881,0.0864,49.87977061\n", + "1962,3.999216389,0.689655172,9.331672945,1.671313753,0.0992,54.65094261\n", + "1963,3.620650112,0.718685832,8.831891186,1.610091701,0.112,54.56121578\n", + "1964,3.402062837,0.677506775,8.051281106,1.657457283,0.12,53.43232706\n", + "1965,2.930260659,0.591269841,7.587247177,1.57470454,0.1192,54.56179126\n", + "1966,2.683282422,0.576379066,8.435300286,1.614422827,0.1304,66.44275153\n", + "1967,2.74792677,0.545217107,9.417795933,1.775500366,0.1336,78.39844224\n", + "1968,2.54364188,0.548510764,9.268454275,1.797265817,0.1488,84.32903122\n", + "1969,2.27378467,0.600160043,8.633263795,1.770108751,0.18,84.99016543\n", + "1970,2.188979696,0.497411659,8.032743584,1.889157918,0.1768,83.407993\n", + "1971,2.131485639,0.48765558,6.943069609,2.077659711,0.1912,78.23797989\n", + "1972,2.011818438,0.536568089,6.519756924,2.233737031,0.2424,80.70807097\n", + "1973,1.832601818,0.544217687,5.893870591,2.363060955,0.3008,81.46979441\n", + "1974,1.783813085,0.565744137,5.954111197,2.809465529,0.4072,89.27892034\n", + "1975,1.863541853,0.57358422,5.622679096,3.18091549,0.5048,92.08092875\n", + "1976,1.765927978,0.598103574,5.191071429,3.581805735,0.531576968,94.71525108\n", + "1977,1.8057636,0.534256205,5.155617351,3.752174526,0.437692986,104.665219\n", + "1978,1.848887401,0.504834431,4.943087248,3.969158477,0.518287193,113.3820637\n", + "1979,1.711245918,0.505297474,4.951991535,4.084145738,0.679663588,126.8799271\n", + "1980,1.764448615,0.416107383,5.153537467,4.744402251,0.810422204,143.6883549\n", + "1981,1.709915638,0.513301014,5.646541256,5.141128191,1.284948561,176.5588753\n", + "1982,1.954343585,0.495419418,6.814057094,6.017321456,0.858130163,221.6735426\n", + "1983,2.081196249,0.522866314,6.32114426,6.947104072,0.778556797,223.427165\n", + "1984,2.117188855,0.65981906,6.23641653,7.349795764,1.155945373,245.1491683\n", + "1985,2.097376234,0.676313139,6.453219205,7.460563318,1.241863652,272.1632293\n", + "1986,2.109197118,0.634622463,6.626522658,7.78013674,0.817296612,295.5462238\n", + "1987,2.062576371,0.580341889,6.420274023,8.694447168,0.813391574,304.0866487\n", + "1988,1.986767119,0.536145374,6.071277702,9.897335684,0.981914646,309.6612693\n", + "1989,1.934614309,0.517255829,5.871206008,10.74713469,1.153375828,321.8665588\n", + "1990,1.958793742,0.433081035,5.605175294,11.41463185,1.210872502,325.129314\n", + "1991,1.895444339,0.435402301,4.883429398,11.3385033,1.459136041,299.3727791\n", + "1992,1.8616877,0.469454656,4.970466808,10.78880312,1.824550066,325.033736\n", + "1993,1.821753504,0.442785494,4.604350295,10.26882262,2.122980338,316.7194437\n", + "1994,1.696680257,0.518830327,4.215264675,9.57737764,2.635284079,308.084\n", + "1995,1.554090071,0.450891531,3.860245792,9.176903908,1.562615372,295.8530977\n", + "1996,1.403752581,0.476484778,3.554982206,8.615884471,1.882873103,287.9606687\n", + "1997,1.246243202,0.458095854,3.405562244,7.945140183,2.184061042,293.1678258\n", + "1998,1.256293902,0.450450487,3.201558499,7.748607984,2.263223453,290.9960551\n", + "1999,1.241703064,0.460988776,3.085676783,8.21077854,2.652912012,298.0948913\n", + "2000,1.11808088,0.44604782,3.112242147,8.299385231,3.031454509,320.0863242\n", + "2001,1.137368973,0.442657004,3.123809803,8.375571425,3.229469276,331.8056106\n", + "2002,1.120852292,0.421606002,3.447618099,8.495399281,3.172268734,378.4631388\n", + "2003,1.115878799,0.405916547,3.827161045,9.958245602,2.960496802,440.5320696\n", + "2004,1.107966027,0.364898723,4.016312736,11.33648983,2.854385965,492.9993762\n", + "2005,1.110669655,0.355958931,4.090034876,12.98813296,3.123454978,533.203\n", + "2006,1.125832408,0.311171936,4.041627237,14.8098928,3.035131019,558.335\n", + "2007,1.188901783,0.401163918,4.079655081,17.41713993,4.223037646,589.586\n", + "2008,1.248621382,0.390513227,4.463827356,19.3420584,4.334654124,656.756\n", + "2009,1.377555631,0.501556275,4.88559968,18.93622605,4.514233914,705.917\n", + "2010,1.194338338,0.452734493,4.922641677,19.31568883,4.789031339,738.005\n", + "2011,1.193291895,0.465777803,4.840173995,21.39372086,5.498458542,752.288\n", + "2012,1.118404598,0.475987281,4.477401219,20.45210711,5.717035575,725.205\n", + "2013,1.0023672,0.507919455,4.046678879,18.51573121,6.473144378,679.229\n", + "2014,0.989925299,0.513829957,3.69589465,17.85364048,6.758693845,647.789\n", + "2015,1.152709374,0.466676122,3.477845166,17.93764189,5.468837812,633.829639\n", + "2016,1.164161567,0.495064414,3.418942337,17.78277554,5.33687574,639.856443\n", + "2017,1.351602232,0.436510296,3.313381294,22.26969632,5.062076646,646.752927\n", + "2018,1.324681094,0.477517407,3.316248808,22.72932758,5.839521271,682.4914\n", + "2019,1.27894142,0.52348249,3.427080181,22.20440844,6.650808254,734.3441\n", + "2020,1.415055841,0.573651659,3.741160091,22.75484713,6.116376582,778.2322\n", + "'''\n", + "\n", + "from io import StringIO\n", + "\n", + "NorthAmerica_Military_USD_PercentGDP_Combined_file = StringIO(NorthAmerica_Military_USD_PercentGDP_Combined_csv)\n", + "\n", + "military = pd.read_csv(NorthAmerica_Military_USD_PercentGDP_Combined_file, index_col='Year')\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "id": "f26e1513", + "metadata": {}, + "source": [ + "## Bar graphs\n", + "\n", + "Bar graphs are a popular method to visualize categorical data. They're simple, concise, and can condense\n", + "large and complex datasets into a simple visual summary. Most bar graphs depict a categorical element as an\n", + "independent variable on the x-axis while the height of the bar corresponds to a numerical value on the y-axis.\n", + "\n", + "We will practice making bar graphs using our military dataset in the context of an ordinal variable.\n", + "\n", + "First, let's create a graph to examine the percent of the GDP spent on the military in Canada. We will look at the years 2018, 2019 and 2020.\n", + "\n", + "To do this, we must extract the data for the years of interest from the column containing the data pertaining to GDP percentage of military spending in Canada. We will call this `can_gdp`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "af3c0e6d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CAN-PercentGDP
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" + ], + "text/plain": [ + " CAN-PercentGDP\n", + "Year \n", + "2018 1.324681\n", + "2019 1.278941\n", + "2020 1.415056" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "can_gdp = military.loc[[2018, 2019, 2020], ['CAN-PercentGDP']]\n", + "\n", + "can_gdp" + ] + }, + { + "cell_type": "markdown", + "id": "62a27826-df76-4802-9414-f1a2b5931a96", + "metadata": {}, + "source": [ + "To make a bar graph from a dataframe, the `plot.bar()` method can be used. We will use this method on `can_gdp`:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7669818c", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "can_gdp.plot.bar()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c53841ab", + "metadata": {}, + "source": [ + "We can also use `plt.bar()` to create a bar graph using the `pyplot` library. The `plt.bar()` function needs two arguments. The first argument, `x`, is an array of values to be plotted on the x-axis. \n", + "\n", + "The second argument, `height`, determines the height of the bars (the y-values). \n", + "\n", + "We will create a list of our years of interest and call it `year_labels` to input as the first argument and use the \"CAN-PercentGDP\" column of `can_gdp` as our second argument." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b2be1224", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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4eVXdn+RHgRng8d34bwK7quobSf4Q+FVG3+p/HnhtVX17PT/P2WDgY/YZRo+Hfhh4c1XduK4f5iywguP1fuCl3TqAk6eevpjkIuBdfPfRJX+0Xp9jvRl0SWqEl1wkqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqRH/B29LNnR+NibcAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "year_labels = ['2018', '2019', '2020']\n", + "plt.bar(year_labels, can_gdp[\"CAN-PercentGDP\"])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5748baf9", + "metadata": {}, + "source": [ + "The above code produced a plot, but this plot needs more descriptive labeling to help others understand the data. \n", + "\n", + "We need to add axis labels and a title to communicate what is being measured. Aesthetically, we can also reduce the width of each bar to give more room on the graph and more rest for our eyes." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b66c30a8-e66a-4ad1-b6c5-cbe53ee5eef9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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gqQdYNdKdbK8EDgMuBm6mODpooaTjJO1ZNvso8O7yTOWzgYNtewzPIyIixqjO4aP/CXwbeIqkTwN7Uxz2OaLynIAFg+YdVbl9E/CK2tVGRMS4q3NC2VmSrqHYhCNgL9s3N15ZRER0xYhBIOnJwFKKTTcD89bNMNQREZNDnX0E11LswP0N8Nvy9u2SrpX04iaLi4iI5tUJgkuA2ban2d4c2B24EHgf/3eNgoiIWEvVCYKdbV88MGH7B8DLbF8BrNdYZRER0RV1jhpaIukIirGCAN4K/Kk8pHTEw0gjImLNVqdHsD/FWcHfKf9mlPOmAPs0VVhERHRHncNH72aYweCAReNbTkREdFudw0d7gI8Dz6UYAgIA269tsK6IiOiSOpuGzgJ+DWwDHAvcTjGOUERETAJ1gmBz26cCK2xfZvudQHoDERGTRJ2jhgbOIF4i6Y0UVxF7cnMlRUREN9UJgk9J2oRipNAvU1xa8kNNFhUREd1TJwiW274PuA94DYCkjBgaETFJ1NlH8OWa8yIiYi00bI9A0suAlwM9kj5SWbQxxclkERExCXTaNDQV2LBss1Fl/v0UF6eJiIhJYNggsH0ZcJmk023f0cWaIiKii+rsLF5P0jxgZrV9ziyOiJgc6gTBN4GTgVOAx5otJyIiuq1OEKy0fdJYVi5pN+BLFDuXT7H92SHa7AMcAxi4zvb+Y3msiIgYmzpB8D+S3gd8G3hkYKbtezrdqbxewQnA64HFwNWS5tu+qdJmFvDPwCtsL5f0lDE8h4iIWA11guCg8t/DK/MMbDvC/XYCFtm+FUDSOcAc4KZKm3cDJ9heDmB7aZ2iIyJi/NS5HsE2Y1z3lsCdlenFwEsHtXkmgKSfU2w+Osb29wevSNJcYC7AjBkzxlhOREQMZcQziyVtIOlfyiOHkDRL0pvG6fHXAWYBuwD7AV+VtOngRrbn2e613dvT0zNODx0REVBviImvAY9SnGUMcBfwqRr3uwuYXpneqpxXtRiYb3uF7duA31AEQ0REdEmdINjO9ucph6O2/WdANe53NTBL0jaSpgL7AvMHtfkORW8ASdMoNhXdWqvyiIgYF3WC4FFJT6TYQYyk7agcPTQc2yuBw4CLgZuB82wvlHScpD3LZhcDyyTdBPwYONz2sjE8j4iIGKM6Rw0dDXwfmC7pLOAVwMF1Vm57AbBg0LyjKrcNfKT8i4iICVDnqKFLJF0L7EyxSeiDtu9uvLKIiOiKOkcN/QPF2cUX2b4QWClpr8Yri4iIrqizj+Do8gplANi+l2JzUURETAJ1gmCoNnX2LURExFqgThD0SfqipO3Kvy8C1zRdWEREdEedIHg/xQll5wLnAA8DhzZZVEREdE/HTTzlCKIX2n5Nl+qJiIgu69gjsP0YsErSJl2qJyIiuqzOTt8HgRskXQI8NDDT9gcaqyoiIrqmThBcUP5FRMQkVOfM4jPKsYZm2L6lCzVFREQX1TmzeA/gVxTjDSHpBZIGjyIaERFrqTqHjx5DcdnJewFs/4qRL1MZERFriTpBsKI6xERpVRPFRERE99XZWbxQ0v7AFEmzgA8AlzdbVkREdEvdM4ufS3Exmm8A9wEfarCmiIjoomF7BJLWB94DPAO4AXhZedWxiIiYRDr1CM4AeilCYHfgC12pKCIiuqrTPoLtbT8PQNKpwFXdKSkiIrqpU49gxcCNbBKKiJi8OgXBjpLuL/8eAJ4/cFvS/XVWLmk3SbdIWiTpyA7t3izJknpH+wQiImL1DLtpyPaU1VlxOYT1CcDrgcXA1ZLm275pULuNgA8CV67O40VExNjUOXx0rHYCFtm+1fajFBe1mTNEu+OBz1Fc8CYiIrqsySDYErizMr24nPdXkl4ETLd9UacVSZorqU9SX39///hXGhHRYk0GQUeSngB8EfjoSG1tz7Pda7u3p6en+eIiIlqkySC4C5hemd6qnDdgI2AH4CeSbgd2BuZnh3FERHc1GQRXA7MkbSNpKrAv8Nfhq23fZ3ua7Zm2ZwJXAHva7muwpoiIGKSxICjPPTgMuBi4GTjP9kJJx0nas6nHjYiI0akz+uiY2V4ALBg076hh2u7SZC0RETG0CdtZHBERa4YEQUREyyUIIiJaLkEQEdFyCYKIiJZLEEREtFyCICKi5RIEEREtlyCIiGi5BEFERMslCCIiWi5BEBHRcgmCiIiWSxBERLRcgiAiouUSBBERLZcgiIhouQRBRETLJQgiIlouQRAR0XKNBoGk3STdImmRpCOHWP4RSTdJul7SpZK2brKeiIh4vMaCQNIU4ARgd2B7YD9J2w9q9kug1/bzgfOBzzdVT0REDK3JHsFOwCLbt9p+FDgHmFNtYPvHtv9cTl4BbNVgPRERMYQmg2BL4M7K9OJy3nAOAb7XYD0RETGEdSa6AABJBwC9wKuHWT4XmAswY8aMLlYWETH5NdkjuAuYXpneqpz3NyTtCnwS2NP2I0OtyPY82722e3t6ehopNiKirZoMgquBWZK2kTQV2BeYX20g6YXAf1GEwNIGa4mIiGE0FgS2VwKHARcDNwPn2V4o6ThJe5bN/h+wIfBNSb+SNH+Y1UVEREMa3UdgewGwYNC8oyq3d23y8SMiYmQ5szgiouUSBBERLZcgiIhouQRBRETLJQgiIlouQRAR0XIJgoiIlksQRES0XIIgIqLlEgQRES2XIIiIaLkEQUREyyUIIiJaLkEQEdFyCYKIiJZLEEREtFyCICKi5RIEEREtlyCIiGi5BEFERMslCCIiWq7RIJC0m6RbJC2SdOQQy9eTdG65/EpJM5usJyIiHq+xIJA0BTgB2B3YHthP0vaDmh0CLLf9DODfgc81VU9ERAytyR7BTsAi27fafhQ4B5gzqM0c4Izy9vnA6ySpwZoiImKQdRpc95bAnZXpxcBLh2tje6Wk+4DNgburjSTNBeaWkw9KuqWRiuuZxqD6Rkvp90xm+XzEcCb6s7H1cAuaDIJxY3seMG+i6wCQ1Ge7d6LriDVTPh8xnDX5s9HkpqG7gOmV6a3KeUO2kbQOsAmwrMGaIiJikCaD4GpglqRtJE0F9gXmD2ozHziovL038CPbbrCmiIgYpLFNQ+U2/8OAi4EpwGm2F0o6DuizPR84FThT0iLgHoqwWNOtEZuoYo2Vz0cMZ439bCg/wCMi2i1nFkdEtFyCICKi5VofBJKmS/qxpJskLZT0wXL+kyVdIum35b+blfOfLekXkh6R9LFB6/pwuY4bJZ0taf2JeE4xfsb58/HB8rOxUNKHJuDpxDgaw2fjbZKul3SDpMsl7VhZV8fheJrW+iAAVgIftb09sDNwaDkUxpHApbZnAZeW01Ds1P4A8IXqSiRtWc7vtb0DxQ7ytWHnd3Q2Xp+PHYB3U5xxvyPwJknP6M5TiIaM9rNxG/Bq288DjqfceVxzOJ5GtT4IbC+xfW15+wHgZooznqvDX5wB7FW2WWr7amDFEKtbB3hieU7EBsAfmq0+mjaOn4/nAFfa/rPtlcBlwD82/wyiKWP4bFxue3k5/wqKc6ug3nA8jWp9EFSVo5++ELgSeKrtJeWiPwJP7XRf23dR/Ar8PbAEuM/2D5qrNrptdT4fwI3AqyRtLmkDYDZ/e8JlrMXG8Nk4BPheeXuo4Xi2bKbSoSUISpI2BL4FfMj2/dVl5UluHY+zLbcDzgG2AZ4OPEnSAQ2VG122up8P2zdTjK77A+D7wK+AxxopNrpqtJ8NSa+hCIIjulbkCBIEgKR1Kd7Is2xfUM7+k6QtyuVbAEtHWM2uwG22+22vAC4AXt5UzdE94/T5wPaptl9s+++A5cBvmqo5umO0nw1JzwdOAebYHhhOp85wPI1qfRCUw16fCtxs+4uVRdXhLw4CvjvCqn4P7Cxpg3Kdr6PYZhhrsXH8fCDpKeW/Myj2D3xjfKuNbhrtZ6N83y8ADrRd/RFQZzieRrX+zGJJrwT+F7gBWFXO/gTFtr7zgBnAHcA+tu+R9DSgD9i4bP8gsL3t+yUdC7yV4miCXwLvsv1IN59PjK9x/nz8L8Uw6yuAj9i+tKtPJsbVGD4bpwBvLucBrBwYjVTSbOA/+L/heD7drecBCYKIiNZr/aahiIi2SxBERLRcgiAiouUSBBERLZcgiIhouQRBxAhU+Jmk3Svz3iLp+xNZV8R4yeGjETWUo4d+k2I8mXUozhPZzfbvxrCudcqB5yLWCAmCiJokfR54CHhS+e/WwA7AusAxtr9bDj52ZtkG4DDbl0vahWLo4eXAs20/s7vVRwwvQRBRk6QnAdcCjwIXAgttf13SpsBVFL0FA6tsPyxpFnC27d4yCC4CdrB920TUHzGcdSa6gIi1he2HJJ1LMWzEPsAelauQrU8xpMAfgK9IegHF6KLVX/5XJQRiTZQgiBidVeWfgDfbvqW6UNIxwJ8orkL2BODhyuKHulRjxKjkqKGIsbkYeH85AiWSXljO3wRYYnsVcCDFIGIRa7QEQcTYHE+xk/h6SQvLaYATgYMkXQc8m/QCYi2QncURES2XHkFERMslCCIiWi5BEBHRcgmCiIiWSxBERLRcgiAiouUSBBERLff/Abwb5FGQ+8qiAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.bar(year_labels, can_gdp[\"CAN-PercentGDP\"], width=0.25)\n", + "\n", + "\n", + "plt.title('Military Spending in Canada')\n", + "\n", + "plt.ylabel('Percentage of GDP')\n", + "plt.xlabel('Year')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "18931cc9", + "metadata": {}, + "source": [ + "This plot looks better and is a lot more descriptive.\n", + "\n", + "Let's add the data from Mexico and the United States.\n", + "\n", + "To do this, we will once again use the `plt.subplots()` function. This time, we will specify our figure with a single axis called `ax`. Because we want to group our data by year, we can call `ax.bar()` to set precise positions on the x-axis for each country.\n", + "\n", + "We will also use `plt.tight_layout()` to automatically adjust the subplot dimensions to give appropriate spacing between the bars and the axes boundaries." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6833fd5b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "can_gdp = military.loc[[2018, 2019, 2020], ['CAN-PercentGDP']]\n", + "mex_gdp = military.loc[[2018, 2019, 2020], ['MEX-PercentGDP']]\n", + "usa_gdp = military.loc[[2018, 2019, 2020], ['USA-PercentGDP']]\n", + "\n", + "index = np.arange(len(year_labels))\n", + "\n", + "(fig, ax) = plt.subplots()\n", + "\n", + "# Offsets the bars for Canada by -0.25in\n", + "ax.bar(index - 0.25, can_gdp[\"CAN-PercentGDP\"], width=0.25) \n", + "\n", + "# Plots the bars for Mexico in the middle\n", + "ax.bar(index, mex_gdp[\"MEX-PercentGDP\"], width=0.25)\n", + "\n", + "# Offsets the bars for the U.S. by +0.25in\n", + "ax.bar(index + 0.25, usa_gdp[\"USA-PercentGDP\"], width=0.25)\n", + "\n", + "plt.tight_layout()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "21c5b1b1", + "metadata": {}, + "source": [ + "We were able to create a bar plot with all three data sets together. Now, let's add the appropriate titles, axis labels, and other details using previously described functions.\n", + "\n", + "If we assign each country's bar to a variable, we can also label each individual bar with the associated numerical value by calling the `bar_label()` method on `ax`. In order to label the bars, the label must be specified when creating each each bar." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "61bdf283", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "(fig, ax) = plt.subplots()\n", + "\n", + "\n", + "# Creates variable for each country and offsets as appropriate\n", + "# Annotates bar with the associated value, rounded to 2 places after the demical\n", + "can_bar = ax.bar(index - 0.25, can_gdp[\"CAN-PercentGDP\"].round(decimals=2), width=0.25, label='Canada') \n", + "mex_bar = ax.bar(index, mex_gdp[\"MEX-PercentGDP\"].round(decimals=2), width=0.25, label='Mexico')\n", + "usa_bar = ax.bar(index + 0.25, usa_gdp[\"USA-PercentGDP\"].round(decimals=2), width=0.25, label='USA')\n", + "\n", + "\n", + "# Add labels and titles for entire figure\n", + "plt.title(\"Military Spending in North America\", pad=10)\n", + "plt.ylabel('Percentage of GDP')\n", + "plt.xlabel('Year')\n", + "plt.xticks(index, year_labels)\n", + "plt.ylim(0, 5)\n", + "\n", + "\n", + "# Creates legend for the entire figure\n", + "plt.legend(loc=4, bbox_to_anchor=(1.3, 0.5))\n", + "\n", + "\n", + "# Add labels for individual bars; gives spacing (padding) between the value and the bar\n", + "ax.bar_label(can_bar, label_type=\"edge\", padding=4)\n", + "ax.bar_label(mex_bar, label_type=\"edge\", padding=4)\n", + "ax.bar_label(usa_bar, label_type=\"edge\", padding=4)\n", + "\n", + "\n", + "plt.tight_layout()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c0a52a3c", + "metadata": {}, + "source": [ + "Great! Now we have a well annotated, visually appealing graph that depicts an important message about the data: the percentage of the GDP spent on the military for each country for the years 2018-2020.\n", + "\n", + "From this graph, we can easily see that during this time period, Canada and Mexico contribute a smaller proportion of their GDP to military spending than the United States. This may not have been easily discernable by just looking at our large data table." + ] + }, + { + "cell_type": "markdown", + "id": "19fadd76", + "metadata": {}, + "source": [ + "### Horizontal Bar Graphs\n", + "\n", + "There may be times when you want to present data as a horizontal bar graph. Using the dollar amounts spent in 2020 for each North American country, we can create a horiztonal bar graph to represent nomimal data. First, we need to extract the data for the year of 2020:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f1baccac", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "year2020 = military.loc[[2020]][['CAN-USD', 'MEX-USD', 'USA-USD']]" + ] + }, + { + "cell_type": "markdown", + "id": "b06712a1", + "metadata": {}, + "source": [ + "From this, we obtain a dataframe with a single row of data with values for three variables. To format this data for plotting, we can tranpose the dataframe using the `transpose()` method:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "2acad7b4", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Year2020
CAN-USD22.754847
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" + ], + "text/plain": [ + "Year 2020\n", + "CAN-USD 22.754847\n", + "MEX-USD 6.116377\n", + "USA-USD 778.232200" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "year2020 = year2020.transpose()\n", + "year2020" + ] + }, + { + "cell_type": "markdown", + "id": "286d0d9e", + "metadata": {}, + "source": [ + "The dataframe in this format can directly be used for plotting. To create a horiztonal bar graph, we can use the `plt.barh()` function. This function requires arguments for the `y` and `width` parameters. The `y` parameter is the categorical variables to be plotted, which are usually displayed on the x-axis of a regular bar graph. The `width` parameter corresponds to the numerical values that are associated with each categorical variable.\n", + "\n", + "We will use the index of the `year2020` dataframe as the an argument for the `y` parameter and the values in the `2020` column as an argument for the `width` parameter:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "6d4d4be5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.barh(y = year2020.index, width = year2020[2020])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "245f26fe", + "metadata": {}, + "source": [ + "In the above graph, the index is used to label the categories on the y-axis. If desired, this can be changed by using a list of the same size as an argument for the `tick_label` parameter. Additional titling and labeling can be added to this graph as well:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "f9d97586", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.barh(y = year2020.index, width = year2020[2020], tick_label = ['Canada', 'Mexico', 'USA'])\n", + "plt.title('Military Spending in North American in 2020 (USD)')\n", + "plt.xlabel('USD')\n", + "plt.ylabel('Country')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "9d60ec90", + "metadata": {}, + "source": [ + "For further customization, the bars can be colored too. To do this, the bar graph will need to be defined as a variable and then each bar color can be set by calling for the index of the bar and using the `set_color()` method like so:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "6643a16c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "barh = plt.barh(y = year2020.index, width = year2020[2020], tick_label = ['Canada', 'Mexico', 'USA'])\n", + "\n", + "barh[0].set_color('red')\n", + "barh[1].set_color('green')\n", + "barh[2].set_color('blue')\n", + "\n", + "plt.title('Military Spending in North American in 2020 (USD)')\n", + "plt.xlabel('USD')\n", + "plt.ylabel('Country')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ed7a75f6", + "metadata": {}, + "source": [ + "The same can be done for regular bar graphs made using `plt.bar()`. Depending on your visualization preferences, `plt.bar()` and `plt.barh()` provide multiple options for constructing a bar graph." + ] + }, + { + "cell_type": "markdown", + "id": "d28b66ff", + "metadata": {}, + "source": [ + "## Pie charts\n", + "\n", + "Pie charts are a commonly used visualization method to represent proportions in datasets. Pie charts use *wedges* to represent the numerical value of a proportion corresponding to a categorial variable.\n", + "\n", + "While pie charts are very common and can be easily interpreted by a layperson audience, they may not be the best way to represent data in certain cases. Firstly, because pie charts use the area of a circle to represent the proportion of a categorical variable, it can be difficult to gauge the numerical value that a wedge represents if the area doesn't appear as an easily discernible fraction (_e.g._ ½, ⅓, ¼). This can be aided with the help of labels and legends that explicitly show the numerical values associated with the wedges of the pie chart. Secondly, if you want to visualize many categorical variables or variables that make up a significantly small proportion of the dataset, it may be difficult to see the variable on a pie chart. Overall, pie charts can be a simple and effective way to communicate proportional categorical data, but before using them, consider what attributes of the data need to be highlighted to help decide if a pie chart is the most appropriate visualization method. \n", + "\n", + "Now, let's look at the total amount of money spent on the military in Mexico from 1970 - 1975 and determine the proportion of the total amount for each year. To do this, we will extract data from the years 1970-1975 and call it `mex70s`. Then, we will make a pie chart using the `plt.pie()` function. We will use `mex70s` to determine the wedge sizes. We'll also set the figure size, in inches, using `plt.figure()`." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "63c723fa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
MEX-USD
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19700.1768
19710.1912
19720.2424
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19750.5048
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" + ], + "text/plain": [ + " MEX-USD\n", + "Year \n", + "1970 0.1768\n", + "1971 0.1912\n", + "1972 0.2424\n", + "1973 0.3008\n", + "1974 0.4072\n", + "1975 0.5048" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mex_70s = military.loc[np.arange(1970, 1976), ['MEX-USD']]\n", + "\n", + "mex_70s" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b156e98f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "\n", + "plt.pie(x = mex_70s['MEX-USD'])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e12d0bf4", + "metadata": {}, + "source": [ + "Now that we have a pie chart, let's add some more detail to it to make it more descriptive.\n", + "\n", + "We can label the sectors of the chart so that we know which year corresponds to which color. Likewise, we can label the percentage of each sector to know the definitive proportion of each years's contribution to the total amount of money spent on the military in Mexico from 1970-1975.\n", + "\n", + "To do this, we will create an array called `years`, containing the integers 1970, 1971, 1972, 1973, 1974, and 1975. We then use `years` as an argument for the `labels` parameter within `plt.pie()` and specify formatting for the `autopct` parameter, which labels the wedges using the printf style format. More information on that format can be found here." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "db7549c6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "\n", + "years = np.arange(1970, 1976)\n", + "\n", + "plt.pie(x = mex_70s['MEX-USD'], labels=years, autopct='%.1f')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e2b162dd", + "metadata": {}, + "source": [ + "This plot is okay, but it can be better.\n", + "\n", + "The percentages may be difficult to see as the labeling competes with the color of the wedge. Instead, let's add the percentages into a legend along with the labels of each sector. Let's also add a title so others can know what they are looking at when they view this chart." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "0d310f32", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "\n", + "\n", + "patches, text = plt.pie(x = mex_70s['MEX-USD'])\n", + "labels = ['1970 (9.7 %)', '1971 (10.5 %)', '1972 (13.3 %)', '1973 (16.5 %)', '1974 (22.3 %)', '1975 (27.7%)']\n", + "\n", + "# Finds the sum of usd_2000 and rounds it to 1 position after the decimal\n", + "total = mex_70s['MEX-USD'].sum().round(decimals=1) \n", + "\n", + "plt.legend(patches, labels, loc=4, bbox_to_anchor=(1.5, 0.3), fontsize=15)\n", + "plt.title(\"Military Spending in Mexico from 1970-1975\" + \" (\" + str(total) + \" Billion USD)\", loc = 'center',\n", + " fontsize = 15)\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "71eb38e3", + "metadata": {}, + "source": [ + "Above, we used `plt.pie()` in a way that we had not used it before.\n", + "\n", + "Under the hood, the `plt.pie()` function returns two default outputs, which we named: `patches` and`text`. The size of each wedge is dictated by the `patches` object. The `text` object consists of a list of labels for our data. Here, we needed to specifically assign `patches` and `text` objects so we could use `patches` as an argument for the `plt.legend()` function. \n", + "\n", + "The `plt.legend()` function has two required arguments. The first argument dictates **what** is being labeled. In our case, the wedges of the pie chart (*i.e.* the `patches` object) are being labeled. The second argument dictates **how** things are labeled. Here, we simply created a variable called labels, which consists of the six strings for the six wedges:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "b4eb218d", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['1970 (9.7 %)',\n", + " '1971 (10.5 %)',\n", + " '1972 (13.3 %)',\n", + " '1973 (16.5 %)',\n", + " '1974 (22.3 %)',\n", + " '1975 (27.7%)']" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "labels" + ] + }, + { + "cell_type": "markdown", + "id": "9721e589", + "metadata": {}, + "source": [ + "The other arguments, `bbox_to_anchor` and `fontsize`, are optional when using the `plt.legend()` function.\n", + "\n", + "The argument `bbox_to_anchor` designates the position in the plotting area where the legend will be, while the `fontsize` argument dictates the font size, in points, of the legend text.\n", + "\n", + "As mentioned previously, labeling can greatly enhance the efficiency of of a pie chart's ability to communicate information. Because some of the wedges are very similar in size, it can be hard to discern the numeric value associated with each year. Labeling each wedge with the percentage and associated category or making a legend that depicts this information leaves less room for ambiguity when it comes to the data." + ] + }, + { + "cell_type": "markdown", + "id": "e64c6b88", + "metadata": {}, + "source": [ + "## Conclusions\n", + "\n", + "In this section, we were introduced to the `plt.bar()` and `plt.pie()` functions to construct bar plots and pie charts, respectively.\n", + "\n", + "The `plt.bar()` function requires `x` and `height` arguments, which can be an array of number values, but other parameters can be included.\n", + "\n", + "The `plt.pie()` function only requires an `x` argument as an array of values and has other parameters that can be utilized well.\n", + "\n", + "Both of these types of visualizations are used for depicting categorical data.\n", + "\n", + "As a reminder, when deciding on whether to use a pie chart, consider certain attributes of the data, such as the number of categorical variables or the size of the proportions to be plotted. Below is a list of functions with linked documentation for your reference and further reading:\n", + "\n", + "- plt.bar( )\n", + "- plt.pie( )\n", + "- plt.boxplot( ) \n", + "- plt.title( )\n", + "- plt.ylim( )\n", + "- plt.xlim( )\n", + "- plt.ylabel( )\n", + "- plt.xlabel( )\n", + "- plt.xticks( )\n", + "- ax.bar( )\n", + "- ax.legend( )\n", + "- plt.tight_layout( )\n", + "- plt.figure( )\n", + "- plt.show( )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69a868f4", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/textbook/09/3/Numerical_Data.ipynb b/textbook/09/3/Numerical_Data.ipynb deleted file mode 100644 index 430a0480..00000000 --- a/textbook/09/3/Numerical_Data.ipynb +++ /dev/null @@ -1,779 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0fb30d10", - "metadata": {}, - "source": [ - "# Numerical Data\n", - "\n", - "Numerical data consists of values including integers and rational numbers. Temperature collections, height recordings, and numerical grades in a class are examples of numerical data. In this section, we will practice making histograms, scatter plots, and line graphs to represent numerical data.\n", - "\n", - "Again, we'll load the necessary libraries and data." - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "3e479dc7-ce8e-4ca7-ae35-79038cf4bf99", - "metadata": { - "tags": [ - "hide_cell" - ] - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "\n", - "from matplotlib import pyplot as plt\n", - "\n", - "sns.set_style('whitegrid')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "NorthAmerica_Military_USD_PercentGDP_Combined_csv = '''\\\n", - "Year,CAN-PercentGDP,MEX-PercentGDP,USA-PercentGDP,CAN-USD,MEX-USD,USA-USD\n", - "1960,4.18525654,0.673508659,8.993124587,1.702442711,0.084,47.34655267\n", - "1961,4.128312243,0.651780326,9.1560315,1.677820881,0.0864,49.87977061\n", - "1962,3.999216389,0.689655172,9.331672945,1.671313753,0.0992,54.65094261\n", - "1963,3.620650112,0.718685832,8.831891186,1.610091701,0.112,54.56121578\n", - "1964,3.402062837,0.677506775,8.051281106,1.657457283,0.12,53.43232706\n", - "1965,2.930260659,0.591269841,7.587247177,1.57470454,0.1192,54.56179126\n", - "1966,2.683282422,0.576379066,8.435300286,1.614422827,0.1304,66.44275153\n", - "1967,2.74792677,0.545217107,9.417795933,1.775500366,0.1336,78.39844224\n", - "1968,2.54364188,0.548510764,9.268454275,1.797265817,0.1488,84.32903122\n", - "1969,2.27378467,0.600160043,8.633263795,1.770108751,0.18,84.99016543\n", - "1970,2.188979696,0.497411659,8.032743584,1.889157918,0.1768,83.407993\n", - "1971,2.131485639,0.48765558,6.943069609,2.077659711,0.1912,78.23797989\n", - "1972,2.011818438,0.536568089,6.519756924,2.233737031,0.2424,80.70807097\n", - "1973,1.832601818,0.544217687,5.893870591,2.363060955,0.3008,81.46979441\n", - "1974,1.783813085,0.565744137,5.954111197,2.809465529,0.4072,89.27892034\n", - "1975,1.863541853,0.57358422,5.622679096,3.18091549,0.5048,92.08092875\n", - "1976,1.765927978,0.598103574,5.191071429,3.581805735,0.531576968,94.71525108\n", - "1977,1.8057636,0.534256205,5.155617351,3.752174526,0.437692986,104.665219\n", - "1978,1.848887401,0.504834431,4.943087248,3.969158477,0.518287193,113.3820637\n", - "1979,1.711245918,0.505297474,4.951991535,4.084145738,0.679663588,126.8799271\n", - "1980,1.764448615,0.416107383,5.153537467,4.744402251,0.810422204,143.6883549\n", - "1981,1.709915638,0.513301014,5.646541256,5.141128191,1.284948561,176.5588753\n", - "1982,1.954343585,0.495419418,6.814057094,6.017321456,0.858130163,221.6735426\n", - "1983,2.081196249,0.522866314,6.32114426,6.947104072,0.778556797,223.427165\n", - "1984,2.117188855,0.65981906,6.23641653,7.349795764,1.155945373,245.1491683\n", - "1985,2.097376234,0.676313139,6.453219205,7.460563318,1.241863652,272.1632293\n", - "1986,2.109197118,0.634622463,6.626522658,7.78013674,0.817296612,295.5462238\n", - "1987,2.062576371,0.580341889,6.420274023,8.694447168,0.813391574,304.0866487\n", - "1988,1.986767119,0.536145374,6.071277702,9.897335684,0.981914646,309.6612693\n", - "1989,1.934614309,0.517255829,5.871206008,10.74713469,1.153375828,321.8665588\n", - "1990,1.958793742,0.433081035,5.605175294,11.41463185,1.210872502,325.129314\n", - "1991,1.895444339,0.435402301,4.883429398,11.3385033,1.459136041,299.3727791\n", - "1992,1.8616877,0.469454656,4.970466808,10.78880312,1.824550066,325.033736\n", - "1993,1.821753504,0.442785494,4.604350295,10.26882262,2.122980338,316.7194437\n", - "1994,1.696680257,0.518830327,4.215264675,9.57737764,2.635284079,308.084\n", - "1995,1.554090071,0.450891531,3.860245792,9.176903908,1.562615372,295.8530977\n", - "1996,1.403752581,0.476484778,3.554982206,8.615884471,1.882873103,287.9606687\n", - "1997,1.246243202,0.458095854,3.405562244,7.945140183,2.184061042,293.1678258\n", - "1998,1.256293902,0.450450487,3.201558499,7.748607984,2.263223453,290.9960551\n", - "1999,1.241703064,0.460988776,3.085676783,8.21077854,2.652912012,298.0948913\n", - "2000,1.11808088,0.44604782,3.112242147,8.299385231,3.031454509,320.0863242\n", - "2001,1.137368973,0.442657004,3.123809803,8.375571425,3.229469276,331.8056106\n", - "2002,1.120852292,0.421606002,3.447618099,8.495399281,3.172268734,378.4631388\n", - "2003,1.115878799,0.405916547,3.827161045,9.958245602,2.960496802,440.5320696\n", - "2004,1.107966027,0.364898723,4.016312736,11.33648983,2.854385965,492.9993762\n", - "2005,1.110669655,0.355958931,4.090034876,12.98813296,3.123454978,533.203\n", - "2006,1.125832408,0.311171936,4.041627237,14.8098928,3.035131019,558.335\n", - "2007,1.188901783,0.401163918,4.079655081,17.41713993,4.223037646,589.586\n", - "2008,1.248621382,0.390513227,4.463827356,19.3420584,4.334654124,656.756\n", - "2009,1.377555631,0.501556275,4.88559968,18.93622605,4.514233914,705.917\n", - "2010,1.194338338,0.452734493,4.922641677,19.31568883,4.789031339,738.005\n", - "2011,1.193291895,0.465777803,4.840173995,21.39372086,5.498458542,752.288\n", - "2012,1.118404598,0.475987281,4.477401219,20.45210711,5.717035575,725.205\n", - "2013,1.0023672,0.507919455,4.046678879,18.51573121,6.473144378,679.229\n", - "2014,0.989925299,0.513829957,3.69589465,17.85364048,6.758693845,647.789\n", - "2015,1.152709374,0.466676122,3.477845166,17.93764189,5.468837812,633.829639\n", - "2016,1.164161567,0.495064414,3.418942337,17.78277554,5.33687574,639.856443\n", - "2017,1.351602232,0.436510296,3.313381294,22.26969632,5.062076646,646.752927\n", - "2018,1.324681094,0.477517407,3.316248808,22.72932758,5.839521271,682.4914\n", - "2019,1.27894142,0.52348249,3.427080181,22.20440844,6.650808254,734.3441\n", - "2020,1.415055841,0.573651659,3.741160091,22.75484713,6.116376582,778.2322\n", - "'''\n", - "\n", - "from io import StringIO\n", - "\n", - "NorthAmerica_Military_USD_PercentGDP_Combined_file = StringIO(NorthAmerica_Military_USD_PercentGDP_Combined_csv)\n", - "\n", - "military = pd.read_csv(NorthAmerica_Military_USD_PercentGDP_Combined_file, index_col='Year')" - ] - }, - { - "cell_type": "markdown", - "id": "5190be46", - "metadata": {}, - "source": [ - "## Histograms\n", - "\n", - "Histograms are a great way to view distributions of numerical data. In histogram plots, a numerical component of data is divided into what are called *bins*. As data points are assigned to their respective bins, the total number of data points in each bin is quantified and plotted, visualizing a distribution of frequencies. In the upcoming exercise, we will explore how to visualize a distribution of values in our dataset.\n", - "\n", - "\n", - "Let's examine military spending in the United States from 1960 to 2020. We can look at multiple ranges of dollar amounts spent on the military as our independent variable and organize them into bins. After, we can determine how many fiscal years fall into each of these bins and visualize the distribution.\n", - "\n", - "First, we will need to extract the data pertaining to the military spending in the United States. We will call it `hist_data`. Then, we will need to determine the minimum and maximum values of this subset of data so that we can determine the range of values." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a45eb875", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'min': 47.34655267, 'max': 778.2322}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hist_data = military[\"USA-USD\"]\n", - "\n", - "{\n", - " 'min': hist_data.min(),\n", - " 'max': hist_data.max(),\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "b21d2e3e", - "metadata": {}, - "source": [ - "We see that the minimum amount the United States spent on the military between the years of 1960 and 2020 was about \\$47 billion, while the maximum amount was about \\$780 billion.\n", - "\n", - "With this information, we will create a range for our bins, named `binnum`, with integers between 0 and 801, so that it is inclusive of all the data values. We make the interval of the range 100, giving us eight evenly spaced bins." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "ccfb3ade", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0, 100, 200, 300, 400, 500, 600, 700, 800]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "binnum = range(0, 801, 100)\n", - "\n", - "list(binnum)" - ] - }, - { - "cell_type": "markdown", - "id": "55e62707", - "metadata": {}, - "source": [ - "To graph the distribution of military spending, we will make a histogram by calling the `plt.hist()` funtion.\n", - "\n", - "Minimally, this function needs an input set of values. Additionally, we will specify the bins, so that they are evenly distributed on the x-axis. We do this by inputting `binnum` as our `bins` argument. If we do not specify the `bin` argument, the data will be divided into 10 bins." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "daae9915", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(hist_data, bins=binnum)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "6043632d", - "metadata": {}, - "source": [ - "Now that we have our plot, let's add additional details to make it more understandable:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "942043f1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(hist_data, bins=binnum)\n", - "plt.title(\"Distribution of Military Spending in the United States from 1960 to 2020\")\n", - "plt.ylabel('Counts of Fiscal Years')\n", - "plt.xlabel(\"Dollar Amount (USD)\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "5e52c962", - "metadata": {}, - "source": [ - "Awesome! From this plot, we can see that the United States had the highest frequency of fiscal years where \\$0 - \\$100 billion was spent on the military. We can also see that the \\$400 - \\$500 billion bin had the lowest frequency with only two years spending that range of money." - ] - }, - { - "cell_type": "markdown", - "id": "1af7e1d3", - "metadata": {}, - "source": [ - "## Scatter plots\n", - "\n", - "Next, we'll examine the use of a scatter plot as another visualization tool for numerical data.\n", - "\n", - "Scatter plots visualize the relationship between two numerical variables. For this exercise, let's examine the percentage of the GDP of Mexico spent on the military versus the absolute dollar amount (in USD) over 1960-2020.\n", - "\n", - "We'll simply extract the columns for this data and assign them to `mex_gdp` and `mex_usd`, respectively. Then, we can plot this data using the `plt.scatter()` function." - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "id": "deb40aae", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mex_gdp = military[['MEX-USD']]\n", - "\n", - "mex_usd = military['MEX-PercentGDP']\n", - "\n", - "plt.scatter(mex_gdp, mex_usd) # mex_gdp on the x-axis, mex_usd on the y-axis\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "4fba0f1f", - "metadata": {}, - "source": [ - "Looking at this scatter plot out of context, it would be hard to understand what the data means. Let's add the important details to make it clear:" - ] - }, - { - "cell_type": "code", - "execution_count": 142, - "id": "bc576939", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "(fig, ax) = plt.subplots()\n", - "\n", - "plt.scatter(mex_gdp, mex_usd)\n", - "\n", - "plt.title(\"% GDP vs. Absolute Spending on Military in Mexico 1960 - 2020\", pad=10)\n", - "\n", - "ax.set_ylabel('Spending in USD (Billions)')\n", - "ax.set_xlabel('Percentage of GDP')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "cd5d171b", - "metadata": {}, - "source": [ - "Now, we have a better understanding of the data, which depicts the relationship between the percentage of the Mexican GDP spent on the military and the total amount spent in USD from the years 1960 to 2020. \n", - "\n", - "In addition to this information, we can add a color scheme that will color each data point based on the year of collection. This adds another dimension of analysis, using year as a feature; the context of the spending relationship can be examined over time:" - ] - }, - { - "cell_type": "code", - "execution_count": 143, - "id": "23a45386", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "(fig, ax) = plt.subplots()\n", - "plt.title(\"% GDP vs. Absolute Spending on Military in Mexico 1960 - 2020\", pad=10)\n", - "ax.set_ylabel('Spending in USD (Billions)')\n", - "ax.set_xlabel('Percentage of GDP')\n", - "\n", - "\n", - "mex_years = mex_gdp.index\n", - "\n", - "plt.scatter(mex_gdp, mex_usd, c=mex_years, alpha=0.4, cmap='winter')\n", - "\n", - "plt.colorbar()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "20146a6e", - "metadata": {}, - "source": [ - "The `plt.scatter()` function minimally needs two arguments - *x* and *y* - which are array-like variables. Other optional arguments include `c`, which determines how to color the data points; `alpha`, which sets the opacity of the data points; and `cmap` which sets the Colormap used to color the data points. \n", - "\n", - "We used the years of the dataset (which we defined as the index earlier in this chapter) as our `c` argument to color the data points based on the year of collection. We used used the 'winter' Colormap as our `cmap` argument, but many other Colormaps are available for your choosing. A list of other possible Colormaps to explore is linked at the end of this section.\n", - "\n", - "The `plt.colorbar()` function displays a scale of the Colormap based on the feature used to color the data, which in our case is the year of collection." - ] - }, - { - "cell_type": "markdown", - "id": "30ba35e7", - "metadata": {}, - "source": [ - "## Line graphs\n", - "\n", - "Line graphs are used to visualize sequential numerical data. Using line graphs, we can easily see trends within data over time.\n", - "\n", - "Let's examine the spending (in USD) on the military in Canada in the 21st century (2000-2020).\n", - "\n", - "This can be done most quickly through the pandas Series." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "b26501e3-def6-48f3-80e5-d238e8220f9f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Year\n", - "2000 8.299385\n", - "2001 8.375571\n", - "2002 8.495399\n", - "2003 9.958246\n", - "2004 11.336490\n", - "2005 12.988133\n", - "2006 14.809893\n", - "2007 17.417140\n", - "2008 19.342058\n", - "2009 18.936226\n", - "2010 19.315689\n", - "2011 21.393721\n", - "2012 20.452107\n", - "2013 18.515731\n", - "2014 17.853640\n", - "2015 17.937642\n", - "2016 17.782776\n", - "2017 22.269696\n", - "2018 22.729328\n", - "2019 22.204408\n", - "2020 22.754847\n", - "Name: CAN-USD, dtype: float64" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "can_usd = military.loc[2000:2020, 'CAN-USD']\n", - "\n", - "can_usd" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "2f9a252d", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "can_usd.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "22bd9379-07f0-4900-99b2-bcafee5b2728", - "metadata": {}, - "source": [ - "To use matplotlib directly, we first extract the years of interest and assign it to the variable `years`." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "ab3bb392-3509-4adc-b1ae-4460db515862", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Int64Index([2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,\n", - " 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020],\n", - " dtype='int64', name='Year')" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "years = can_usd.index\n", - "\n", - "years" - ] - }, - { - "cell_type": "markdown", - "id": "148a691a-3fd4-4501-800d-f926099108db", - "metadata": {}, - "source": [ - "Then, we call `plt.plot()` and specifiy the years on the x-axis and the spending on the y-axis." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "88bcffa6-027e-4208-aeab-9c61649b913c", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(years, can_usd);" - ] - }, - { - "cell_type": "markdown", - "id": "d90f500d", - "metadata": {}, - "source": [ - "We can see from the graph that Canada's spending on the military has increased overall since 2000. The country had a period of time (around 2011 to 2017) where military spending was decreasing consistently.\n", - "\n", - "Let's add the data for Mexico as well to see the country's spending in the 21st century." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "3eff4b2a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mex_usd = military.loc[2000:2099, 'MEX-USD']\n", - "\n", - "plt.plot(years, can_usd)\n", - "plt.plot(years, mex_usd);" - ] - }, - { - "cell_type": "markdown", - "id": "81e3e39f", - "metadata": {}, - "source": [ - "We can now see that the military spending for both Mexico and Canada is vastly different. However, just looking at this graph out of context, we wouldn't be able to tell which line corresponds to which country. Let's add some descriptive details, such as line labels, a title, and axis labels. Let's also fix the x-axis ticks to set an interval for every five years." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "50c364ea", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(years, can_usd, label='Canada')\n", - "plt.plot(years, mex_usd, label='Mexico')\n", - "\n", - "plt.title(\"Military Spending in Mexico and Canada in the 21st Century\", pad=10)\n", - "plt.ylabel('USD (Billions)')\n", - "plt.xlabel('Year')\n", - "plt.xticks(range(2000, 2025, 5))\n", - "\n", - "plt.legend(loc=\"best\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "3ab35d3c", - "metadata": {}, - "source": [ - "We can see that the overall trend of military spending in Mexico also increased from 2000 to 2020. However, this increase was a lot less drastic than observed in Canada. Mexico's military spending was a steady rise from about \\$3 billion to \\$6 billion over the course of 20 years, while Canada's spending rose from \\$8 billion to about \\$23 billion over the same period of time.\n", - "\n", - "Let's add data from the United States to see the trends in all North American countries." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "18c12059", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "usa_usd = military.loc[2000:2099, 'USA-USD']\n", - "\n", - "plt.plot(years, can_usd, label='Canada')\n", - "plt.plot(years, mex_usd, label='Mexico')\n", - "plt.plot(years, usa_usd, label='United States')\n", - "\n", - "plt.title(\"Military Spending in North America in the 21st Century\", pad=10)\n", - "plt.ylabel('USD (Billions)')\n", - "plt.xlabel('Year')\n", - "plt.xticks(range(2000, 2025, 5))\n", - "\n", - "plt.legend(loc=\"best\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "ae241382", - "metadata": {}, - "source": [ - "With the addition of the data from the United States, it's diffiult to discern the data from Canada and Mexico. Because the spending on the military in the United States was a lot higher, plotting all three datasets on the same graph with the same axis does not allow us to clearly see trends in the other countries.\n", - "\n", - "To address this, we can graph the data for each country separately with axis limits that are tailored to each country. If we graph this data side by side, we can see the trends in each country while acknowledging that the axis intervals for each country provides a numerical context for cross-comparisons." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "af08cad2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "(fig, (ax1, ax2, ax3)) = plt.subplots(1, 3, figsize=(15, 3))\n", - "\n", - "fig.suptitle('Military Spending in North America in the 21st Century', y=1.1, fontsize=15)\n", - "\n", - "ax1.plot(years, can_usd)\n", - "ax1.set_title('Canada')\n", - "ax1.set_ylim([8, 24])\n", - "ax1.set_xlabel('Years')\n", - "ax1.set_ylabel('USD (Billions)')\n", - "\n", - "ax2.plot(years, mex_usd, color='orange')\n", - "ax2.set_title('Mexico')\n", - "ax2.set_ylim([2.5, 7])\n", - "ax2.set_xlabel('Years')\n", - "ax2.set_ylabel('USD (Billions)')\n", - "\n", - "ax3.plot(years, usa_usd, color='green')\n", - "ax3.set_title('United States')\n", - "ax3.set_ylim([300, 800])\n", - "ax3.set_xlabel('Years')\n", - "ax3.set_ylabel('USD (Billions)')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "efee0c99", - "metadata": {}, - "source": [ - "Now that we've created separate plots, we can see the trends for all three countries over the last 20 years. All three countries seem to have decreased spending around 2011 and 2018. By observing the difference in scale, we can also see that while the trends are similar, the magnitude of spending was very different between Canada, Mexico, and the United States.\n", - "\n", - "In creating these plots, we used `plt.subplots()` in a way that we had not used it before. Here, we defined our `fig` argument, as well as three `axes` arguments, `ax1`, `ax2`, and `ax3`. This allowed us to create three separate plotting areas, bounded by three different axes, that are contained within one figure." - ] - }, - { - "cell_type": "markdown", - "id": "886787a1", - "metadata": {}, - "source": [ - "## Conclusions\n", - "\n", - "In this section, we learned how to use `plt.hist()`, `plt.scatter()`, and `plt.plot()` to create histograms, scatter plots, and line graphs as a means of visualizing numerical data.\n", - "\n", - "The `plt.scatter()` and `plt.plot()` functions require numerical arrays that serve as `x` and `y` arugments.\n", - "\n", - "The `plt.hist()` function requires an array of values for plotting distributions of data.\n", - "\n", - "We also learned about a number of other functions that can be used to enhance and annotate our plots. Documentation for the functions used in this section, and related functions, are listed below:\n", - "\n", - "- plt.hist( )\n", - "- plt.scatter( )\n", - "- plt.plot( )\n", - "- fig.suptitle( )\n", - "- ax.set_title( )\n", - "- ax.set_ylim( )\n", - "- ax.set_xlim( )\n", - "- ax.set_ylabel( )\n", - "- ax.set_xlabel( )\n", - "- plt.colorbar( )\n", - "- Colormap options" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "37ae5ff6", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/textbook/09/4/img/BoxandWhisker.png 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z+)}J>1#NprPirZ3&sB5t#y_+{yYZ3NB(d7(_C7%%%Fc00}yz+`njxgN@xNA3Am{W literal 0 HcmV?d00001 diff --git a/textbook/09/4/other-viz.ipynb b/textbook/09/4/other-viz.ipynb new file mode 100644 index 00000000..36644a6b --- /dev/null +++ b/textbook/09/4/other-viz.ipynb @@ -0,0 +1,1021 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "02ee0b6e", + "metadata": {}, + "source": [ + "# Other Visualization Techniques\n", + "\n", + "In this section, we will introduce other data visualizations that can be used to represent categorical or numerical data. We will discuss another visualization library called `seaborn`. While the `matplotlib` library can be used to create most data visualizations in Python, there are some restrictions when it comes to customization. The `seaborn` library provides many flexible options when creating visualizations. In the upcoming exercises, we will use a combination of `seaborn` and `matplotlib` to make visualizations, including box and whisker plots, heatmaps, and area plots.\n", + "\n", + "Along with the previous data and libraries we have been using, we will import `seaborn` as a common convention: `sns`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "41e82e0c", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from matplotlib import pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "plt.style.use('fast')\n", + "\n", + "\n", + "NorthAmerica_Military_USD_PercentGDP_Combined_csv = '''\\\n", + "Year,CAN-PercentGDP,MEX-PercentGDP,USA-PercentGDP,CAN-USD,MEX-USD,USA-USD\n", + "1960,4.18525654,0.673508659,8.993124587,1.702442711,0.084,47.34655267\n", + "1961,4.128312243,0.651780326,9.1560315,1.677820881,0.0864,49.87977061\n", + "1962,3.999216389,0.689655172,9.331672945,1.671313753,0.0992,54.65094261\n", + "1963,3.620650112,0.718685832,8.831891186,1.610091701,0.112,54.56121578\n", + "1964,3.402062837,0.677506775,8.051281106,1.657457283,0.12,53.43232706\n", + "1965,2.930260659,0.591269841,7.587247177,1.57470454,0.1192,54.56179126\n", + "1966,2.683282422,0.576379066,8.435300286,1.614422827,0.1304,66.44275153\n", + "1967,2.74792677,0.545217107,9.417795933,1.775500366,0.1336,78.39844224\n", + "1968,2.54364188,0.548510764,9.268454275,1.797265817,0.1488,84.32903122\n", + "1969,2.27378467,0.600160043,8.633263795,1.770108751,0.18,84.99016543\n", + "1970,2.188979696,0.497411659,8.032743584,1.889157918,0.1768,83.407993\n", + "1971,2.131485639,0.48765558,6.943069609,2.077659711,0.1912,78.23797989\n", + "1972,2.011818438,0.536568089,6.519756924,2.233737031,0.2424,80.70807097\n", + "1973,1.832601818,0.544217687,5.893870591,2.363060955,0.3008,81.46979441\n", + "1974,1.783813085,0.565744137,5.954111197,2.809465529,0.4072,89.27892034\n", + "1975,1.863541853,0.57358422,5.622679096,3.18091549,0.5048,92.08092875\n", + "1976,1.765927978,0.598103574,5.191071429,3.581805735,0.531576968,94.71525108\n", + "1977,1.8057636,0.534256205,5.155617351,3.752174526,0.437692986,104.665219\n", + "1978,1.848887401,0.504834431,4.943087248,3.969158477,0.518287193,113.3820637\n", + "1979,1.711245918,0.505297474,4.951991535,4.084145738,0.679663588,126.8799271\n", + "1980,1.764448615,0.416107383,5.153537467,4.744402251,0.810422204,143.6883549\n", + "1981,1.709915638,0.513301014,5.646541256,5.141128191,1.284948561,176.5588753\n", + "1982,1.954343585,0.495419418,6.814057094,6.017321456,0.858130163,221.6735426\n", + "1983,2.081196249,0.522866314,6.32114426,6.947104072,0.778556797,223.427165\n", + "1984,2.117188855,0.65981906,6.23641653,7.349795764,1.155945373,245.1491683\n", + "1985,2.097376234,0.676313139,6.453219205,7.460563318,1.241863652,272.1632293\n", + "1986,2.109197118,0.634622463,6.626522658,7.78013674,0.817296612,295.5462238\n", + "1987,2.062576371,0.580341889,6.420274023,8.694447168,0.813391574,304.0866487\n", + "1988,1.986767119,0.536145374,6.071277702,9.897335684,0.981914646,309.6612693\n", + "1989,1.934614309,0.517255829,5.871206008,10.74713469,1.153375828,321.8665588\n", + "1990,1.958793742,0.433081035,5.605175294,11.41463185,1.210872502,325.129314\n", + "1991,1.895444339,0.435402301,4.883429398,11.3385033,1.459136041,299.3727791\n", + "1992,1.8616877,0.469454656,4.970466808,10.78880312,1.824550066,325.033736\n", + "1993,1.821753504,0.442785494,4.604350295,10.26882262,2.122980338,316.7194437\n", + "1994,1.696680257,0.518830327,4.215264675,9.57737764,2.635284079,308.084\n", + "1995,1.554090071,0.450891531,3.860245792,9.176903908,1.562615372,295.8530977\n", + "1996,1.403752581,0.476484778,3.554982206,8.615884471,1.882873103,287.9606687\n", + "1997,1.246243202,0.458095854,3.405562244,7.945140183,2.184061042,293.1678258\n", + "1998,1.256293902,0.450450487,3.201558499,7.748607984,2.263223453,290.9960551\n", + "1999,1.241703064,0.460988776,3.085676783,8.21077854,2.652912012,298.0948913\n", + "2000,1.11808088,0.44604782,3.112242147,8.299385231,3.031454509,320.0863242\n", + "2001,1.137368973,0.442657004,3.123809803,8.375571425,3.229469276,331.8056106\n", + "2002,1.120852292,0.421606002,3.447618099,8.495399281,3.172268734,378.4631388\n", + "2003,1.115878799,0.405916547,3.827161045,9.958245602,2.960496802,440.5320696\n", + "2004,1.107966027,0.364898723,4.016312736,11.33648983,2.854385965,492.9993762\n", + "2005,1.110669655,0.355958931,4.090034876,12.98813296,3.123454978,533.203\n", + "2006,1.125832408,0.311171936,4.041627237,14.8098928,3.035131019,558.335\n", + "2007,1.188901783,0.401163918,4.079655081,17.41713993,4.223037646,589.586\n", + "2008,1.248621382,0.390513227,4.463827356,19.3420584,4.334654124,656.756\n", + "2009,1.377555631,0.501556275,4.88559968,18.93622605,4.514233914,705.917\n", + "2010,1.194338338,0.452734493,4.922641677,19.31568883,4.789031339,738.005\n", + "2011,1.193291895,0.465777803,4.840173995,21.39372086,5.498458542,752.288\n", + "2012,1.118404598,0.475987281,4.477401219,20.45210711,5.717035575,725.205\n", + "2013,1.0023672,0.507919455,4.046678879,18.51573121,6.473144378,679.229\n", + "2014,0.989925299,0.513829957,3.69589465,17.85364048,6.758693845,647.789\n", + "2015,1.152709374,0.466676122,3.477845166,17.93764189,5.468837812,633.829639\n", + "2016,1.164161567,0.495064414,3.418942337,17.78277554,5.33687574,639.856443\n", + "2017,1.351602232,0.436510296,3.313381294,22.26969632,5.062076646,646.752927\n", + "2018,1.324681094,0.477517407,3.316248808,22.72932758,5.839521271,682.4914\n", + "2019,1.27894142,0.52348249,3.427080181,22.20440844,6.650808254,734.3441\n", + "2020,1.415055841,0.573651659,3.741160091,22.75484713,6.116376582,778.2322\n", + "'''\n", + "\n", + "from io import StringIO\n", + "\n", + "NorthAmerica_Military_USD_PercentGDP_Combined_file = StringIO(NorthAmerica_Military_USD_PercentGDP_Combined_csv)\n", + "\n", + "military = pd.read_csv(NorthAmerica_Military_USD_PercentGDP_Combined_file, index_col='Year')\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "id": "589f64b7", + "metadata": {}, + "source": [ + "## Box and Whisker Plots\n", + "\n", + "Box and whisker plots are a useful data visualization method because they intrinsically display multiple summary statistics simultaneously. The central line of each box within a box and whisker plot is the *median*. The median (also known as the second quartile, $Q_2$) is a value within a dataset that lies within the middle, separating the higher half and the lower half of the dataset. The median is a valuable measure of center for a dataset because it is not greatly affected by outliers, as opposed to the *mean*.\n", + "\n", + "If *A* represents a dataset with the values listed below, the median of *A* can be determined by sorting the numbers from low to high and determining the value that falls into the middle (which in this case is 109):\n", + "\n", + "\n", + "\n", + "\n", + "In the case of a dataset with an even number of values, the median can be calculated as the mean of the middle two values, as shown in the example dataset *B* below:\n", + "\n", + "\n", + "\n", + "\n", + "Box and whisker plots also show the *lower quartile*, *the upper quartile*, the *interquartile range*, *outliers*, the *minimum*, and the *maximum*. The lower quartile $\\left( Q_1\\right)$ is the value where the lowest 25% of the data points within a dataset lie. It is represented by the lower end of the box. \n", + "\n", + "On the other side, the upper quartile $\\left( Q_3\\right)$ is the value in which the highest 25% of the dataset resides. It is represented by the higher end of a box.\n", + "\n", + "\n", + "The interquartile range (IQR), is the difference between the upper quartile and the lower quartile. The IQR is used to make the length of a box and is represented by the equation:\n", + "\n", + " > $IQR=Q_3-Q_1$\n", + " \n", + "\n", + "Outliers are data points that are less than $Q_1-1.5×IQR$ or greater than $Q_3+1.5×IQR$. These data points are shown beyond the extremity of the whiskers.\n", + "\n", + "Lastly, the minimum and maximum values are represented by the lowest and highest values, respectively, that are within the range $Q_1-1.5×IQR$ and $Q_3+1.5×IQR$. Essentially, they are the lowest and highest values that do not qualify as outliers. The lower whisker represents the minimum value, while the upper whisker represents the maximum value.\n", + "\n", + "Below is a pictorial summary of the major components of a box and whisker plot with an accompanying set of numbers, *A*:\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "0b21bf29", + "metadata": {}, + "source": [ + "We will use a box and whisker plot to examine the percentage GDP spending on the military for each country in the '60s as a way to examine multiple statistics for each country in this time period.\n", + "\n", + "First, we extract the data of interest:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3f2494b4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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19692.2737850.6001608.633264
\n", + "
" + ], + "text/plain": [ + " CAN-PercentGDP MEX-PercentGDP USA-PercentGDP\n", + "Year \n", + "1960 4.185257 0.673509 8.993125\n", + "1961 4.128312 0.651780 9.156031\n", + "1962 3.999216 0.689655 9.331673\n", + "1963 3.620650 0.718686 8.831891\n", + "1964 3.402063 0.677507 8.051281\n", + "1965 2.930261 0.591270 7.587247\n", + "1966 2.683282 0.576379 8.435300\n", + "1967 2.747927 0.545217 9.417796\n", + "1968 2.543642 0.548511 9.268454\n", + "1969 2.273785 0.600160 8.633264" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "the60s = military.loc[1960:1969, ['CAN-PercentGDP', 'MEX-PercentGDP', 'USA-PercentGDP']]\n", + "\n", + "the60s" + ] + }, + { + "cell_type": "markdown", + "id": "097daad0", + "metadata": {}, + "source": [ + "It is possible to make boxplots using using `pyplot` or the dataframe method `plot.box()`, as shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "34aca023", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Creates a boxplot using pyplot from matplotlib\n", + "plt.boxplot(the60s)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "adda1f5f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Creates a boxplot using the dataframe method plot.box()\n", + "the60s.plot.box()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a12f1be2", + "metadata": {}, + "source": [ + "Plotting using these approaches, the graphs show data within the columns of interest, but depending on the approach used, we see that the column title may or may not be used as categorical indicators on the x-axis. Furthermore, while these plots do the job of displaying the median and interquartile range, adding individual data points will allow for viewers to more easily see the spread of the data. The addition of axis labels, a title, and some color would also enhance this plot and make it more aesthetically pleasing. \n", + "\n", + "We can accomplish this using a combination of functions from `matplotlib` and `seaborn`. Using the `sns.boxplot()` and `sns.swarmplot()` functions will allow us to create a boxplot with data points overlayed on top:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f5f1e20c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = sns.boxplot(data=the60s, palette=\"Set2\", linewidth=1)\n", + "ax = sns.swarmplot(data=the60s, palette=\"Set2\", linewidth=0.5, edgecolor = \"black\")\n", + "plt.xticks(ticks = [0,1,2], labels = ['Canada', 'Mexico', 'United States'])\n", + "plt.ylabel(\"Percent of GDP\")\n", + "plt.title(\"% GDP spent on the military in North America from 1960-1969\")\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "62687a9d", + "metadata": {}, + "source": [ + "Now that we have proper labeling, we can see the median, upper quartile, and lower quartile of the percentage of the each country's GDP spent on the military from 1960 to 1969. A noticeable observation this plot shows is that Mexico not only spent a small percentage of their GDP on the military (less than 2%), but the percentage of spending during this decade had very little variability. This makes it hard to see what the median, upper quartile, and lower quartile are for Mexico. The issue of being able to visually resolve displays of data is a common one that data scientists encounter." + ] + }, + { + "cell_type": "markdown", + "id": "913ed316", + "metadata": {}, + "source": [ + "## Heatmaps\n", + "\n", + "A heatmap is a matrix of data points depicted through a color gradient. Heatmaps are a great way to visualize data when you want to look at a multidimensional comparison of many variables. Heatmaps can be made from `matplotlib`, but this process may not be as straightforward to some. On the other hand, `seaborn` has a function dedicated to generations of heatmaps called `sns.heatmap()`. For your reference, both the `matplotlib` and `seaborn` approaches for constructing heatmaps are listed below.\n", + "\n", + "\n", + "We can use a heatmap to visualize variables that have a large number of values, such as the percentage of a country's GDP spent on the military from 1960 to 2020. Because Mexico has not spent over 1% of its GDP on the military in this time frame, we will look at these numbers for the U.S. and Canada. We will do this using the `seaborn` library.\n", + "\n", + "\n", + "To do this, we will first subset our data:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1ab14198", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CAN-PercentGDPUSA-PercentGDP
Year
19604.1852578.993125
19614.1283129.156031
19623.9992169.331673
19633.6206508.831891
19643.4020638.051281
.........
20161.1641623.418942
20171.3516023.313381
20181.3246813.316249
20191.2789413.427080
20201.4150563.741160
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61 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " CAN-PercentGDP USA-PercentGDP\n", + "Year \n", + "1960 4.185257 8.993125\n", + "1961 4.128312 9.156031\n", + "1962 3.999216 9.331673\n", + "1963 3.620650 8.831891\n", + "1964 3.402063 8.051281\n", + "... ... ...\n", + "2016 1.164162 3.418942\n", + "2017 1.351602 3.313381\n", + "2018 1.324681 3.316249\n", + "2019 1.278941 3.427080\n", + "2020 1.415056 3.741160\n", + "\n", + "[61 rows x 2 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "perc_gdp = military[['CAN-PercentGDP', 'USA-PercentGDP']]\n", + "perc_gdp" + ] + }, + { + "cell_type": "markdown", + "id": "93db9fb9", + "metadata": {}, + "source": [ + "Next, we will use the `sns.heatmap()` function to generate a heatmap for the subsetted data. We will use the dataframe `perc_gdp` as an argument for the `data` parameter. Other parameters such as `cmap`, `linewidth`, and `linecolor` allow for customization of heatmap. The `cbar_kws` parameter alters components of the color bar, and the `vmin` and `vmax` parameters set the minimum and maximum values of the colorbar, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4125b38d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(6,15)) \n", + "\n", + "sns.heatmap(data = perc_gdp, cmap='BuGn', linewidth=2, linecolor=\"grey\", # colormap, line width, and color specified,\n", + " cbar_kws={'label': 'Percentage of GDP'}, vmin=0, vmax=10) # color bar labeled, min and max values set\n", + "plt.title('Births per year in each state') # title added\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0a5f744d", + "metadata": {}, + "source": [ + "Above, we see the matrix of values of the percentage of the GDP spent on the military in the U.S. and Canada from 1960 to 2020. Interestingly, Canada's percentage of the GDP going toward the military appeared to decrease steadily over the years." + ] + }, + { + "cell_type": "markdown", + "id": "334804bf", + "metadata": {}, + "source": [ + "## Area Plots\n", + "\n", + "An area plot is a specialized line graph that can be used to show trends of multiple variables in a dataset over a period of time. In an area plot, data points over time are connected to create a trend line and the region formed under the line is filled with a solid color. A useful adaption of an area plot is that it can be constructed in a way that shows the a proportional relationship of each variable to all variables over time, which can be a great alternative to using multiple pie charts to examine temporal trends.\n", + "\n", + "We will use an area plot to examine the proportion of USD spent between Canada and Mexico from 1960 to 2020. To do this, we will need to extract the data of interest and then calculate the proportion of money spent for each country. The following code takes the USD spent on the military for Canada and Mexico:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4be3c8fe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CAN-USDMEX-USD
Year
19601.7024430.084000
19611.6778210.086400
19621.6713140.099200
19631.6100920.112000
19641.6574570.120000
.........
201617.7827765.336876
201722.2696965.062077
201822.7293285.839521
201922.2044086.650808
202022.7548476.116377
\n", + "

61 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " CAN-USD MEX-USD\n", + "Year \n", + "1960 1.702443 0.084000\n", + "1961 1.677821 0.086400\n", + "1962 1.671314 0.099200\n", + "1963 1.610092 0.112000\n", + "1964 1.657457 0.120000\n", + "... ... ...\n", + "2016 17.782776 5.336876\n", + "2017 22.269696 5.062077\n", + "2018 22.729328 5.839521\n", + "2019 22.204408 6.650808\n", + "2020 22.754847 6.116377\n", + "\n", + "[61 rows x 2 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "usd = military[['CAN-USD', 'MEX-USD']]\n", + "usd" + ] + }, + { + "cell_type": "markdown", + "id": "8b013073", + "metadata": {}, + "source": [ + "Now that we have this data, we can begin to calculate the proportion of USD for each country. First, let's find the total between the two countries. We can do this by using the `apply()` method that we learned about in [Chapter 7](../7/1/Functions_to_DataFrames.ipynb). Because we want to find the total for each year, we need to apply the `np.sum()` function across columns (in other words, `axis=1`):" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b4eb14b0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CAN-USDMEX-USDTotalCAN-PropMEX-Prop
Year
19601.7024430.0840001.7864430.9529790.047021
19611.6778210.0864001.7642210.9510270.048973
19621.6713140.0992001.7705140.9439710.056029
19631.6100920.1120001.7220920.9349630.065037
19641.6574570.1200001.7774570.9324880.067512
..................
201617.7827765.33687623.1196510.7691630.230837
201722.2696965.06207727.3317730.8147920.185208
201822.7293285.83952128.5688490.7955980.204402
201922.2044086.65080828.8552170.7695110.230489
202022.7548476.11637728.8712240.7881500.211850
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61 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " CAN-USD MEX-USD Total CAN-Prop MEX-Prop\n", + "Year \n", + "1960 1.702443 0.084000 1.786443 0.952979 0.047021\n", + "1961 1.677821 0.086400 1.764221 0.951027 0.048973\n", + "1962 1.671314 0.099200 1.770514 0.943971 0.056029\n", + "1963 1.610092 0.112000 1.722092 0.934963 0.065037\n", + "1964 1.657457 0.120000 1.777457 0.932488 0.067512\n", + "... ... ... ... ... ...\n", + "2016 17.782776 5.336876 23.119651 0.769163 0.230837\n", + "2017 22.269696 5.062077 27.331773 0.814792 0.185208\n", + "2018 22.729328 5.839521 28.568849 0.795598 0.204402\n", + "2019 22.204408 6.650808 28.855217 0.769511 0.230489\n", + "2020 22.754847 6.116377 28.871224 0.788150 0.211850\n", + "\n", + "[61 rows x 5 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "usd['Total'] = usd.apply(np.sum, axis=1)\n", + "usd['CAN-Prop'] = usd['CAN-USD']/usd['Total']\n", + "usd['MEX-Prop'] = usd['MEX-USD']/usd['Total']\n", + "usd" + ] + }, + { + "cell_type": "markdown", + "id": "8868b9a1", + "metadata": {}, + "source": [ + "Now we can begin to make the area plot with the calculated proportions. To do these, we will use the `plt.stackplot()` function. This function first takes an array-like object for the x-values, followed by arrays of the y-values that are to be stacked. The `labels` parameter dictates how each y-value is to be labeled, which can be visualized in the legend using `plt.legend()`:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f005d3c1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.stackplot(usd.index, usd['CAN-Prop'], usd['MEX-Prop'], labels=['Canada','Mexico'])\n", + "plt.legend(loc='lower left')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "dfad8561", + "metadata": {}, + "source": [ + "Now that we have made the plot, we can improve it by adding a descriptive title. As an aesthetic feature, we can also make this graph so that it takes up the entire plotting area by using `plt.margins`. This function accepts an x and y value, respectively, to indicate where the margins begin on each axis. To get rid of the margins, we will use 0 for each value. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "01ade042", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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jjFvRdvwIcGtovrYL1h/MrAe+seoo/KXdP5nZHuWU8RU+ubk9WM6NzGz/oF/UfXVlamL/uomZNcIvU4D04HtJv13MrJmZNTSzUfhlfQ+Ac2468F/gz2aWHuwzRrD5GPIMcFaw/FriGz8+FSOM8o5Xcc/HYN36CN9o9ZFyyn8G+GOwDXXGtxF6Khi3OfAu8LlzbnSMceOtxybVmjw45z4Arsdn7vPxjY5GlBnsDfxO7Hv8zvnxoPsT+APIZ/jW6OuAiyuY1hrgVuBz86fn+pfpn49vGX4F/hTYn4AjnXNLaqheUd0CTMJntz8C3wbdcM79jF+xZgR164xvsX0S/lTSo1TxVkXn3Dzn3NY+o+EjfLa6wMxK5ucFwE1mVoBPhGKdDi3rGXxDy+cqGGYZvjV2Dj6Beg74uyt9q+sL+J3vUnzj0lEAzrkC/A5hBD7TX4BvYV7pfczBunI8vm1APv464udx1qusR/GZ/2T8XSnj8QfHogrGedPMVuHrfCu+UVjJr4mr8Y2tJpg/Xf4B/sxCrPUHfJKQxuZfVp/iD36fhaZZUbmT8MvhQfwyycVfPorMObcUfzfURmBisN58iD+7kIvf2b2Db6Q2C78/iOuyZXBW5W58G6eF+PUrvNweDcr/Ab/dvRYatwCfJI4N6ngS/oxaLDfiLwWtwO/PXqtg2LJxvo1vG/QRvs4fRRi3ou34fnzM7wXzdQL+0l0qftu5wzn3g3MuB5+EPGtl7ut3zhXhb/fbEX+nwBx8+yGIuK+Ooy41sX8NK7kTDvzZ2/Bl9yPwl3CW4RPpwc658G2vI/FnB/Lxy/f64Mwnzrl38G1YPsbPo1nESABjHK+izMez8YnHDRZ6dlCo/7/wbcJ+xJ9leyvoBv5s4974BC383KHtotYjrOQ2slphZg5/2j231iYqSc3MTsXfYjRgK8p4Ct/Qa4vnjySr4BftI865spfWRESSXr16PLXULcGp2gvw91bXa2aWYWZDzV9Cy8Rn9q8nOi4RkapQ8hAn84/sXGRmP8Xob2b2DzPLNbPJZrZnbcdYlwTX0RfjTyu/kOBwaoPhT3Evw1+2mIa/vCMiUufU6mWLuszMDsRfN3vGObdbOf2H4q9XDcXfR31/0HpXRESkXtGZhzg55z7DN8aLZRg+sXDOuQlASwu9uERERKS+SLYXTNVlmZRuDT4n6Da/7IBmdi7+RSY0adRwr507x3sHmoiIAHwzY8kS51xFD0aTGqTkIQGcc2MIGgn27drCTTplQ4IjEhGpW+zGuJ/IKTVAly2qz1xKPy2u5BniIiIi9YqSh+ozDjg1uOuiP7DCObfFJQsREZG6Tpct4mRmL+JfLNTWzObg79NPAwgeFzoef6dFLv7lN2ckJlIREZGapeQhTs65kZX0d/j3BoiIiNRrumwhIiIikSh5EBERkUiUPIiIiEgkSh5EREQkEjWYTLD1qU1Z0HlfGhetIH3DClLXLyVl3TKsuDDRoYmIiJRLyUOCzd3YjP4zSt/VaeY4st0Shrf4hT4bvqX5km+xIj2FUkREkoOShyTknPHmona8uagdMIA2DTcyqkMeQxv9SPf8D0lZsyTRIYqIyDZMyUMdkL8hjfvzunE/3UhPOYqzM2dzfPoEuiz6CNuwKtHhiYjINkbJQx2zvjiFh/K68hBdaZZ6HJdmTWeftBm0cktpvjGfjA35pK1ZhK1fkehQRUSknlLyUIcVFKZyy8ydgJ226NcirZDdmq5mp8Yr2SF9BdkNltGBfNoVzqdFQS4NVsV+7UZx43Ysb74Tq1Nb0GnF96QW6P1eIiKymZKHemrFxlQ+X9aCz5e1oPTLPr3MRus5qFU+fRsvpHvKPBbTkklrM/lgWQd+XZoBS0uGPJl9Wq7guNYz6Zcyjazlk2iwal5tVkVERJKM+VcySKK07rKzaz7y7kSHEclt2//IiOWPkrJWDTdFJDHsxpXfOOf6JjqObZUeEiWRXftbLw5edye52cNxWIXDuvRmFDXtxIaWO7Km7e4s77gvyzruj7MGtRTttqmy5SIisjV02UKqZPbaRhyWM5yTOu3HX+xRGi39GYCipp2Y03JvPi/elRcXd+PHFU3KHX9gm5Hc2uINOs57vzbDrvc2tOzGhGZHcM/CPizdmMphrRezT8Y8drJZdFybS6NlOVjR+kSHKSJ1nC5bJFhdvGxRVnpKMVdsl8vnBe34NL9VpHGP77iA69LH0mLhhBqKrv4rbtSSnHaH81hBf15Z0LHCYZs0KOb4DvMY0uRXeq7/nqZLfsCKN9ZSpCLVR5ctEkvJQ4LVh+ShOvwheyaXFD9PRv6URIdSp7i0xhzJ/UwpKP8MT2XaNdzIiI5z6NFwKauLG7K6OI1VxQ0pKEqjgTku4zkylvxUzVGLbD0lD4ml5CHBlDxs1sCKubvb9xy99AlS1i6tfIQ6zqWk8lPWCHbLewFzxVUq45vsMxieM6iaI9sso0ERz+/wIX3ynqlyjCI1QclDYqnBpCSNIpfCZdP35MC1d/Nz9ok12qgyL+tIbmpzBws6D0pI402X2oh/tL2Bo34dynfZp1apjOKM1lyUd1A1R1ba2qIG/P7Xw7mh1R0UNsus0WmJSN2h5EGSzpx16QzOGcYfmt7Hig79q7386dnDOXj6CJ6Ym03/GWcwvOHDfJd9GsWNorXXqCrXsCk3Nr+Je2d3A2Bk7mGsatcncjkftBnF/HUNqzu8cj09L5MDCm4lL+t3tTI9EUluSh4kab2zuA27z7qE+9vfwrrWO1dLmdOyR3BY7u8pcptX/W9XNOXYnCPos/p+xmddjkupuZuQijNac0XGLTw1L2tTt/XFKZy+8nxcevO4yylslsnlM/euiRBjmr+uIQfknsyX2efW6nRFJPkoeZCkd+/sbuw6/zqe73wthc23fFpmvL7PPpUhOUfjXPnPQFixMZULcvfm+Y5X18hzEoqadOSclJt4bWH7LfpNWtGMJ1pfFndZY5udyurCxDwrY2TOwczRGQiRbZqSB6kTilwKf56xG7vn/43xWZdT3LhdpPEnZp/NMTmD4xr2uhm78mbWH6sSZkwurQkji27gw/zWMYe5+bedmZ49vNKy1rXemet/27U6w4tsyMwTWd1uj4TGICKJo+RB6pTVRSlckLs3vVbcy5OdrmdFh34xh3XWgIL2ffl356s5MefQSNO5JHcvPs6+cGvD3WRu+4P4annllyWOmzmMDa26VzjMww1GlbrskggFhakMX3aRGlGKbKOUPEidtLoohRt/24XdZ13K6Y0f4sfskylu1BKX3ox5mYN5vvO1HMKj9Jr9R66csXuVpnFGzv58k31GtcT7n8L4Gn4u25jKJYWX4NLKf27Dyg79uD+vW7XEtLV+XtWYy7g6ZqwiUn/pOQ8Jpuc8VJ9mqYUUuxRWF1VvTvx293HskvdSlcd36c3ZbfVDkdooNEstZGTHuQzO+Jmd135LRv4UzBVzVct7Kn2KZG27sksuFy78K4b2JVJ79JyHxNK7LaTeKCismdV5aO5R3L59V3o0mE+H4oW0WD+fjNVz436r6Kx2B7N6RbTGjQWFqYyZ04UxdAGOYLuMdQxqs5hX5iZX4gBw16wd6dn9Ag7NeyjRoYhILVHyIFIJ54yrZ/QGepfqntVoPZ9lXFHp0zBf3RC7XUa8Zq9txONzq36nSU07M2d//tO9gD3ynqlyGV9kn8u+i17E1hdUY2QiUhPU5kGkiuasS2dC2+MqHKY4ozWPzd2uliJKrGNyBvNz9olVGtc1bMqFMwfwaKsrqjkqEakJSh5EtsKf8vbFNWwas/+Mtoewtigxz2NIhCG5RzMj+/eRx5vZ/jCWbUzltpk9mJI9sgYiE5HqpORBZCvMWZfO5A7Hxuw/dm3tPgUy0ZwzBuX+PvJjrJ9fu++mz8fNGKpnSIgkOSUPIltp9PyDcA3St+he3LgdT85L3nYKNaXIpXDYjJEsyIzvbZ+FzTJ5PPS47rVFDThl5R9q7V0jIhKdkgeRrTRtVWNyOh+9Rfdf2xzKxuLqf8x1XbC+OIWBM09jecf9Kh32h5aDtnhk+LcrmvGP5lfUyGPCk0l+pwNZ32qnRIchEpmSB5FqcP3igVu82vv51dv2Leiri1L4w4pTcA0qfvPnQ0vLv7Rz3+xufJd9Wk2ElhTysn7HgNnnMHTFn1jXepdEhyMSiZIHkWowcXlz5mRufndGUdPOPDe/cwIjSg5fLmvB951jN4Bc07YXH+XHvjxx4vRBFLSvf0nYz9kncuD0k1hb1IDpazI4fNlVrG27W6LDEombkgeRanLz8iGbTrNPbXVIzLd3bmvOm30oRU22fJMowKeNBlY47sZi46yVZ+PSm9VEaAkxMftsBucMK7V+zF7biMOWXBGzoWhhs0zeybqURzrcwMLOA3EpabUUbc3YmrfjSnJQ8hCBmQ02s1/MLNfMRpfTfzsz+9jMvjOzyWY2NBFxSmK8t6Q1SzofAsAzBXslOJrksWh9Gv9uceYW3V1KKnfN61Xp+F8tb86LbS6qidBqlcN4J+vSmC9pm7sunYGLLmNV+83rzoaWO/Jy59H0Wno75+f24/ZZPeg34ywG8i/GZ13Gmra9yy0rma1p24vd8//Gt9mn1/s2LfWZ3m0RJzNrAPwKDALmAF8DI51zU0PDjAG+c849bGY9gfHOua4Vlat3W9QvJ3ZawK1F97HjotsTHUpSMXNMybyDxksmb+q2uPMh7D3jnLjL+HyHZ8mc+3ZNhFejXEoaK9vtyVgbzK0zK28c2a7hRl7u8gbvbtidO2fvWOkZrIPaLOP4VtPp46bScfl3NFi9sLpCrxE3tL6Tp4K7ay7e7jcuL7iHlLX5kcvRuy0SS4+njt8+QK5zbgaAmb0EDAOmhoZxQMl7l1sA82o1Qkm4l+d3pP125yU6jKTjnHFL0WncxuYnSI4rPiBSGSfMPYFPm00mtWBudYdX7QqbZTKzZX/e39Cbpxd0YcGsihuNhi3ekMahORU/uTTs0/xWfJrfF+gLnMq+rVZwTKtZ7J3yC1mrJpO2fEbSvLRsYefDeGrG5ttyH5i9PZ80u53n24+h+aKvExiZRKUzD3Eys+OAwc65s4PvpwD9nHMXhYbpBLwHtAKaAIc5574pp6xzgXMBGrfusFe7cx6vhRqIJN7/dnyerDlv4dKb03v1g5FfZnZmZh7XL70Gc8U1FOHWey3zKv44vU+iw9ika8Y6jmk3jwHp0+mxYSrNFn6VkGTCpaQxKv0ffL6sxRb90lIcL+3wAXvlPRl3eTrzkFhq81C9RgJPOeeygKHAs2a2xTx2zo1xzvV1zvVNb9qytmMUSZgLFg3DpTVmevtBVXoL6hNzs/k++5QaiKx6fJJ9QVIlDgAz1zbivtndOC5nEL1nXcplLe5lecd9Kx+xmv2UeUK5iQP4hrHDcwbxbtaltRyVVJWSh/jNBcJNhLOCbmFnAWMBnHNfAo2AtrUSnUgdMHllU77sOIpnVlf94DVq+iDWtkm+2xp/3G4Up+cMSHQYlXpjYXv2mHkxd7W7rdaeL1HcqBV/yDus0uHOy+3Hh9kXVzpcXWwoWt8oeYjf10B3M9vezBoCI4BxZYaZDQwEMLNd8MnD4lqNUiTJnffbAJ6d36nK468uSmFkwSVMzR6JSy//l2xtm5V1NEfnDEl0GJE8mNeVXef/mRc7X0NRk441Oq13257KnHVbPsK9PGfl7Msn2ReU289hfJ99KnvNv6o6w5MqUPIQJ+dcIXAR8C4wDRjrnJtiZjeZWcmzia8AzjGzH4AXgdOdGpWIlFJQmLrVz8D4fmVThuYcxZ5rHmBs56tZ3Xb3aoouusWdD+HwGSfUyed6FLkUrpnRiz+lXo3b8gprtdjQsht//C1a04TTcwbwv+zSDY+LmrTnzna3cUzO4G3qTbXJSg0mE0y3aopUj2EdFnFNk7foOO/9WptmQfu+DJh/CSs21v0b18Z3f5OeeS9We7kPtr+Ju2bvWKVxX+r+Ef3zHiO/00GcsPBUpq/J2NRv1h1HqsFkAtX9NV5EBH8t/w3O4PZufTlxyQPYhlU1Or3ijDb8bvEF9SJxADht1mC+bPHZVt0K6zCKm7RjfUZHChq2Y2aD7bkrp2qJA8CInEM5N2tHHp2ZXSfP7NRn9WOtFxEJjJ7Ri9db3sXj7cbQdPG3NTadV1udxewZjWqs/Nq2eEMaDzc+n4sLro88bmGzLE7eeB2TC5qwNr96LymMmbNdtZYn1UNtHkSk3pm4vDl95l7BF9nn4lKq/zfSqnZ9+NNviWtnUVPunrUD8zMPjzzegxnnM3F5c7VF2IYoeRCRemljsXFSzsGMbnFnzBdzVYWzFP684bR6exr9zIUn4NKbVz5gYH7m4dw3u1sNRiTJSMmDiNRrL8/vyElFN7GxRddqKe/XrON4Y2H1JSPJZtqqxoxre25cw7r0Zpy96PgajkiSkZIHEan3Ji5vzu9WXb/VD5cqzmjLWXl163kOVXHZjD4UtK/8Rob/a3sOUwqa1EJEkmyUPIjINuHX1RkcsuRKVnToX+Uy/t36rLgfdlSXOWdctvp0ihu1ijnM6nZ7cNmMPWsxKkkmSh5EZJuxYH1D9pt7IQuq0ChwVbs+XD1j23ks8of5rTl0/d1Mz/o9jtLtO1xKKleuO5Mip0PItkpLXkS2KasLG7D/jFP5KvusuB9v7SyFa9afXm8bScYyc20jBuYexzWt7mJtm103df8h8yTeXqzX9mzLlDyIyDanyKVwQs5A9lzzAK9lXsW6Nj3LHc41SGdlh3683vkK3lzUrpajTB4vze9E7/nXMj7rcta16ckZMyt/yZXUb3o8dYLp8dQiyeGUznM5r/HHNNu4lKkNe/He6h3596JOVXp1eH1m5pLiDIweT51Y2ipERIBn52XyLKMSHUbSS4bEQRJPly1EREQkEiUPIiIiEomSBxEREYlEyYOIiIhEouRBREREIlHyICIiIpEoeRAREZFIlDyIiIhIJEoeREREJBIlDyIiIhKJkgcRERGJRMmDiIiIRKLkQURERCJR8iAiIiKRKHkQERGRSJQ8iIiISCRKHkRERCQSJQ8iIiISiZIHERERiUTJg4iIiESi5EFEREQiUfIgIiIikSh5iMDMBpvZL2aWa2ajYwxzgplNNbMpZvZCbccoIiJS01ITHUBdYWYNgIeAQcAc4GszG+ecmxoapjtwDbC/c26ZmbVPTLQiIiI1R2ce4rcPkOucm+Gc2wC8BAwrM8w5wEPOuWUAzrlFtRyjiIhIjVPyEL9MIC/0fU7QLawH0MPMPjezCWY2uLyCzOxcM5tkZpPWr1peM9GKiIjUEF22qF6pQHfgYCAL+MzMejnnlocHcs6NAcYAtO6ys6vlGEVERLaKzjzEby6QHfqeFXQLmwOMc85tdM79BvyKTyZERETqDSUP8fsa6G5m25tZQ2AEMK7MMP/Bn3XAzNriL2PMqMUYRUREapyShzg55wqBi4B3gWnAWOfcFDO7ycyODgZ7F8g3s6nAx8BVzrn8xEQsIiJSM9TmIQLn3HhgfJlufwl9dsAfgz8REZF6SWceREREJBIlDyIiIhKJkgcRERGJRMmDiIiIRKLkQURERCJR8iAiIiKRKHkQERGRSJQ8iIiISCRKHkRERCQSJQ8iIiISiZIHERERiUTJg4iIiESi5EFEREQiUfIgIiIikSh5EBERkUiUPIiIiEgkSh5EREQkEiUPIiIiEomSBxEREYlEyYOIiIhEouRBREREIlHyICIiIpEoeRAREZFIlDyIiIhIJEoeREREJBIlDyIiIhKJkgcRERGJRMmDiIiIRKLkQURERCJR8iAiIiKRKHkQERGRSJQ8iIiISCRKHkRERCQSJQ8iIiISiZIHERERiUTJQwRmNtjMfjGzXDMbXcFww83MmVnf2oxPRESkNih5iJOZNQAeAoYAPYGRZtaznOGaAZcCE2s3QhERkdqh5CF++wC5zrkZzrkNwEvAsHKGuxm4A1hXm8GJiIjUFiUP8csE8kLf5wTdNjGzPYFs59xbFRVkZuea2SQzm7R+1fJqD1RERKQmKXmoJmaWAtwDXFHZsM65Mc65vs65vulNW9Z4bCIiItVJyUP85gLZoe9ZQbcSzYDdgE/MbCbQHxinRpMiIlLfKHmI39dAdzPb3swaAiOAcSU9nXMrnHNtnXNdnXNdgQnA0c65SYkJV0REpGYoeYiTc64QuAh4F5gGjHXOTTGzm8zs6MRGJyIiUntSEx1AXeKcGw+ML9PtLzGGPbg2YhIREaltOvMgIiIikSh5EBERkUiUPIiIiEgkSh5EREQkEiUPIiIiEomSBxEREYlEyYOIiIhEouRBREREIlHyICIiIpEoeRAREZFIlDyIiIhIJEoeREREJBIlDyIiIhKJkgcRERGJRMmDiIiIRKLkQURERCJR8iAiIiKRKHkQERGRSJQ8iIiISCRKHkRERCQSJQ8iIiISiZIHERERiUTJg4iIiESi5EFEREQiUfIgIiIikSh5EBERkUiUPIiIiEgkSh5EREQkEiUPIiIiEomSBxEREYlEyYOIiIhEouRBREREIlHyICIiIpEoeRAREZFIlDxEYGaDzewXM8s1s9Hl9P+jmU01s8lm9qGZdUlEnCIiIjVJyUOczKwB8BAwBOgJjDSznmUG+w7o65zrDfwbuLN2oxQREal5Sh7itw+Q65yb4ZzbALwEDAsP4Jz72Dm3Jvg6Aciq5RhFRERqnJKH+GUCeaHvc4JusZwFvF1eDzM718wmmdmk9auWV1+EIiIitSA10QHUR2Y2CugLHFRef+fcGGAMQOsuO7taDE1ERGSrKXmI31wgO/Q9K+hWipkdBvwZOMg5t76WYhMREak1umwRv6+B7ma2vZk1BEYA48IDmFkf4F/A0c65RQmIUUREpMYpeYiTc64QuAh4F5gGjHXOTTGzm8zs6GCwvwNNgVfM7HszGxejOBERkTpLly0icM6NB8aX6faX0OfDaj0oERGRWqbkIQk1T0/h4n6t6NIyDcMSHU7ScThmLd/IAxOXsXJ9caLDERHZ5ih5SEIX92vFnjt0JrVxM8yUPJTlnKNNm5VcDNz6WX6iwxER2eaozUMS6tIyTYlDBcyM1MbN6dIyLdGhiIhsk5Q8JCHDlDhUwsx0SUdEJEGUPIiIiEgkavNQBxz94OfVWt64i/aPa7glixZy5w3XMOWH72jWogVt2rbjqhv+RtduO1ZrPCX675TFhF/m1EjZIiJSfZQ8SLmcc1x+zikcddwI7vznEwD8MvVHli5eVGPJg4iI1A1KHqRcX33xX1JTUznhlDM3ddupZy/WrF7FOSOGsXLFcgo3buSiq67jkCOGMjdvNheeejx99u7P9998RfsOnbj/8edplJHBqy88zavPP83GjRvI7tqNW+9/hIyMxsyZPYtrLj6HNatXccjhQzdNZ83qVVx61slbTENERJKD2jxIuXJ/mUbPXnts0b1heiPuffRZXn77Ux4b+yZ333wdzvl3e83+bTonnnY2r3/4Jc1btOCDt/0DNgcOOYoX3vqIV977H9127MHrLz0HwJ1/Hc0Jp5zJqx98Qdv2HeKahoiIJJ7OPEgkzjn+ccfNfDvxC1JSUli0YD75i/1rPDKzu7Dzrr0A2KXX7szL828wz/15Gg/+/RYKVq5gzZrV7HfQoQB8P2kid495BoAjh5/IfX+7scJphBMMERFJHCUPUq4de+zMB2+9sUX38a+/wrL8fF4c/wlpaWkM2bc369f7l4emNWy4abgGKQ1YX7QOgOuvuID7HnuOnXr24o2xLzDpy/9tGq68W1IrmoaIiCSeLltIufbZ/0A2bNjAv59/alO3X6f9xPy5ebRu25a0tDS++uK/zJuTV2lZa1atom37jmzcuJHx/3llU/c9+vbjnXGvAj5hKLGqYGXkaYiISO3RmYc6IN5bK6uTmXHvo8/y9xuv5cl/3k/DRo3IzMrm/MtHc8dfRzP8sP3o2bsP2+/Yo9KyLrzyWkYdfRitWrelV5+9WLNqFQB/uvF2rrn4HJ785/2lGkwOPfZ4LjljZKRpiIhI7TE1REus1l12ds1H3l2q26NHd6LDdt0SFFHdsXD2DM4ZNz/RYYhIAsy648hvnHN9Ex3HtkqXLURERCQSJQ8iIiISiZIHERERiUTJg4iIiESi5EFEREQiUfIgIiIikeg5D3VA78e6VGt5k8+eVekwu2e3Yuixx/O3f4wBoLCwkMP22pnd+uzFg0+9HHmaY599goyMxhx13IjI44qISHJR8iDlymjchOm/TGPd2rU0yshgwn8/pn3HTlUuL/x2ThERqdt02UJiGnDIIP770XsAvP3GqwweNnxTvzVrVvOXKy7ipCMHcsLgA/n43fEA3PHX0Txy350AfP7Jh5wxfCjFxcU8fM/tPP3IAwDM/m0G5448huMPH8CJQw4ib+ZvOOe455br+f3AfRl+2H68M+61Wq6tiIjES2ceJKbBw37Pv+77OwcOPIKcaVM45sRRfPvVlwA89o+72Wf/A7jp7gdZuWIFJx81kH4HHMQlo//CSUcOZM999uWOv47moafHkpJSOke95pJzOfOCyxg45EjWr1tHsSvmw7ff5JepP/HKe/9j+dJ8TjryUPbqtx/tOnRMRNVFRKQCOvMgMfXYZTfm5c3m7TdeZcAhg0r1+/Kzj3niofs44YgDOPuEI9mwfh0L5s4hI6Mxf73jPs4/6VhGnH4O2V23LzXe6lUFLFown4FDjgQgvVEjMjIa891XExh89HAaNGhAm3bt2av//kz54dtaq6uIiMRPZx6kQgcdPoR7brmex8e+yfLlyzZ1dzjuGfMMXXfovsU4OT9PpUWr1ixeqPdOiIjURzrzIBU69sSTOe/yq+m+y66luu934KG88OQYSl6sNu2nyQDMmzObZ8Y8xMtvf8rnH3/A5O8mlRqvSdNmdOjUmY/eeQuADevXs3btGvr025d333ydoqIiluYv4duJX7DbHnvVQg1FRCQqnXmoA+K5tbKmdOiUyclnnrdF93MvvYo7b7yG4wbtT7FzZGZvxwNPvsQNV13CFdfdRPuOnbjh7w9w/R8v4IX/+6jUuLfe/wg3j76cf959G6lpadz18FMMHHwkk7/5iuMPH4CZcdm1N9K2fYfaqqaIiESgV3InmF7JXXV6JbfItkuv5E4sXbYQERGRSJQ8iIiISCRKHpKQw6HLSRVzzuHQPBIRSQQlD0lo1vKNFK5ZqQQiBucchWtWMmv5xkSHIiKyTdLdFknogYnLuBjo0nIJhiU6nKTjcMxavpEHJi6rfGAREal2Sh6S0Mr1xdz6WX6iwxARESmXLltEYGaDzewXM8s1s9Hl9E83s5eD/hPNrGsCwhQREalRSh7iZGYNgIeAIUBPYKSZ9Swz2FnAMufcjsC9wB21G6WIiEjN02WL+O0D5DrnZgCY2UvAMGBqaJhhwA3B538DD5qZuQpaPjZMbcBumc1rJmIRkXoqcc/dFVDyEEUmkBf6PgfoF2sY51yhma0A2gBLwgOZ2bnAucHX9W9dcuBPNRJxcmhLmfrXM6pf3VWf6wb1v347JTqAbZmShwRwzo0BxgCY2aT6/IhV1a9uq8/1q891g22jfomOYVumNg/xmwtkh75nBd3KHcbMUoEWgG6bEBGRekXJQ/y+Brqb2fZm1hAYAYwrM8w44LTg83HARxW1dxAREamLdNkiTkEbhouAd4EGwBPOuSlmdhMwyTk3DngceNbMcoGl+ASjMmNqLOjkoPrVbfW5fvW5bqD6SQ3SK7lFREQkEl22EBERkUiUPIiIiEgkSh5qgJk9YWaLzOynULfdzexLM/vRzN40s+ahfr2DflOC/o2C7nsF33PN7B9mlvC3ZEWpm5mdbGbfh/6KzWyPoF/S1Q0i1y/NzJ4Ouk8zs2tC41T4KPNEiVi/hmb2ZND9BzM7ODROsi6/bDP72MymBtvTpUH31mb2vpnlBP9bBd0tiD/XzCab2Z6hsk4Lhs8xs9NiTbO2VKFuOwfLdb2ZXVmmrKRbP6tQv5ODZfajmX1hZruHykq6+tU7zjn9VfMfcCCwJ/BTqNvXwEHB5zOBm4PPqcBkYPfgexugQfD5K6A/YMDbwJC6VLcy4/UCpoe+J13dqrDsTgJeCj43BmYCXfENaqcD3YCGwA9Az0TXrQr1uxB4MvjcHvgGSEny5dcJ2DP43Az4Ff84+TuB0UH30cAdweehQfwW1Gdi0L01MCP43yr43KqO1a09sDdwK3BlqJykXD+rUL/9SpYJ/rUBJcsuKetX3/505qEGOOc+w99tEdYD+Cz4/D4wPPh8ODDZOfdDMG6+c67IzDoBzZ1zE5zfIp4Bjqnx4CsRsW5hI4GXAJK1bhC5fg5oYv6ZHhnABmAloUeZO+c24Os9rKZjj0fE+vUEPgrGWwQsB/om+fKb75z7NvhcAEzDP/l1GPB0MNjTbI53GPCM8yYALYP6HQG875xb6pxbhp8vg2uvJluKWjfn3CLn3NfAxjJFJeX6WYX6fREsG4AJ+GfvQJLWr75R8lB7prB5BT6ezQ+c6gE4M3vXzL41sz8F3TPxj8AuMSfoloxi1S3sRODF4HNdqhvErt+/gdXAfGA2cJdzbinlP8q8LtbvB+BoM0s1s+2BvYJ+dWL5mX+rbR9gItDBOTc/6LUA6BB8jrWsknoZxlm3WJK6blCl+p2FP4MEdaB+9YGSh9pzJnCBmX2DPyW3IeieCgwATg7+H2tmAxMTYpXFqhsAZtYPWOOcq6vv8IhVv32AIqAzsD1whZl1S0yIWyVW/Z7A73gnAfcBX+Drm/TMrCnwKnCZc25luF9wtqTO3qNen+sG0etnZofgk4eray1I0UOiaotz7mf8JQrMrAfwu6DXHOAz59ySoN94/DXp59h8Gg7Kfxx2UqigbiVGsPmsA/h61Im6QYX1Owl4xzm3EVhkZp8DffG/eip7lHnSiFU/51whcHnJcGb2Bf469DKSePmZWRr+4PO8c+61oPNCM+vknJsfXJZYFHSP9dj5ucDBZbp/UpNxxyNi3WKJ51H7CRG1fmbWG3gM3+am5FUASVu/+kRnHmqJmbUP/qcA1wGPBL3eBXqZWePg2vlBwNTgNN1KM+sftGQ/FXgjAaFXqoK6lXQ7gaC9A/hrm9SRukGF9ZsNHBr0a4JvcPcz8T3KPGnEql+wTjYJPg8CCp1zSb1uBvE8Dkxzzt0T6hV+dPxpbI53HHBqcNdFf2BFUL93gcPNrFXQuv/woFvCVKFusSTl+hm1fma2HfAacIpz7tfQ8ElZv3on0S026+Mf/lf2fHxDpTn4U2qX4n+1/QrcTvB0z2D4Ufjrzj8Bd4a69w26TQceDI9Th+p2MDChnHKSrm5R6wc0BV4Jlt1U4KpQOUOD4acDf050vapYv67AL/iGax8AXerA8huAP609Gfg++BuKv4vpQyAnqEvrYHgDHgrq8SPQN1TWmUBu8HdGHaxbx2AZr8Q3dp2Db+ialOtnFer3GP4sWMmwk0JlJV396tufHk8tIiIikeiyhYiIiESi5EFEREQiUfIgIiIikSh5EBERkUiUPIiIiEgkSh5EREQkEiUPIiIiEsn/AwL93ZFl4l02AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.stackplot(usd.index, usd['CAN-Prop'], usd['MEX-Prop'], labels=['Canada','Mexico'])\n", + "plt.legend(loc='lower left')\n", + "plt.margins(0,0)\n", + "plt.title('Proportion of Total Military Spending Between Canada and Mexico from 1960 to 2020')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ca499c7e", + "metadata": {}, + "source": [ + "We can now see the changes in the proportion of military spending between Canada and Mexico from 1960 to 2020. We see that the proportion of spending by Mexico increased over time." + ] + }, + { + "cell_type": "markdown", + "id": "b90364f6", + "metadata": {}, + "source": [ + "## Conclusions\n", + "\n", + "In this section, we were introduced to a new data visualization library: `seaborn`. The `seaborn` library can make many of the same visualizations available in `matplotlib` and can be used as an alternative in cases where more flexibility is needed. \n", + "\n", + "We learned how to make box and whisker plots and the multiiple statistics that these plots innately show. Box and whisker plots can be made in both `matplotlib` and `seaborn`, but using `seaborn` to construct these plots allows for an easy way to overlay data points upon the box and whisker plot.\n", + "\n", + "We also learned about heatmaps and their ability to show multidimensional data.\n", + "\n", + "Lastly, we learned how to construct area plots as another way to show proportional trends overtime, combining benefits of both a line graph and pie chart.\n", + "\n", + "Documentation to functions introduced in this section can be found below:\n", + "\n", + "\n", + "- Seaborn documentaion and user guide\n", + "- Seaborn data visualization gallery\n", + "- DataFrame.plot.box( )\n", + "- plt.boxplot( )\n", + "- sns.boxplot( )\n", + "- sns.swarmplot( )\n", + "- Heatmaps in Matplotlib\n", + "- sns.heatmap( )\n", + "- plt.stackplot( )\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31d3ff63", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/textbook/09/data-visualization.md b/textbook/09/data-visualization.md index 13791d38..d75e8d3d 100644 --- a/textbook/09/data-visualization.md +++ b/textbook/09/data-visualization.md @@ -6,4 +6,4 @@ Distilling large and complex sets of data into more easily digestible forms is a The visual that we choose is dependent on the type of data. Two major data types that can be visualized graphically are **numerical data** and **categorical data**. -Numerical data is commonly visualized using histograms, scatter plots and line graphs. Categorical data can be depicted using bar graphs, box and whisker plots, and pie charts. There are a vast number of other methods to visualize these data types, (e.g. cartograms, heatmaps, etc.), but the aforementioned graphs are the most commonly used among data scientists. +Numerical data is commonly visualized using histograms, scatter plots and line graphs. Categorical data can be depicted using bar graphs and pie charts. There are a vast number of other methods to visualize these data types, (e.g. box and whisker plots, heatmaps, etc.), but the aforementioned graphs can greatly distill complex datasets and are commonly used throughout multiple disciplines. diff --git a/textbook/15/1/ethics-and-law.ipynb b/textbook/15/1/ethics-and-law.ipynb index a3a17132..99231100 100644 --- a/textbook/15/1/ethics-and-law.ipynb +++ b/textbook/15/1/ethics-and-law.ipynb @@ -28,10 +28,10 @@ "Many legal and ethical concerns surrounding data-centered conduct and decision-making involves maximizing human benefits while minimizing unintended damage. Using a synopsis of the major emphasized topics outlined in state-level, federal, and international guides (see references below and throughout the chapter), this approach can be summarized into 4 major pillars that may serve as the basis of decisions, research, and usage of data involving human subjects:\n", "\n", "\n", - "1. The attainment, usage, storage, analysis, and maintenance of human data should be as transparent, accountable, and honest as possible and intended for some sort of human benefit.\n", + "1. Communication, interpretation, and application of human data should be accurate and consider social, political, and economic contexts and ramifications, especially when involving vulnerable populations. \n", "2. Collected human data should be shared and maintained in a way that protects the privacy of subjects.\n", "3. Acquisition and collection of data should involve volition and informed consent from human subjects. \n", - "4. Communication, interpretation, and application of human data should be accurate and consider social, political, and economic contexts and ramifications, especially when involving vulnerable populations. \n", + "4. The attainment, usage, storage, analysis, and maintenance of human data should be as transparent, accountable, and honest as possible and intended for some sort of human benefit.\n", "\n", "While these pillars have not been adopted by government agencies verbatim, they serve as a basis for data science students to consider as they explore and move throughout data science courses and professional opportunities." ] diff --git a/textbook/15/5/img/Dresseletal.png b/textbook/15/2/img/Dresseletal.png similarity index 100% rename from textbook/15/5/img/Dresseletal.png rename to textbook/15/2/img/Dresseletal.png diff --git a/textbook/15/5/img/indiana-eugenics.jpeg b/textbook/15/2/img/indiana-eugenics.jpeg similarity index 100% rename from textbook/15/5/img/indiana-eugenics.jpeg rename to textbook/15/2/img/indiana-eugenics.jpeg diff --git a/textbook/15/2/img/lifecycleofdata.png b/textbook/15/2/img/lifecycleofdata.png deleted file mode 100644 index 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Pamphlet of the Virginia Health Bulletin reporting passing of the Racial Integrity Act of 1924. “Virginia Health Bulletin: The New Virginia Law To Preserve Racial Integrity, March 1924,” Document Bank of Virginia, accessed August 1, 2023, https://edu.lva.virginia.gov/dbva/items/show/226.
\n", "\n", + "
\n", + "
\n", + "While we as a society have evolved our moral compass, remnants of previous ethical shortcomings creep into scientific and technological practices in the digital age. As the rapid utilization of rich datasets drive discoveries in the fields of medicine, physics, engineering, artificial intelligence, and other sciences, few are slowing down to ask the questions that weigh innovation against morality. Most of the time, such ethical considerations are not acknowledged until cumulative damage is done. A well-known example is the report published by ProPublica[^******] in 2016, which described how a recidivism algorithm called COMPAS exhibited racial biases toward offenders, rating Black offenders as more likely to re-offend than White offenders. This algorithm has been used in several states in judicial decision-making processes.\n", "\n", - "In 2015, the Harvard Business Review published an article surveying consumers across several countries on sentiments around data privacy and security[^**********]. Interestingly, the entities people trusted most with their data were healthcare providers and financial institutions, while the least trusted were entertainment companies and social media firms. Arguably, the level of trust may coincide with mechanisms of accountability, such as HIPPA legislations and financial privacy laws like the Gramm–Leach–Bliley Act, which may not apply to entertainment and social media platforms in the same way. While legal obligation is a strong way to enforce accountability, providing open and accessible information on conduct around data security, privacy, and collection and protocols for when someone has questions or ailments about them shows an organization’s intrinsic commitment to transparent practices, which can enhance trust between the organization and customers, users, and other subjects from which data may be collected. \n", + "Surprisingly, the reported analysis showed that of those deemed “high risk” to reoffend by COMPAS, 28.0% of Black offenders and 47.7% of White offenders did in fact reoffend. On the other hand, 44.9% of Black offenders and 23.5% of White offenders actually did not reoffend. How may an algorithm exhibit such suboptimal predictions and biases in its outputs? This would depend on the lens of what is considered \"suboptimal\" or even \"fair.\" According to a report published by Northpointe Incorporated [^*******], the creator of COMPAS, the algorithm does not confer biases with the statistical measures they used to assess accuracy and fairness. Affiliates of the United States Courts have also criticized the Propublica report [^********], stating that the study did not apply appropriate calculations in determining recidivism rates. Since the publishing of the original Propublica report, representatives of the academic, government, and private sectors have been in debate about what the appropriate measures of fairness are and the importance of these measures for crucial decisions such as recidivism[^*********][^**********].\n", "\n", - "\n", + "Following up on this report, Dressel and Farid (2018)[^***********] compared the accuracy of COMPAS’s prediction of recidivism to predictions made by human participants using an online survey. They found that human prediction was slightly more accurate than COMPAS (67% vs 65.2%) but was not significantly different. Using a developed classifier, they determined that age and total number of previous convictions yielded a similar racial bias as was reported in the Propublica report. Out of context, this could be viewed at most as a major flaw of the data inputs and outputs for this algorithm (garbage in, garbage out). A more contextual interpretation of how either of these factors lead to a racial bias in recidivism ratings could be supported by the numerous studies documenting racial bias arrests and confrontations with law enforcement[^************], as well as the track record of controversial police encounters in America. Thus, it is important to understand the greater macrocosm from which this data is derived to garner understanding of what the data may mean. Furthermore, it is imperative to acknowledge the strengths and limitations of statistical models and measures in relevant applications involving human behavior.\n", "\n", - "As laws relevant to companies outside of the healthcare and financial sectors are still developing, accountability for such entities is currently nebulous. The data lifecycle is a pipeline that considers the past, present, and future of data and its application in technologies by said companies. This pipeline involves numerous steps, such as data collection, processessing, analysis, dissemination, and maintenance, which can be lead by numerous teams or organizations of people. Thus, for terminal applications of data, how do we determine accountability when things go wrong? While assignment of accountability is complex and multifaceted, Virginia Dignum, an AI ethicist and researcher, suggests that when thinking about accountability in the data lifecycle, the larger sociotechnical ecosystem must be considered within a *Accountability-Responsibility-Transparency* (ART) framework [^***********]. Those involved throughout various phases of the data lifecycle should be able to explain and justify their decisions around data (*accountability*), acknowledge the role that they play in the data lifecyle (*responsibility*), and describe, inspect and reproduce mechanisms contributing to end products of the life cycle (*transparency*). Utilizing this framework requires a level of open discussion with stakeholders, whether they be clients, users, or society. Such discussions facilitate iterative modification and reworking to improve products and services from an ethical and technological standpoint.\n", "\n", - "\n", + "\n", "\n", - "
The data lifecycle. Adapted from Communications of the ACM, July 2020, Vol. 63 No. 7, Pages 58-66\n", - "10.1145/3360646
\n", + "
Human (no-race condition) versus COMPAS algorithmic predictions. Adapted from Dressel and Farrid, 2018.
\n", "\n", + "
\n", "\n", + "While the use of tools like COMPAS is still an ongoing debate, important questions and considerations have emerged from this discussion. Particularly, how do tools like COMPAS affect decisions made by judges presented with recidivism predictions? Do these tools significantly impact one's right to due process? Does automation bias work in concert with implicit biases that are already prominent in the justice system[^*************]? Should we be using algorithmic predictions about human behavior in these circumstances at all?\n", "\n", - "## The Trade-Off Between Transparency and Accuracy\n", - "\n", - "The development of artificial intelligence algorithms has aided, informed, and/or influenced human decision-making processes in a number of low-stakes and high-stakes scenarios. Because of this, conversation around the transparency of these algorithms has highlighted important perspectives and viewpoints regarding the implications of these technologies. \n", - "\n", - "Some algorithms disclose the use of parameters and models applied (i.e., \"white-box\" algorithms), while others may not readily explain the parameters and models used or may utilize another model that approximates the original model(s) (i.e., \"black-box algorithms). Some black-box algorithms also may utilize more sophistocated and complex methods, such as random forests and neural networks, which may not be readily explainable. Depending on the context of use, the methods backing white-box and black-box algorithms can influence the accuracy, and thus impact terminal services, decisions, products, or actions. \n", - "\n", - "Explainable AI (XAI) and ML (XML) explores the intersection of transparency and technical applicability in AI and ML. While transparency can help promote accountability and trust, limitations to prioritizing transparency may present in the use of AI/ML. Some of these include malicous and unintended misuse of developed AI/ML tools [^***********], exposure of trade secrets [^************], domain and technical knowledge requirements for full comprehension, and several others. In high-stakes situations, such as the use of AI/ML in healthcare decisions, some argue that accuracy should be more important than transperancy [^*************], while others suggest the use of *interpretable* models instead of black-box algorithms where possible [^**************]. Sometimes, black-box algorithms can be replaced with more simpler and transparent ones with little compromise of accuracy [^***************], [^****************]. In cases where black-box methods must be used, attempts to provide transparency through justification as opposed to explanation may be the best case scenario [^*****************]. An evalulation of the situation and associated risks and rewards, as well as testing of multiple black-box, white-box, and interpretable options, is important in determining the the best way to balance transparency and accuracy. \n", - "\n", - "\n", - "## Honest Statistical Practices\n", - "\n", - "A part of getting accurate insights from data includes using honest and appropriate statistical methods during data analysis. Doing such can enhance reproducibility, allowing for economical allocation of time and resources toward follow up studies. Some common pitfalls in research and analysis include hypothesizing after results are known (HARKing), p-hacking, and cherry-picking. Below is a discussion of each and how they impact research and knowledge generation.\n", - "\n", - "#### HARKing\n", - "As the acroynm states, HARKing is developing a hypothesis about data after knowing the results that the data depict and then reporting conclusions as if they were hypothesized *a priori*. HARKing does not fully disclose the process leading to the hypothesis and conclusions from data and thus can be seen as dishonest in nature. HARKing may or may not include performing statistical analysis and determining significant variables in a dataset; plotting and cross-examining variables can also be a part of HARKing. When performing exploratory studies, examining the relationships between multiple variables from a dataset can be a useful process to generate new hypotheses, but these hypotheses should be tested with a new dataset to confirm previous observations.\n", - "\n", - "#### P-hacking\n", - "\n", - "P-hacking can involve the use of multiple testing, various kinds of statistical tests, and/or specific subsetting of data in order to generate a significant p-value. Like HARKing, p-hacking does not fully account for the process leading up to a significant result. Because hypothesis testing reports the probability of an observed outcome occuring given that the null hypothesis is true, testing multiple hypothesis on the same data will give a false positive at some point. To address this, multiple testing corrections can be used [^******************].\n", - "\n", - "#### Cherry-picking\n", - "Cherry-picking involves biased selection of data for analysis or reporting conclusions. Cherry-picking can be used to fuel p-hacking or paint an incomplete picture of a research process. Reporting only data that aligns with a hypothesis can be an impediment to those trying to repeat a reported experiment because it can lead researchers down an avoidable rabbit hole. Furthermore, not reporting null data that is not in alignment with a hypothesis can similarly hinder the scientific process. When outliers are found within data, it is only appropriate to exclude it if there is sound justification based on the data collection process or statistical backing.\n", - "\n", - "All-in-all, when it comes to data, especially when derived from humans, centering honesty as much as possible is the best policy.\n" + "Past ethical faults within the science and technology sectors may not have fully considered the damaging ramifications of certain studies, methods, and drawn conclusions on society. Moreover, it is impossible to anticipate the myriad of ways scientific findings may positively or negatively influence society. However, when it comes to working with human-derived data, or even data that can greatly affect human lives, these damages can be mitigated by staying well informed about the populations from which the data are collected and using ethical and contextual discernment when interpreting, communicating, and utilizing these data. As data scientists, it is our job to have ethics and morality as a basis by which we make these decisions around data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "[^*]: Andrade, Chittaranjan. “HARKing, Cherry-Picking, P-Hacking, Fishing Expeditions, and Data Dredging and Mining as Questionable Research Practices.” The Journal of Clinical Psychiatry, vol. 82, no. 1, Feb. 2021, p. 20f13804, https://doi.org/10.4088/JCP.20f13804.\n", - "\n", - "[^**]: Lin, D., Crabtree, J., Dillo, I. et al. The TRUST Principles for digital repositories. Sci Data 7, 144 (2020). https://doi.org/10.1038/s41597-020-0486-7\n", - "\n", - "[^***]: Atske, Sara. “2. Americans Concerned, Feel Lack of Control over Personal Data Collected by Both Companies and the Government.” Pew Research Center: Internet, Science & Tech, 15 Nov. 2019, https://www.pewresearch.org/internet/2019/11/15/americans-concerned-feel-lack-of-control-over-personal-data-collected-by-both-companies-and-the-government/.\n", - "\n", - "[^****]: Robles, Pedro, and Daniel J. Mallinson. “Artificial Intelligence Technology, Public Trust, and Effective Governance.” Review of Policy Research, May 2023, p. ropr.12555, https://doi.org/10.1111/ropr.12555.\n", - "\n", - "[^*****]: Mathews, Lee. “Equifax Data Breach Impacts 143 Million Americans.” Forbes, https://www.forbes.com/sites/leemathews/2017/09/07/equifax-data-breach-impacts-143-million-americans/. Accessed 31 July 2023.\n", - "\n", - "[^******]: U.S. Department of Homeland Security. Increasing Threat of DEEPFAKE Identities. Accessed 31 July 2023. https://www.dhs.gov/sites/default/files/publications/increasing_threats_of_deepfake_identities_0.pdf\n", + "[^*]: General Assembly. \"Preservation of Racial Integrity (1924)\" Encyclopedia Virginia. Virginia Humanities, (07 Dec. 2020). Web. 01 Dec. 2022\n", "\n", - "[^*******]: “Americans Widely Distrust Facebook, TikTok and Instagram with Their Data, Poll Finds.” Washington Post, 22 Dec. 2021, https://www.washingtonpost.com/technology/2021/12/22/tech-trust-survey/.\n", + "[^**]: BUCK v. BELL. 2 May 1927, https://www.loc.gov/item/usrep274200/.\n", "\n", - "[^********]: Nadeem, Reem. “Public Awareness of Artificial Intelligence in Everyday Activities.” Pew Research Center Science & Society, 15 Feb. 2023, https://www.pewresearch.org/science/2023/02/15/public-awareness-of-artificial-intelligence-in-everyday-activities/.\n", + "[^***]: Laws of Indiana, 1907, pp. 377-78 (B050823). https://www.in.gov/history/state-historical-markers/find-a-marker/1907-indiana-eugenics-law/\n", "\n", - "[^**********]: Morey, Timothy, et al. “Customer Data: Designing for Transparency and Trust.” Harvard Business Review, 1 May 2015, https://hbr.org/2015/05/customer-data-designing-for-transparency-and-trust.\n", + "[^****]: Reilly, Philip R. “Eugenics and Involuntary Sterilization: 1907-2015.” Annual Review of Genomics and Human Genetics, vol. 16, 2015, pp. 351–68, https://doi.org/10.1146/annurev-genom-090314-024930.\n", "\n", - "[^***********]: Virginia Dignum. The role and challenges of education for responsible AI. London Review of Education. 2021. Vol. 19(1). DOI: 10.14324/LRE.19.1.01\n", + "[^*****]: Office of the New York State Attorney General. Investigative Report on the role of online platforms in the tragic mass shooting in Buffalo on May 14, 2022. Published OCTOBER 18, 2022. https://ag.ny.gov/sites/default/files/buffaloshooting-onlineplatformsreport.pdf\n", "\n", - "[^***********]: Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, and Peter Eckersley. 2020. Explainable machine learning in deployment. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20). Association for Computing Machinery, New York, NY, USA, 648–657. https://doi.org/10.1145/3351095.3375624\n", + "[^******]: Angwin, Julia, et al. “Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks,” ProPublica, 23 May 2016, www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.\n", "\n", - "[^************]: Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1). https://doi.org/10.1177/2053951715622512\n", + "[^*******]: Dieterich, William, et al. \"COMPAS Risk Scales: Demonstrating\n", + "Accuracy Equity and Predictive Parity,\" 8 July 2016, https://go.volarisgroup.com/rs/430-MBX-989/images/ProPublica_Commentary_Final_070616.pdf.\n", "\n", - "[^*************]: Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021 Nov;3(11):e745-e750. doi: 10.1016/S2589-7500(21)00208-9. PMID: 34711379.\n", + "[^********]: Flores, Anthony, et al. \"False Positives, False Negatives, and False Analyses: A Rejoinder to 'Machine Bias: There's Software Used Across the Country to Predict Future Criminals. And It's Biased Against Blacks.'\" Federal Probation, Vol. 80 Number 2, September 2016, https://www.uscourts.gov/federal-probation-journal/2016/09/false-positives-false-negatives-and-false-analyses-rejoinder.\n", "\n", - "[^**************]: Rudin C. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nat Mach Intell. 2019 May;1(5):206-215. doi: 10.1038/s42256-019-0048-x. Epub 2019 May 13. PMID: 35603010; PMCID: PMC9122117.\n", + "[^*********]: Corbett-Davies, Sam, and Sharad Goel. The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning. arXiv, 14 Aug. 2018, https://arxiv.org/abs/1808.00023.\n", "\n", - "[^***************]: Chaofan Chen, Oscar Li, Chaofan Tao, Alina Jade Barnett, Jonathan Su, and Cynthia Rudin. 2019. This looks like that: deep learning for interpretable image recognition. Proceedings of the 33rd International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA, Article 801, 8930–8941.\n", + "[^**********]: Pleiss, Geoff, et al. “On Fairness and Calibration.” Advances in Neural Information Processing Systems, vol. 30, Curran Associates, Inc., 2017, https://papers.nips.cc/paper/2017/hash/b8b9c74ac526fffbeb2d39ab038d1cd7-Abstract.html.\n", "\n", - "[^****************]: Barnett, A.J., Schwartz, F.R., Tao, C. et al. A case-based interpretable deep learning model for classification of mass lesions in digital mammography. Nat Mach Intell 3, 1061–1070 (2021). https://doi.org/10.1038/s42256-021-00423-x\n", + "[^***********]: Dressel, Julia and Farid, Hany. “The Accuracy, Fairness, and Limits of Predicting Recidivism.” Science Advances, vol. 4, no. 1, Jan. 2018, p. eaao5580, https://doi.org/10.1126/sciadv.aao5580.\n", "\n", - "[^*****************]: Biran, Or and Courtenay V. Cotton. “Explanation and Justification in Machine Learning : A Survey Or.” (2017).\n", + "[^************]: Eberhardt, Jennifer. L. “Strategies for change: Research initiatives and recommendations to improve police- community relations in Oakland, Calif.” 2016. Stanford University, SPARQ: Social Psychological Answers to Real-world Questions. \n", "\n", - "[^******************]: Andrade C. Multiple Testing and Protection Against a Type 1 (False Positive) Error Using the Bonferroni and Hochberg Corrections. Indian Journal of Psychological Medicine. 2019;41(1):99-100. doi:10.4103/IJPSYM.IJPSYM_499_18\n" + "[^*************]: Jeffrey J. Rachlinski & Sheri L. Johnson, Does Unconscious Racial Bias Affect Trial Judges, 84 Notre Dame L. Rev. 1195 (2009). Available at: http://scholarship.law.nd.edu/ndlr/vol84/iss3/4.\n" ] }, { diff --git a/textbook/15/5/img/lifecycleofdata.png b/textbook/15/5/img/lifecycleofdata.png new file mode 100644 index 0000000000000000000000000000000000000000..53eaa8db71c4208ec65c59a0b2f9e93150b87f55 GIT binary patch literal 507162 zcmdRW2UJsA)MjiT=mSxjND*ux?V*U&APOibAiaZv3eq8T2%v~Ws!CG`gd!~n5s=n6tLJvgx*B5tnZ~n!~#|i?0 z{?fXoZU6!ueFp-ukPkBf-^g9i3I=|#c-%6FfIw1b=zsS4=c@XGK&L@k>S{**@TIXy z&#A8iOcTA9X`oa4PwZ!#pnVb^1pb>icKYCnFY*TsH_qL={GbMpmM0*V zjQsCDeoUIO27c|vBo#$5JU_6_Kc5e%CMlJFuIRr|G}P!6{_}|yy|wS33lJ!i-uER7r0iOQ-bitl-{(A)i@#(+Z{~xZeonZyU@bAa*C*d|b<8p%2Kzka- z%M}_N1-t*v63ZbYP78Vqv4ID=?A;Dc@xr7C9-v=c6F1P-D;2vQptyFo9A+k}yAMB1IF--XpMBrj3dGdNH*S5{XH+LGtNB7fQW7~`2*wcn!a zQWDF(&!HI-+sm09OghFPB$nHK5*!_;E%ujzHi~0aFwO4VM9h0N#zS-5zXf^?^)9iE zWA!AS{S3RkVd5n)5#O^ir|q^kw3fO|H$Z`typgUptr^lR$0V^qx6S@ zw04|=7!6X3m%$lolOCd{Den@;&s-NiZUwt?n6cZ~yWi@Xi(qOMZHK+V!|_!9t9WH` 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zJi^WjSWc9+)bKn8W{Jhw;lKn^vQAh2EyquZW580`?fN=kZS*3h`smLW>se!F1N&k~ zK;UN1_S=`;8P{Ab3s0S^v+-TsvE1)`J2rf%`kuVmwETTuVjyd!iOt2Tc{wuq{r0x2 z1pM#)e0R7c*=lQre%iaYkM?$aPyB!9>zkvGYG2H${ugKS$nMOY`&WSlhxAN~^qP0S z=5;2%D*L?gf4%9`HQydfyrdcd6<&VrcWOq z{(f)$s#W~=>VYJ%dPsR>11j%#%D=5y^K0geo%TO1WaO3s3xH|BMk|r59pAUCire+a zufY=72|)@4ha;E`DI{qI!B!Dut`Zk&oQb60%Wwv9Ic&@%SiNY&uEc3T4atEnMgULp<}ej94#`D5(L9&kvUpP z44F#e|NVWk+@RFU^J%->;eVev5Ggr95M{(Oq6H\n", + "The aforementioned practices may help to facilitate trust between human subjects/users and technologies that rely on data acquired from them [^**], [^***]. The establishment of trust within communities can have several advantages, including increased use of a product or technology, expanded collection of data, improved productivity and efficiency in tasks, and other benefits [^****]. However, with the occurence of numerous malevolent incidences, including massive data breaches that revealed sensitive financial information [^*****] or the use of \"synthetic\" media that has contributed to social and political confusion and misinformation [^******], it is unsurprising that some of the population has a level of distrust and concern when it comes to collection of personal data \n", + "[^*******] and emergent technologies [^********] such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP), in the public and private sectors.\n", "\n", - "

Pamphlet of the Virginia Health Bulletin reporting passing of the Racial Integrity Act of 1924. “Virginia Health Bulletin: The New Virginia Law To Preserve Racial Integrity, March 1924,” Document Bank of Virginia, accessed August 1, 2023, https://edu.lva.virginia.gov/dbva/items/show/226.
\n", "\n", - "
\n", - "
\n", - "While we as a society have evolved our moral compass, remnants of previous ethical shortcomings creep into scientific and technological practices in the digital age. As the rapid utilization of rich datasets drive discoveries in the fields of medicine, physics, engineering, artificial intelligence, and other sciences, few are slowing down to ask the questions that weigh innovation against morality. Most of the time, such ethical considerations are not acknowledged until cumulative damage is done. A well-known example is the report published by ProPublica[^******] in 2016, which described how a recidivism algorithm called COMPAS exhibited racial biases toward offenders, rating Black offenders as more likely to re-offend than White offenders. This algorithm has been used in several states in judicial decision-making processes.\n", "\n", - "Surprisingly, the reported analysis showed that of those deemed “high risk” to reoffend by COMPAS, 28.0% of Black offenders and 47.7% of White offenders did in fact reoffend. On the other hand, 44.9% of Black offenders and 23.5% of White offenders actually did not reoffend. How may an algorithm exhibit such suboptimal predictions and biases in its outputs? This would depend on the lens of what is considered \"suboptimal\" or even \"fair.\" According to a report published by Northpointe Incorporated [^*******], the creator of COMPAS, the algorithm does not confer biases with the statistical measures they used to assess accuracy and fairness. Affiliates of the United States Courts have also criticized the Propublica report [^********], stating that the study did not apply appropriate calculations in determining recidivism rates. Since the publishing of the original Propublica report, representatives of the academic, government, and private sectors have been in debate about what the appropriate measures of fairness are and the importance of these measures for crucial decisions such as recidivism[^*********][^**********].\n", + "In 2015, the Harvard Business Review published an article surveying consumers across several countries on sentiments around data privacy and security[^**********]. Interestingly, the entities people trusted most with their data were healthcare providers and financial institutions, while the least trusted were entertainment companies and social media firms. Arguably, the level of trust may coincide with mechanisms of accountability, such as HIPPA legislations and financial privacy laws like the Gramm–Leach–Bliley Act, which may not apply to entertainment and social media platforms in the same way. While legal obligation is a strong way to enforce accountability, providing open and accessible information on conduct around data security, privacy, and collection and protocols for when someone has questions or ailments about them shows an organization’s intrinsic commitment to transparent practices, which can enhance trust between the organization and customers, users, and other subjects from which data may be collected. \n", "\n", - "Following up on this report, Dressel and Farid (2018)[^***********] compared the accuracy of COMPAS’s prediction of recidivism to predictions made by human participants using an online survey. They found that human prediction was slightly more accurate than COMPAS (67% vs 65.2%) but was not significantly different. Using a developed classifier, they determined that age and total number of previous convictions yielded a similar racial bias as was reported in the Propublica report. Out of context, this could be viewed at most as a major flaw of the data inputs and outputs for this algorithm (garbage in, garbage out). A more contextual interpretation of how either of these factors lead to a racial bias in recidivism ratings could be supported by the numerous studies documenting racial bias arrests and confrontations with law enforcement[^************], as well as the track record of controversial police encounters in America. Thus, it is important to understand the greater macrocosm from which this data is derived to garner understanding of what the data may mean. Furthermore, it is imperative to acknowledge the strengths and limitations of statistical models and measures in relevant applications involving human behavior.\n", + "\n", "\n", + "As laws relevant to companies outside of the healthcare and financial sectors are still developing, accountability for such entities is currently nebulous. The data lifecycle is a pipeline that considers the past, present, and future of data and its application in technologies by said companies. This pipeline involves numerous steps, such as data collection, processessing, analysis, dissemination, and maintenance, which can be lead by numerous teams or organizations of people. Thus, for terminal applications of data, how do we determine accountability when things go wrong? While assignment of accountability is complex and multifaceted, Virginia Dignum, an AI ethicist and researcher, suggests that when thinking about accountability in the data lifecycle, the larger sociotechnical ecosystem must be considered within a *Accountability-Responsibility-Transparency* (ART) framework [^***********]. Those involved throughout various phases of the data lifecycle should be able to explain and justify their decisions around data (*accountability*), acknowledge the role that they play in the data lifecyle (*responsibility*), and describe, inspect and reproduce mechanisms contributing to end products of the life cycle (*transparency*). Utilizing this framework requires a level of open discussion with stakeholders, whether they be clients, users, or society. Such discussions facilitate iterative modification and reworking to improve products and services from an ethical and technological standpoint.\n", "\n", - "\n", + "\n", "\n", - "
Human (no-race condition) versus COMPAS algorithmic predictions. Adapted from Dressel and Farrid, 2018.
\n", + "
The data lifecycle. Adapted from Communications of the ACM, July 2020, Vol. 63 No. 7, Pages 58-66\n", + "10.1145/3360646
\n", "\n", - "
\n", "\n", - "While the use of tools like COMPAS is still an ongoing debate, important questions and considerations have emerged from this discussion. Particularly, how do tools like COMPAS affect decisions made by judges presented with recidivism predictions? Do these tools significantly impact one's right to due process? Does automation bias work in concert with implicit biases that are already prominent in the justice system[^*************]? Should we be using algorithmic predictions about human behavior in these circumstances at all?\n", "\n", - "Past ethical faults within the science and technology sectors may not have fully considered the damaging ramifications of certain studies, methods, and drawn conclusions on society. Moreover, it is impossible to anticipate the myriad of ways scientific findings may positively or negatively influence society. However, when it comes to working with human-derived data, or even data that can greatly affect human lives, these damages can be mitigated by staying well informed about the populations from which the data are collected and using ethical and contextual discernment when interpreting, communicating, and utilizing these data. As data scientists, it is our job to have ethics and morality as a basis by which we make these decisions around data." + "## The Trade-Off Between Transparency and Accuracy\n", + "\n", + "The development of artificial intelligence algorithms has aided, informed, and/or influenced human decision-making processes in a number of low-stakes and high-stakes scenarios. Because of this, conversation around the transparency of these algorithms has highlighted important perspectives and viewpoints regarding the implications of these technologies. \n", + "\n", + "Some algorithms disclose the use of parameters and models applied (i.e., \"white-box\" algorithms), while others may not readily explain the parameters and models used or may utilize another model that approximates the original model(s) (i.e., \"black-box\" algorithms). Some black-box algorithms also may utilize more sophistocated and complex methods, such as random forests and neural networks, which may not be readily explainable. Depending on the context of use, the methods backing white-box and black-box algorithms can influence the accuracy, and thus impact terminal services, decisions, products, or actions. \n", + "\n", + "Explainable AI (XAI) and ML (XML) explores the intersection of transparency and technical applicability in AI and ML. While transparency can help promote accountability and trust, limitations to prioritizing transparency may present in the use of AI/ML. Some of these include malicous and unintended misuse of developed AI/ML tools [^***********], exposure of trade secrets [^************], domain and technical knowledge requirements for full comprehension, and several others. In high-stakes situations, such as the use of AI/ML in healthcare decisions, some argue that accuracy should be more important than transperancy [^*************], while others suggest the use of *interpretable* models instead of black-box algorithms where possible [^**************]. Sometimes, black-box algorithms can be replaced with more simpler and transparent ones with little compromise of accuracy [^***************], [^****************]. In cases where black-box methods must be used, attempts to provide transparency through justification as opposed to explanation may be the best case scenario [^*****************]. An evalulation of the situation and associated risks and rewards, as well as testing of multiple black-box, white-box, and interpretable options, is important in determining the the best way to balance transparency and accuracy. \n", + "\n", + "\n", + "## Dishonest Statistical Practices\n", + "\n", + "A part of getting accurate insights from data includes using honest and appropriate statistical methods during data analysis. Doing such can enhance reproducibility, allowing for economical allocation of time and resources toward follow up studies. Some common pitfalls in research and analysis include hypothesizing after results are known (HARKing), p-hacking, and cherry-picking. Below is a discussion of each and how they impact research and knowledge generation.\n", + "\n", + "### HARKing\n", + "As the acroynm states, HARKing is developing a hypothesis about data after knowing the results that the data depict and then reporting conclusions as if they were hypothesized *a priori*. HARKing does not fully disclose the process leading to the hypothesis and conclusions from data and thus can be seen as dishonest in nature. HARKing may or may not include performing statistical analysis and determining significant variables in a dataset; plotting and cross-examining variables can also be a part of HARKing. When performing exploratory studies, examining the relationships between multiple variables from a dataset can be a useful process to generate new hypotheses, but these hypotheses should be tested with a new dataset to confirm previous observations.\n", + "\n", + "### P-hacking\n", + "\n", + "P-hacking can involve the use of multiple testing, various kinds of statistical tests, and/or specific subsetting of data in order to generate a significant p-value. Like HARKing, p-hacking does not fully account for the process leading up to a significant result. Analyzing data in various ways with the specific intent to show a significant p-value, rather than analyzing in an objective fashion, can lead to erroneous conclusions, misguided research directions, and retraction of scientific papers, as seen in the research of Dr. Brian Wansink [^******************]. Because hypothesis testing reports the probability of an observed outcome occuring given that the null hypothesis is true, testing multiple hypothesis on the same data will give a false positive at some point. To address this, multiple testing corrections and adjustments should be used [^*******************], [^********************].\n", + "\n", + "### Cherry-picking\n", + "Cherry-picking involves biased selection of data for analysis or reporting conclusions. Cherry-picking can be used to fuel p-hacking or paint an incomplete picture of a research process. Reporting only data that aligns with a hypothesis can be an impediment to those trying to repeat a reported experiment because it can lead researchers down an avoidable rabbit hole. Furthermore, not reporting null data that is not in alignment with a hypothesis can similarly hinder the scientific process. \n", + "\n", + "### Data manipulation\n", + "Data manipulation includes practices of fabrication and falsification that can be fueled by the omission, addition, and/or alteration of raw data. According to the National Science Foundation's policy (45 CFR 689)[^*********************], fabrication means “making up data or results and recording or reporting them”, while falsification means “manipulating research materials, equipment, or processes, or changing or omitting data or results such that the research is not accurately represented in the research record.” \n", + "\n", + "Data manipulation is one of the most blatantly dishonest research and statistical practices an investigator can do. It is greatly frowned upon within the scientific community and can reduce one's credibility as a researcher, as seen with Dr. Francesca Gino[^**********************] and Dr. Dan Ariely.[^***********************] During the research process, it may be tempting to exclude perceived outliers within a dataset, but this should be avoided unless there is sound justification based on the data collection process or statistical backing; otherwise, keeping data points that deviate highly from others within the dataset is the most honest thing to do.\n", + "\n", + "\n", + "All-in-all, when it comes to data, especially when derived from humans, centering honesty as much as possible is the best policy.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "[^*]: General Assembly. \"Preservation of Racial Integrity (1924)\" Encyclopedia Virginia. Virginia Humanities, (07 Dec. 2020). Web. 01 Dec. 2022\n", + "[^*]: Andrade, Chittaranjan. “HARKing, Cherry-Picking, P-Hacking, Fishing Expeditions, and Data Dredging and Mining as Questionable Research Practices.” The Journal of Clinical Psychiatry, vol. 82, no. 1, Feb. 2021, p. 20f13804, https://doi.org/10.4088/JCP.20f13804.\n", + "\n", + "[^**]: Lin, D., Crabtree, J., Dillo, I. et al. The TRUST Principles for digital repositories. Sci Data 7, 144 (2020). https://doi.org/10.1038/s41597-020-0486-7\n", + "\n", + "[^***]: Atske, Sara. “2. Americans Concerned, Feel Lack of Control over Personal Data Collected by Both Companies and the Government.” Pew Research Center: Internet, Science & Tech, 15 Nov. 2019, https://www.pewresearch.org/internet/2019/11/15/americans-concerned-feel-lack-of-control-over-personal-data-collected-by-both-companies-and-the-government/.\n", + "\n", + "[^****]: Robles, Pedro, and Daniel J. Mallinson. “Artificial Intelligence Technology, Public Trust, and Effective Governance.” Review of Policy Research, May 2023, p. ropr.12555, https://doi.org/10.1111/ropr.12555.\n", + "\n", + "[^*****]: Mathews, Lee. “Equifax Data Breach Impacts 143 Million Americans.” Forbes, https://www.forbes.com/sites/leemathews/2017/09/07/equifax-data-breach-impacts-143-million-americans/. Accessed 31 July 2023.\n", + "\n", + "[^******]: U.S. Department of Homeland Security. Increasing Threat of DEEPFAKE Identities. Accessed 31 July 2023. https://www.dhs.gov/sites/default/files/publications/increasing_threats_of_deepfake_identities_0.pdf\n", + "\n", + "[^*******]: “Americans Widely Distrust Facebook, TikTok and Instagram with Their Data, Poll Finds.” Washington Post, 22 Dec. 2021, https://www.washingtonpost.com/technology/2021/12/22/tech-trust-survey/.\n", + "\n", + "[^********]: Nadeem, Reem. “Public Awareness of Artificial Intelligence in Everyday Activities.” Pew Research Center Science & Society, 15 Feb. 2023, https://www.pewresearch.org/science/2023/02/15/public-awareness-of-artificial-intelligence-in-everyday-activities/.\n", + "\n", + "[^**********]: Morey, Timothy, et al. “Customer Data: Designing for Transparency and Trust.” Harvard Business Review, 1 May 2015, https://hbr.org/2015/05/customer-data-designing-for-transparency-and-trust.\n", + "\n", + "[^***********]: Virginia Dignum. The role and challenges of education for responsible AI. London Review of Education. 2021. Vol. 19(1). DOI: 10.14324/LRE.19.1.01\n", + "\n", + "[^***********]: Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, and Peter Eckersley. 2020. Explainable machine learning in deployment. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20). Association for Computing Machinery, New York, NY, USA, 648–657. https://doi.org/10.1145/3351095.3375624\n", + "\n", + "[^************]: Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1). https://doi.org/10.1177/2053951715622512\n", "\n", - "[^**]: BUCK v. BELL. 2 May 1927, https://www.loc.gov/item/usrep274200/.\n", + "[^*************]: Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021 Nov;3(11):e745-e750. doi: 10.1016/S2589-7500(21)00208-9. PMID: 34711379.\n", "\n", - "[^***]: Laws of Indiana, 1907, pp. 377-78 (B050823). https://www.in.gov/history/state-historical-markers/find-a-marker/1907-indiana-eugenics-law/\n", + "[^**************]: Rudin C. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nat Mach Intell. 2019 May;1(5):206-215. doi: 10.1038/s42256-019-0048-x. Epub 2019 May 13. PMID: 35603010; PMCID: PMC9122117.\n", "\n", - "[^****]: Reilly, Philip R. “Eugenics and Involuntary Sterilization: 1907-2015.” Annual Review of Genomics and Human Genetics, vol. 16, 2015, pp. 351–68, https://doi.org/10.1146/annurev-genom-090314-024930.\n", + "[^***************]: Chaofan Chen, Oscar Li, Chaofan Tao, Alina Jade Barnett, Jonathan Su, and Cynthia Rudin. 2019. This looks like that: deep learning for interpretable image recognition. Proceedings of the 33rd International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA, Article 801, 8930–8941.\n", "\n", - "[^*****]: Office of the New York State Attorney General. Investigative Report on the role of online platforms in the tragic mass shooting in Buffalo on May 14, 2022. Published OCTOBER 18, 2022. https://ag.ny.gov/sites/default/files/buffaloshooting-onlineplatformsreport.pdf\n", + "[^****************]: Barnett, A.J., Schwartz, F.R., Tao, C. et al. A case-based interpretable deep learning model for classification of mass lesions in digital mammography. Nat Mach Intell 3, 1061–1070 (2021). https://doi.org/10.1038/s42256-021-00423-x\n", "\n", - "[^******]: Angwin, Julia, et al. “Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks,” ProPublica, 23 May 2016, www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.\n", + "[^*****************]: Biran, Or and Courtenay V. Cotton. “Explanation and Justification in Machine Learning : A Survey Or.” (2017).\n", "\n", - "[^*******]: Dieterich, William, et al. \"COMPAS Risk Scales: Demonstrating\n", - "Accuracy Equity and Predictive Parity,\" 8 July 2016, https://go.volarisgroup.com/rs/430-MBX-989/images/ProPublica_Commentary_Final_070616.pdf.\n", + "[^******************]: “More Evidence That Nutrition Studies Don’t Always Add Up.” The New York Times, 29 Sept. 2018, https://www.nytimes.com/2018/09/29/sunday-review/cornell-food-scientist-wansink-misconduct.html.\n", "\n", - "[^********]: Flores, Anthony, et al. \"False Positives, False Negatives, and False Analyses: A Rejoinder to 'Machine Bias: There's Software Used Across the Country to Predict Future Criminals. And It's Biased Against Blacks.'\" Federal Probation, Vol. 80 Number 2, September 2016, https://www.uscourts.gov/federal-probation-journal/2016/09/false-positives-false-negatives-and-false-analyses-rejoinder.\n", "\n", - "[^*********]: Corbett-Davies, Sam, and Sharad Goel. The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning. arXiv, 14 Aug. 2018, https://arxiv.org/abs/1808.00023.\n", + "[^*******************]: Benjamini, Yoav, and Yosef Hochberg. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society. Series B (Methodological), vol. 57, no. 1, 1995, pp. 289–300. JSTOR, http://www.jstor.org/stable/2346101. Accessed 22 Aug. 2023.\n", "\n", - "[^**********]: Pleiss, Geoff, et al. “On Fairness and Calibration.” Advances in Neural Information Processing Systems, vol. 30, Curran Associates, Inc., 2017, https://papers.nips.cc/paper/2017/hash/b8b9c74ac526fffbeb2d39ab038d1cd7-Abstract.html.\n", + "[^********************]: Zbyněk Šidák (1967) Rectangular Confidence Regions for the Means of Multivariate Normal Distributions, Journal of the American Statistical Association, 62:318, 626-633, DOI: 10.1080/01621459.1967.10482935\n", "\n", - "[^***********]: Dressel, Julia and Farid, Hany. “The Accuracy, Fairness, and Limits of Predicting Recidivism.” Science Advances, vol. 4, no. 1, Jan. 2018, p. eaao5580, https://doi.org/10.1126/sciadv.aao5580.\n", + "[^*********************]: Code of Federal Regulations. Title 45, Part 689 - Research Misconduct. 18 Mar. 2002. https://www.ecfr.gov/current/title-45/subtitle-B/chapter-VI/part-689#.\n", "\n", - "[^************]: Eberhardt, Jennifer. L. “Strategies for change: Research initiatives and recommendations to improve police- community relations in Oakland, Calif.” 2016. Stanford University, SPARQ: Social Psychological Answers to Real-world Questions. \n", + "[^**********************]: \"Professor accused of faking data in studies on dishonesty sues Harvard.\" Washington Post, 3 Aug. 2023, https://www.washingtonpost.com/education/2023/08/03/harvard-honesty-lawsuit-research-misconduct/.\n", "\n", - "[^*************]: Jeffrey J. Rachlinski & Sheri L. Johnson, Does Unconscious Racial Bias Affect Trial Judges, 84 Notre Dame L. Rev. 1195 (2009). Available at: http://scholarship.law.nd.edu/ndlr/vol84/iss3/4.\n" + "[^***********************]: \"An Influential Study Of Dishonesty Was Dishonest.\" Forbes, 30 Aug. 2021. https://www.forbes.com/sites/christianmiller/2021/08/30/an-influential-study-of-dishonesty-was-dishonest/?sh=6e9e992c2c72." ] }, { diff --git a/textbook/_toc.yml b/textbook/_toc.yml index 1e70df33..8d595612 100644 --- a/textbook/_toc.yml +++ b/textbook/_toc.yml @@ -40,8 +40,6 @@ parts: file: 04/2/Dictionaries - title: "4.3 Arrays" file: 04/3/Arrays-Intro - - title: "4.4 Array Indexing and Slicing" - file: 04/4/Arrays-Slicing - title: "4.5 Assignment for Mutable Data Types" file: 04/5/assignment @@ -100,12 +98,16 @@ parts: - title: "9. Data Visualization" file: 09/data-visualization sections: - - title: "9.1 Libraries" - file: 09/1/Libraries - - title: "9.2 Categorical Data" - file: 09/2/Categorical_Data - - title: "9.3 Numerical Data" - file: 09/3/Numerical_Data + - title: "9.1 Introduction to Matplotlib & Pyplot" + file: 09/1/Intro-to-Matplotlib + - title: "9.2 Numerical Data" + file: 09/2/Numerical_Data + - title: "9.3 Categorical Data" + file: 09/3/Categorical_Data + - title: "9.4 Other Visualization Techniques" + file: 09/4/other-viz + + - title: "10. Data Collection" file: 10/data-collection @@ -172,13 +174,13 @@ parts: sections: - title: "15.1 Data Ethics and the Law" file: 15/1/ethics-and-law - - title: "15.2 Pillar 1: Data Transparency & Accountability" + - title: "15.2 Pillar 1: Mitigating Unintended Consequences" file: 15/2/pillar1 - title: "15.3 Pillar 2: Data Privacy" file: 15/3/pillar2 - title: "15.4 Pillar 3: Informed Consent" file: 15/4/pillar3 - - title: "15.5 Pillar 4: Mitigating Unintended Consequences" + - title: "15.5 Pillar 4: Data Transparency & Accountability" file: 15/5/pillar4 - title: "16. Traffic Stops Case Study"