{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"}},"nbformat":4,"nbformat_minor":2,"cells":[{"cell_type":"markdown","source":["# Data Visualization\n","<div style=\"position: relative; padding-bottom: 62.5%; height: 0;\"><iframe src=\"https://www.loom.com/embed/c96651521c6241c690c6e4f4e35a2cdd\" frameborder=\"0\" webkitallowfullscreen mozallowfullscreen allowfullscreen style=\"position: absolute; top: 0; left: 0; width: 100%; height: 100%;\"></iframe></div>\n","\n","---\n","Link to slides: [link](https://docs.google.com/presentation/d/1UaBuUSXQEMqzYpIB_sTf5TaSeq-MNG4ny_i2NOemmDo/edit#slide=id.g556781fa9e_4_0) \n","\n","```{jupyter-info}\n","{rel-data-download}`pokemon.csv`\n","```"],"metadata":{}},{"cell_type":"markdown","source":["In Lesson 10, we will discuss the guiding principles for why we want to visualize data and how we can make effective visualizations. In this notebook, we introduce the libraries `matplotlib` and `seaborn` to make visualizations.\n","\n","Before we begin, let's put a word of caution about how to approach learning these libraries:\n","> Trying to memorize all of these function calls and patterns is a ridiculous task. We will throw a lot of new functions at you very quickly and the intent is not for you to be able to memorize them all. The more important thing is to understand how to use them as examples and **adapt** those examples to the problem you are trying to solve. \n",">\n","> The most important thing is to understand the big ideas we highlight about the code we are showing!\n","\n","This means we won't always be able to explain every bit of code. The purpose is to give you some examples that you can run for your own projects or homeworks, even if you don't have the hundreds of pages of documentation memorized (because no one acually does that!).\n","\n","We will discuss how to read documentation more in Lesson 10. This notebook is meant to be an introduction to see the general workflow of the code.\n","\n","We will be using a modified dataset of Pokemon to explore how to visualize our data!"],"metadata":{},"attachments":{}},{"cell_type":"code","execution_count":2,"source":["import pandas as pd\n","\n","data = pd.read_csv('pokemon.csv')\n","\n","data  # For display"],"outputs":[{"output_type":"execute_result","data":{"text/plain":["     Num        Name   Type 1  Type 2  Total   HP  Attack  Defense  Sp. Atk  \\\n","0      1   Bulbasaur    Grass  Poison    318   45      49       49       65   \n","1      2     Ivysaur    Grass  Poison    405   60      62       63       80   \n","2      3    Venusaur    Grass  Poison    525   80      82       83      100   \n","3      4  Charmander     Fire     NaN    309   39      52       43       60   \n","4      5  Charmeleon     Fire     NaN    405   58      64       58       80   \n","..   ...         ...      ...     ...    ...  ...     ...      ...      ...   \n","146  147     Dratini   Dragon     NaN    300   41      64       45       50   \n","147  148   Dragonair   Dragon     NaN    420   61      84       65       70   \n","148  149   Dragonite   Dragon  Flying    600   91     134       95      100   \n","149  150      Mewtwo  Psychic     NaN    680  106     110       90      154   \n","150  151         Mew  Psychic     NaN    600  100     100      100      100   \n","\n","     Sp. Def  Speed  Stage  Legendary  \n","0         65     45      1      False  \n","1         80     60      2      False  \n","2        100     80      3      False  \n","3         50     65      1      False  \n","4         65     80      2      False  \n","..       ...    ...    ...        ...  \n","146       50     50      1      False  \n","147       70     70      2      False  \n","148      100     80      3      False  \n","149       90    130      1       True  \n","150      100    100      1      False  \n","\n","[151 rows x 13 columns]"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Num</th>\n","      <th>Name</th>\n","      <th>Type 1</th>\n","      <th>Type 2</th>\n","      <th>Total</th>\n","      <th>HP</th>\n","      <th>Attack</th>\n","      <th>Defense</th>\n","      <th>Sp. Atk</th>\n","      <th>Sp. Def</th>\n","      <th>Speed</th>\n","      <th>Stage</th>\n","      <th>Legendary</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>Bulbasaur</td>\n","      <td>Grass</td>\n","      <td>Poison</td>\n","      <td>318</td>\n","      <td>45</td>\n","      <td>49</td>\n","      <td>49</td>\n","      <td>65</td>\n","      <td>65</td>\n","      <td>45</td>\n","      <td>1</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>Ivysaur</td>\n","      <td>Grass</td>\n","      <td>Poison</td>\n","      <td>405</td>\n","      <td>60</td>\n","      <td>62</td>\n","      <td>63</td>\n","      <td>80</td>\n","      <td>80</td>\n","      <td>60</td>\n","      <td>2</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>Venusaur</td>\n","      <td>Grass</td>\n","      <td>Poison</td>\n","      <td>525</td>\n","      <td>80</td>\n","      <td>82</td>\n","      <td>83</td>\n","      <td>100</td>\n","      <td>100</td>\n","      <td>80</td>\n","      <td>3</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>Charmander</td>\n","      <td>Fire</td>\n","      <td>NaN</td>\n","      <td>309</td>\n","      <td>39</td>\n","      <td>52</td>\n","      <td>43</td>\n","      <td>60</td>\n","      <td>50</td>\n","      <td>65</td>\n","      <td>1</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>Charmeleon</td>\n","      <td>Fire</td>\n","      <td>NaN</td>\n","      <td>405</td>\n","      <td>58</td>\n","      <td>64</td>\n","      <td>58</td>\n","      <td>80</td>\n","      <td>65</td>\n","      <td>80</td>\n","      <td>2</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>146</th>\n","      <td>147</td>\n","      <td>Dratini</td>\n","      <td>Dragon</td>\n","      <td>NaN</td>\n","      <td>300</td>\n","      <td>41</td>\n","      <td>64</td>\n","      <td>45</td>\n","      <td>50</td>\n","      <td>50</td>\n","      <td>50</td>\n","      <td>1</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>147</th>\n","      <td>148</td>\n","      <td>Dragonair</td>\n","      <td>Dragon</td>\n","      <td>NaN</td>\n","      <td>420</td>\n","      <td>61</td>\n","      <td>84</td>\n","      <td>65</td>\n","      <td>70</td>\n","      <td>70</td>\n","      <td>70</td>\n","      <td>2</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>148</th>\n","      <td>149</td>\n","      <td>Dragonite</td>\n","      <td>Dragon</td>\n","      <td>Flying</td>\n","      <td>600</td>\n","      <td>91</td>\n","      <td>134</td>\n","      <td>95</td>\n","      <td>100</td>\n","      <td>100</td>\n","      <td>80</td>\n","      <td>3</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>149</th>\n","      <td>150</td>\n","      <td>Mewtwo</td>\n","      <td>Psychic</td>\n","      <td>NaN</td>\n","      <td>680</td>\n","      <td>106</td>\n","      <td>110</td>\n","      <td>90</td>\n","      <td>154</td>\n","      <td>90</td>\n","      <td>130</td>\n","      <td>1</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>150</th>\n","      <td>151</td>\n","      <td>Mew</td>\n","      <td>Psychic</td>\n","      <td>NaN</td>\n","      <td>600</td>\n","      <td>100</td>\n","      <td>100</td>\n","      <td>100</td>\n","      <td>100</td>\n","      <td>100</td>\n","      <td>100</td>\n","      <td>1</td>\n","      <td>False</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>151 rows × 13 columns</p>\n","</div>"]},"metadata":{},"execution_count":2}],"metadata":{}},{"cell_type":"markdown","source":["We first start by importing our plotting libraries \n","* `matplotlib` (commonly abreviated by `plt`) is a much older and very powerful plotting library.\n","* `seaborn` (commonly abbreviated by `sns`) is a newer visualization library popular for data science.\n","\n","We primarily use `seaborn` due to its popularity in data science and it makes creating useful visualizations very easy. We will rely on `matplotlib` for some abilities to customize these visualizations. \n","\n","We also need to \"set up\" `seaborn` by calling `sns.set()` after importing it. \n","\n","Also, because this is in a Jupyter Notebook, we need to add a special directive to show the plots after the cell. This last step is something that only needs to be done in Jupyter Notebooks."],"metadata":{},"attachments":{}},{"cell_type":"code","execution_count":3,"source":["import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","sns.set()  # Don't forget this!\n","\n","# Only for Jupyter\n","%matplotlib inline"],"outputs":[],"metadata":{}},{"cell_type":"markdown","source":["Let's start by making our first plot! We want to make a scatter plot that shows how Pokemon Attack and Defense compare. \n","\n","We will explain what this code does after this cell."],"metadata":{},"attachments":{}},{"cell_type":"code","execution_count":4,"source":["sns.relplot(x='Attack', y='Defense', data=data)"],"outputs":[{"output_type":"execute_result","data":{"text/plain":["<seaborn.axisgrid.FacetGrid at 0x7f349b681bb0>"]},"metadata":{},"execution_count":4},{"output_type":"display_data","data":{"text/plain":["<Figure size 360x360 with 1 Axes>"],"image/png":"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"},"metadata":{"needs_background":"light"}}],"metadata":{}},{"cell_type":"markdown","source":["That's so cool that we can generate a pretty plot from so little code! \n","\n","The `relplot` function is just one example function you can call from `seaborn` (we will see more later). You can view the documentation for `relplot` [here](https://seaborn.pydata.org/generated/seaborn.relplot.html).\n","\n","Here is how I would go about reading that documentation I just linked:\n","1. Skim examples, don’t focus too much on code\n","2. Read overview\n","3. Look at examples and the code. Look at documentation for relevant parameters\n","4. (Sometimes, if necessary) Skim parameter list\n","\n","Looking through the examples on that page, I see that I can set the size of the dots by some other value. Let's go ahead and do that!"],"metadata":{},"attachments":{}},{"cell_type":"code","execution_count":9,"source":["sns.relplot(x='Attack', y='Defense', size='Stage', data=data)"],"outputs":[{"output_type":"execute_result","data":{"text/plain":["<seaborn.axisgrid.FacetGrid at 0x7f349b318220>"]},"metadata":{},"execution_count":9},{"output_type":"display_data","data":{"text/plain":["<Figure size 431.6x360 with 1 Axes>"],"image/png":"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"},"metadata":{"needs_background":"light"}}],"metadata":{}},{"cell_type":"markdown","source":["We recommend using the following functions for possible things to plot (notice that most of them can generate different types of plots with the `kind` parameter).\n","* [Bar/Violin Plot](https://seaborn.pydata.org/generated/seaborn.catplot.html)\n","* [Plot a Distribution](https://seaborn.pydata.org/generated/seaborn.kdeplot.html)\n","* [Scatter/Line Plot](https://seaborn.pydata.org/generated/seaborn.relplot.html)\n","* [Linear Regression Plot](https://seaborn.pydata.org/generated/seaborn.regplot.html)\n","* [Compare Two Variables](https://seaborn.pydata.org/generated/seaborn.jointplot.html)\n","* [Heatmap](https://seaborn.pydata.org/generated/seaborn.heatmap.html#seaborn.heatmap)\n","\n","Again, you do not need to memorize these functions or their parameters. You might want to look through their examples to see all the different types of plots you can make!\n","\n","> **Warning**: These pages of documentation link to other functions in `seaborn` for plotting. We recommend you stick to these ones using the `kind` parameter rather than using the other functions since they have a slightly different behavior. 80% of bugs on HW3 related to plotting are students using other `seaborn` functions that aren't one of the 6 listed above! \n","\n","Let's try using another one of those functions to make a plot to show how many Pokemon are of each type!"],"metadata":{},"attachments":{}},{"cell_type":"code","execution_count":10,"source":["sns.catplot(x='Type 1', kind='count', data=data)"],"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 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"},"metadata":{"needs_background":"light"}}],"metadata":{}},{"cell_type":"markdown","source":["This plot has one big problem and one minor one. \n","1. Big: You can't read the x-axis which violates the major rule of making your visualization readable.\n","2. Minor: It's not necessarily a bad idea to encode the bars with different colors to emphasize they are different types (in fact, it sometimes can help by encoding information twice), but in this case, it doesn't necessarily make anything clearer.\n","\n","Looking at the documentation for `catplot`, we see we can make the bars the same color with the `color` parameter. `'b'` in this context means \"blue\"."],"metadata":{},"attachments":{}},{"cell_type":"code","execution_count":10,"source":["sns.catplot(x='Type 1', kind='count', color='b', data=data)"],"outputs":[{"output_type":"execute_result","data":{"text/plain":["<seaborn.axisgrid.FacetGrid at 0x7f349b2a23a0>"]},"metadata":{},"execution_count":10},{"output_type":"display_data","data":{"text/plain":["<Figure size 360x360 with 1 Axes>"],"image/png":"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"},"metadata":{"needs_background":"light"}}],"metadata":{}},{"cell_type":"markdown","source":["Then we need to fix the axis issue. `seaborn` does a great job making visualizations that look pretty good by default, but makes it really hard to customize them in small ways. This is is where `matplotlib` comes in to help us customize the chart. \n","\n","To fix this specific issue of being unable to read the x-axis, we need to rotate the x-axis ticks. The following cell does that using `matplotlib`."],"metadata":{},"attachments":{}},{"cell_type":"code","execution_count":11,"source":["sns.catplot(x='Type 1', kind='count', color='b', data=data)\n","plt.xticks(rotation=-45)"],"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14]),\n"," <a list of 15 Text xticklabel objects>)"]},"metadata":{},"execution_count":11},{"output_type":"display_data","data":{"text/plain":["<Figure size 360x360 with 1 Axes>"],"image/png":"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"},"metadata":{"needs_background":"light"}}],"metadata":{}},{"cell_type":"markdown","source":["For your homework, we would not expect you to try to learn the `matplotlib` library (as it is huge and complex) to make minor adjustments to your plots. Instead, we will commonly ask you to use the following three:\n","* `plt.title('My Title')`: To set the title of the chart\n","* `plt.xlabel('X-Axis Label')`: To set the x-axis label\n","* `plt.ylabel('Y-Axis Label')`: To set the y-axis label\n","\n","The following cell shows how to set all of these for the bar-plot."],"metadata":{},"attachments":{}},{"cell_type":"code","execution_count":15,"source":["sns.catplot(x='Type 1', kind='count', color='b', data=data)\n","plt.xticks(rotation=-45)\n","\n","plt.title('Count of Each Primary Pokemon Type')\n","plt.xlabel('Primary Type')\n","plt.ylabel('Number of Pokemon')"],"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(10.175000000000004, 0.5, 'Number of Pokemon')"]},"metadata":{},"execution_count":15},{"output_type":"display_data","data":{"text/plain":["<Figure size 360x360 with 1 Axes>"],"image/png":"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"},"metadata":{"needs_background":"light"}}],"metadata":{}},{"cell_type":"markdown","source":["## Expectations\n","For the most part, that's all we are going to discuss in depth for data visualization at this time. We will revisit this topic later in the book to show how to make more complex plots (e.g., ones where you make two plots side-by-side).\n","\n","The rationale here is we actually want to use data visualization as an opportunity for you to learn the skill of looking at documentation to build your own working knowledge. This much better reflects the learning you will need to do in the real world, and `seaborn` is such a great case-study for this because their documentation is incredible.\n","\n","You won't have to dig super deep though, for the most part we show you that you only need to look at the documentation for the pages we listed above (shown below for convenience).\n","\n","* [Bar/Violin Plot](https://seaborn.pydata.org/generated/seaborn.catplot.html)\n","* [Plot a Distribution](https://seaborn.pydata.org/generated/seaborn.kdeplot.html)\n","* [Scatter/Line Plot](https://seaborn.pydata.org/generated/seaborn.relplot.html)\n","* [Linear Regression Plot](https://seaborn.pydata.org/generated/seaborn.regplot.html)\n","* [Compare Two Variable](https://seaborn.pydata.org/generated/seaborn.jointplot.html)\n","* [Heatmap](https://seaborn.pydata.org/generated/seaborn.heatmap.html#seaborn.heatmap)"],"metadata":{},"attachments":{}}]}