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1134 lines
1.1 MiB
1134 lines
1.1 MiB
2 years ago
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"___\n",
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"\n",
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"<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>\n",
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"___\n",
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"<center><em>Copyright by Pierian Data Inc.</em></center>\n",
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"<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Categorical Plots - Distribution within Categories"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"So far we've seen how to apply a statistical estimation (like mean or count) to categories and compare them to one another. Let's now explore how to visualize the distribution within categories. We already know about distplot() which allows to view the distribution of a single feature, now we will break down that same distribution per category."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Imports"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## The Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv(\"StudentsPerformance.csv\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>gender</th>\n",
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" <th>race/ethnicity</th>\n",
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" <th>parental level of education</th>\n",
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" <th>lunch</th>\n",
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" <th>test preparation course</th>\n",
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" <th>math score</th>\n",
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" <th>reading score</th>\n",
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" <th>writing score</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>female</td>\n",
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" <td>group B</td>\n",
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" <td>bachelor's degree</td>\n",
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" <td>standard</td>\n",
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" <td>none</td>\n",
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" <td>72</td>\n",
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" <td>72</td>\n",
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" <td>74</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>female</td>\n",
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" <td>group C</td>\n",
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" <td>some college</td>\n",
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" <td>standard</td>\n",
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" <td>completed</td>\n",
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" <td>69</td>\n",
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" <td>90</td>\n",
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" <td>88</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>female</td>\n",
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" <td>group B</td>\n",
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" <td>master's degree</td>\n",
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" <td>standard</td>\n",
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" <td>none</td>\n",
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" <td>90</td>\n",
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" <td>95</td>\n",
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" <td>93</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>male</td>\n",
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" <td>group A</td>\n",
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" <td>associate's degree</td>\n",
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" <td>free/reduced</td>\n",
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" <td>none</td>\n",
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" <td>47</td>\n",
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" <td>57</td>\n",
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" <td>44</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>male</td>\n",
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" <td>group C</td>\n",
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|
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" <td>some college</td>\n",
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" <td>standard</td>\n",
|
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" <td>none</td>\n",
|
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" <td>76</td>\n",
|
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" <td>78</td>\n",
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" <td>75</td>\n",
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|
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" </tr>\n",
|
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|
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" </tbody>\n",
|
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|
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"</table>\n",
|
||
|
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"</div>"
|
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|
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],
|
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|
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"text/plain": [
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" gender race/ethnicity parental level of education lunch \\\n",
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|
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"0 female group B bachelor's degree standard \n",
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|
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"1 female group C some college standard \n",
|
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|
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"2 female group B master's degree standard \n",
|
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"3 male group A associate's degree free/reduced \n",
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"4 male group C some college standard \n",
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"\n",
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" test preparation course math score reading score writing score \n",
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"0 none 72 72 74 \n",
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"1 completed 69 90 88 \n",
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"2 none 90 95 93 \n",
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"3 none 47 57 44 \n",
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"4 none 76 78 75 "
|
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]
|
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|
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},
|
||
|
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"execution_count": 7,
|
||
|
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"metadata": {},
|
||
|
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"output_type": "execute_result"
|
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|
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}
|
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|
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],
|
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|
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"source": [
|
||
|
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"df.head()"
|
||
|
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]
|
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|
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},
|
||
|
|
{
|
||
|
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"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
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|
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"source": [
|
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|
|
"## Boxplot\n",
|
||
|
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"\n",
|
||
|
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"As described in the video, a boxplot display distribution through the use of quartiles and an IQR for outliers."
|
||
|
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]
|
||
|
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},
|
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|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
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"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='parental level of education', ylabel='math score'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
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"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x432 with 1 Axes>"
|
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|
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]
|
||
|
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},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,6))\n",
|
||
|
|
"sns.boxplot(x='parental level of education',y='math score',data=df)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Adding hue for further segmentation"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.legend.Legend at 0x2116e6b9948>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x432 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,6))\n",
|
||
|
|
"sns.boxplot(x='parental level of education',y='math score',data=df,hue='gender')\n",
|
||
|
|
"\n",
|
||
|
|
"# Optional move the legend outside\n",
|
||
|
|
"plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Boxplot Styling Parameters\n",
|
||
|
|
"\n",
|
||
|
|
"#### Orientation"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='math score', ylabel='parental level of education'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# NOTICE HOW WE HAVE TO SWITCH X AND Y FOR THE ORIENTATION TO MAKE SENSE!\n",
|
||
|
|
"sns.boxplot(x='math score',y='parental level of education',data=df,orient='h')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"#### Width"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='parental level of education', ylabel='math score'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 29,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x432 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,6))\n",
|
||
|
|
"sns.boxplot(x='parental level of education',y='math score',data=df,hue='gender',width=0.3)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Violinplot\n",
|
||
|
|
"\n",
|
||
|
|
"A violin plot plays a similar role as a box and whisker plot. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. Unlike a box plot, in which all of the plot components correspond to actual datapoints, the violin plot features a kernel density estimation of the underlying distribution."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 30,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='parental level of education', ylabel='math score'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 30,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x432 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,6))\n",
|
||
|
|
"sns.violinplot(x='parental level of education',y='math score',data=df)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 31,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='parental level of education', ylabel='math score'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 31,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x432 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,6))\n",
|
||
|
|
"sns.violinplot(x='parental level of education',y='math score',data=df,hue='gender')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Violinplot Parameters\n",
|
||
|
|
"\n",
|
||
|
|
"#### split\n",
|
||
|
|
"When using hue nesting with a variable that takes two levels, setting split to True will draw half of a violin for each level. This can make it easier to directly compare the distributions."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 32,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='parental level of education', ylabel='math score'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 32,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x432 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,6))\n",
|
||
|
|
"sns.violinplot(x='parental level of education',y='math score',data=df,hue='gender',split=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"#### inner\n",
|
||
|
|
"\n",
|
||
|
|
"Representation of the datapoints in the violin interior. If box, draw a miniature boxplot. If quartiles, draw the quartiles of the distribution. If point or stick, show each underlying datapoint. Using None will draw unadorned violins."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 38,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='parental level of education', ylabel='math score'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 38,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x432 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,6))\n",
|
||
|
|
"sns.violinplot(x='parental level of education',y='math score',data=df,inner=None)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 39,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='parental level of education', ylabel='math score'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 39,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x432 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,6))\n",
|
||
|
|
"sns.violinplot(x='parental level of education',y='math score',data=df,inner='box')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 35,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='parental level of education', ylabel='math score'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 35,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAAFzCAYAAAAe3J47AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAACnNElEQVR4nOzdd3hcxfXw8e/cbepdVrEty7bk3m3ABkNMMR1C7wQIBFIIBBISIL+EhORNgABJIIFAQiehY7ApptiYZrBxt3EvcpFkWb2stPXO+8dq5SZZbXfv3d35PI8fS1vuHEve3XPnnjkjpJQoiqIoiqIoitI3mtEBKIqiKIqiKEo0Uwm1oiiKoiiKovSDSqgVRVEURVEUpR9UQq0oiqIoiqIo/aASakVRFEVRFEXpB5VQK4qiKIqiKEo/WI0OoD9ycnJkcXGx0WEoiqIoiqIoMW758uU1Usrczu6L6oS6uLiYZcuWGR2GoiiKoiiKEuOEEDu7uk+VfCiKoiiKoihKP6iEWlEURVEURVH6QSXUiqIoiqIoitIPKqFWFEVRFEVRlH5QCbWiKIqiKIqi9INKqBVFURRFURSlH1RCrSiKoiiKoij9oBJqRVEURVEURekHlVAriqIoiqIoSj+ohFpRFEVRFEVR+kEl1IqiKIqiKIrSDyqhVhRFURRFUZR+UAm1SX3xxRfceNNNPPPMM0aHoiiKoiiKohyBSqhN6tNPP2Xjhg3Me+cdo0NRFEVRFEVRjkAl1CbV0NAAQFNTk7GBKIqiKIqiKEekEmqTqqmtBcDjdtPa2mpwNIqiKNHhyy+/5NZbb2XTpk1Gh6IoShxRCbVJ7dtXjbTY2r/eZ3A0Sm9t2rSJhQsX4vF4jA5FUeLKRx99xMqVK1m6dKnRoSiKEkdUQm1Cbreb5qZG/Cl5gEqoo9FPf3ozv/vd7/jiiy+MDkVR4orL5QKgpaXF4EiUvpBSIqU0OgxF6TWVUJtQRUUFAP70gQCUl5cbGY7SS7qu43K5AaipqTE4GkWJL83NzYBafxKt7rrrLm695Vajw1CUXrMaHYByuD179gCgp+YhLLaO75XoEPxAB2hsbDQwEqUvWlpaWLt2LYmJiUycOBEhhNEhKb3QUFcX+Lt9YbcSXRYvXmx0CEo/SCnj9j1TzVCb0I4dOwDQEzPQE9I7vleiw4Gz0mqGOvo8++yz/OpXv+KWW25h8+bNRoej9FJ9eyJd355YK4oSGdu3b+ekk05izpw5RodiCJVQm9D27dsRiWlgseNLzGTL1q1Gh6T0QnV1dcfXqv49+lRUVIA44GslarhcLlqcTgD2VVUZHI2ixJc9e/bg9/tZtGiR0aEYQiXUJrRh40a8CZkA6MnZNDY0qJnOKFLV/kFemu5jb6Wqf482VfuqkJmBRVHqhCi6BH9fGUBdfT0+n8/QeBQlnrjdbqNDMJRKqE2msbGRyooK9JQBAOjJuQCsX7/eyLCUXtizZw82C4zK9LK3ap/6UI8yFRUVyAyJsAsqKyuNDkfpheAC7mGALmXHya2iKOEXTKjjtUuLSqhN5ttvvwXAnxJIpPXkbNAsrFu3zsiwlF7Ys2cPeUmS/CQdv19n7969Roek9FBzczPOFiekAMmqw060CS7gHnHI90r08fv9Roeg9FJbW5vRIRhKJdQms2LFCoRm6ZihRrOiJ+eyfPkKYwNTemz7tq0MTPIyMDnwgaAWlUaPsrIyAGSaxJ/iZ3vZdmMDUnqlrKyMRE2j+IDvlehx4Mym2hQr+gR3dVYz1IopLFu+Al/KAND2dzT0pRWydesW1Vc1CrS2tlK5t4pByf6OhHr7dpWURYuOk580IB2qq6pxti9yU8xv65Yt5Os6yQjSNI2takF3VDkwiY73etxoFEyo4/V3pxJqE6murmb7tq340wcddLs/fRBSSrWVbhTYtm0bAEWpfhKtkJcMW7ZsMTgqpac2bdqE5tAgCWRGYJZF/f6ig8/nY/v27eS3f5+n62zeuNHQmJTeCe5yeejXSnSI902VVEJtIl999RUAvozBB92up+Qg7Imq4X0U2LRpEwDD0gILEYemeNi4QS0ojRbfrv8Wf4Y/0DYv0GiHDRs2GBqT0jPbtm3D7fEQfPccDJTt2qWuMESRA2tw470eNxoFNzJTCbViuE8//RQS0pCJmQffITQ8aYP48svFqq7M5DZu3EhGAmQ6ArObw9J87KuuoU5tMmF6LS0t7Ni+A5ndXv+XACJVsGbNGmMDU3okuHA7mFAXEajlDC70VsxPJdTRrakpkFA7nc64XFSqEmqTaGxsZPny5Xgyi6GTbTv92cNoa2vlm2++iXxwSo+tW7Oa0tT9Jz0l6YGZavWhbn6rV69GSonM3b+gxp/jZ+Wqlar1YRRYvnw5mZpGevv3gwCLECxfvtzIsJReOPBqQrAeV4kewf0ypJQ0tO9YGk9UQm0SixYtQtd1/NnDOr3fn1aIsDn46KOPIhyZ0lO1tbVU7K2iNGN/8lWc6seiodoeRoElS5YgrAKy998m8yStzlZV9mFyPp+PFcuXM1zXEe3bXDoQDJaSb9Tak6hxYBLd0tJiYCRKX9TV1pFgCaSVtbW1BkcTeSqhNon358+HpEz0pOzOH6BZ8GQO4/PPv1A1gSa1du1aAEYckFDbLTAszc+a1auNCkvpASklXyz+Aj1XB8sBd+QBArV+weTWrl1La1sbJYfcXgJs3bZN7TQbJQ5MotXnXHRpbW2lzeUizW4DiMvXnEqoTWD37t2s//ZbPNklnZZ7BPlySvF6PXzyyScRjE7pqdWrV+OwCopTD64dG5nuZdOmTXHbSigabNiwgZp9NchBh/RPtYMcIFmwcEHc9laNBl988QVWIQ5LqEe1//3ll19GOiSlD4JdIg79WjG/4K6yWQm2g76PJyqhNoF33nkHhMCXU3rEx+kpuZCUydtz50YoMqU31qxexfA0L9ZDXlWjMr34/H5VR21iH3/8MUITyMLDk2Y5WLK3cq8q+zApXdf59JNPGC4lDg6ekBgAZGuamoSIEiqhjl7BHYHT7TYsmojLHYJVQm0wr9fLu+++hy+jCGlPOvKDhcCdO5JNGzeqDQtMpqWlha3btjMqw3vYfaXpfgSobhEm5fF4mP/BfPyFfrAffr8cJBFWwfvvvx/54JRurVu3jn01NYzv5D6BYJyus2rlyri8BB1tGhsbsVpsJNiTOlqwKdGhvLwcgCSrhSSrteP7eKISaoMtWrSIpqZGfANG9+jxvpxShMXKnDlzwhyZ0htr165FSsnIjMO7QSTbJEVpOqtXrYp8YEq3PvvsM1qaW5BDuyjpsIF/oJ8PPvxA1XWa0IcffohdiI7yjkNNBHQpWbhwYSTDUvqgoaEBhy0JhzUpLrtERLOdO3fisFqwWzQSLdr+XWfjSNgSaiHE00KIfUKIdQfcliWE+EgIsaX978wD7rtLCLFVCLFJCHFauOIymzfefBMS0/GnD+zZE6wOPFnD+ODDD9UlMRNZu3YtFgHD0ztvrzYizcO3336r2q+Z0CuvvoJIFYEFiF2QJRJXm0vNUpuMy+Xi448+YnQn5R5BuQgGCsG78+apOniTa2howGFJxG5JVAl1lCkrKyOpvcNHis1CZWUlXu/hV2xjWThnqJ8FTj/ktjuBBVLKUmBB+/cIIcYAlwFj25/zmBDCQozbtGkT67/9Fnfu6CMuRjyUL28MHreb9957L4zRKb2xds0ahqT5Sejif+2IDB8ut7tja3LFHFatWsWmjZvwl7TvjtiVLCAHXn7lZXVSZCKLFi2ita2Nqd08bqqU7Ni5k/Xr1a6lZlZbW4vDmkSCNYnamvhruxatpJRs37aNZFvgAzDFZkXXdXbu3GlwZJEVtoRaSvkZcOj2cN8Fnmv/+jngvANuf1lK6ZZS7gC2AkeHKzazeO211xBWG77cEb16np6cg56Wz2uvvx6XuxGZjc/nY8OGDYxI6/psPNhKL9haTzGHZ597FpEokMXdz1z6R/rZV7VP9YI3kbfmzCFHaBR387jxgF0I3n777QhEpfRVXW0dCbZkEuwp1NWr3WWjRWVlJc7W1o6WeWl2KwCbN28
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x432 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,6))\n",
|
||
|
|
"sns.violinplot(x='parental level of education',y='math score',data=df,inner='quartile')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 43,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='parental level of education', ylabel='math score'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 43,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x432 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,6))\n",
|
||
|
|
"sns.violinplot(x='parental level of education',y='math score',data=df,inner='stick')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"#### orientation"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 45,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='math score', ylabel='parental level of education'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 45,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Simply switch the continuous variable to y and the categorical to x\n",
|
||
|
|
"sns.violinplot(x='math score',y='parental level of education',data=df,)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"#### bandwidth\n",
|
||
|
|
"\n",
|
||
|
|
"Similar to bandwidth argument for kdeplot"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 48,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='parental level of education', ylabel='math score'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 48,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x432 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,6))\n",
|
||
|
|
"sns.violinplot(x='parental level of education',y='math score',data=df,bw=0.1)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Advanced Plots\n",
|
||
|
|
"\n",
|
||
|
|
"We can use a boxenplot and swarmplot to achieve the same effect as the boxplot and violinplot, but with slightly more information included. Be careful when using these plots, as they often require you to educate the viewer with how the plot is actually constructed. Only use these if you are sure your audience will understand the visualization."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 49,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"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>gender</th>\n",
|
||
|
|
" <th>race/ethnicity</th>\n",
|
||
|
|
" <th>parental level of education</th>\n",
|
||
|
|
" <th>lunch</th>\n",
|
||
|
|
" <th>test preparation course</th>\n",
|
||
|
|
" <th>math score</th>\n",
|
||
|
|
" <th>reading score</th>\n",
|
||
|
|
" <th>writing score</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>female</td>\n",
|
||
|
|
" <td>group B</td>\n",
|
||
|
|
" <td>bachelor's degree</td>\n",
|
||
|
|
" <td>standard</td>\n",
|
||
|
|
" <td>none</td>\n",
|
||
|
|
" <td>72</td>\n",
|
||
|
|
" <td>72</td>\n",
|
||
|
|
" <td>74</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1</th>\n",
|
||
|
|
" <td>female</td>\n",
|
||
|
|
" <td>group C</td>\n",
|
||
|
|
" <td>some college</td>\n",
|
||
|
|
" <td>standard</td>\n",
|
||
|
|
" <td>completed</td>\n",
|
||
|
|
" <td>69</td>\n",
|
||
|
|
" <td>90</td>\n",
|
||
|
|
" <td>88</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2</th>\n",
|
||
|
|
" <td>female</td>\n",
|
||
|
|
" <td>group B</td>\n",
|
||
|
|
" <td>master's degree</td>\n",
|
||
|
|
" <td>standard</td>\n",
|
||
|
|
" <td>none</td>\n",
|
||
|
|
" <td>90</td>\n",
|
||
|
|
" <td>95</td>\n",
|
||
|
|
" <td>93</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>3</th>\n",
|
||
|
|
" <td>male</td>\n",
|
||
|
|
" <td>group A</td>\n",
|
||
|
|
" <td>associate's degree</td>\n",
|
||
|
|
" <td>free/reduced</td>\n",
|
||
|
|
" <td>none</td>\n",
|
||
|
|
" <td>47</td>\n",
|
||
|
|
" <td>57</td>\n",
|
||
|
|
" <td>44</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>4</th>\n",
|
||
|
|
" <td>male</td>\n",
|
||
|
|
" <td>group C</td>\n",
|
||
|
|
" <td>some college</td>\n",
|
||
|
|
" <td>standard</td>\n",
|
||
|
|
" <td>none</td>\n",
|
||
|
|
" <td>76</td>\n",
|
||
|
|
" <td>78</td>\n",
|
||
|
|
" <td>75</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" gender race/ethnicity parental level of education lunch \\\n",
|
||
|
|
"0 female group B bachelor's degree standard \n",
|
||
|
|
"1 female group C some college standard \n",
|
||
|
|
"2 female group B master's degree standard \n",
|
||
|
|
"3 male group A associate's degree free/reduced \n",
|
||
|
|
"4 male group C some college standard \n",
|
||
|
|
"\n",
|
||
|
|
" test preparation course math score reading score writing score \n",
|
||
|
|
"0 none 72 72 74 \n",
|
||
|
|
"1 completed 69 90 88 \n",
|
||
|
|
"2 none 90 95 93 \n",
|
||
|
|
"3 none 47 57 44 \n",
|
||
|
|
"4 none 76 78 75 "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 49,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df.head()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## swarmplot"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 50,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='math score'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 50,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.swarmplot(x='math score',data=df)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 53,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='math score', ylabel='race/ethnicity'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 53,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.swarmplot(x='math score',y='race/ethnicity',data=df)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 54,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='race/ethnicity', ylabel='math score'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 54,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.swarmplot(x='race/ethnicity',y='math score',data=df)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 56,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='race/ethnicity', ylabel='math score'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 56,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x432 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,6))\n",
|
||
|
|
"sns.swarmplot(x='race/ethnicity',y='math score',data=df,hue='gender')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 57,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='race/ethnicity', ylabel='math score'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 57,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x432 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,6))\n",
|
||
|
|
"sns.swarmplot(x='race/ethnicity',y='math score',data=df,hue='gender',dodge=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### boxenplot (letter-value plot)\n",
|
||
|
|
"\n",
|
||
|
|
"Official Paper on this plot: https://vita.had.co.nz/papers/letter-value-plot.html\n",
|
||
|
|
"\n",
|
||
|
|
"This style of plot was originally named a “letter value” plot because it shows a large number of quantiles that are defined as “letter values”. It is similar to a box plot in plotting a nonparametric representation of a distribution in which all features correspond to actual observations. By plotting more quantiles, it provides more information about the shape of the distribution, particularly in the tails."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 59,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='math score', ylabel='race/ethnicity'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 59,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.boxenplot(x='math score',y='race/ethnicity',data=df)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 60,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='race/ethnicity', ylabel='math score'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 60,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.boxenplot(x='race/ethnicity',y='math score',data=df)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 62,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='race/ethnicity', ylabel='math score'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 62,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x432 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,6))\n",
|
||
|
|
"sns.boxenplot(x='race/ethnicity',y='math score',data=df,hue='gender')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"----\n",
|
||
|
|
"----"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"anaconda-cloud": {},
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"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": "3.7.6"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 1
|
||
|
|
}
|