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682 lines
1.1 MiB
682 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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"# Scatter Plots\n",
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"\n",
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"Scatter plots can show how different features are related to one another, the main theme between all relational plot types is they display how features are interconnected to each other. There are many different types of plots that can be used to show this, so let's explore the scatterplot() as well as general seaborn parameters applicable to other plot types."
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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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"----\n",
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"## Data\n",
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"\n",
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"We'll use some generated data from: http://roycekimmons.com/tools/generated_data\n",
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"\n"
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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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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tools\\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
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" import pandas.util.testing as tm\n"
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]
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}
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],
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"source": [
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"import pandas as pd\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": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv(\"dm_office_sales.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": 4,
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"metadata": {},
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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>division</th>\n",
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" <th>level of education</th>\n",
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" <th>training level</th>\n",
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" <th>work experience</th>\n",
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" <th>salary</th>\n",
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" <th>sales</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>printers</td>\n",
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" <td>some college</td>\n",
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" <td>2</td>\n",
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" <td>6</td>\n",
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" <td>91684</td>\n",
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" <td>372302</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>printers</td>\n",
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" <td>associate's degree</td>\n",
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" <td>2</td>\n",
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" <td>10</td>\n",
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" <td>119679</td>\n",
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" <td>495660</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>peripherals</td>\n",
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" <td>high school</td>\n",
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" <td>0</td>\n",
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" <td>9</td>\n",
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" <td>82045</td>\n",
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" <td>320453</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>office supplies</td>\n",
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" <td>associate's degree</td>\n",
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" <td>2</td>\n",
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" <td>5</td>\n",
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" <td>92949</td>\n",
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" <td>377148</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>office supplies</td>\n",
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" <td>high school</td>\n",
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" <td>1</td>\n",
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" <td>5</td>\n",
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" <td>71280</td>\n",
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" <td>312802</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" division level of education training level work experience \\\n",
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"0 printers some college 2 6 \n",
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"1 printers associate's degree 2 10 \n",
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"2 peripherals high school 0 9 \n",
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"3 office supplies associate's degree 2 5 \n",
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"4 office supplies high school 1 5 \n",
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"\n",
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" salary sales \n",
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"0 91684 372302 \n",
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"1 119679 495660 \n",
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"2 82045 320453 \n",
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"3 92949 377148 \n",
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"4 71280 312802 "
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]
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},
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"execution_count": 4,
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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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"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": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 1000 entries, 0 to 999\n",
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"Data columns (total 6 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 division 1000 non-null object\n",
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" 1 level of education 1000 non-null object\n",
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" 2 training level 1000 non-null int64 \n",
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" 3 work experience 1000 non-null int64 \n",
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" 4 salary 1000 non-null int64 \n",
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" 5 sales 1000 non-null int64 \n",
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"dtypes: int64(4), object(2)\n",
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"memory usage: 47.0+ KB\n"
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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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|
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"df.info()"
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|
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]
|
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|
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},
|
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|
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{
|
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|
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"cell_type": "markdown",
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|
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"metadata": {},
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"source": [
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"-----"
|
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]
|
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},
|
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|
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{
|
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|
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"cell_type": "markdown",
|
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|
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"metadata": {},
|
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"source": [
|
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|
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"# Scatterplot"
|
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]
|
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},
|
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|
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{
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"cell_type": "code",
|
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|
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"execution_count": 6,
|
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|
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"metadata": {},
|
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|
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"outputs": [
|
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|
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{
|
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|
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"data": {
|
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|
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"text/plain": [
|
||
|
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"<matplotlib.axes._subplots.AxesSubplot at 0x2089e370088>"
|
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|
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]
|
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|
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},
|
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|
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"execution_count": 6,
|
||
|
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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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"data": {
|
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|
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"image/png": "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
|
||
|
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"text/plain": [
|
||
|
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"<Figure size 432x288 with 1 Axes>"
|
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]
|
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},
|
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|
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"metadata": {
|
||
|
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"needs_background": "light"
|
||
|
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},
|
||
|
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"output_type": "display_data"
|
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|
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}
|
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|
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],
|
||
|
|
"source": [
|
||
|
|
"sns.scatterplot(x='salary',y='sales',data=df)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Connecting to Figure in Matplotlib"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"**Note how matplotlib is still connected to seaborn underneath (even without importing matplotlib.pyplot), since seaborn itself is directly making a Figure call with matplotlib. We can import matplotlib.pyplot and make calls to directly effect the seaborn figure.**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import matplotlib.pyplot as plt"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x2089fb16a08>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x576 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,8))\n",
|
||
|
|
"sns.scatterplot(x='salary',y='sales',data=df)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Seaborn Parameters\n",
|
||
|
|
"\n",
|
||
|
|
"The hue and palette parameters are commonly available around many plot calls in seaborn.\n",
|
||
|
|
"\n",
|
||
|
|
"### hue\n",
|
||
|
|
"\n",
|
||
|
|
"Color points based off a categorical feature in the DataFrame"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x2089fb0fe88>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x576 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,8))\n",
|
||
|
|
"sns.scatterplot(x='salary',y='sales',data=df,hue='division')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x2089fc3d848>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x576 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,8))\n",
|
||
|
|
"sns.scatterplot(x='salary',y='sales',data=df,hue='work experience')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"**Choosing a palette from Matplotlib's cmap: https://matplotlib.org/tutorials/colors/colormaps.html**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x2089fcbbdc8>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x576 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,8))\n",
|
||
|
|
"sns.scatterplot(x='salary',y='sales',data=df,hue='work experience',palette='viridis')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Scatterplot Parameters\n",
|
||
|
|
"\n",
|
||
|
|
"These parameters are more specific to the scatterplot() call\n",
|
||
|
|
"\n",
|
||
|
|
"### size\n",
|
||
|
|
"\n",
|
||
|
|
"Allows you to size based on another column "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x2089fcb7188>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAHgCAYAAADpKKjTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd5hU5dn48e+ZPruzvQBbYOksIL1Kk6agsWsUNYBoiN1oEpOY96fRJGpMscS8tkSKBdsbaxQLiiJFWHrvbXeB7WV2+szz+2OHcZedXWB3lh2W+3Nde8Hc55znPLPEK/c8c5/70ZRSCCGEEEIIIdqWrq0nIIQQQgghhJDEXAghhBBCiKggibkQQgghhBBRQBJzIYQQQgghooAk5kIIIYQQQkQBScyFEEIIIYSIAoa2nkC0SE1NVTk5OW09DSGEEEII0Y6tXbu2RCmVFu6YJOZBOTk55OXltfU0hBBCCCFEO6Zp2sHGjkkpixBCCCGEEFFAEnMhhBBCCCGigCTmQgghhBBCRAGpMW+C1+slPz8fl8vV1lMREWKxWMjKysJoNLb1VIQQQggh6pHEvAn5+fnExcWRk5ODpmltPR3RQkopSktLyc/Pp2vXrm09HSGEEEKIeqSUpQkul4uUlBRJytsJTdNISUmRb0CEEEIIEZUkMT8JScrbF/n3FEIIIUS0ksT8HJGTk0NJSUlbT6OeDz/8kCeeeKKtpyGEEEIIERWkxvwc4Pf723oKDfh8Pi677DIuu+yytp6KEEIIIURUkBXzKPbkk0/y7LPPAnDfffcxadIkAJYsWcJNN90EwKJFizjvvPPo378/v/71r0PX2mw2HnroIUaOHMnKlStDcafTybRp03j55Zcb3O/zzz9n9OjRDBkyhGuvvRa73U5lZSW9e/dm586dAMyYMSN0rc1m4xe/+AVDhgxh8uTJFBcXA7B3716mTZvG0KFDGTduHDt27ABg9uzZ3H///UycOJFf//rXzJ8/n7vuuguA4uJirr76aoYPH87w4cNZvnw5AL///e+ZM2cOF1xwAd26dQv9PgAWLlzIgAEDGDhwID/5yU+aHEcIIYQQIuoppeRHKYYOHapOtG3btgaxM2nlypXqmmuuUUopNXbsWDV8+HDl8XjU73//e/XCCy+ogoIClZ2drYqKipTX61UTJ05U7733nlJKKUC99dZbobG6dOmi9u/fryZPnqwWLFjQ4F7FxcVq3Lhxym63K6WUeuKJJ9QjjzyilFLq888/V6NGjVKLFi1SF110UegaQL322mtKKaUeeeQRdeeddyqllJo0aZLatWuXUkqpVatWqYkTJyqllJo1a5a65JJLlM/nU0opNW/evNA1M2bMUMuWLVNKKXXw4EHVp08fpZRSDz/8sBo9erRyuVyquLhYJScnK4/Ho7Zs2aJ69eqliouLlVJKlZaWNjlOXW397yqEEEKIcxeQpxrJR6WUJYoNHTqUtWvXUl1djdlsZsiQIeTl5bFs2TKeffZZ1qxZwwUXXEBaWhoAN954I99++y1XXHEFer2eq6++ut54l19+OQ888AA33nhjg3utWrWKbdu2MWbMGAA8Hg+jR48GYOrUqbzzzjvceeedbNy4MXSNTqfjuuuuA+Cmm27iqquuwm63s2LFCq699trQeW63O/T3a6+9Fr1e3+D+X375Jdu2bQu9rqqqorq6GoBLLrkEs9mM2WwmPT2dY8eO8dVXX3HNNdeQmpoKQHJycpPjxMXFnfT3LYQQQgjRliQxj2JGo5GcnBzmzZvH+eefz4ABA/j666/Zu3cvubm57Nq1q9FrLRZLgwR4zJgxfPrpp9xwww0NupMopZg6dSqLFi1qMFYgEGD79u1YrVbKysrIysoKe09N0wgEAiQmJrJhw4aw58TGxoaNBwIBVq5cidVqbXDMbDaH/q7X6/H5fCilwnZYaWocIYQQQohoJjXmUW78+PH89a9/Zfz48YwbN44XXniBQYMGoWkaI0eO5JtvvqGkpAS/38+iRYuYMGFCo2M9+uijpKSkcMcddzQ4NmrUKJYvX86ePXsAcDgcocT/qaeeIjc3l0WLFjFnzhy8Xi9QmwS/++67ALzxxhuMHTuW+Ph4unbtyjvvvAPUJvx1V9kbc+GFF/Lcc8+FXjeW2B83efJk3n77bUpLSwEoKytr1jhCCCGEOLdUOb0cq3JxrMpFldPb1tOpRxLzKDdu3DiOHDnC6NGj6dChAxaLhXHjxgHQqVMnHn/8cSZOnMjAgQMZMmQIl19+eZPjPf3007hcLh544IF68bS0NObPn8+MGTMYMGAAo0aNYseOHezatYt//etf/O1vf2PcuHGMHz+eP/7xj0Dt6vfWrVsZOnQoX331FQ899BAAr7/+Ov/+978ZOHAg/fr144MPPjjp+3z22WfJy8tjwIAB9O3blxdeeKHJ8/v168fvfvc7JkyYwMCBA7n//vubNY4QQgghzg3+gOJASQ33vrmeUY8vYfTjS7jvrQ0cLK0hEFBtPT0AtNoadDFs2DCVl5dXL7Z9+3Zyc3PbaEbRz2azYbfb23oap03+XYUQQohzz9FKFxc9/S2VJ6ySJ8UYWfzz8XSIt5yReWiatlYpNSzcMVkxF0IIIYQQ7ZrPH+CtNYcaJOUA5Q4v760rwO8PtMHM6pPEXDTb2bhaLoQQQohzT43bz/K9pY0e/25PCQ5v22/IKIm5EEIIIYRo10wGHR3izY0e75hgxqRv+7S47WcghBBCCCFEK7Ka9Mwd373R47eM7YbZ2HCflTNNEnMhhBBCCNHudUmO4eFL+2LQ/bAPilGv8ccr+pOZFB37n8gGQ0IIIYQQot2Ltxq5dlg20/t3YsPhctA0BmUlEGcxEmuOjpRYVsyj3OLFi+nduzc9evTgiSeeaOvpCCGEEEKctWxmAx0TLEzr34lp/TrSMcEaNUk5SGIe1fx+P3feeSeffvop27ZtY9GiRWzbtq2tpyWEEEIIIVqBJOYR5vP5KCkpwefztXis1atX06NHD7p164bJZOL6668/pV00hRBCCCHE2UcS8wjauHEjU6ZM4bLLLmPKlCls3LixReMVFBSQnZ0dep2VlUVBQUFLpymEEEIIIaKQJOYR4vP5uPfee7Hb7Xg8Hux2O/feey9+f/Ob1SulGsQ0TQtzphBCCCGEONtJYh4hFRUVeDyeejGPx0N5eXmzx8zKyuLw4cOh1/n5+WRkZDR7PCGEEEIIEb0kMY+QxMRETCZTvZjJZCIpKanZYw4fPpzdu3ezf/9+PB4Pb775JpdddllLpyqEEEIIIaKQJOYRYjAYeOaZZ7DZbJhMJmw2G8888wx6ffN3kTIYDDz33HNcdNFF5Obm8uMf/5h+/fpFcNZCCCGEECJaRE/jxnZg4MCBLFmyhPLycpKSklqUlB938cUXc/HFF0dgdkIIIYQQIppJYh5her2e1NTUtp6GEEIIIYQ4y0gpixBCCCGEEFFAEnMhhBBCCCGigCTmQgghhBBCRAFJzIUQQgghhIgCkpgLIYQQQggRBSQxPwtUVFRwzTXX0KdPH3Jzc1m5cmVbT0kIIYQQQkSYtEuMMJ/Ph91ux2azYTBE5td77733Mm3aNN599108Hg8OhyMi4wohhBBCiOghiXmEKKV45ZVXWLhwIV6vF6PRyMyZM5kzZw6apjV73KqqKr799lvmz58PgMlkwmQyRWjWQgghhBAiWkgpS4S88sorzJs3j5qaGjweDzU1NcybN4958+a1aNx9+/aRlpbGzTffzODBg7n11lupqamJ0KyFEEIIIUS0kMQ8Anw+HwsXLsTlctWLu1wuFixYgM/na9HY69at4/bbb2f9+vXExsbyxBNPtHTKQgghhBAiykhiHgF2ux2v1xv2mNfrxW63N3vsrKwssrKyGDlyJADXXHMN69ata/Z4QgghhBAiOkliHgE2mw2j0Rj2mNFoxGazNXvsjh07kp2dzc6dOwFYsmQJffv2bfZ4QgghhBAiOkliHgEGg4GZM2disVjqxS0WC7NmzWpxd5Z//OMf3HjjjQwYMIANGzbw4IMPtmg8IYQQQgg
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x576 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,8))\n",
|
||
|
|
"sns.scatterplot(x='salary',y='sales',data=df,size='work experience')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"**Use *s=* if you want to change the marker size to be some uniform integer value**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x208a00c1708>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x576 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,8))\n",
|
||
|
|
"sns.scatterplot(x='salary',y='sales',data=df,s=200)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x208a077b908>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x576 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,8))\n",
|
||
|
|
"sns.scatterplot(x='salary',y='sales',data=df,s=200,linewidth=0,alpha=0.2)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### style\n",
|
||
|
|
"\n",
|
||
|
|
"Automatically choose styles based on another categorical feature in the dataset. Optionally use the **markers=** parameter to pass a list of marker choices based off matplotlib, for example: ['*','+','o']"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='salary', ylabel='sales'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x576 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,8))\n",
|
||
|
|
"sns.scatterplot(x='salary',y='sales',data=df,style='level of education')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='salary', ylabel='sales'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x576 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,8))\n",
|
||
|
|
"# Sometimes its nice to do BOTH hue and style off the same column\n",
|
||
|
|
"sns.scatterplot(x='salary',y='sales',data=df,style='level of education',hue='level of education',s=100)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Exporting a Seaborn Figure"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x576 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,8))\n",
|
||
|
|
"sns.scatterplot(x='salary',y='sales',data=df,style='level of education',hue='level of education',s=100)\n",
|
||
|
|
"\n",
|
||
|
|
"# Call savefig in the same cell\n",
|
||
|
|
"plt.savefig('example_scatter.jpg')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"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": "3.7.4"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 1
|
||
|
|
}
|