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bzhr/Correlation Plot in Seaborn

Last active August 29, 2015 14:13
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Correlation Plot in Seaborn
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"worksheets": [
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"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import pandas as pd\n",
"from pandas import DataFrame, Series"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cols = ['Proffesional or Managerial', 'Technical or Supervisory', 'Other White-Collar', 'Skilled Blue-Collar', 'Unskilled Blue-Collar']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data = pd.read_csv('/Users/bozhidarhristov/Documents/Werk/PyPlot_task/data.csv', names=cols)\n",
"df = pd.DataFrame(data)\n",
"df"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Proffesional or Managerial</th>\n",
" <th>Technical or Supervisory</th>\n",
" <th>Other White-Collar</th>\n",
" <th>Skilled Blue-Collar</th>\n",
" <th>Unskilled Blue-Collar</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Wage</th>\n",
" <td> 10109.028000</td>\n",
" <td> 4383.736300</td>\n",
" <td> 6801.609900</td>\n",
" <td> 4150.532200</td>\n",
" <td> 2801.408700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Completed College</th>\n",
" <td> 0.013535</td>\n",
" <td> 0.171981</td>\n",
" <td> 0.227155</td>\n",
" <td> 0.446282</td>\n",
" <td> 0.100670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Some College</th>\n",
" <td> 0.067696</td>\n",
" <td> 0.227766</td>\n",
" <td> 0.191410</td>\n",
" <td> 0.315064</td>\n",
" <td> 0.129937</td>\n",
" </tr>\n",
" <tr>\n",
" <th>High School</th>\n",
" <td> 0.134360</td>\n",
" <td> 0.232467</td>\n",
" <td> 0.178927</td>\n",
" <td> 0.288863</td>\n",
" <td> 0.108454</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Prim. or Mid. School</th>\n",
" <td> 0.175677</td>\n",
" <td> 0.245064</td>\n",
" <td> 0.150863</td>\n",
" <td> 0.283132</td>\n",
" <td> 0.101641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NaN</th>\n",
" <td> 0.175677</td>\n",
" <td> 0.245064</td>\n",
" <td> 0.150863</td>\n",
" <td> 0.283132</td>\n",
" <td> 0.101641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NaN</th>\n",
" <td> 0.200496</td>\n",
" <td> 0.236459</td>\n",
" <td> 0.132581</td>\n",
" <td> 0.279265</td>\n",
" <td> 0.112305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NaN</th>\n",
" <td> 0.167431</td>\n",
" <td> 0.217742</td>\n",
" <td> 0.155650</td>\n",
" <td> 0.267490</td>\n",
" <td> 0.156447</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NaN</th>\n",
" <td> 0.201875</td>\n",
" <td> 0.243127</td>\n",
" <td> 0.174218</td>\n",
" <td> 0.201489</td>\n",
" <td> 0.145106</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NaN</th>\n",
" <td> 0.057260</td>\n",
" <td> 0.132611</td>\n",
" <td> 0.162644</td>\n",
" <td> 0.418414</td>\n",
" <td> 0.174429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NaN</th>\n",
" <td> 0.188967</td>\n",
" <td> 0.413302</td>\n",
" <td> 0.196293</td>\n",
" <td> 0.126078</td>\n",
" <td> 0.031322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NaN</th>\n",
" <td> 0.313410</td>\n",
" <td> 0.448558</td>\n",
" <td> 0.174808</td>\n",
" <td> 0.038068</td>\n",
" <td> 0.009104</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NaN</th>\n",
" <td> 0.567438</td>\n",
" <td> 0.325516</td>\n",
" <td> 0.064623</td>\n",
" <td> 0.009721</td>\n",
" <td> 0.002870</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NaN</th>\n",
" <td> 0.208980</td>\n",
" <td> 0.323734</td>\n",
" <td> 0.190809</td>\n",
" <td> 0.141833</td>\n",
" <td> 0.111784</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Female</th>\n",
" <td> 0.010975</td>\n",
" <td> 0.119982</td>\n",
" <td> 0.177752</td>\n",
" <td> 0.544431</td>\n",
" <td> 0.105883</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tenure</th>\n",
" <td> 91.535072</td>\n",
" <td> 86.124855</td>\n",
" <td> 54.792229</td>\n",
" <td> 45.623550</td>\n",
" <td> 47.287083</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 137,
"text": [
" Proffesional or Managerial Technical or Supervisory \\\n",
"Wage 10109.028000 4383.736300 \n",
"Completed College 0.013535 0.171981 \n",
"Some College 0.067696 0.227766 \n",
"High School 0.134360 0.232467 \n",
"Prim. or Mid. School 0.175677 0.245064 \n",
"NaN 0.175677 0.245064 \n",
"NaN 0.200496 0.236459 \n",
"NaN 0.167431 0.217742 \n",
"NaN 0.201875 0.243127 \n",
"NaN 0.057260 0.132611 \n",
"NaN 0.188967 0.413302 \n",
"NaN 0.313410 0.448558 \n",
"NaN 0.567438 0.325516 \n",
"NaN 0.208980 0.323734 \n",
"Female 0.010975 0.119982 \n",
"Tenure 91.535072 86.124855 \n",
"\n",
" Other White-Collar Skilled Blue-Collar \\\n",
"Wage 6801.609900 4150.532200 \n",
"Completed College 0.227155 0.446282 \n",
"Some College 0.191410 0.315064 \n",
"High School 0.178927 0.288863 \n",
"Prim. or Mid. School 0.150863 0.283132 \n",
"NaN 0.150863 0.283132 \n",
"NaN 0.132581 0.279265 \n",
"NaN 0.155650 0.267490 \n",
"NaN 0.174218 0.201489 \n",
"NaN 0.162644 0.418414 \n",
"NaN 0.196293 0.126078 \n",
"NaN 0.174808 0.038068 \n",
"NaN 0.064623 0.009721 \n",
"NaN 0.190809 0.141833 \n",
"Female 0.177752 0.544431 \n",
"Tenure 54.792229 45.623550 \n",
"\n",
" Unskilled Blue-Collar \n",
"Wage 2801.408700 \n",
"Completed College 0.100670 \n",
"Some College 0.129937 \n",
"High School 0.108454 \n",
"Prim. or Mid. School 0.101641 \n",
"NaN 0.101641 \n",
"NaN 0.112305 \n",
"NaN 0.156447 \n",
"NaN 0.145106 \n",
"NaN 0.174429 \n",
"NaN 0.031322 \n",
"NaN 0.009104 \n",
"NaN 0.002870 \n",
"NaN 0.111784 \n",
"Female 0.105883 \n",
"Tenure 47.287083 "
]
}
],
"prompt_number": 137
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.gridspec as gs\n",
"import seaborn as sns\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"sns.set(style=\"darkgrid\")\n",
"\n",
"gs = gs.GridSpec(30, 1) # nrows, ncols\n",
"f = plt.figure(figsize=(30, 30))\n",
"\n",
"ax1 = f.add_subplot(gs[0:2, :]) # row, col\n",
"ax2 = f.add_subplot(gs[2:28, :]) # row, col\n",
"ax3 = f.add_subplot(gs[28:30, :]) # row, col\n",
"\n",
"cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
"heatmap1 = sns.heatmap(data[1:15], vmin=0, vmax=1, annot=False, square=True, cmap=cmap, ax=ax2, yticklabels=True, cbar=False, xticklabels=False)\n",
"heatmap2 = sns.heatmap(data[15:16], vmin=40, vmax=100, annot=False, square=True, cmap=cmap, ax=ax3, yticklabels=True, cbar=False)\n",
"heatmap3 = sns.heatmap(data[0:1], vmin=2000, vmax=10000, annot=False, square=True, cmap=cmap, ax=ax1, yticklabels=True, cbar=False, xticklabels=False)\n",
"f.tight_layout()\n",
"#plt.savefig('heatmap.png')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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h0QgAwJBoBABgSDQCADAkGgEAGBKNAAAMiUYAAIZEIwAAQ6IRAIAh0QgAwJBoBABgSDQC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\nRIts3y0pU90mSNHYIklvBF5EacFzGrAJ8IaqoaJTtl9VO0NERLRL0nr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"text": [
"<matplotlib.figure.Figure at 0x10de39690>"
]
}
],
"prompt_number": 146
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 103
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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