843 lines
494 KiB
Plaintext
843 lines
494 KiB
Plaintext
|
{
|
||
|
|
"cells": [
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"___\n",
|
||
|
|
"\n",
|
||
|
|
"<a href='http://www.pieriandata.com'> <img src='../../Pierian_Data_Logo.png' /></a>\n",
|
||
|
|
"___\n",
|
||
|
|
"# Pandas Built-in Data Visualization\n",
|
||
|
|
"\n",
|
||
|
|
"In this lecture we will learn about pandas built-in capabilities for data visualization! It's built-off of matplotlib, but it baked into pandas for easier usage! \n",
|
||
|
|
"\n",
|
||
|
|
"Let's take a look!"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Imports"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 69,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import numpy as np\n",
|
||
|
|
"import pandas as pd\n",
|
||
|
|
"%matplotlib inline"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## The Data\n",
|
||
|
|
"\n",
|
||
|
|
"There are some fake data csv files you can read in as dataframes:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 70,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"df1 = pd.read_csv('df1',index_col=0)\n",
|
||
|
|
"df2 = pd.read_csv('df2')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Style Sheets\n",
|
||
|
|
"\n",
|
||
|
|
"Matplotlib has [style sheets](http://matplotlib.org/gallery.html#style_sheets) you can use to make your plots look a little nicer. These style sheets include plot_bmh,plot_fivethirtyeight,plot_ggplot and more. They basically create a set of style rules that your plots follow. I recommend using them, they make all your plots have the same look and feel more professional. You can even create your own if you want your company's plots to all have the same look (it is a bit tedious to create on though).\n",
|
||
|
|
"\n",
|
||
|
|
"Here is how to use them.\n",
|
||
|
|
"\n",
|
||
|
|
"**Before plt.style.use() your plots look like this:**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 71,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x125853940>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 71,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAEACAYAAAD1KqK3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYQFJkkpYQJKkEt36OAZ
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x12530e748>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df1['A'].hist()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Call the style:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 72,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"plt.style.use('ggplot')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Now your plots look like this:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 73,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x12588d358>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 73,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAEACAYAAAD1KqK3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRQFh6iLdGtq3PeW7udP
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x125bd8a90>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df1['A'].hist()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 74,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x125bec080>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 74,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAEACAYAAAD1KqK3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwG2v+TYUG5ny9QNILgo
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x1259b86a0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.style.use('bmh')\n",
|
||
|
|
"df1['A'].hist()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 75,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1259eb780>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 75,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAEACAYAAAD1KqK3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwbRpEgUEJBUOwQEATFB
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x125c36908>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.style.use('dark_background')\n",
|
||
|
|
"df1['A'].hist()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 76,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x125fad2b0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 76,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEWCAYAAADcsGj7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndV0rhHC6dwMAuCpH9aQ
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x12611d8d0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.style.use('fivethirtyeight')\n",
|
||
|
|
"df1['A'].hist()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 77,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"plt.style.use('ggplot')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Let's stick with the ggplot style and actually show you how to utilize pandas built-in plotting capabilities!"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Plot Types\n",
|
||
|
|
"\n",
|
||
|
|
"There are several plot types built-in to pandas, most of them statistical plots by nature:\n",
|
||
|
|
"\n",
|
||
|
|
"* df.plot.area \n",
|
||
|
|
"* df.plot.barh \n",
|
||
|
|
"* df.plot.density \n",
|
||
|
|
"* df.plot.hist \n",
|
||
|
|
"* df.plot.line \n",
|
||
|
|
"* df.plot.scatter\n",
|
||
|
|
"* df.plot.bar \n",
|
||
|
|
"* df.plot.box \n",
|
||
|
|
"* df.plot.hexbin \n",
|
||
|
|
"* df.plot.kde \n",
|
||
|
|
"* df.plot.pie\n",
|
||
|
|
"\n",
|
||
|
|
"You can also just call df.plot(kind='hist') or replace that kind argument with any of the key terms shown in the list above (e.g. 'box','barh', etc..)\n",
|
||
|
|
"___"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Let's start going through them!\n",
|
||
|
|
"\n",
|
||
|
|
"## Area"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 78,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x126222978>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 78,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ0AAAEQCAYAAABr8amkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQggajQYUReE3tUmSYHd
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x12627b4a8>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df2.plot.area(alpha=0.4)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Barplots"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 79,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<div>\n",
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>a</th>\n",
|
||
|
|
" <th>b</th>\n",
|
||
|
|
" <th>c</th>\n",
|
||
|
|
" <th>d</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>0.039762</td>\n",
|
||
|
|
" <td>0.218517</td>\n",
|
||
|
|
" <td>0.103423</td>\n",
|
||
|
|
" <td>0.957904</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1</th>\n",
|
||
|
|
" <td>0.937288</td>\n",
|
||
|
|
" <td>0.041567</td>\n",
|
||
|
|
" <td>0.899125</td>\n",
|
||
|
|
" <td>0.977680</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2</th>\n",
|
||
|
|
" <td>0.780504</td>\n",
|
||
|
|
" <td>0.008948</td>\n",
|
||
|
|
" <td>0.557808</td>\n",
|
||
|
|
" <td>0.797510</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>3</th>\n",
|
||
|
|
" <td>0.672717</td>\n",
|
||
|
|
" <td>0.247870</td>\n",
|
||
|
|
" <td>0.264071</td>\n",
|
||
|
|
" <td>0.444358</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>4</th>\n",
|
||
|
|
" <td>0.053829</td>\n",
|
||
|
|
" <td>0.520124</td>\n",
|
||
|
|
" <td>0.552264</td>\n",
|
||
|
|
" <td>0.190008</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" a b c d\n",
|
||
|
|
"0 0.039762 0.218517 0.103423 0.957904\n",
|
||
|
|
"1 0.937288 0.041567 0.899125 0.977680\n",
|
||
|
|
"2 0.780504 0.008948 0.557808 0.797510\n",
|
||
|
|
"3 0.672717 0.247870 0.264071 0.444358\n",
|
||
|
|
"4 0.053829 0.520124 0.552264 0.190008"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 79,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df2.head()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 80,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x126388630>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 80,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZoAAAEMCAYAAAD9OXA9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5b9aAEDJycv3aBKJhP7
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x1263dfb70>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df2.plot.bar()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 81,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x12657bb38>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 81,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZoAAAEMCAYAAAD9OXA9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nf//9WcsEAIXAFUUjjT+
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x1265dbfd0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df2.plot.bar(stacked=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Histograms"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 82,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x12685e9b0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 82,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAawAAAEQCAYAAADswECiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nf+o3tn379sQlAgAgAse
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x1264485f8>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df1['A'].plot.hist(bins=50)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Line Plots"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 83,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1243875f8>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 83,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAxsAAADUCAYAAAD0iD41AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRk4Wyho/KcxGMmejiLT
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x124e87160>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df1.plot.line(x=df1.index,y='B',figsize=(12,3),lw=1)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Scatter Plots"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 84,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x126b90fd0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 84,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAa4AAAEiCAYAAAChhzY5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqidXOtzIWZI7vK5Fr2q
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x126dcfc50>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df1.plot.scatter(x='A',y='B')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"You can use c to color based off another column value\n",
|
||
|
|
"Use cmap to indicate colormap to use. \n",
|
||
|
|
"For all the colormaps, check out: http://matplotlib.org/users/colormaps.html"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 85,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x126f7b400>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 85,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAawAAAEiCAYAAAClcuYEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nvH7mnxCWQJ17uvxp+0M
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x126f83438>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df1.plot.scatter(x='A',y='B',c='C',cmap='coolwarm')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Or use s to indicate size based off another column. s parameter needs to be an array, not just the name of a column:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 86,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x126f94c18>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 86,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAa4AAAEiCAYAAAChhzY5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZwsFNS02Z73qszfrHcK
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x127066160>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df1.plot.scatter(x='A',y='B',s=df1['C']*200)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## BoxPlots"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 87,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1271fa198>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 87,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZoAAAEQCAYAAACJLbLdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUXNz81zUCQAIqKzWaOr
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x127243f28>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df2.plot.box() # Can also pass a by= argument for groupby"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Hexagonal Bin Plot\n",
|
||
|
|
"\n",
|
||
|
|
"Useful for Bivariate Data, alternative to scatterplot:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 88,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x127413358>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 88,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEdCAYAAAAxRnE+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nD7nat4ZpOfZX9f/rxpe
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x12740cbe0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])\n",
|
||
|
|
"df.plot.hexbin(x='a',y='b',gridsize=25,cmap='Oranges')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"____"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Kernel Density Estimation plot (KDE)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 89,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1276d9160>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 89,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAbQAAAEQCAYAAADYuYG+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQhfO893Y4kxYt94LKXr
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x1256c06d8>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df2['a'].plot.kde()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 90,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1276f7940>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 90,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAbQAAAEQCAYAAADYuYG+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4XNW1t3/7TB/13pvV3eQmV1wxGIPpvdwvQEKIE8AQ\nyk1CAhdiLknAlMTgG4gTWiCAKaaDjXETLrJlWZZlSZYsWb3XaZpy9vfH2BptzUgaSdMk7/d5/Fiz\nz9lnlo5mztpr7VUIpZSCw+FwOJwJjuBtATgcDofDcQVcoXE4HA5nUsAVGofD4XAmBVyhcTgcDmdS\nwBUah8PhcCYFXKFxOBwOZ1LgMYW2ZcsW3HPPPXjkkUeGPa+iogK33norDh065CHJOBwOhzMZ8JhC\nW7lyJR5//PFhzxFFEe+++y5ycnI8JBWHw+FwJgseU2hZWVnw8/Mb9pxvvvkGCxcuRGBgoIek4nA4\nHM5kwWf20Do6OpCfn49LL73U26JwOBwOZwLiMwrtjTfewO23397/mlfk4nA4HM5okHpbgPOcOXMG\nL730Eiil6O3txbFjxyCVSjFv3jxvi8bhcDicCYBHFRqldEjLa/Pmzf0/v/rqq5g7d+6wyqyhocHl\n8nGGJzY2lt93L8Dvu3fg9907xMbGjnmuxxTayy+/jJKSEvT29mL9+vW46aabYDabQQjB6tWrPSUG\nh8PhcCYpZKK2j+ErJ8/DV6zegd9378Dvu3cYj4XmM0EhHA6Hw+GMB67QOBwOhzMp8JkoRw6Hw+GM\njFwuh0Kh8LYY40IURWi1Wpdflys0DofDmSCoVCoAQG9vr5clGR9SqRR+fn4uV2rc5cjhcDgTBKlU\nCr1e720xxo3ZbIYguF79cIXG4XA4nEkBV2gcDofDmRRwhcbhcDicSQFXaBwOh8OZFHCFxuFwOJxJ\nAQ/b53A4nEmE5Z6rxjVf8vpnY57r7+8PtVoNQRBgsVjQ29sLg8EwLnlGA1doHA6Hw3EJZrMZbW1t\nEEURSqUSwcHBaGlpgSiKHnl/7nLkcDgcjkswGAz9ystgMMBisUAmk3ns/bmFxuFwOByXoFKp4O/v\nD4lEAgAghLglgXoouELjcDicScR49sDG9b4SCYKDg9HW1gaTyQQAiIiI8KgM3OXI4XA4nHFDCAGl\ntN/lqFKpIJV61mbiFhqHw+Fwxo3ZbIZWq0VERAQopdDr9TAajR6VgSs0DofD4biE3t5er3YC4C5H\nDofD4UwKuELjcDgczqSAKzQOh8PhTAr4HhpnQqM1WlDd1YdWrQkaowUCIVBJBcQEyBEfJIe/XOJt\nEV2CRqNBVVUVmpub0dXVBaPRCEEQoFarER4ejujoaCQkJHg0iZXD8TW4QuNMOLoNZnxf2Y0Dtb2o\n6DBApI7PIwDSwpSYF+uPlVMCEeUv96ic44VSirq6Onz99dc4ffr0kOdVV1cDAGQyGaZMmYLZs2cj\nMjLSQ1JyOL4DV2icCUOX3ox3i9rw/ZlumIfSYgOgAE63G3C63YD3TrRhdowfbpkRjqwIlfuFHSca\njQa7d+/GmTNnnJ5jMplQVlaGsrIyJCUlYdGiRVyxcS4ouELj+Dwipfi8tBPvFrXBYB57kdNjjVoc\na9Ribqwf7p4TifgghQuldB2VlZXYsWPHuHJ4zp49i5qaGsyYMQOLFi2CQuGbvyuH40o8ptC2bNmC\ngoICBAUF4fnnn7c7vn//fmzfvh0AoFQqcc899yAxMdFT4nF8lA69GS/92IDjTbohz4kPlCMhSIFg\npQQUVpdkbbcRdT2OFcLRBi2ON1XjhmmhuGFaGGQS34iNopTiyJEjOHDggMPjkZGRSE1NRUREBFQq\nFURRRHd3N5qamlBZWQmtVmt3vaKiIlRVVeHSSy9FXFycJ34NDsdrEErpyL4bF1BaWgqlUonNmzc7\nVGjl5eWIj4+HWq1GYWEhPvzwQzzzzDNDXq+hocGd4nIcEBsb69H7XtVpwB9/qEO73mx3LMpfhrXp\nwViWHIgwteNAiE69GYfrNNhR2YXT7Y57MqWEKPDoRXGIC/Tu/hqlFHl5eSgoKLA7lpCQgAULFiA2\nNnbY+WfPnsWRI0eG/BvNmzcPCxcu9Gix2ImMpz/vzhAQEODVxOWRiIyM7A9aGomhfpfhPucj4TEL\nLSsrC62trUMez8jI6P85PT0dHR0dnhCL46Mcb9Li2T310A9yMfrJBdw2MxyXpYdAKpBhrxGikmJN\nejDWpAejqEmL94raUNKqZ86p6uzDr7+uwi/nR2N5SpDLfw9nOXDggJ0yI4RgyZIluPzyy9HU1DTs\nfEIIkpOTkZycjKqqKuzZswc9PT3MOUeOHEFzczPWrl0LpVLp8t+Bw/E2PrmH9v3332PWrFneFoPj\nJYqatNi4uw5GC+s8mB6lxoOLYhDhN/rQ9JnRfpgRpcb+s714/Wgzug2W/mMGM8ULPzaiptuI23PC\nIZDhFaWrKSoqwpEjR5gxhUKByy+/HAkJCaO2qFJSUpCQkIADBw7g2LFjzLHa2lr85z//wbp16xAe\nHj5u2TkcX8LnFFpxcTF2796Np59+2tuicLzAqRadQ2W2LjMEd8+JhGQEq2w4CCFYmhyI2TF+ePVw\nE/JqWHfHtpPtaOg14sFFMVBIPeOWq6mpwZ49e5gxhUKBa6+9dlwRilKpFEuXLkVSUhJ27NjB7K/1\n9PTgww8/xNq1a5GcnDzm9+D4Jlf/u3Rc87ffnjWu+XK5HEFBQZBIJNDr9eju7h7X9UaDTym0s2fP\n4rXXXsPvfvc7+Pv7D3vuePysnLHjzvte16nDs/sr0TdImW1YkYY7cl0bIPRicjw+KWrApu9Pw2ix\nuTV/rOmF1iLgpetz4K9w79eju7sb3333HQZuY8vlctxzzz2Ij49nzh3rfY+NjcW0adPwzjvv4OzZ\ns/3jJpMJX3zxBa699lrk5uaO7Re4APC150xfX5+3RRgRlUqF9vZ2UEoRGho65F6ZXC53+f31qEKj\nlGKoGJS2tjZs2rQJ9913H6Kjo0e8lq9t1l4IuHOTXGO04L+/PYtuvYkZv3tOJFbFSd3yvgsjCDau\nTsAze+oYF+Tx+m7c885hPLkqAYEK91QaEUURn376KXQ6NnrzkksugSAIzO/rivu+bt067NmzB8XF\nxYwMH330EWpra7FgwQIQD7tafR1fDQrxdbRabX9PNI1Gg6CgIIcKzWg0or293W58QgSFvPzyyygp\nKUFvby/Wr1+Pm266CWazGYQQrF69Gtu2bYNGo8HWrVtBKYVEIsGzzz7rKfE4XkSkFC/kNdiF2d88\nIwxXZ4e69b0zw1V4bk0Sntldj7PdttVvRYcBv99Rg6cuTkCIyvVfk4KCAtTV1TFjixYtQmpqqsvf\nC7B2E161ahVCQ0Oxd+9e5tjhw4fR29uLVatWQSKZHKXCON7DYrEwP3syqtZjYfuuxtdWThcC7lqx\nfnqqHf8qYCNglyYF4OElsR6zGjRGC57+oQ5lbWwUZGKQHM9ckuRSS629vR3vvfde/yoWAOLj43Ht\ntdc6/H1dfd8rKirw7bffMg8ewJoecPnll/Mk7HP4qoXm62H7Go2m3/OgUCgQFBSElpYWu3PdEbbP\nE1I4XuV0ux5vF7LKLD1MifsXxnjUBeYvl+CpVQmYEaVmxmu6jXhqVy10JssQM0cHpRS7du1ilJlS\nqcSll17qsd83LS0N1113nV3ofm1tLT766CNoNBqPyMGZnPj5+UEQBBBC4O/vD71eP/IkF8EVGsdr\nGMwiNuU1YGCqmZ9cwGMXxXksynAgKpmAP6yIx9xYP2a8osOa4N03jrJb5zl58iQaGxuZsZUrV44Y\nBOVqYmJicOONNyIwMJAZb2trwwcffOBwb4PDcQa9Xo+wsDBERUXBYrF41KLkCo3jNd493orGXjYI\n5P4FMYj0914LFIVUwH8vjbOz1Epa9fjT3nqniiIPhU6nQ15eHjOWnJyMtLS0MV9zPISEhOCmm25C\nVFQUM67RaPDhhx+itrb
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x12779ee80>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df2.plot.density()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"That's it! Hopefully you can see why this method of plotting will be a lot easier to use than full-on matplotlib, it balances ease of use with control over the figure. A lot of the plot calls also accept additional arguments of their parent matplotlib plt. call. \n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"# Great Job!"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"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": "3.6.1"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 1
|
||
|
|
}
|