465 lines
208 KiB
Plaintext
465 lines
208 KiB
Plaintext
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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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"# Pandas Data Visualization Exercise\n",
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"\n",
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"This is just a quick exercise for you to review the various plots we showed earlier. Use **df3** to replicate the following plots. "
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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": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"df3 = pd.read_csv('df3')\n",
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"%matplotlib inline"
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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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"collapsed": false
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},
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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: 500 entries, 0 to 499\n",
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"Data columns (total 4 columns):\n",
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"a 500 non-null float64\n",
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"b 500 non-null float64\n",
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"c 500 non-null float64\n",
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"d 500 non-null float64\n",
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"dtypes: float64(4)\n",
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"memory usage: 15.7 KB\n"
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]
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}
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],
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"source": [
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"df3.info()"
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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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"collapsed": false
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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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"<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>a</th>\n",
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" <th>b</th>\n",
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" <th>c</th>\n",
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" <th>d</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>0.336272</td>\n",
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" <td>0.325011</td>\n",
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" <td>0.001020</td>\n",
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" <td>0.401402</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>0.980265</td>\n",
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" <td>0.831835</td>\n",
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" <td>0.772288</td>\n",
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" <td>0.076485</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>0.480387</td>\n",
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" <td>0.686839</td>\n",
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" <td>0.000575</td>\n",
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" <td>0.746758</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>0.502106</td>\n",
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" <td>0.305142</td>\n",
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" <td>0.768608</td>\n",
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" <td>0.654685</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>0.856602</td>\n",
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" <td>0.171448</td>\n",
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" <td>0.157971</td>\n",
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" <td>0.321231</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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" a b c d\n",
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"0 0.336272 0.325011 0.001020 0.401402\n",
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"1 0.980265 0.831835 0.772288 0.076485\n",
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"2 0.480387 0.686839 0.000575 0.746758\n",
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"3 0.502106 0.305142 0.768608 0.654685\n",
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"4 0.856602 0.171448 0.157971 0.321231"
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]
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},
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"execution_count": 3,
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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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"df3.head()"
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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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"** Recreate this scatter plot of b vs a. Note the color and size of the points. Also note the figure size. See if you can figure out how to stretch it in a similar fashion. Remeber back to your matplotlib lecture...**"
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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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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.axes._subplots.AxesSubplot at 0x1176a7da0>"
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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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"data": {
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"text/plain": [
|
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"<matplotlib.figure.Figure at 0x10357ae10>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": []
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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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|
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"** Create a histogram of the 'a' column.**"
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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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|
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"collapsed": false
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|
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},
|
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|
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"outputs": [
|
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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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|
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"<matplotlib.axes._subplots.AxesSubplot at 0x1177a2860>"
|
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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": 5,
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|
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"metadata": {},
|
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|
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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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"data": {
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|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEACAYAAACznAEdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrwcWq2o5yQJwU1Wdt8r
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11776b5c0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"** These plots are okay, but they don't look very polished. Use style sheets to set the style to 'ggplot' and redo the histogram from above. Also figure out how to add more bins to it.***"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x11a87b908>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEECAYAAADZBhiGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKZ4ks4/zF1BufZ1O/M9
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11a97e550>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"** Create a boxplot comparing the a and b columns.**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1177c4a20>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEECAYAAADXg6SsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3d2t1tZWeZ6n6upq1dX
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11ab3b3c8>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"** Create a kde plot of the 'd' column **"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x11abb6278>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEECAYAAADQ7bj8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nA1Uwyewo5AFueQQnlge
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11ac541d0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"** Figure out how to increase the linewidth and make the linestyle dashed. (Note: You would usually not dash a kde plot line)**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x11ab9acc0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEECAYAAADQ7bj8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+Xl0Y+gv1yFEuJDkISK
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11c3e84a8>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"** Create an area plot of all the columns for just the rows up to 30. (hint: use .ix).**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x11ccdfbe0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEECAYAAADJSpQfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvVuMJGd5//99D3Xqw5z2hO21MU6cGG0AQQxCRoEErGBx\nCIkIK5EbiFCILAQBKUCkBJGbBGEHJAgHKfBD3EUxueAKBXEVESUm/oMdwjo2PrHrXe/uzM5Mz/Sx\n6j39L2renuru6u6q7pqZnnV9JMu7Oz1db1dXPfW8z+H7EGOMQUlJSUnJTQ096gWUlJSUlBw8pbEv\nKSkpeRlQGvuSkpKSlwGlsS8pKSl5GVAa+5KSkpKXAaWxLykpKXkZwKe9QAiBz3/+85BSQimFN7/5\nzfjABz4w8rrvfOc7eOKJJ+B5Hj72sY/hzjvvPIj1lpSUlJTMwFTP3nEcfP7zn8dDDz2Ehx9+GE88\n8QSeffbZgdc8/vjjuH79Or761a/iox/9KL71rW9lOviFCxdmW/Uxofx8x5ub+fPdzJ8NKD9fGpnC\nOJ7nAYi9fKXUyM8fe+wxvO1tbwMA3H333eh0Omg0GlPft/xCjjfl5zu+3MyfDSg/XxpTwzgAoLXG\nX/3VX+H69et45zvfiV//9V8f+PnW1hZOnDjR//va2hq2trawsrKSe0ElJSUlJcWTybOnlOKhhx7C\nN7/5TTzzzDO4fPnyQa+rpKSkpKRASF5tnH/913+F7/t4z3ve0/+3f/qnf8Jv/dZv4b777gMAfPKT\nn8Tf/u3fjnj2Fy5cGNh+nD9/fp61l5SUlLxseeSRR/p/PnfuHM6dOzfx9VPDOLu7u+Cco1KpIIoi\n/O///i/e9773Dbzm3nvvxQ9/+EPcd999+OUvf4lqtZoawklb0EsvvTRtCceWer2OZrN51Ms4MMrP\nd3y5mT8bcPN/vltvvTW3szzV2DcaDXz961+H1hrGGNx33314wxvegB/96EcghOD+++/HG97wBjz+\n+OP4+Mc/Dt/38eCDD878IUpKSkpKiid3GKdoSs/++FJ+vuPLzfzZgJv/89166625f6fsoC0pKSl5\nGVAa+5KSkpKXAZnq7EtKSkqOC7VaDYwx1Ov1o17K3Bhj0Gq1Cnmv0tiXlJTcVBBCMnXwHweKfGCV\nYZySkpKSlwGlsS8pKSl5GVAa+5KSkpKXAaWxLykpKXkZUCZoS0pKbn5au/F/B0VtKf5vgSmNfUlJ\nyc1Paxf6l784sLenv/FbC2/syzBOSUlJySHy9a9/HW95y1vwm7/5m3j729+Of/u3fzuU45bGvqSk\npOQQufPOO/H9738fTz/9ND71qU/h4x//ODY2Ng78uKWxLykpKTlE3v3ud+PUqVMAgPe+97141ate\nhccff/zAj1vG7EtKSkoOke9973v41re+1Z/41+l0sL29feDHLY19SUlJySFx5coVfPazn8UjjzyC\ne++9FwDw+7//+zgMpfnS2JeUlJQcEp1OB4QQrK2tQWuN733ve3j66acP5dilsS8pKbn5qS3F5ZEH\n+P5ZuPvuu/Hnf/7neO973wvGGP74j/8Yb3zjGw9uXQnKSVUHyM0+Laf8fMeX8rMdD8Z9lnJSVUlJ\nSUlJKqWxLykpKXkZcCyMvVLqUJoOSkpKSm5WjoWxD8MQzz//PLrd7lEvpaSkpORYciyMvZQS3W4X\nW1tbR72UkpKSkmPJsTH2Wmtsbm4e9VJKSkpKjiXHxtgDKGzKeklJScnLjWNj7F3XRbvdPpS24pKS\nkpKbjWNh7MNQQPQqiKIIvV7vqJdTUlJSMjNvfvOb8R//8R+HftxjIZcQhQKdlgEcis3NTZw9e/ao\nl1RSUnKM2A0VdkN9YO+/5FEseezA3r8Iphr7zc1NfO1rX8POzg4IIXjHO96Bd73rXQOvefLJJ/HQ\nQw/hzJkzAIA3velNeP/731/YInu9CIZwONzF1tZWaexLSkpysRtqXLjePrD3P3emevyNPWMMH/rQ\nh3DnnXei1+vhs5/9LF73utfhtttuG3jdq1/9anz2s589kEWGUQQCCkLcMklbUlJy7HniiSfwuc99\nDuvr63jggQfwhS98Aa7rHugxp8bsV1ZWcOeddwIAfN/HbbfdllrvflCJU6UUpFDg1AGFVyZpS0pK\njj3f//738c///M/4z//8Tzz33HP4yle+cuDHzJWgXV9fx8WLF3H33XeP/OyZZ57Bpz/9aXzhC1/o\nT2ApgrjG3oBSDkZ9CCHKJG1JScmx5k//9E/xile8AsvLy/jEJz6B73//+wd+zMzGvtfr4ctf/jI+\n/OEPw/f9gZ/ddddd+MY3voGHH34YDzzwAB5++OHCFiilhFYAZRSEOHAcBzdu3Cjs/UtKSkoOm1tu\nuaX/57Nnz+L69esHfsxM1ThKKXzpS1/CW9/61lSh/aTxf/3rX49vf/vbaLVaqNVqA6+7cOECLly4\n0P/7+fPnUa/XJx5bSgnOXFCPg1AK36uj3W5P/b1FwHXdY7HOWSk/3/HlZv5sjC12ohQYnONx+fLl\nfnHLMIyxsd/TI4880v/zuXPncO7cuYnHzGTsv/nNb+Ls2bMjVTiWRqOBlZUVAMCzzz4LACOGftyC\npg0Z2NnZQSQUCCSMIOA+webm5rEYTnAzDVFIo/x8x5eb/bMtOt/97nfxjne8A77v4x//8R/xvve9\nL/V1SqnU76ler+P8+fO5jjnV2D/11FP48Y9/jDvuuAOf+cxnQAjBBz/4QWxsbIAQgvvvvx+PPvoo\nfvSjH4ExBtd18clPfjLXIiZhwzi+xxGF8ZNud3cXxhgQQgo7TklJyeHSarXguu6BV6EAcR38uTPV\nA33/rBBC8Ed/9Ef4kz/5E6yvr+Od73wnPvGJTxzY2vrHXfSxhJcvX8ZTv9jA8vIp9DoMlZWruH79\nGn73d38XlUrlkFY5Gzez9wSUn+84swif7emnn0YURXjNa15T6PsuwmcripfVWEIpJZQCHMcBpRxC\naLiuWypglpQcYzqdDtrtNi5dugSl1FEv52XBwhv7MBQwAFzHheM4UILA87xS276k5Bizs7MDYww4\n51hfXz/q5bwsWHhj3+uFIIaAOQyOQ6EkgeuWnbQlJccVYwy2t7dhjEGtVsOVK1eOekkvCxba2Btj\nEIURDAE4Y3BcDq0oHMdBp9MpO2lLSo4hrVYLURSBUoogCPqGvyjs/IuSQRba2CuloJQBJQScc1AK\nYK+xSghRzqQtKTmGNBqN/owK13WhlCo0B1ca+3QW2thLKWE0wGi8TEoJYDgIiUM5ZSdtySLQ6XTK\nJGNGtNbY2dkZ+LdarYYXX3yxkPcXQpTGfgwLbeyFEFBK9zviYpsf/7lM0pYsCi+99BKuXr161Ms4\nFuzu7kIIAc73W3yCICjsXraJ35JRFtrYxyJoBGTIswfQH1NYUnLUhGE4tV+kJKbRaEBrPWDsXddF\nFEVoNBqFvH9JOgtv7AEKQuNOWcoAAg4pVZmkLVkIjDEIw/CmaeI5SKSU/e73JIQQVKtVXLx4ca73\n7/V6ZZXeBBZ6LKGUEsZQ0D1ZBEoBRh2ISMIP3H6SdtE7aUtuXoQQEEIgDMNSwmMKOzs7kFLCcZyR\nn1UqlblDOdvb21BKgRAy8kARERAe4FhCz6NwDl71YS4W39hrAsbjG4gQAu44kNL0k7QbGxt45Stf\necQrLXm5IqUEpRRaa4RhOCL/XbJPo9GAMSZVldLzPKyvr6eq5WbBGNN//zRjH4Yam+ti5rVP48Rp\nB4670IGSxQ/jaA2wRHzPcTikiJft+z62t7ePanklJRBCwBgDx3FGqkwOE6XUQocwwjBEq9UaG3ad\nN5TTarUQhiEoXWiTBiBO6P/Zn/0ZXvva1+I1r3kNPve5zx3KcRf6zERRBK0IHMfr/5vjUGhF9v7s\nlEnakiNFCNHfZR6l47G7u4tnnnlmYXNYNoQzSeGyUqnMXE6drN1fZLTW+NCHPoTbb78d//3f/42f\n/vSn+IM/+INDOfZCG/s
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x115f97160>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Bonus Challenge!\n",
|
||
|
|
"Note, you may find this really hard, reference the solutions if you can't figure it out!\n",
|
||
|
|
"** Notice how the legend in our previous figure overlapped some of actual diagram. Can you figure out how to display the legend outside of the plot as shown below?**\n",
|
||
|
|
"\n",
|
||
|
|
"** Try searching Google for a good stackoverflow link on this topic. If you can't find it on your own - [use this one for a hint.](http://stackoverflow.com/questions/23556153/how-to-put-legend-outside-the-plot-with-pandas)**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11ca5fdd8>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# 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": "3.5.1"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 0
|
||
|
|
}
|