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iqbalbasyariqbalbasyar
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Add computing logo
Add and update computing logo to each notebook
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‎2018/Data-Capstone-Projects/911 Calls Data Capstone Project - Solutions.ipynb

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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.google.com'> <img src='Logo_Padded.png' /></a>\n",
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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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"metadata": {},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {
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"collapsed": false
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [
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"name": "stdout",
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},
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{
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"cell_type": "markdown",
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"collapsed": false
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"metadata": {},
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"source": [
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"** Check the head of df **"
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]
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},
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
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"outputs": [
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"data": {
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{
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
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"outputs": [
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"data": {
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{
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"cell_type": "code",
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"execution_count": 30,
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"metadata": {},
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"outputs": [
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
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"outputs": [
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"data": {
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [
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"data": {
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{
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"cell_type": "code",
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"execution_count": 34,
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"metadata": {},
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"outputs": [
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"data": {
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{
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"cell_type": "code",
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"execution_count": 35,
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"metadata": {
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"collapsed": false
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"metadata": {},
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"outputs": [
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"data": {
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{
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"cell_type": "code",
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"execution_count": 37,
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"collapsed": false
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"metadata": {},
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"outputs": [],
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"source": [
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"df['Hour'] = df['timeStamp'].apply(lambda time: time.hour)\n",
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"source": [
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"** Notice how the Day of Week is an integer 0-6. Use the .map() with this dictionary to map the actual string names to the day of the week: **\n",
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"\n",
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{
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"cell_type": "code",
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"execution_count": 38,
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"collapsed": false
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"metadata": {},
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"outputs": [],
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"source": [
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"dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}"
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{
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"cell_type": "code",
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"execution_count": 39,
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"metadata": {
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"collapsed": false
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"metadata": {},
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"outputs": [],
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"source": [
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"df['Day of Week'] = df['Day of Week'].map(dmap)"
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]
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},
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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"metadata": {},
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"source": [
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"** Now use seaborn to create a countplot of the Day of Week column with the hue based off of the Reason column. **"
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]
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},
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"outputs": [
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"data": {
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"data": {
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"data": {
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"execution_count": 44,
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"metadata": {},
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"____\n",
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"** Now let's move on to creating heatmaps with seaborn and our data. We'll first need to restructure the dataframe so that the columns become the Hours and the Index becomes the Day of the Week. There are lots of ways to do this, but I would recommend trying to combine groupby with an [unstack](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.unstack.html) method. Reference the solutions if you get stuck on this!**"
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"cell_type": "markdown",
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"metadata": {},
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"** Now create a HeatMap using this new DataFrame. **"
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"metadata": {},
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"** Now create a clustermap using this DataFrame. **"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.1"
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"version": "3.6.3"
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"nbformat": 4,
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"nbformat_minor": 1
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}

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