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alexjj/pancham | pandas-cookbook/cookbook/Chapter 4 - Find out on which weekday people bike the most with groupby and aggregate.ipynb | 1 | 140728 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier\n",
"plt.rcParams['figure.figsize'] = (15, 5)\n",
"\n",
"# This is necessary to show lots of columns in pandas 0.12. \n",
"# Not necessary in pandas 0.13.\n",
"pd.set_option('display.line_width', 5000) \n",
"pd.set_option('display.max_columns', 60)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay! We're going back to our bike path dataset here. I live in Montreal, and I was curious about whether we're more of a commuter city or a biking-for-fun city -- do people bike more on weekends, or on weekdays?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 4.1 Adding a 'weekday' column to our dataframe"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, we need to load up the data. We've done this before."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bikes = pd.read_csv('../data/bikes.csv', sep=';', encoding='latin1', parse_dates=['Date'], dayfirst=True, index_col='Date')\n",
"bikes['Berri 1'].plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"<matplotlib.axes.AxesSubplot at 0x1079e5090>"
]
},
{
"metadata": {},
"output_type": "display_data",
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NaU3kJKxqiwTmgfvGq0OYLThrLbQHbjov4WSqiHeu1LPP8RDn+QHSYvkcSasxLfLAEQRB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UAAAg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\nAAAaQsABAAA0hIADAABoCAEHAADQEN/0h8XWuQDOWQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1079de7d0>"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next up, we're just going to look at the Berri bike path. Berri is a street in Montreal, with a pretty important bike path. I use it mostly on my way to the library now, but I used to take it to work sometimes when I worked in Old Montreal. \n",
"\n",
"So we're going to create a dataframe with just the Berri bikepath in it"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"berri_bikes = bikes[['Berri 1']]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"berri_bikes[:5]"
],
"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>Berri 1</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2012-01-01</th>\n",
" <td> 35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-02</th>\n",
" <td> 83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-03</th>\n",
" <td> 135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-04</th>\n",
" <td> 144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-05</th>\n",
" <td> 197</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 1 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
" Berri 1\n",
"Date \n",
"2012-01-01 35\n",
"2012-01-02 83\n",
"2012-01-03 135\n",
"2012-01-04 144\n",
"2012-01-05 197\n",
"\n",
"[5 rows x 1 columns]"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we need to add a 'weekday' column. Firstly, we can get the weekday from the index. We haven't talked about indexes yet, but the index is what's on the left on the above dataframe, under 'Date'. It's basically all the days of the year."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"berri_bikes.index"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"<class 'pandas.tseries.index.DatetimeIndex'>\n",
"[2012-01-01, ..., 2012-11-05]\n",
"Length: 310, Freq: None, Timezone: None"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can see that actually some of the days are missing -- only 310 days of the year are actually there. Who knows why.\n",
"\n",
"Pandas has a bunch of really great time series functionality, so if we wanted to get the day of the month for each row, we could do it like this:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"berri_bikes.index.day"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
" 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 1, 2, 3,\n",
" 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,\n",
" 21, 22, 23, 24, 25, 26, 27, 28, 29, 1, 2, 3, 4, 5, 6, 7, 8,\n",
" 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n",
" 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,\n",
" 29, 30, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n",
" 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 1,\n",
" 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
" 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 1, 2, 3, 4, 5,\n",
" 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,\n",
" 23, 24, 25, 26, 27, 28, 29, 30, 31, 1, 2, 3, 4, 5, 6, 7, 8,\n",
" 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n",
" 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,\n",
" 29, 30, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n",
" 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 1,\n",
" 2, 3, 4, 5], dtype=int32)"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We actually want the weekday, though:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"berri_bikes.index.weekday"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"array([6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
" 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
" 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,\n",
" 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
" 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
" 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
" 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
" 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
" 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
" 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,\n",
" 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
" 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
" 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
" 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0], dtype=int32)"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These are the days of the week, where 0 is Monday. I found out that 0 was Monday by checking on a calendar.\n",
"\n",
"Now that we know how to *get* the weekday, we can add it as a column in our dataframe like this:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"berri_bikes['weekday'] = berri_bikes.index.weekday\n",
"berri_bikes[:5]"
],
"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>Berri 1</th>\n",
" <th>weekday</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2012-01-01</th>\n",
" <td> 35</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-02</th>\n",
" <td> 83</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-03</th>\n",
" <td> 135</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-04</th>\n",
" <td> 144</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-05</th>\n",
" <td> 197</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 2 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
" Berri 1 weekday\n",
"Date \n",
"2012-01-01 35 6\n",
"2012-01-02 83 0\n",
"2012-01-03 135 1\n",
"2012-01-04 144 2\n",
"2012-01-05 197 3\n",
"\n",
"[5 rows x 2 columns]"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 4.2 Adding up the cyclists by weekday"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This turns out to be really easy!\n",
"\n",
"Dataframes have a `.groupby()` method that is similar to SQL groupby, if you're familiar with that. I'm not going to explain more about it right now -- if you want to to know more, [the documentation](http://pandas.pydata.org/pandas-docs/stable/groupby.html) is really good.\n",
"\n",
"In this case, `berri_bikes.groupby('weekday').aggregate(sum)` means \"Group the rows by weekday and then add up all the values with the same weekday\"."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"weekday_counts = berri_bikes.groupby('weekday').aggregate(sum)\n",
"weekday_counts"
],
"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>Berri 1</th>\n",
" </tr>\n",
" <tr>\n",
" <th>weekday</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 134298</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 135305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 152972</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 160131</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 141771</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td> 101578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td> 99310</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>7 rows \u00d7 1 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
" Berri 1\n",
"weekday \n",
"0 134298\n",
"1 135305\n",
"2 152972\n",
"3 160131\n",
"4 141771\n",
"5 101578\n",
"6 99310\n",
"\n",
"[7 rows x 1 columns]"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's hard to remember what 0, 1, 2, 3, 4, 5, 6 mean, so we can fix it up and graph it:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"weekday_counts.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
"weekday_counts"
],
"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>Berri 1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Monday</th>\n",
" <td> 134298</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tuesday</th>\n",
" <td> 135305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wednesday</th>\n",
" <td> 152972</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Thursday</th>\n",
" <td> 160131</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Friday</th>\n",
" <td> 141771</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Saturday</th>\n",
" <td> 101578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sunday</th>\n",
" <td> 99310</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>7 rows \u00d7 1 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
" Berri 1\n",
"Monday 134298\n",
"Tuesday 135305\n",
"Wednesday 152972\n",
"Thursday 160131\n",
"Friday 141771\n",
"Saturday 101578\n",
"Sunday 99310\n",
"\n",
"[7 rows x 1 columns]"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"weekday_counts.plot(kind='bar')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"<matplotlib.axes.AxesSubplot at 0x107bf1450>"
]
},
{
"metadata": {},
"output_type": "display_data",
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E7e3tikajkqTW1laFw2E5jjPp9erqagUCgUn/oAAAAABwNfFU9s6bOnWqCgsL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\nTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x107bed390>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So it looks like Montrealers are commuter cyclists -- they bike much more during the week. Neat!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 4.3 Putting it together"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's put all that together, to prove how easy it is. 6 lines of magical pandas!\n",
"\n",
"If you want to play around, try changing `sum` to `max`, `numpy.median`, or any other function you like."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bikes = pd.read_csv('../data/bikes.csv', \n",
" sep=';', encoding='latin1', \n",
" parse_dates=['Date'], dayfirst=True, \n",
" index_col='Date')\n",
"# Add the weekday column\n",
"berri_bikes = bikes[['Berri 1']]\n",
"berri_bikes['weekday'] = berri_bikes.index.weekday\n",
"\n",
"# Add up the number of cyclists by weekday, and plot!\n",
"weekday_counts = berri_bikes.groupby('weekday').aggregate(sum)\n",
"weekday_counts.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
"weekday_counts.plot(kind='bar')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"<matplotlib.axes.AxesSubplot at 0x107a4ad10>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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| unlicense |
pk-ai/training | machine-learning/deep-learning/udacity/ud730/1_notmnist.ipynb | 1 | 68918 | "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"t(...TRUNCATED) | mit |
flaviostutz/datascience-snippets | kaggle-sea-lion/07-train-lion-patches-single.ipynb | 1 | 100711 | "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true(...TRUNCATED) | mit |
analyticsguru/NUPredict480FinalProject | predict480FinalProjectDataPrep.ipynb | 1 | 125571 | "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\(...TRUNCATED) | mit |
wd15/extremefill2D | notebooks/fig3a_sim.ipynb | 1 | 1599 | "{\n \"metadata\": {\n \"name\": \"fig3a_sim_nx200\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": (...TRUNCATED) | mit |
rafaelscnunes/COS738-AutomaticPatentClassification | GitHub/classification-TF-1gram.ipynb | 1 | 303043 | "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n (...TRUNCATED) | mit |
CompPhysics/MachineLearning | "doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/KNNeighbors Example from Sci-Kit L(...TRUNCATED) | 1 | 67660 | "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {}(...TRUNCATED) | cc0-1.0 |
birdsarah/bokeh-miscellany | old/slider_example/Gapminder homage 0_3 html with population - better slice.ipynb | 1 | 2256903 | null | gpl-2.0 |
luwei0917/awsemmd_script | notebook/GlpG_paper/apr_week2_Fourth.ipynb | 1 | 4681388 | null | mit |
mattgiguere/doglodge | code/.ipynb_checkpoints/bf_qt_scraping-checkpoint.ipynb | 1 | 14699 | "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n (...TRUNCATED) | mit |
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GitHub Jupyter Dataset
Dataset Description
The dataset was extracted from Jupyter Notebooks on BigQuery.
Licenses
Each example has the license of its associated repository. There are in total 15 licenses:
[
'mit',
'apache-2.0',
'gpl-3.0',
'gpl-2.0',
'bsd-3-clause',
'agpl-3.0',
'lgpl-3.0',
'lgpl-2.1',
'bsd-2-clause',
'cc0-1.0',
'epl-1.0',
'mpl-2.0',
'unlicense',
'isc',
'artistic-2.0'
]
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