{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Counting bikes in Zurich\n",
"\n",
"---\n",
"\n",
"[Table of contents](index.ipynb)\n",
"\n",
"---\n",
"\n",
"All around the city of Zurich there are bike counting stations. They count the number of bikes in each direction at their location. We can get these counts for each 15minute period of the last few years. \n",
"\n",
"In this notebook we will look at how the total number of cyclists varies across the year and what the distribution of cyclists looks like on an average day.\n",
"\n",
"Let's use data from 2015.\n",
"\n",
"To get started we import a few libraries that we will need later:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Usage\n",
"\n",
"This is a jupyter notebook, where you can have text \"cells\" (like this text here) and code \"cells\" i.e. boxes where you can write python code to be executed (like the one below). For a short introduction to python and jupyter read the [quick start guide](introduction.ipynb).\n",
"\n",
"
\n",
"
\n",
" \n",
"
\n",
" To run the selected code cell, press
Shift + Enter
\n",
"
\n",
"
"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"\n",
"from utils import get_velo_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fetch and load data\n",
"\n",
"Fetch the data from the city of Zurich server and load it into a [pandas](http://pandas.pydata.org/) dataframe. We can think of a pandas dataframe as an Excel spreadsheet that can be manipulated by programming instead of clicking buttons.\n",
"\n",
"There are two things we need to know in order to load the data:\n",
"1. which year we are interested in, in our case 2015\n",
"1. which of the many bike counters we want to look at\n",
"\n",
"The bike counters are specified with their own special naming system. This means their names are things like \"ECO09113499\" or \"U15G3104442\". Not exactly human friendly. It makes sense though as the counters themselves have been in use for many years during which they might have been moved from one location to another. We will focus on a counter that has been placed at Mythenquai for a long time. It is named: \"ECO09113499\". To find out what counters at other locations are called visit: XXX and check when they were located where.\n",
"\n",
"Now that we know the name of the counter and the time period, let's finally load the data (if it looks like nothing is happening it is because this can take a while to fetch the data)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mythenquai = get_velo_data('ECO09113499', year=2015)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To see what our data looks like we can examine the first five rows of it using:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
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" \n",
" \n",
" | \n",
" Velo_in | \n",
" Velo_out | \n",
" Total | \n",
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" Datum | \n",
" | \n",
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" 2016-01-22 20:00:00 | \n",
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"text/plain": [
" Velo_in Velo_out Total\n",
"Datum \n",
"2016-01-22 20:00:00 2.0 1.0 3.0\n",
"2016-01-06 00:00:00 0.0 0.0 0.0\n",
"2016-01-06 00:15:00 0.0 2.0 2.0\n",
"2016-01-06 00:30:00 0.0 0.0 0.0\n",
"2016-01-06 00:45:00 0.0 0.0 0.0"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mythenquai.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can probably see why it makes sense to think of dataframes as excel spreadsheets. They both have rows and columns. In this dataset each row is one 15minute period. The columns \"Velo_in\" and \"Velo_out\" are the number of bikes that went in each direction. For this particular counter \"Velo_in\" counts how many bikes are going north, or towards the city center of Zurich. \"Velo_out\" counts the bikes going south. The column \"Total\" shows the sum of the two other columns.\n",
"\n",
"To make it a bit easier to think about our data let's rename the first two columns to: \"North\" and \"South\":"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" \n",
" \n",
" | \n",
" North | \n",
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" \n",
" Datum | \n",
" | \n",
" | \n",
" | \n",
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" 2016-01-22 20:00:00 | \n",
" 2.0 | \n",
" 1.0 | \n",
" 3.0 | \n",
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" 2016-01-06 00:00:00 | \n",
" 0.0 | \n",
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" 2016-01-06 00:45:00 | \n",
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"text/plain": [
" North South Total\n",
"Datum \n",
"2016-01-22 20:00:00 2.0 1.0 3.0\n",
"2016-01-06 00:00:00 0.0 0.0 0.0\n",
"2016-01-06 00:15:00 0.0 2.0 2.0\n",
"2016-01-06 00:30:00 0.0 0.0 0.0\n",
"2016-01-06 00:45:00 0.0 0.0 0.0"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# rename for easier plotting\n",
"mythenquai.columns = [\"North\", \"South\", \"Total\"]\n",
"mythenquai.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have the data, let's find out when people cycle past Mythenquai!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bike users per week\n",
"\n",
"To get started let's look at the total number of bikes that go north and south each week. As our data gives the number of bikes counted in each 15m period we need to sum those in the same week to get the total count for a day. In pandas you can do this with the `resample` function which combines entries according to the rule you specify as the argument. For weekly use 'W', for daily use 'D', and for hourly use 'H', etc. Once that is done, plotting is easy: we call the `plot` function of the dataframe."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"weekly = mythenquai.resample('W').sum()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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lCYmkpjWwBcRqs9LY3ugziG40GEkMTxwSAqIYePyxQGYB1wE7hBDb7GOPAFcLIfLRXFgH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\n1VLF3Wvv5oGvHyAxPJGKlgqW7vL+geJ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fD4jKEsWFA8W9JzjT0kpTs1xpuW2o7TKbVt9+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\nyBFGgRgMBoMhRxgFYjAYDIYc8f9jJ/3zvMaeHAAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"weekly.plot();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"#### Challenge\n",
"\n",
"Can you modify the example above from showing results per Week to instead showing the number of cyclists per Day?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# what needs to be on the right of the eqaul sign on the next line?\n",
"# check the weekly example above for inspiration\n",
"daily = \n",
"daily.plot();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"We can see that the total number of cyclists increases over the months, presumably as the weather gets better. Then after some time in September it drops back down as autumn approaches. There seems to be a pattern of a week with more cyclists followed by a low one which repeats. It would be interesting to look at what causes this.\n",
"\n",
"\n",
"As an exercise let's make the same plot but showing aggregates for each month instead of each week:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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CY9Aa08OAcGCzYftCoIIQIgyYhKEnlmJ5Tsae5L1979HYvTEfPvGh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\nZqIxZlUnbULnnvdpYAGAiIwGoowxW/y4h+NxxBgzGfgzcE8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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mythenquai.resample('M').sum().plot()\n",
"plt.title(\"Total cyclists per month 2016\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Average cyclists per hour\n",
"\n",
"A further question that is interesting is: do people commute to work (and when) or do they only cycle for fun? One way to look at this is to calculate the average number of cyclists per hour of the day. If people commute to work we should see a spike early in the morning and in the afternoon. Corresponding to people going to work and returning home. If they only cycle for fun, the distribution would probably be smoother.\n",
"\n",
"To do this we compute the number of cyclists per hour, just as we did before. Then we group each hour together by averaging them. This means the hour from 8am - 9am of every day of the year get's averegaged together and then plotted:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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lFCTQx75PoznlcRnqT+zamjpap9mCsVMAvyf9Tm55rsGsdG2r0qpSvo//nq9j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\n2o48mjvbsrZxMO8gd/a9s9owc9am0yo0kGvPr5u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ZsBwE7NZa7wFQSi0ExgBuFUG9CY2C\nHlebzR1aQ0meUQoVSiLHOj4JxTkEFJ0ioiiHiKJTtCmyFEjRMXTRKSg6iSorOTPdImurghOFE3/K\nlL/L3o8y67gMs0U7syiKSKTr3YvwD4mwpSgEQRAai6cUQTvggMv5QWCwh/I6E6UgOMJs9Zx5WfH/\nvLTIUg6u20mzlRYZPwFOBzjL8HM68HM6CLDOT9+rch4QQvDwGSBKQBAEH8JrRu2VUpOByQAdOnTw\nlhjVExhitojW3pZEEATBo3jK1tAhIMHlvL11rQKt9Rta62StdXJsbKyHxBAEQRBqw1OKYBPQTSnV\nSSkVBEwAPvZQXoIgCEIj8EjXkNbaoZSaAnyOmT46R2ud6om8BEEQhMbhsTECrfVnwGeeSl8QBEGw\nB5/wRyAIgiB4D1EEgiAIzRxRBIIgCM0cUQSCIAjNHJ+wPqqUygV2eVuOWogBsmoN5T18XT7wfRlF\nvsbj6zL6unxQPxk7aq0bvRDLayuLq7DLDsNJnkQpleLLMvq6fOD7Mop8jcfXZfR1+cA7MkrXkCAI\nQjNHFIEgCEIzx1cUwRveFqAO+LqMvi4f+L6MIl/j8XUZfV0+8IKMPjFYLAiCIHgPX2kRCIIgCN5C\na13vDRiFme65G3jQuhYFfAmkWftWdY3rgfg/WntX+Z4GfgF+Bj4Ezqnh+fyBLcDyKtenWmmkAk/V\nJ34d5HsUY6p7q7Vd6SbdEGAj8JMlw2Mu92Zaz7YV+AJo6yZ+ArAa4ykuFfh7DeU/zk0d9wN+sPJI\nAQY1Mo/G1PEh4Biw3eX+dVaeTiC5Pu+vB+rYnXy11pEVbi+wrbycPVjH7wGZrjLaXAaeqOO+wHqr\nfD4BIutTBjY/37fAd1XzoY7fGmBONeVvdz27rYNK6dUWoJrC+Q3oDARhPko9gac4/cF4EJhV17jW\nPVviW2GygdeqyDcSCLDCznKXvks+04EFVV6AS4GvgGDrPK6e8WuT71HgH7WUvQLCreNAYANwgXUe\n6RLub8BrbuLHA/2t4wiMX2l35f8QcMJNHX8BjLbCXAmsaUQejX1HXgPepvJHogdwLrCGahRBLenb\nUsc1yFdrHVn39gIxbq7bWccPAvOB/lVktLMMPFHHm4Bh1vFtwMx6loGdz/cE8HbVfKjjtwa4pGr5\ne6ieq/3WVaRXWwA3AgwBPnc5f8jadgHxLkLuqmtc69iW+FaY1eXxXcO4xPsTML+a52sPrAIuq/IC\nLAIur0P5VBe/RvmogyKokk8opmUx2M29h4BX65DGMmCEm/L/I5Dvpo4/B663rt0ALGhEHna8I79R\n5Udk3VtD9YqgpvRtqePa5KutjqhGEdhcx/HWeSKVP7R2l4GtdQyc4vTYZgKwo55l4LHnc83H5Vq1\n3xrrfqXy91Q91xa/IWME7vwRtwNaa62PWNcygNYASqm2SqnPaomLjfHbYV6e1m7ClHMbsMJN+gAv\nAA9guhdc6Q5crJTaoJT6Rik1sJ7x6yLfVKXUz0qpOUqpVu7SV0r5K6W2YpqUX2qtN7jce0IpdQC4\nCfjfauQrD5sInI9pVbjKB6YLynWxYbmM9wJPW3k8g3lJG5qHHe9InVZU1iN9u+q4WvnqWEca+Eop\ntdly6Vrf+OVhE6lD+VfB7jKwu45TgTHW8XVYnhDrUQaeer6q+ZRT07emWpqgnivhkcFibVSRto4P\na62vbMr45cm4u6iUehhwYJrFldJXSv0RyNRab3YTNQDT93YBcD+wSCml6hG/NvlexTSV+wFHgGer\nymedl2mt+2H+sQxSSiW53HtYa51gPdsUd/EtOcOBJcC9WuucOshazl3ANCuPacDshuZh5ztSh7B1\nTd+2Oq5OvjrW0VCrjkcD9yilLqln/HqVf1OUgY11fBtwt1JqM6ZLpKS69KspA9ufr7qyrulbU4dn\n93Q9V6IhiqA6f8RHlVLxlnDxmH+sdY2LjfEPAV1c4leEUUr9BdPtcZNVQFW5CLhGKbUXWAhcppR6\nz7p3EFiqDRsx/xRi6hG/Rvm01ketj7wTeBMY5Ea+CrTWJzFdTKPc3J4P/NldPKVUIObFma+1Xupy\nq6L8gSLMC1xOeRlOBMrjfFCdjHXJw6Z3pCE2Y2pK3646rot81daR1vqQtc/EDDa6K+dG1XEN5W93\nGdhax1rrX7TWI7XWA4D3Ma3r+pSBJ57vjHzq8K2pK56q50o0RBFU54/4Y8yHAmu/rB5xsTH+JqAP\nsMY1jFJqFKapd43WusDdg2mtH9Jat9daJ1rxvtZa32zd/ggz0IRSqjtmkKvqS1pT/Nrki3dJ6k/A\n9qryKaVilVLnWMctgBGY2Qkopbq5BB1Tfr1KfIX5F79Ta/1clduu5d8HKHVTzoeBYVaYyzCzEhqa\nhx3vyJdu4tdGTenbVcdu5atjHYUppSLKjzEDj9vrEb+x5W93Gdhax0qpOGvvBzyCGVCmSpiaysDu\n5yutmk9dvjU10UT1XBldyyCCuw0zY+RXjDZ+2LoWjRlcScOMykdZ19sCn9UU1wPxt2CmpbnKtxvT\nL1k+PfM1d+m7pDecyoNEQZjpdtsxg7SX1TN+bfK9i5kS97NVkfFV08d8oLdYYbYD/+uS/hLr2s+Y\naXXt3MQfimkmlk9Nq5im6qb8x7up46HAZswsjw3AgEbm0Zg6zrC2Usy/vNsxCvQgUAwcxRqQrEf6\ndtaxO/nqUkedrfItnyL8sAfreAmmG9JVRjvLwBN1/Hcr3q/Ak5weOK5rGdj5fJvc5UMdvzWYFk2l\n8vdQPUfV9k2XlcWCIAjNHFlZLAiC0MwRRSAIgtDMEUUgCILQzBFFIAiC0MwRRSAIgtDMEUUgNEuU\nUucope62jtsqpRZ7WyZB8BYyfVRollj2WZZrrZNqCSoIZz0BtQcRhLOSJ4EuyhjwSwN6aK2TLNMA\n1wJhQDeMcb0g4BbMQrUrtdbHlVJdgJcxRtEKgDu01mesABWE3wPSNSQ0Vx4EftPGuNv9Ve4lAWOB\ngRib8wVa6/MxDlFutcK8AUzVxubNP4BXmkRqQfAA0iIQhDNZrbXOBXKVUqcwy/zBmADpY1l8vBD4\nwJh8ASC46cUUBHsQRSAIZ1Lscux0OXdifjN+wEmrNSEIv3uka0horuRi7NnXG23svqcrpa4DYwlS\nKdXXTuEEoSkRRSA0S7TW2cA6pdR2jLPx+nITcLtSqtxK6JhawguCzyLTRwVBEJo50iIQBEFo5ogi\nEARBaOaIIhAEQWjmiCIQBEFo5ogiEARBaOaIIhAEQWjmiCIQBEFo5ogiEARBaOb8P+in/ibN6fdm\nAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"hourly = mythenquai.resample('H').sum()\n",
"hourly.groupby(hourly.index.time).mean().plot();"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"As we suspected there is a big bump of people going North (blue) in the morning and a smaller bump of people going South (orange) around five in the afternoon. However there is also a significant number of people cycling at the hours inbetween. How can we learn more about that trend across the day?\n",
"\n",
"The pattern made by the commuters should only exist for working days. If we split up working days (Monday to Friday) and the weekend (Saturday and Sunday) we might learn something more.\n",
"\n",
"The first step to check this idea is to select only measurements that were taken on a Saturday or Sunday. We can do that using the following bit of code:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" North | \n",
" South | \n",
" Total | \n",
"
\n",
" \n",
" Datum | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" 2016-09-04 14:15:00 | \n",
" 10.0 | \n",
" 13.0 | \n",
" 23.0 | \n",
"
\n",
" \n",
" 2016-03-13 01:00:00 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" 2016-03-26 09:15:00 | \n",
" 2.0 | \n",
" 6.0 | \n",
" 8.0 | \n",
"
\n",
" \n",
" 2016-06-19 01:45:00 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" 2016-06-25 21:30:00 | \n",
" 1.0 | \n",
" 1.0 | \n",
" 2.0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" North South Total\n",
"Datum \n",
"2016-09-04 14:15:00 10.0 13.0 23.0\n",
"2016-03-13 01:00:00 0.0 0.0 0.0\n",
"2016-03-26 09:15:00 2.0 6.0 8.0\n",
"2016-06-19 01:45:00 0.0 0.0 0.0\n",
"2016-06-25 21:30:00 1.0 1.0 2.0"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"\n",
"weekend = mythenquai[np.logical_or(mythenquai.index.weekday_name == 'Saturday',\n",
" mythenquai.index.weekday_name == 'Sunday')]\n",
"# select five random entries, if you check the dates in a calendar you will see they\n",
"# are all on the weekend\n",
"weekend.sample(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we sum the counts within the same hour of the day, and then we average. Just as before."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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t4Mzmc/cJffA0y8+vaFcRt8JucnHZa2S7ECiKEgmECSH+7tjVDLgMbAH80+/z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\nVt4X75qB5aWrITJxQEQZ1G6onXG0uIa7X91LXXM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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"weekend = weekend.resample('H').sum()\n",
"\n",
"weekend.groupby(weekend.index.time).mean().plot();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"#### Challenge\n",
"\n",
"Based on the weekend example, can you show what the distribution of cyclists looks like during the working week?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}