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- pandas-cookbook
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In [1]:
import pandas as pd pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier figsize(15, 5)
1.1 Reading data from a csv file¶
You can read data from a CSV file using the read_csv function. By default, it assumes that the fields are comma-separated.
We're going to be looking some cyclist data from Montréal. Here's the original page (in French), but it's already included in this repository. We're using the data from 2012.
This dataset is a list of how many people were on 7 different bike paths in Montreal, each day.
In [2]:
broken_df = pd.read_csv('../data/bikes.csv')
In [3]:
Look at the first 3 rows
broken_df[:3]
Out[3]:
| Date;Berri 1;Br�beuf (donn�es non disponibles);C�te-Sainte-Catherine;Maisonneuve 1;Maisonneuve 2;du Parc;Pierre-Dupuy;Rachel1;St-Urbain (donn�es non disponibles) | |
|---|---|
| 0 | 01/01/2012;35;;0;38;51;26;10;16; |
| 1 | 02/01/2012;83;;1;68;153;53;6;43; |
| 2 | 03/01/2012;135;;2;104;248;89;3;58; |
You'll notice that this is totally broken! read_csv has a bunch of options that will let us fix that, though. Here we'll
- change the column separator to a
; - Set the encoding to
'latin1'(the default is'utf8') - Parse the dates in the 'Date' column
- Tell it that our dates have the date first instead of the month first
- Set the index to be the 'Date' column
In [4]:
fixed_df = pd.read_csv('../data/bikes.csv', sep=';', encoding='latin1', parse_dates=['Date'], dayfirst=True, index_col='Date') fixed_df[:3]
Out[4]:
| Berri 1 | Brébeuf (données non disponibles) | Côte-Sainte-Catherine | Maisonneuve 1 | Maisonneuve 2 | du Parc | Pierre-Dupuy | Rachel1 | St-Urbain (données non disponibles) | |
|---|---|---|---|---|---|---|---|---|---|
| Date | |||||||||
| 2012-01-01 | 35 | NaN | 0 | 38 | 51 | 26 | 10 | 16 | NaN |
| 2012-01-02 | 83 | NaN | 1 | 68 | 153 | 53 | 6 | 43 | NaN |
| 2012-01-03 | 135 | NaN | 2 | 104 | 248 | 89 | 3 | 58 | NaN |
1.2 Selecting a column¶
When you read a CSV, you get a kind of object called a DataFrame, which is made up of rows and columns. You get columns out of a DataFrame the same way you get elements out of a dictionary.
Here's an example:
Out[5]:
Date 2012-01-01 35 2012-01-02 83 2012-01-03 135 2012-01-04 144 2012-01-05 197 2012-01-06 146 2012-01-07 98 2012-01-08 95 2012-01-09 244 2012-01-10 397 2012-01-11 273 2012-01-12 157 2012-01-13 75 2012-01-14 32 2012-01-15 54 ... 2012-10-22 3650 2012-10-23 4177 2012-10-24 3744 2012-10-25 3735 2012-10-26 4290 2012-10-27 1857 2012-10-28 1310 2012-10-29 2919 2012-10-30 2887 2012-10-31 2634 2012-11-01 2405 2012-11-02 1582 2012-11-03 844 2012-11-04 966 2012-11-05 2247 Name: Berri 1, Length: 310, dtype: int64
1.3 Plotting a column¶
Just add .plot() to the end! How could it be easier? =)
We can see that, unsurprisingly, not many people are biking in January, February, and March,
In [6]:
fixed_df['Berri 1'].plot()
Out[6]:
<matplotlib.axes.AxesSubplot at 0x3ea1490>
We can also plot all the columns just as easily. We'll make it a little bigger, too. You can see that it's more squished together, but all the bike paths behave basically the same -- if it's a bad day for cyclists, it's a bad day everywhere.
In [7]:
fixed_df.plot(figsize=(15, 10))
Out[7]:
<matplotlib.axes.AxesSubplot at 0x3fc2110>
1.4 Putting all that together¶
Here's the code we needed to write do draw that graph, all together:
In [8]:
df = pd.read_csv('../data/bikes.csv', sep=';', encoding='latin1', parse_dates=['Date'], dayfirst=True, index_col='Date') df['Berri 1'].plot()
Out[8]:
<matplotlib.axes.AxesSubplot at 0x4751750>