In [21]:
# Render our plots inline
%matplotlib inline

import pandas as pd
import matplotlib.pyplot as plt

# Make the graphs a bit prettier, and bigger'ggplot')
plt.rcParams['figure.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 [22]:
broken_df = pd.read_csv('../data/bikes.csv',encoding = "ISO-8859-1")
In [23]:
# Look at the first 3 rows
Date;Berri 1;Brébeuf (données non disponibles);Côte-Sainte-Catherine;Maisonneuve 1;Maisonneuve 2;du Parc;Pierre-Dupuy;Rachel1;St-Urbain (données 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 day first instead of the month first
  • Set the index to be the 'Date' column
In [24]:
fixed_df = pd.read_csv('../data/bikes.csv', sep=';', encoding='latin1', parse_dates=['Date'], dayfirst=True, index_col='Date')
Berri 1 Brébeuf (données non disponibles) Côte-Sainte-Catherine Maisonneuve 1 Maisonneuve 2 du Parc Pierre-Dupuy Rachel1 St-Urbain (données non disponibles)
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:

In [25]:
fixed_df['Berri 1']
2012-01-01      35
2012-01-02      83
2012-01-03     135
2012-01-04     144
2012-01-05     197
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 [26]:
fixed_df['Berri 1'].plot()
<matplotlib.axes._subplots.AxesSubplot at 0x11da42eb0>

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 [27]:
fixed_df.plot(figsize=(15, 10))
<matplotlib.axes._subplots.AxesSubplot at 0x11dadd8e0>

1.4 Putting all that together

Here's the code we needed to write do draw that graph, all together:

In [28]:
df = pd.read_csv('../data/bikes.csv', sep=';', encoding='latin1', parse_dates=['Date'], dayfirst=True, index_col='Date')
df['Berri 1'].plot()
<matplotlib.axes._subplots.AxesSubplot at 0x11d6bb9d0>