The very first step is to import the scientific packages we will be using in this recipe, namely NumPy, pandas, and Matplotlib. We also instruct Matplotlib to render the figures as inline images in the Notebook:

>>> import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    %matplotlib inline

from IPython.display import set_matplotlib_formats
set_matplotlib_formats('retina')

Now, we create a new Python variable called url that contains the address to a Comma-separated Values (CSV) data file. This standard text-based file format is used to store tabular data:

>>> url = ("https://raw.githubusercontent.com/"
           "ipython-books/cookbook-2nd-data/"
           "master/bikes.csv")

pandas defines a read_csv() function that can read any CSV file. Here, we pass the URL to the file. pandas will automatically download the file, parse it, and return a DataFrame object. We need to specify a few options to make sure that the dates are parsed correctly:

>>> df = pd.read_csv(url, index_col='Date',
                     parse_dates=True, dayfirst=True)

The df variable contains a DataFrame object, a specific pandas data structure that contains 2D tabular data. The head(n) method displays the first n rows of this table. In the Notebook, pandas displays a DataFrame object in an HTML table, as shown in the following screenshot:

>>> df.head(2)

Here, every row contains the number of bicycles on every track of the city, for every day of the year.

We can get some summary statistics of the table with the describe() method:

>>> df.describe()

Let's display some figures. We will plot the daily attendance of two tracks. First, we select the two columns, Berri1 and PierDup. Then, we call the plot() method:

>>> df[['Berri1', 'PierDup']].plot(figsize=(10, 6),
                                   style=['-', '--'],
                                   lw=2)

Now, we move to a slightly more advanced analysis. We will look at the attendance of all tracks as a function of the weekday. We can get the weekday easily with pandas: the index attribute of the DataFrame object contains the dates of all rows in the table. This index has a few date-related attributes, including weekday_name:

>>> df.index.weekday_name
Index(['Tuesday', 'Wednesday', 'Thursday', 'Friday',
       'Saturday', 'Sunday', 'Monday', 'Tuesday',
       ...
       'Friday', 'Saturday', 'Sunday', 'Monday',
       'Tuesday', 'Wednesday'],
      dtype='object', name='Date', length=261)

To get the attendance as a function of the weekday, we need to group the table elements by the weekday. The groupby() method lets us do just that. We use weekday instead of weekday_name to keep the weekday order (Monday is 0, Tuesday is 1, and so on). Once grouped, we can sum all rows in every group:

>>> df_week = df.groupby(df.index.weekday).sum()
>>> df_week

We can now display this information in a figure. We create a Matplotlib figure, and we use the plot() method of DataFrame to create our plot:

>>> fig, ax = plt.subplots(1, 1, figsize=(10, 8))
    df_week.plot(style='-o', lw=3, ax=ax)
    ax.set_xlabel('Weekday')
    # We replace the labels 0, 1, 2... by the weekday
    # names.
    ax.set_xticklabels(
        ('Monday,Tuesday,Wednesday,Thursday,'
         'Friday,Saturday,Sunday').split(','))
    ax.set_ylim(0)  # Set the bottom axis to 0.

Finally, let's illustrate the interactive capabilities of the Notebook. We will plot a smoothed version of the track attendance as a function of time (rolling mean). The idea is to compute the mean value in the neighborhood of any day. The larger the neighborhood, the smoother the curve. We will create an interactive slider in the Notebook to vary this parameter in real time in the plot. All we have to do is add the @interact decorator above our plotting function:

>>> from ipywidgets import interact
    
    @interact
    def plot(n=(1, 30)):
        fig, ax = plt.subplots(1, 1, figsize=(10, 8))
        df['Berri1'].rolling(window=n).mean().plot(ax=ax)
        ax.set_ylim(0, 7000)
        plt.show()