2 Every picture tells a story

It can be difficult and confusing to look at a table of rows of numbers and make any meaningful interpretation especially if there are many rows and columns.

Handily, pandas has a method called which will visualise data for us by producing a chart.

Before using the method, the following line of code must be executed (once) which tells Jupyter to display all charts inside this notebook, immediately after each call to

%matplotlib inline

To plot ’, it’s as simple as this code:

london['Max Wind SpeedKm/h'].plot(grid=True)

Figure 10

The argument makes the gridlines (the dotted lines in the image above) appear, which make values easier to read on the chart. The chart comes out a bit small, so you can make it bigger by giving the method some extra information. The figsize units are inches.

london['Max Wind SpeedKm/h'].plot(grid=True, figsize=(10,5))

Figure 11

That’s better! The argument given to the method, simply tells that the x-axis should be 10 units wide and the y-axis should be 5 units high. In the above graph the x-axis (the numbers at the bottom) shows the dataframe’s index, so 0 is 1 January and 50 is 18 February.

The y-axis (the numbers on the side) shows the range of wind speed in kilometres per hour. It is clear that the windiest day in 2014 was somewhere in mid-February and the wind reached about 66 kilometers per hour.

By default, the method will try to generate a line, although as you’ll see in a later week, it can produce other chart types too.