Learn to code for data analysis
Learn to code for data analysis

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Learn to code for data analysis

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 plot() which will visualise data for us by producing a chart.

Before using the plot() 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 plot():

In []:

%matplotlib inline

To plot ‘Max Wind SpeedKm/h ’, it’s as simple as this code:

In []:

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

Out[]:

The grid=True 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 plot() method some extra information. The figsize units are inches.

In []:

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

Out[]:

That’s better! The argument given to the plot() method, figsize=(10,5) simply tells plot() 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 plot() method will try to generate a line, although as you’ll see in a later week, it can produce other chart types too.

Exercise 5 Every picture tells a story

Now try Exercise 5 in the Exercise notebook 2.

If you’re using Anaconda, remember that to open the notebook you’ll need to navigate to the notebook using Jupyter.

LCDAB_1

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