Plotting DataFrames

Comparing data from several columns can be very illuminating. Pandas makes doing so easy with multi-column DataFrames. By default, calling df.plot() will cause pandas to over-plot all column data, with each column as a single line. In this exercise, we have pre-loaded three columns of data from a weather data set - temperature, dew point, and pressure - but the problem is that pressure has different units of measure. The pressure data, measured in Atmospheres, has a different vertical scaling than that of the other two data columns, which are both measured in degrees Fahrenheit.

Your job is to plot all columns as a multi-line plot, to see the nature of vertical scaling problem. Then, use a list of column names passed into the DataFrame df[column_list] to limit plotting to just one column, and then just 2 columns of data. When you are finished, you will have created 4 plots. You can cycle through them by clicking on the 'Previous Plot' and 'Next Plot' buttons.

As in the previous exercise, inspect the DataFrame df in the IPython Shell using the .head() and .info() methods.

Instructions

Plot all columns together on one figure by calling df.plot(), and noting the vertical scaling problem.
Plot all columns as subplots. To do so, you need to specify subplots=True inside .plot().
Plot a single column of dew point data. To do this, define a column list containing a single column name 'Dew Point (deg F)', and call df[column_list1].plot().
Plot two columns of data, 'Temperature (deg F)' and 'Dew Point (deg F)'. To do this, define a list containing those column names and pass it into df[], as df[column_list2].plot().

Take Hint (-30xp)