Expanding on panda data frames in Jupyter

There are more functions built-in for working with data frames than we have used so far. If we were to take one of the data frames from a prior example in this chapter, the Titanic dataset from an Excel file, we could use additional functions to help portray and work with the dataset.

As a repeat, we load the dataset using the script:

import pandas as pddf = pd.read_excel('http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic3.xls')

We can then inspect the data frame using the info function, which displays the characteristics of the data frame:

df.info()

Some of the interesting points are as follows:

• 1309 entries
• 14 columns
• Not many fields with valid data in the body column—most were lost
• Does give a good overview of the types of data involved

We can also use the describe function, which gives us a statistical breakdown of the number columns in the data frame.

df.describe()

This produces the following tabular display:

For each numerical column we have...