Hands-On Exploratory Data Analysis with Python

Applying descriptive statistics

Having preprocessed the dataset, let's do some sanity checking using descriptive statistics techniques.

We can implement this as shown here:

dfs.info()

The output of the preceding code is as follows:

<class 'pandas.core.frame.DataFrame'>Int64Index: 37554 entries, 1 to 78442Data columns (total 6 columns):subject 37367 non-null objectfrom 37554 non-null objectdate 37554 non-null datetime64[ns, UTC]to 36882 non-null objectlabel 36962 non-null objectthread 37554 non-null objectdtypes: datetime64[ns, UTC](1), object(5)memory usage: 2.0+ MB

We will learn more about descriptive statistics in Chapter 5, Descriptive Statistics. Note that there are 37,554 emails, with each email containing six columns—subject, from, ...