Similar to Numpy, pandas offers an easy way to load text files into a pandas dataframe:

import pandas as pd
pd.read_csv(usecols=1)

Here the separation can be denoted by either sep or delimiter, which is set as comma , by default (CSV stands for comma-separated values).

There is a long list of less commonly used options available as to determine how different data formats, data types, and errors should be handled. You may refer to the documentation at http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html. Besides flat CSV files, Pandas also has other built-in functions for reading other common data formats, such as Excel, JSON, HTML, HDF5, SQL, and Google BigQuery.

To stay focused on data visualization, we will not dig deep into the methods of data cleaning in this book, but this is a survival skill set very helpful in data science. If interested, you can check out resources on data handling with Python.