Complex operations

We've seen how to load, select, filter, and operate on data with pandas. In this section, we will show more complex manipulations that are typically done on full-blown databases based on SQL.

Tip

SQL

Structured Query Language is a domain-specific language widely used to manage data in relational database management systems (RDBMS). pandas is somewhat inspired by SQL, which is familiar to many data analysts. Additionally, pandas can connect to SQL databases. You will find more information about the links between pandas and SQL at http://pandas.pydata.org/pandas-docs/stable/comparison_with_sql.html.

Let's first import our NYC taxi dataset as in the previous sections.

In [1]: import numpy as np
        import pandas as pd
        import matplotlib.pyplot as plt
        import seaborn
        %matplotlib inline
        data = pd.read_csv('data/nyc_data.csv',
                           parse_dates=['pickup_datetime',
                                        'dropoff_datetime...