Manipulating data

Visualizing raw data and computing basic statistics is particularly easy with pandas. All we have to do is choose a couple of columns in a DataFrame and use built-in statistical or visualization functions.

However, more sophisticated data manipulations methods quickly become necessary as we explore a dataset. In this section, we will first see how to make selections of a DataFrame. Then, we will see how to efficiently make transformations and computations on columns.

We first import the NYC taxi dataset, as in the previous section.

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