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Learning IPython for Interactive Computing and Data Visualization, Second Edition

You're reading from  Learning IPython for Interactive Computing and Data Visualization, Second Edition

Product type Book
Published in Oct 2015
Publisher
ISBN-13 9781783986989
Pages 200 pages
Edition 1st Edition
Languages
Author (1):
Cyrille Rossant Cyrille Rossant
Profile icon Cyrille Rossant

Computing with NumPy arrays


We now get to the substance of array programming with NumPy. We will perform manipulations and computations on ndarrays.

Let's first import NumPy, pandas, matplotlib, and seaborn:

In [1]: import numpy as np
        import pandas as pd
        import matplotlib.pyplot as plt
        import seaborn as sns
        %matplotlib inline

We load the NYC taxi dataset with pandas:

In [2]: data = pd.read_csv('../chapter2/data/nyc_data.csv',
                           parse_dates=['pickup_datetime',
                                        'dropoff_datetime'])

We get the pickup and dropoff locations of the taxi rides as ndarrays, using the .values attribute of pandas DataFrames:

In [3]: pickup = data[['pickup_longitude', 'pickup_latitude']].values
        dropoff = data[['dropoff_longitude',
                        'dropoff_latitude']].values
        pickup
Out[3]: array([[-73.955925,  40.781887],
               [-74.005501,  40.745735],
               [-73.969955,  40.79977...
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