A pivot table, as known from Excel, summarizes data. The data in CSV files that we have seen in this chapter so far has been in flat files. The pivot table aggregates data from a flat file for certain columns and rows. The aggregating operation can be sum, mean, standard deviations, and so on. We will reuse the data generating code from data_aggregation.py. The pandas API has a top-level pivot_table() function and corresponding DataFrame method. With the aggfunc parameter, we can specify the aggregation function to use the NumPy sum() function, for instance. The cols parameter tells pandas the column to be aggregated. Create a pivot table on the Food column as follows:

print pd.pivot_table(df, cols=['Food'], aggfunc=np.sum)

The pivot table we get contains totals for each food item:

Food    chocolate   icecream      soup
Number   8.000000  15.000000  19.00000
Price    5.986585  10.440071  13.83338

[2 rows x 3 columns]

The following code can be found in pivot_demo.py in this book's code bundle:

import pandas as pd
from numpy.random import seed
from numpy.random import rand
from numpy.random import random_integers
import numpy as np

seed(42)
N = 7
df = pd.DataFrame({
   'Weather' : ['cold', 'hot', 'cold', 'hot',
   'cold', 'hot', 'cold'],
   'Food' : ['soup', 'soup', 'icecream', 'chocolate',
   'icecream', 'icecream', 'soup'],
   'Price' : 10 * rand(N), 'Number' : random_integers(1, 9, size=(N,))})

print "DataFrame
", df
print pd.pivot_table(df, cols=['Food'], aggfunc=np.sum)