Home TechnologyHow-to How to: Pandas Tutorial – Summary Functions and Maps.

How to: Pandas Tutorial – Summary Functions and Maps.

by Ivan

Pandas Tutorial part 3: Extract insights from your data.

How To Pandas tutorial summary functions and maps
getting insights from your data


In the last Pandas Tutorial, we learned how to use Pandas to select relevant data out of a DataFrame or Series. Plucking the right data out of our data representation is critical to getting work done, as we demonstrated in the exercises.

However, the data does not always come out of memory in the format we want it in right out of the bat. Sometimes we have to do some more work ourselves to reformat it for the task at hand. This tutorial will cover different operations we can apply to our data to get the input “just right”.

For this exercise we will continue using the famous wine review dataframe. You can obtain this from Kaggle, following this link.

import pandas as pd
pd.set_option('max_rows', 5)
import numpy as np
reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv", index_col=0)

Download the most comprehensive Python Pandas Cheat Sheet here



Summary functions

Pandas provides many simple “summary functions” (not an official name) which restructure the data in some useful way. For example, consider the describe() method:In [3]:



count    129971.000000
mean         88.447138
75%          91.000000
max         100.000000
Name: points, Length: 8, dtype: float64

This method generates a high-level summary of the attributes of the given column. It is type-aware, meaning that its output changes based on the data type of the input. The output above only makes sense for numerical data; for string data here’s what we get:In [4]:



count         103727
unique            19
top       Roger Voss
freq           25514
Name: taster_name, dtype: object

If you want to get some particular simple summary statistic about a column in a DataFrame or a Series, there is usually a helpful pandas function that makes it happen.

For example, to see the mean of the points allotted (e.g. how well an averagely rated wine does), we can use the mean() function:


To see a list of unique values we can use the unique() function:In [6]:



array(['Kerin O’Keefe', 'Roger Voss', 'Paul Gregutt',
       'Alexander Peartree', 'Michael Schachner', 'Anna Lee C. Iijima',
       'Virginie Boone', 'Matt Kettmann', nan, 'Sean P. Sullivan',
       'Jim Gordon', 'Joe Czerwinski', 'Anne Krebiehlxa0MW',
       'Lauren Buzzeo', 'Mike DeSimone', 'Jeff Jenssen',
       'Susan Kostrzewa', 'Carrie Dykes', 'Fiona Adams',
       'Christina Pickard'], dtype=object)

To see a list of unique values and how often they occur in the dataset, we can use the value_counts() method:In [7]:



Roger Voss           25514
Michael Schachner    15134
Fiona Adams             27
Christina Pickard        6
Name: taster_name, Length: 19, dtype: int64


A map is a term, borrowed from mathematics, for a function that takes one set of values and “maps” them to another set of values. In data science we often have a need for creating new representations from existing data, or for transforming data from the format it is in now to the format that we want it to be in later. Maps are what handle this work, making them extremely important for getting your work done!

There are two mapping methods that you will use often.

map() is the first, and slightly simpler one. For example, suppose that we wanted to remean the scores the wines received to 0. We can do this as follows:In [8]:

review_points_mean = reviews.points.mean()
reviews.points.map(lambda p: p - review_points_mean)


0        -1.447138
1        -1.447138
129969    1.552862
129970    1.552862
Name: points, Length: 129971, dtype: float64

The function you pass to map() should expect a single value from the Series (a point value, in the above example), and return a transformed version of that value. map() returns a new Series where all the values have been transformed by your function.

apply() is the equivalent method if we want to transform a whole DataFrame by calling a custom method on each row:

def remean_points(row):
    row.points = row.points - review_points_mean
    return row

reviews.apply(remean_points, axis='columns')


129971 rows × 13 columns

If we had called reviews.apply() with axis='index', then instead of passing a function to transform each row, we would need to give a function to transform each column.

Note that map() and apply() return new, transformed Series and DataFrames, respectively. They don’t modify the original data they’re called on. If we look at the first row of reviews, we can see that it still has its original points value:


Pandas provides many common mapping operations as built-ins. For example, here’s a faster way of remeaning our points column:In [11]:

review_points_mean = reviews.points.mean()
reviews.points - review_points_mean


0        -1.447138
1        -1.447138
129969    1.552862
129970    1.552862
Name: points, Length: 129971, dtype: float64

In this code we are performing an operation between a lot of values on the left-hand side (everything in the Series) and a single value on the right-hand side (the mean value). Pandas looks at this expression and figures out that we must mean to subtract that mean value from every value in the dataset.

Pandas will also understand what to do if we perform these operations between Series of equal length. For example, an easy way of combining country and region information in the dataset would be to do the following:In [12]:

reviews.country + " - " + reviews.region_1
0            Italy - Etna
1                     NaN
129969    France - Alsace
129970    France - Alsace
Length: 129971, dtype: object

These operators are faster than map() or apply() because they use speed ups built into pandas. All of the standard Python operators (>, <, ==, and so on) work in this manner.

However, they are not as flexible as map() or apply(), which can do more advanced things, like applying conditional logic, which cannot be done with addition and subtraction alone.

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