Pandas Pivot: A Guide with Examples

Python’s Pandas library is one of the most popular tools in the data scientist’s toolbelt. Data scientists use Pandas to explore, clean, and understand datasets.

In a previous article, we saw how to work with the Pandas DataFrame, the core data structure in the Pandas library. If you’re new to Pandas, that post is a great way to get started.

In this post, we’ll learn how to reshape your DataFrames by using the Pivot method. Pivot allows you to twist your data into a different shape for easier analysis.

We’ll cover:

  • Why to pivot your data;
  • How to use the pivot method;
  • When to use pivot method vs pivot_table method;
  • How to use the pivot_table method.

Let’s get started.

Why to pivot your data

Before we get into details how to pivot, it’s important to know why you want to pivot.

Pivoting your data allows you to reshape it in a way that makes it easier to understand or analyze. Often you’ll use a pivot to demonstrate the relationship between two columns that can be difficult to reason about before the pivot.

If you’re an Excel wizard who has spent a lot of time in spreadsheets, the idea of a pivot may be easy for you. If not, it can be hard to understand without an example. Let’s see how it works.

In the example below, I’ll create a Pandas DataFrame from some stock trading data that I’ve used in the previous Pandas articles.

>>> import pandas as pd
>>> stocks = pd.read_csv('')
>>> stocks
          date symbol     open     high      low    close    volume
0   2019-03-01   AMZN  1655.13  1674.26  1651.00  1671.73   4974877
1   2019-03-04   AMZN  1685.00  1709.43  1674.36  1696.17   6167358
2   2019-03-05   AMZN  1702.95  1707.80  1689.01  1692.43   3681522
3   2019-03-06   AMZN  1695.97  1697.75  1668.28  1668.95   3996001
4   2019-03-07   AMZN  1667.37  1669.75  1620.51  1625.95   4957017
5   2019-03-01   AAPL   174.28   175.15   172.89   174.97  25886167
6   2019-03-04   AAPL   175.69   177.75   173.97   175.85  27436203
7   2019-03-05   AAPL   175.94   176.00   174.54   175.53  19737419
8   2019-03-06   AAPL   174.67   175.49   173.94   174.52  20810384
9   2019-03-07   AAPL   173.87   174.44   172.02   172.50  24796374
10  2019-03-01   GOOG  1124.90  1142.97  1124.75  1140.99   1450316
11  2019-03-04   GOOG  1146.99  1158.28  1130.69  1147.80   1446047
12  2019-03-05   GOOG  1150.06  1169.61  1146.19  1162.03   1443174
13  2019-03-06   GOOG  1162.49  1167.57  1155.49  1157.86   1099289
14  2019-03-07   GOOG  1155.72  1156.76  1134.91  1143.30   1166559

When we print out the code, you can see that the data has a number of columns and that the rows are organized by trading date and stock symbol.

That organization may be helpful for some analysis, but it can be hard to glean information about trading volume across dates and stock symbols. Let’s reshape our data to look closer at volume.

>>> stocks.pivot(index='symbol', columns='date', values='volume')
date    2019-03-01  2019-03-04  2019-03-05  2019-03-06  2019-03-07
AAPL      25886167    27436203    19737419    20810384    24796374
AMZN       4974877     6167358     3681522     3996001     4957017
GOOG       1450316     1446047     1443174     1099289     1166559

In the example above, I use the pivot method to reshape the data so that the rows are indexed by stock symbol and the columns are trading dates. The value in each cell is the volume on that day. This makes it easy to compare the volume for a stock over time, by reading horizontally, or to compare volume across stocks on a particular day, by reading vertically.

This reshaping power of pivot makes it much easier to understand relationships in your datasets.

How to use the Pandas pivot method

To use the pivot method in Pandas, you need to specify three parameters:

  • Index: Which column should be used to identify and order your rows vertically
  • Columns: Which column should be used to create the new columns in our reshaped DataFrame. Each unique value in the column stated here will create a column in our new DataFrame.
  • Values: Which column(s) should be used to fill the values in the cells of our DataFrame.

Let’s walk through these in an example with our stock trading data. In the example below, I use pivot to examine the closing trading price for each stock symbol over our trading window.

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