Reshaping pandas Objects

Sometimes the data you receive is inconvenient — all of it can be useful, but you might have received it with too many redundant columns or a column that would be more useful as an index. To accomodate this, pandas has several functions and practices that allow you to reshape your data.


The pivot method is the main method for reshaping, taking arguments for index, columns, and values, whose applications are very straightforward.

  • index: the original DataFrame column whose values will become the pivoted DataFrame indices.

  • columns: the original DataFrame column whose values will become the pivoted DataFrame column names.

  • values: the original DataFrame column whose values will occupy the pivoted DataFrame values.

Given the sample DataFrame below, let’s use pivot to set location as our index values, week_of_season as the new columns, and attendance as our values:

import pandas as pd

list_1 = ['Wisconsin', 'IU', 'Rutgers', 'Michigan State', 'Ohio State']
list_2 = ['home', 'away', 'home', 'away', 'home']
list_3 = [85, 78, 90, 75, 74]
list_4 = [500, 1000, 430, 4800, 10000]
list_5 = [1, 2, 3, 4, 5]

myDF = pd.DataFrame(zip(list_1, list_2, list_3, list_4, list_5),
                    columns=['opponent', 'location', 'temp', 'attendance', 'week_of_season'])

print(myDF.pivot(index='location', columns='week_of_season', values='attendance'))
week_of_season      1       2      3       4        5
away              NaN  1000.0    NaN  4800.0      NaN
home            500.0     NaN  430.0     NaN  10000.0

pivot is easy to use and manipulate, as any column can take the place of index, columns, and values depending on your application.

A notable feature is that values can be omitted from the function call, resulting in a higher-order column name for each of the remaining columns, then your values specified in the columns parameter. We’ll repeat the above example, now making week_of_season our index and location our column values.

football_pivot = myDF.pivot(index='week_of_season', columns='location')
                      opponent              temp       attendance
location                  away        home  away  home       away     home
1                          NaN   Wisconsin   NaN  85.0        NaN    500.0
2                           IU         NaN  78.0   NaN     1000.0      NaN
3                          NaN     Rutgers   NaN  90.0        NaN    430.0
4               Michigan State         NaN  75.0   NaN     4800.0      NaN
5                          NaN  Ohio State   NaN  74.0        NaN  10000.0

Cool! The benefit here is that you have a correctly-shaped DataFrame that has all the values you (or your partners) might need in the future. Selecting one column uses standard indexing from this point:

location                  away        home
1                          NaN   Wisconsin
2                           IU         NaN
3                          NaN     Rutgers
4               Michigan State         NaN
5                          NaN  Ohio State


The features of melt are split into identifier variables and measured variables:

  • The identifiers are not indices, but they are static parts of your data that you want to maintain, such as names or locations. These are usually duplicated for each measured variable you have.

  • Measured variables are all the non-identifier columns. They are melted into the column variable, which contains the names of each measured column, and value, each column’s corresponding value for that identifier.

We’ll show an example before we get too wordy:

import pandas as pd

hail = pd.DataFrame(
        "first": ["Purdue", "Dr"],
        "last": ["Pete", "Ward"],
        "topic": ["Cheerleading", "Progeny"],
        "score": [100, 7],

    first  last         topic  score
0  Purdue  Pete  Cheerleading    100
1      Dr  Ward       Progeny      7
print(hail.melt(id_vars=['first', 'last']))
    first  last variable         value
0  Purdue  Pete    topic  Cheerleading
1      Dr  Ward    topic       Progeny
2  Purdue  Pete    score           100
3      Dr  Ward    score             7

We can interpret melt as an anti-pivot — we’re minimizing the number of columns by putting more data in rows, resulting in repetition of our identifier variable(s) for each measured variable.

In addition to selecting your identifier variables, you can select your measured variables using value_vars=, change "variable" to something else using var_name=, and change "value" to something else using value_name=.