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Pandas DataFrame | stack method

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Python
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Pandas
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DataFrame
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Sorting and Restructuring DataFrames
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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Pandas DataFrame.stack(~) method converts the specified column levels to row levels. This is the reverse of unstack(~).

Parameters

1. levellink | int or string | optional

The integer index or name(s) of the column level to convert into a row level. By default, level=-1, which means that the inner-most column level is converted.

2. dropnalink | boolean | optional

Whether or not to drop resulting rows that contain just NaN. By default, dropna=True.

Return Value

A Series or a DataFrame.

Examples

Stacking single-level DataFrames

Consider the following single-level DataFrame:

df = pd.DataFrame([[2,3],[4,5]], columns=["alice","bob"], index=["age","height"])
df
alice bob
age 2 3
height 4 5

Calling stack() on df gives:

df.stack()
age alice 2
bob 3
height alice 4
bob 5
dtype: int64

Here, note the following:

  • the return type is Series with a 2-level index.

  • the row labels and the column labels in df have merged to form a multi-index.

Stacking DataFrames with multi-level columns

Consider the following DataFrame with multi-level columns:

index = [("A", "alice"), ("A", "bob"), ("B","cathy")]
multi_index = pd.MultiIndex.from_tuples(index)
df = pd.DataFrame([[2,3,4],[5,6,7]], columns=multi_index, index=["age","height"])
df
A B
alice bob cathy
age 2 3 4
height 5 6 7

By default, level=-1, which means that the inner-most column level ([alice,bob,cathy]) will be converted into a row level:

df.stack()
A B
age alice 2.0 NaN
bob 3.0 NaN
cathy NaN 4.0
height alice 5.0 NaN
bob 6.0 NaN
cathy NaN 7.0

Note the following:

  • the inner-most column level ([alice, bob, cathy]) became a row index, and is positioned as the inner-most level.

  • stacking columns with multi-levels often yield many NaN since, for instance, no data exists about the age of alice in group B.

To specify which levels to convert, pass the level parameter like so:

df.stack(level=0)
alice bob cathy
age A 2.0 3.0 NaN
B NaN NaN 4.0
height A 5.0 6.0 NaN
B NaN NaN 7.0

Here, level=0 means that that outermost column level ([A,B]) is converted into a row level.

Specifying dropna

Consider the following DataFrame:

index = [("A", "alice"), ("A", "bob"), ("B","cathy")]
multi_index = pd.MultiIndex.from_tuples(index)
df = pd.DataFrame([[2,3,None],[5,6,7]], columns=multi_index, index=["age","height"])
df
A B
alice bob cathy
age 2 3 NaN
height 5 6 7.0

By default, dropna=True, which means that rows that contain just NaN will be removed from the result:

df.stack()
A B
age alice 2.0 NaN
bob 3.0 NaN
height alice 5.0 NaN
bob 6.0 NaN
cathy NaN 7.0

Notice how cathy's row for the age level is missing. This is because it only contains NaN.

To keep all rows, pass dropna=False like so:

df.stack(dropna=False)
A B
age alice 2.0 NaN
bob 3.0 NaN
cathy NaN NaN
height alice 5.0 NaN
bob 6.0 NaN
cathy NaN 7.0

Notice how we now have cathy's row under age.

robocat
Published by Isshin Inada
Edited by 0 others
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