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Pandas DataFrame | unstack method

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

Parameters

1. levellink | int or string or list of such | optional

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

2. fill_valuelink | int or string or dict | optional

The value to fill NaN in the resulting Series/DataFrame. Note that NaN in the original DataFrame will not be filled - only those that appear due to this method will be filled. By default, the NaN is left as is.

Return Value

A Series or a DataFrame.

Examples

Unstacking single-level DataFrames

Consider the following single-level DataFrame:

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

Calling unstack() on df gives:

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

Here, note the following:

  • the return type is Series, with two levels.

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

Unstacking DataFrames with multi-level rows

Consider the following DataFrame with multi-level rows:

index = [("A","alice"), ("A","bob"),("B","cathy"),("B","david")]
multi_index = pd.MultiIndex.from_tuples(index)
df = pd.DataFrame({"age":[2,3,4,5],"height":[6,7,8,9]}, index=multi_index)
df
age height
A alice 2 6
bob 3 7
B cathy 4 8
david 5 9

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

df.unstack()
age height
alice bob cathy david alice bob cathy david
A 2.0 3.0 NaN NaN 6.0 7.0 NaN NaN
B NaN NaN 4.0 5.0 NaN NaN 8.0 9.0

Note the following:

  • the inner-most row level ([alice, bob, cathy, david]) became a column level, 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.unstack(level=0)
age height
A B A B
alice 2.0 NaN 6.0 NaN
bob 3.0 NaN 7.0 NaN
cathy NaN 4.0 NaN 8.0
david NaN 5.0 NaN 9.0

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

Specifying fill_value

By default, fill_value=None, which means that NaN in the resulting Series/DataFrame is left as is.

To fill all NaN with a value instead, pass in fill_value like so:

df.unstack(level=0, fill_value="@")
age height
A B A B
alice 2 @ 6 @
bob 3 @ 7 @
cathy @ 4 @ 8
david @ 5 @ 9

Note that NaN that pre-existed in the original DataFrame will not be filled - only those caused by this unstacking process will be filled by fill_value.

robocat
Published by Isshin Inada
Edited by 0 others
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