Pandas DataFrame unstack() method with examples

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The unstack() method in pandas converts the specified row levels to column levels. This is the opposite of stack() method.

syntax –

DataFrame.unstack(level=- 1, fill_value=None)

level – 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.

fill_value – Replace NaN with this value if the unstack produces missing values.

Examples –

1 . Unstacking a DataFrames with Single-Level Index –

Let’s create a dataframe that has single level rows.

df = pd.DataFrame([[60, 5],[70, 6],[50, 4]],

Calling unstack on this dataframe will create a multi-level index.


2 . Unstacking a DataFrame with Multi-Level Index –

index = [('A', 'Max'), ('A','Steve'),
         ('B','Dustin'), ('B','Lucas')]

multi_index = pd.MultiIndex.from_tuples(index)
df = pd.DataFrame({'Weight':[60, 70, 50, 55], 
                   'height':[5, 6, 4, 5]},

By Default the Level= -1 which means the innermost level [Max, steve, Dustin, Lucas] will be converted to columns.


The outermost index [A, B] is called the level 0. The index after that [Max, Steve, Dustin, Lucas] is called level 1. The index after that is called level 2. So, for example if we want to convert the outermost index [A, B] to columns then we need to pass level=0.


3 . Fill Value –

To fill NaN values with some other values use the fill_value parameter.

By Default when fill_value=None –


When fill_value has some other values –


Related Posts –

  1. Pandas DataFrame stack() method with examples

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