How to Unnest List-Containing Columns in Pandas DataFrames?

How to Unnest (Explode) a Column in a Pandas DataFrame, into Multiple Rows

In pandas, you may encounter situations where a column contains lists or objects as elements. To transform such a column into individual rows, a process known as "unnesting" or "exploding" is necessary. This allows you to visualize and analyze data more effectively.

Problem:

Consider a DataFrame where one of the columns, 'B', contains lists:

df = pd.DataFrame({'A': [1, 2], 'B': [[1, 2], [1, 2]]})

   A       B
0  1  [1, 2]
1  2  [1, 2]

Expected Output:

The desired output is a DataFrame where each element of the 'B' column is represented as a separate row:

   A  B
0  1  1
1  1  2
3  2  1
4  2  2

Solution:

Method 1: Explode Function

Starting with Pandas version 0.25, you can use the pandas.DataFrame.explode function for unnesting. This function efficiently explodes specific columns, creating new rows for each list element.

df.explode('B')

   A  B
0  1  1
1  1  2
0  2  1
1  2  2

Method 2: Apply pd.Series

Another approach is to combine the apply function with pd.Series. This method processes each row of the 'B' column and splits its elements into separate Series objects.

df.set_index('A').B.apply(pd.Series).stack().reset_index(level=0).rename(columns={0:'B'})

Method 3: DataFrame Constructor

Alternatively, you can use the DataFrame constructor to reshape the data. This involves repeating the row indices to match the number of elements in the lists and concatenating them into a single column.

df = pd.DataFrame({'A':df.A.repeat(df.B.str.len()), 'B':np.concatenate(df.B.values)})

Method 4: Reindex or loc

Using reindex or loc allows you to expand the DataFrame to accommodate the exploded values. Fill the missing values with the elements from the 'B' column.

df.reindex(df.index.repeat(df.B.str.len())).assign(B=np.concatenate(df.B.values))

Method 5: List Comprehension

A concise method involves creating a list of lists using list comprehension and then converting it into a DataFrame.

pd.DataFrame([[x] + [z] for x, y in df.values for z in y],columns=df.columns)

Method 6: Numpy

For performance-intensive scenarios, numpy offers vectorized operations. This method reshapes the data using np.dstack and creates a new DataFrame.

newvalues=np.dstack((np.repeat(df.A.values,list(map(len,df.B.values))),np.concatenate(df.B.values)))
pd.DataFrame(data=newvalues[0],columns=df.columns)

Method 7: Itertools

Utilizing the itertools package, you can iterate through the elements and combine them to create a new DataFrame.

from itertools import cycle, chain
l=df.values.tolist()
l1=[list(zip([x[0]], cycle(x[1])) if len([x[0]]) > len(x[1]) else list(zip(cycle([x[0]]), x[1]))) for x in l]
pd.DataFrame(list(chain.from_iterable(l1)),columns=df.columns)

Generalizing to Multiple Columns:

To extend these methods to multiple columns, you can define a custom function that takes the column names as input and performs the unnesting operation.

def unnesting(df, explode):
    idx = df.index.repeat(df[explode[0]].str.len())
    df1 = pd.concat([pd.DataFrame({x: np.concatenate(df[x].values)}) for x in explode], axis=1)
    df1.index = idx

    return df1.join(df.drop(explode, 1), how='left')

Column-Wise Unnesting:

If you want to "unnest" horizontally, meaning expanding elements in a row, you can use the DataFrame constructor.

df.join(pd.DataFrame(df.B.tolist(),index=df.index).add_prefix('B_'))

Conclusion:

These methods provide flexible options for unnesting data in pandas DataFrames. Choose the approach that best suits your performance and readability requirements.

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