How Can Pandas\' `.explode()` Method Transform List-Based Columns into Separate Rows?

Exploiting Pandas' .explode() Method to Unpack Lists into Rows

In Pandas, dealing with DataFrames containing columns of lists can be challenging when you want to have each list element represented as a separate row. This is where the .explode() method comes into play.

Pandas ≥ 0.25

For Pandas versions 0.25 and above, the game-changer is the .explode() method. It allows you to explode a list-like column into separate rows, thus converting it from its long form to a wide form.

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'trial_num': [1, 2, 3, 1, 2, 3],
    'subject': [1, 1, 1, 2, 2, 2],
    'samples': [list(np.random.randn(3).round(2)) for i in range(6)]
})

# Explode using .explode()
exploded_df = df.explode('samples')

# Reset index to be monotonically increasing (optional)
exploded_df = exploded_df.reset_index(drop=True)

print(exploded_df)

# Output:
#    subject  trial_num  sample
# 0        1          1    0.57
# 1        1          1   -0.83
# 2        1          1    1.44
# 3        1          2   -0.01
# 4        1          2    1.13
# 5        1          2    0.36
# 6        1          3    1.18
# 7        2          1   -0.08
# 8        2          1   -4.22
# 9        2          1   -2.05
# 10       2          2    0.72
# 11       2          2    0.79
# 12       2          2    0.53
# 13       2          3    0.4
# 14       2          3   -0.32
# 15       2          3   -0.13

Note that .explode() operates on a single column at a time. For columns containing mixed types (e.g., lists and scalars), .explode() behaves sensibly and preserves empty lists and NaNs.

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