Shuffle DataFrame rows

python

dataframe

shuffling

performance

byAlex Kataev·Aug 18, 2024

To shuffle rows in a pandas DataFrame, leverage df.sample(frac=1). If you fancy consistent shuffling, add random_state=some_number.

shuffled_df = df.sample(frac=1, random_state=42) # 42, the Answer to the Ultimate Question of Life, the Universe, and Everything

For a cleaner shuffle without messing with the indices, utilize reset_index(drop=True):

shuffled_df = df.sample(frac=1, random_state=42).reset_index(drop=True) # shuffle, drop previous index, insert new

Shuffle strategies: Python's got cards up its sleeve

Different strategies can be employed to shuffle your DataFrame rows, each offering a unique flavor:

In-place shuffling: numpy.random.shuffle() shuffles numpy arrays in-place:

np.random.shuffle(df.values) # Shuffle party in the DataFrame house!

Warning: This straight-up guts your DataFrame, maintaining values but waving goodbye to axis labels!

Customized shuffling with sklearn: sklearn.utils.shuffle() lets you steer the randomness:

from sklearn.utils import shuffle
shuffled_df = shuffle(df, random_state=42) # Known fact - universe loves 42

Memory muncher alert: Shuffling large DataFrames may feast on memory. Keep an eye with some memory profiling tools.

Keeping the element of surprise under control

Reproducibility is key when dealing with randomness in data:

Master of randomness: Add random_state when shuffling to ensure repeatability.
Pinning the chaos: Prior to shuffling, set np.random.seed(some_seed) for consistent outcomes.

Efficiency: The need for speed

DataFrame size? Computing resources? Performance matters:

Time is money: Employ timeit to clock your shuffling moves.
Size doesn't matter: Different methods may offer speed but compromise on in-place shuffling or index alignment. Choose wisely.

Faithful shuffle, with a twist

Sometimes, you need shuffling with a serving of more sophisticated sample control:

Sample buffet: replace=True coupled with df.sample simulates a hearty resampling.
Partial Shuffle: Use frac=<0.0-1.0> to shuffle a fraction of your DataFrame, great for creating random smidgens of your data.