Randomly shuffling rows in a Pandas DataFrame is a frequent operation in data science, crucial for tasks like creating training and testing datasets, random sampling, or simply randomizing data for analysis. This article explores three efficient methods for achieving this, highlighting their strengths and weaknesses.
Table of Contents
Pandas sample() Method
The Pandas sample() method offers a user-friendly approach to shuffling DataFrame rows. By setting the frac parameter to 1, you obtain a completely randomized order of the original DataFrame.
import pandas as pd
# Sample DataFrame
data = {'col1': [1, 2, 3, 4, 5], 'col2': ['A', 'B', 'C', 'D', 'E']}
df = pd.DataFrame(data)
# Shuffle using sample()
shuffled_df = df.sample(frac=1, random_state=42) # random_state for reproducibility
print("Original DataFrame:n", df)
print("nShuffled DataFrame:n", shuffled_df)
The random_state argument is vital for reproducibility. Specifying an integer ensures consistent shuffling across multiple runs. Omitting it will result in different shuffles each time.
NumPy permutation() Function
NumPy’s permutation() function generates a random permutation of indices. This approach is generally faster than sample(), especially for large DataFrames, as it operates directly on NumPy arrays which are more efficient.
import pandas as pd
import numpy as np
# Sample DataFrame
data = {'col1': [1, 2, 3, 4, 5], 'col2': ['A', 'B', 'C', 'D', 'E']}
df = pd.DataFrame(data)
# Shuffle using numpy.random.permutation()
shuffled_indices = np.random.permutation(len(df))
shuffled_df = df.iloc[shuffled_indices]
print("Original DataFrame:n", df)
print("nShuffled DataFrame:n", shuffled_df)
For reproducible results, use np.random.seed(42) before calling np.random.permutation().
Scikit-learn shuffle() Function
Scikit-learn’s shuffle() function is particularly beneficial when shuffling a DataFrame alongside a related array (e.g., labels in a supervised learning setting). It ensures that the DataFrame and array remain synchronized after shuffling.
import pandas as pd
from sklearn.utils import shuffle
# Sample DataFrame
data = {'col1': [1, 2, 3, 4, 5], 'col2': ['A', 'B', 'C', 'D', 'E']}
df = pd.DataFrame(data)
# Shuffle DataFrame and a separate array (if needed)
shuffled_df, _ = shuffle(df, random_state=42) # underscore ignores the second return value
print("Original DataFrame:n", df)
print("nShuffled DataFrame:n", shuffled_df)
Like the previous methods, random_state controls reproducibility. The underscore _ discards the second return value (which would be a shuffled array if one were provided).
Conclusion: Each method effectively shuffles DataFrame rows. Pandas sample() is the most intuitive, while NumPy’s permutation() often offers superior performance for larger datasets. Scikit-learn’s shuffle() is ideal for simultaneous shuffling of a DataFrame and a corresponding array. Select the method best suited to your needs and always utilize random_state for reproducible results.