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We can use sample()the shuffle method of the Pandas Dataframe object, the shuffle function from the NumPy module permutation(), and the shuffle function from the sklearn package shuffle()to shuffle the rows of a Pandas DataFrame.

pandas.DataFrame.sample()Methods to Shuffle Rows in Pandas DataFrame

pandas.DataFrame.sample() can be used to return a random sample of items starting from an axis of a DataFrame object. We need to axisset the parameter to 0 because we need to sample elements row-wise, which is axisthe default value of the parameter.

fracThe parameter determines what fraction of the total number of instances to return. If you want a random order, fracset the value of to 1.

import pandas as pd

dates = ["April-10", "April-11", "April-12", "April-13"]
fruits = ["Apple", "Papaya", "Banana", "Mango"]
prices = [3, 1, 2, 4]

df = pd.DataFrame({"Date": dates, "Fruit": fruits, "Price": prices})
print(df)

df_shuffled = df.sample(frac=1).reset_index(drop=True)
print(df_shuffled)

Output:

       Date   Fruit  Price
0  April-10   Apple      3
1  April-11  Papaya      1
2  April-12  Banana      2
3  April-13   Mango      4
       Date   Fruit  Price
3  April-13   Mango      4
2  April-12  Banana      2
0  April-10   Apple      3
1  April-11  Papaya      1

As shown above, Dataframe.shuttlethe method shuffles the rows of a Pandas DataFrame. The index of the DataFrame rows is the same as the initial index.

We can add a reset_index() method to reset DataFramethe index.

import pandas as pd

dates = ["April-10", "April-11", "April-12", "April-13"]
fruits = ["Apple", "Papaya", "Banana", "Mango"]
prices = [3, 1, 2, 4]

df = pd.DataFrame({"Date": dates, "Fruit": fruits, "Price": prices})
print(df)

df_shuffled = df.sample(frac=1).reset_index(drop=True)
print(df_shuffled)

Output:

       Date   Fruit  Price
0  April-10   Apple      3
1  April-11  Papaya      1
2  April-12  Banana      2
3  April-13   Mango      4
       Date   Fruit  Price
0  April-11  Papaya      1
1  April-13   Mango      4
2  April-10   Apple      3
3  April-12  Banana      2

Here, drop=Truethe option prevents indexthe column from being added as a new column.

numpy.random.permutation() randomly permutes Pandas DataFrame rows

We can use numpy.random.permutation() to permute the index of a DataFrame. When iloc()the randomly permuted index is used to select rows using the method, we will get randomly permuted rows.

import pandas as pd
import numpy as np

dates = ["April-10", "April-11", "April-12", "April-13"]
fruits = ["Apple", "Papaya", "Banana", "Mango"]
prices = [3, 1, 2, 4]

df = pd.DataFrame({"Date": dates, "Fruit": fruits, "Price": prices})

df_shuffled = df.iloc[np.random.permutation(df.index)].reset_index(drop=True)
print(df_shuffled)

Output:

       Date   Fruit  Price
0  April-13   Mango      4
1  April-12  Banana      2
2  April-10   Apple      3
3  April-11  Papaya      1

You may get different results when running the same code. This is because np.random.permutation()the function generates a different arrangement of numbers each time.

sklearn.utils.shuffle()Shuffle Pandas DataFrame Rows

We can also use sklearn.utils.shuffle() to shuffle the rows of a Pandas DataFrame.

import pandas as pd
import numpy as np
import sklearn

dates = ["April-10", "April-11", "April-12", "April-13"]
fruits = ["Apple", "Papaya", "Banana", "Mango"]
prices = [3, 1, 2, 4]

df = pd.DataFrame({"Date": dates, "Fruit": fruits, "Price": prices})

df_shuffled = sklearn.utils.shuffle(df)
print(df_shuffled)

Output:

       Date   Fruit  Price
3  April-13   Mango      4
0  April-10   Apple      3
1  April-11  Papaya      1
2  April-12  Banana      2

If you don't have sklearnthe package installed, you can install it using the following script:

pip install -U scikit-learn