Randomly Shuffle DataFrame Rows in Pandas

You can use the following methods to shuffle DataFrame rows:

Using pandas

 
pandas.DataFrame.sample()

Using numpy

 
numpy.random.permutation()

Using sklearn

 
sklearn.utils.shuffle()

Lets create a DataFrame..

 
import pandas as pd
import numpy as np
df = pd.DataFrame()
df['Name'] = ['John', 'Doe', 'Bill','Jim','Harry','Ben']
df['TotalMarks'] = [82, 38, 63,22,55,40]
df['Grade'] = ['A', 'E', 'B','E','C','D']
df['Promoted'] = [True, False,True,False,True,True]
df

 
    Name  TotalMarks Grade  Promoted
0   John          82     A      True
1    Doe          38     E     False
2   Bill          63     B      True
3    Jim          22     E     False
4  Harry          55     C      True
5    Ben          40     D      True

pandas.DataFrame.sample()

Shuffling the rows of the Pandas DataFrame using the sample() method with the parameter frac, The frac argument specifies the fraction of rows to return in the random sample.

 
df.sample(frac=1)

 
    Name  TotalMarks Grade  Promoted
3    Jim          22     E     False
0   John          82     A      True
5    Ben          40     D      True
1    Doe          38     E     False
2   Bill          63     B      True
4  Harry          55     C      True

Argument frac=1 means return all rows.

If you wish to shuffle your dataframe in-place and reset the index, you could do e.g.

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

numpy.random.permutation()

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

 
    Name  TotalMarks Grade  Promoted
0    Jim          22     E     False
1   John          82     A      True
2    Ben          40     D      True
3    Doe          38     E     False
4   Bill          63     B      True
5  Harry          55     C      True

Here, specifying drop=True prevents .reset_index from creating a column containing the old index entries.

sklearn.utils.shuffle()

 
from sklearn.utils import shuffle
shuffle(df)

 
    Name  TotalMarks Grade  Promoted
5    Ben          40     D      True
4  Harry          55     C      True
1    Doe          38     E     False
3    Jim          22     E     False
0   John          82     A      True
2   Bill          63     B      True

Next : Import multiple csv files into one pandas DataFrame