Shuffle DataFrame rows

You can shuffle the rows of a data frame by indexing with a shuffled index. For this, you can eg use np.random.permutation (but np.random.choice is also a possibility):

In [12]: df = pd.read_csv(StringIO(s), sep="s+")

In [13]: df
Out[13]: 
    Col1  Col2  Col3  Type
0      1     2     3     1
1      4     5     6     1
20     7     8     9     2
21    10    11    12     2
45    13    14    15     3
46    16    17    18     3

In [14]: df.iloc[np.random.permutation(len(df))]
Out[14]: 
    Col1  Col2  Col3  Type
46    16    17    18     3
45    13    14    15     3
20     7     8     9     2
0      1     2     3     1
1      4     5     6     1
21    10    11    12     2

If you want to keep the index numbered from 1, 2, .., n as in your example, you can simply reset the index: df_shuffled.reset_index(drop=True)

You can simply use sklearn for this

from sklearn.utils import shuffle
df = shuffle(df)

TL;DR: np.random.shuffle(ndarray) can do the job.
So, in your case

np.random.shuffle(DataFrame.values)

---

DataFrame, under the hood, uses NumPy ndarray as a data holder. (You can check from DataFrame source code)

So if you use np.random.shuffle(), it would shuffle the array along the first axis of a multi-dimensional array. But the index of the DataFrame remains unshuffled.

Though, there are some points to consider.

• function returns none. In case you want to keep a copy of the original object, you have to do so before you pass to the function.
• sklearn.utils.shuffle(), as user tj89 suggested, can designate random_state along with another option to control output. You may want that for dev purposes.
• sklearn.utils.shuffle() is faster. But WILL SHUFFLE the axis info(index, column) of the DataFrame along with the ndarray it contains.

Benchmark result

between sklearn.utils.shuffle() and np.random.shuffle().

ndarray

nd = sklearn.utils.shuffle(nd)

0.10793248389381915 sec. 8x faster

np.random.shuffle(nd)

0.8897626010002568 sec

DataFrame

df = sklearn.utils.shuffle(df)

0.3183923360193148 sec. 3x faster

np.random.shuffle(df.values)

0.9357550159329548 sec

Conclusion: If it is okay to axis info(index, column) to be shuffled along with ndarray, use sklearn.utils.shuffle(). Otherwise, use np.random.shuffle()

used code

import timeit
setup = '''
import numpy as np
import pandas as pd
import sklearn
nd = np.random.random((1000, 100))
df = pd.DataFrame(nd)
'''

timeit.timeit('nd = sklearn.utils.shuffle(nd)', setup=setup, number=1000)
timeit.timeit('np.random.shuffle(nd)', setup=setup, number=1000)
timeit.timeit('df = sklearn.utils.shuffle(df)', setup=setup, number=1000)
timeit.timeit('np.random.shuffle(df.values)', setup=setup, number=1000)

pythonbenchmarking

shuffle the pandas data frame by taking a sample array in this case index and randomize its order then set the array as an index of data frame. Now sort the data frame according to index. Here goes your shuffled dataframe

import random
df = pd.DataFrame({"a":[1,2,3,4],"b":[5,6,7,8]})
index = [i for i in range(df.shape[0])]
random.shuffle(index)
df.set_index([index]).sort_index()

output

    a   b
0   2   6
1   1   5
2   3   7
3   4   8

Insert you data frame in the place of mine in above code .

Here is another way to do this:

df_shuffled = df.reindex(np.random.permutation(df.index))

(I don’t have enough reputation to comment this on the top post, so I hope someone else can do that for me.) There was a concern raised that the first method:

df.sample(frac=1)

It makes a deep copy or just changed the dataframe. I ran the following code:

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

and my results were:

0x1f8a784d400
0x1f8b9d65e10
0x1f8b9d65b70

which means the method is not returning the same object, as was suggested in the last comment. So this method does indeed make a shuffled copy.

Here is another way:

df['rnd'] = np.random.rand(len(df))
df = df.sort_values(by='rnd', inplace=True).drop('rnd', axis=1)

What is also useful, if you use it for Machine_learning and want to separate always the same data, you could use:

df.sample(n=len(df), random_state=42)

This makes sure, that you keep your random choice always replicable

Following could be one of ways:

dataframe = dataframe.sample(frac=1, random_state=42).reset_index(drop=True)

where

frac=1 means all rows of a data frame

random_state=42 means keeping the same order in each execution

reset_index(drop=True) means reinitialize index for randomized dataframe

Shuffle the DataFrame using sample() by passing the frac parameter.
Save the shuffled DataFrame to a new variable.

new_variable = DataFrame.sample(frac=1)

I propose this:

for x in df.columns:
    np.random.seed(42);
    np.random.shuffle(df[x].values)

With my test with a column of arbitrary length strings (with dtype: object), it was 30x faster than @haku’s answer, presumably because it avoids creating a copy which may be expensive.

My variant was about 3x faster than the accepted @Kris’es answer which also does not seem to avoid a copy (based on RES column in Linux top).

Without numpy/sklean 🙂 and in case you want to shuffle all values, but keep rows & columns names in place.

df_c = df.copy()
df_c.iloc[:,:] = df_c.sample(frac=1,random_state=123,ignore_index=True)