How to round the values of a pandas DataFrame ?

Python solutions for rounding the values of a pandas DataFrame

Synthetic data

To start, let's generate a DataFrame using synthetic data:

import pandas as pd
import numpy as np

data = np.random.random_sample((5,3))

df = pd.DataFrame(data=data, columns=['A','B','C'])

print(df)

The code displayed above will generate for example:

A         B         C
0  0.105679  0.874900  0.686569
1  0.418339  0.858791  0.368795
2  0.728953  0.806365  0.405427
3  0.699630  0.911417  0.940126
4  0.906818  0.306024  0.478962

Using the pandas round() method

Round all columns

When working with Pandas DataFrames, the data can easily be rounded using the round() method. The round() method takes as argument the number of decimal.

For example, if we want to round the values down to two decimal place, then the syntax would be:

df.round(2)

With the above code, you'll get:

A     B     C
0  0.11  0.87  0.69
1  0.42  0.86  0.37
2  0.73  0.81  0.41
3  0.70  0.91  0.94
4  0.91  0.31  0.48

Round values of a given column

    df_new = df.copy()
    df_new['B'] = df['B'].round(2)

    print(df_new)

gives

A     B         C
0  0.105679  0.87  0.686569
1  0.418339  0.86  0.368795
2  0.728953  0.81  0.405427
3  0.699630  0.91  0.940126
4  0.906818  0.31  0.478962

Round multiple columns

Another example with two columns

    df_new = df.copy()
    df_new[['A','C']] = df[['A','C']].round(2)

    print(df_new)

gives

A         B     C
0  0.11  0.874900  0.69
1  0.42  0.858791  0.37
2  0.73  0.806365  0.41
3  0.70  0.911417  0.94
4  0.91  0.306024  0.48

Round is not working

If your round operations are not yielding the desired results, try changing the type of DataFrame column first:

df = df.astype('float64')

Using numpy around

Another solution

data = df.to_numpy()

data = np.around(data,2)

df_new = pd.DataFrame(data=data, columns=['A','B','C'])

print(df_new)

With the above code, you'll get:

A     B     C
0  0.11  0.87  0.69
1  0.42  0.86  0.37
2  0.73  0.81  0.41
3  0.70  0.91  0.94
4  0.91  0.31  0.48

References