Note that decimals=1 would mean that values like 1.52 will be rounded to 1.5. In contrast, decimals=-1 would round values like 13 to 10 (nearest 10th).

As for data types dict and Series, the key and indexes will be the names of the columns you want to round, with the corresponding values being the number of decimals to round to. See examples below for clarification.

By default, decimals=0, which means that the values are rounded to the nearest integer.

WARNING

Numbers ending in 5 will be rounded down. For instance, numbers such as 2.5 and 3.45 will be rounded down to 2 and 3.4, respectively, instead of rounding up.

Return Value

A DataFrame with its values rounded according to the supplied parameter.

Examples

Rounding all values to nearest integer

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":[1.05,2.42],"B":[3.45,4.9]})
df
                
            
               A     B
0  1.05  3.45
1  2.42  4.90

To round to the nearest integer, just call round() directly:


        
        
            
                
                
                    df.round()
                
            
               A    B
0  1.0  3.0
1  2.0  5.0

Rounding all values to the nearest 10th

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":[4,6],"B":[12,18]})
df
                
            
               A  B
0  4  12
1  6  18

To round to the nearest 10th, supply -1 as the argument:


        
        
            
                
                
                    df.round(-1)
                
            
               A   B
0  0   10
1  10  20

Rounding all values to the 1st decimal place

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":[1.07,2.42],"B":[3.45,4.9]})
df
                
            
               A     B
0  1.07  3.45
1  2.42  4.90

To round to the 1st decimal place, supply 1 as the argument:


        
        
            
                
                
                    df.round(1)
                
            
               A    B
0  1.1  3.4
1  2.4  4.9

Notice how 3.45 was rounded down to 3.4 instead of rounding up to 3.5.

Rounding certain columns only

Instead of providing an int, we can provide either a dict or a Series to round certain columns.

Passing in Dict

When providing a dict, the key must be the column name, while the value must be the decimal place to round to.

As an example, consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":[4.2,6.6],"B":[12.4,18.9]})
df
                
            
               A    B
0  4.2  12.4
1  6.6  18.9

To round only column A to the nearest integer, and leave column B intact:


        
        
            
                
                
                    df.round({"A":0})
                
            
               A    B
0  4.0  12.4
1  7.0  18.9

Notice how column B was left as is.

Passing in Series

Similarly, when providing a Series, the indexes must be the column name, while the value must be the decimal place to round to.

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A": [4.2,6.6], "B": [12.4,18.9]})
df
                
            
               A    B
0  4.2  12.4
1  6.6  18.9

To round only column A to the nearest integer, and leave column B intact:


        
        
            
                
                
                    decimals = pd.Series([0], index=["A"])
df.round(decimals)
                
            
               A    B
0  4.0  12.4
1  7.0  18.9

Notice how column B was left as is.

Published by Isshin Inada

Edited by 0 others

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Official Pandas Documentation

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.round.html

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