
        
        
            
                
                
                    df = pd.DataFrame({"A":["3","4"],"B":["5","6"]})
df
                
            
               A  B
0  3  5
1  4  6

Currently, the column types are as follows:


        
        
            
                
                
                    df.dtypes
                
            
            A    object
B    object
dtype: object

Using astype method

To convert column A into type int, use the Series' astype() method:


        
        
            
                
                
                    df["A"] = df["A"].astype("int")
df.dtypes
                
            
            A     int64
B    object
dtype: object

Using as_numeric method

To convert column A into type int, use the Pandas' to_numeric(~) method:


        
        
            
                
                
                    df["A"] = pd.to_numeric(df["A"])
df.dtypes
                
            
            A     int64
B    object
dtype: object

Case when conversion is not possible

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":["3#","4"]})
df
                
            
               A
0  3#
1  4

Here, the value "3#" cannot be converted into a numeric type. By default, the to_numeric(~) type will throw an error in such cases:


        
        
            
                
                
                    df["A"] = pd.to_numeric(df["A"])
                
            
            ValueError: Unable to parse string "3#" at position 0

We can map values that cannot be converted into NaN instead:


        
        
            
                
                
                    df["A"] = pd.to_numeric(df["A"], errors='coerce')
df.dtypes
                
            
            A    float64
B     object
dtype: object

Note that Pandas will only allow columns containing NaN to be of type float.

Example - converting data type of multiple columns to integer

To convert the data type of multiple columns to integer, use Pandas' apply(~) method with to_numeric(~).

Case when conversion is possible

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":["3","4"],"B":["5","6"]})
df
                
            
               A  B
0  3  5
1  4  6

Currently, the column types are as follows:


        
        
            
                
                
                    df.dtypes
                
            
            A    object
B    object
dtype: object

To convert the type of all the columns, use the DataFrame's apply(~) method:


        
        
            
                
                
                    df = df.apply(pd.to_numeric)
df.dtypes
                
            
            A    int64
B    int64
dtype: object

Here, we are iteratively applying Pandas' to_numeric(~) method to each column of the DataFrame. The to_numeric(~) method takes as argument a single column (Series) and converts its type to numeric (e.g. int or float).

Case when conversion is not possible

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":["3","4"],"B":["5#","6"]})
df
                
            
               A  B
0  3  5#
1  4  6

Here, column B cannot be converted into numeric type since 5# is not a valid number. Applying the to_numeric(~) method without arguments will result in an error:


        
        
            
                
                
                    df = df.apply(pd.to_numeric)
                
            
            ValueError: Unable to parse string "5#" at position 0

Ignoring unsuccessful columns

Instead of throwing an error, we can supply the following keyword argument to to_numeric() in order to ignore columns where the conversion is not possible:


        
        
            
                
                
                    df = df.apply(pd.to_numeric, errors='ignore')
df.dtypes
                
            
            A     int64
B    object
dtype: object

Replace with NaN for unsuccessful values

To fill values that cannot be successfully converted into the specified data type with NaN:


        
        
            
                
                
                    df = df.apply(pd.to_numeric, errors='coerce')
df
                
            
               A  B
0  3  NaN
1  4  6.0

Here, the value "5#" could not be converted into a numeric type and therefore we end up with a NaN instead. The converted data types are as follows:


        
        
            
                
                
                    df.dtypes
                
            
            A      int64
B    float64
dtype: object

Pandas DataFrame | astype method

Pandas DataFrame.astype(~) method converts the data type of the columns of a DataFrame to the specified type.

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Pandas | to_numeric method

Pandas to_numeric(~) method converts the input to a numerical type. By default, either int64 or float64 will be used.

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