Change data type for one or more columns in Pandas Dataframe

Method # 1: Using

# import pandas as pd

import pandas as pd

# sample data frame

df = pd.DataFrame ({

`A` : [ 1 , 2 , 3 , 4 , 5 ],

`B` : [ `a` , `b` , ` c` , `d` , ` e` ],

    `C`  : [  1.1  ,   `1.0`  ,   `1.3`  ,   2  ,   5  ]}) 
   
  # convert all columns to string  
   df   =   df.astype (  str  )  
   print   (df.dtypes)

Output:

# import pandas as pd

import pandas as pd

# sample data frame

df = pd.DataFrame ({

` A` : [ 1 , 2 , 3 , 4 , 5 ],

    `B`  : [  `a`  ,  ` b`  ,   `c`  ,  ` d`  ,   `e`  ],  
    `C`  : [  1.1  ,   `1.0`  ,  ` 1.3`  ,   2  ,   5  ]}) 
   
  # using a dictionary to convert specific columns  
   convert_dict   =   {  `A`  :   int  , 
    `C`  :   float  
   } 
    
   df   =   df.astype ( convert_dict) 
   print   (df.dtypes)

Output:

Method # 2: Using

# import pandas as pd

import pandas as pd

# sample data frame

df = pd.DataFrame ({

` A` : [ 1 , 2 , 3 , `4` , `5` ],

    ` B`  : [  `a`  ,   `b`  ,  ` c`  ,   `d`  ,  ` e`  ],

`C` : [ < code class = "value"> 1.1 , `2.1` , 3.0 , `4.1` , ` 5.1` ]})

# using the apply method

df [[ `A` , ` C` ]] = df [[ `A` , `C` ]]. apply (pd.to_numeric)

print (df.dtypes)

Output:

Method # 3: Using DataFrame.infer_objects ()
This method attempts to gently convert by inferring the data type of the object type columns. Non-object and non-reversible columns remain unchanged.

# import pandas as pd

import pandas as pd

# sample data frame

df = pd.DataFrame ({

`A` : [ 1 , 2 , 3 , 4 , 5 ],

`B` : [ ` a` , `b` , `c` , `d` , ` e` ],

`C` : [ 1.1 , 2.1 , 3.0 , 4.1 , 5.1 ]
}, dtype = `object` ) # data type conversion х df = df.infer_objects ( ) print (df.dtypes)

Output:

Change data type for one or more columns in Pandas Dataframe

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