Change Data Type of columns in Pandas Dataframe

Method #1: Using DataFrame.astype()

# importing pandas as pd 
import pandas as pd 

# sample dataframe 
df = pd.DataFrame({ 
  'A': [1, 2, 3, 4, 5], 
  'B': ['a', 'b', 'c', 'd', 'e'], 
  'C': [1.1, '1.0', '1.3', 2, 5] }) 

# converting all columns to string type 
df = df.astype(str) 
print(df.dtypes) 
# importing pandas as pd 
import pandas as pd 

# sample dataframe 
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 dictionary to convert specific columns 
convert_dict = {'A': int, 
        'C': float
      } 

df = df.astype(convert_dict) 
print(df.dtypes) 

Method #2: Using DataFrame.apply()

We can pass pandas.to_numeric, pandas.to_datetime and pandas.to_timedelta as argument to apply() function to change the datatype of one or more columns to numeric, datetime and timedelta respectively.

# importing pandas as pd 
import pandas as pd 

# sample dataframe 
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'] }) 

# using apply method 
df[['A', 'C']] = df[['A', 'C']].apply(pd.to_numeric) 
print(df.dtypes) 

Method #3: Using DataFrame.infer_objects()

# importing pandas as pd 
import pandas as pd 

# sample dataframe 
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') 

# converting datatypes 
df = df.infer_objects() 
print(df.dtypes) 

References
https://www.geeksforgeeks.org/change-data-type-for-one-or-more-columns-in-pandas-dataframe/
https://towardsdatascience.com/my-pandas-cheat-sheet-b71437ab26f

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