Let’s see the different ways of changing Data Type for one or more columns in Pandas Dataframe.
Method #1: Using DataFrame.astype()
We can pass any Python, Numpy or Pandas datatype to change all columns of a dataframe to that type, or we can pass a dictionary having column names as keys and datatype as values to change type of selected columns.
# 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) |
Output:
# 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) |
Output:
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) |
Output:
Method #3: Using DataFrame.infer_objects()
This method attempts soft-conversion by inferring data type of ‘object’-type columns. Non-object and unconvertible columns are left unchanged.
# 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) |
Output:
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