Method # 1: Using Output: Output: Method # 2: Using Output: Output: Latest questions # 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)
# 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)
# 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)
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)
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