How To Change Float To String In Python

In this position, we'll see different shipway to Convert Floats to String section in Pandas Dataframe? Pandas Dataframe provides the freedom to alteration the information type of column values. We can change them from Integers to Float type, Integer to String, Draw to Integer, Float to String, etc.

There are ternion methods to exchange Float to Strand:

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Method 1: Using DataFrame.astype().

Syntax :

DataFrame.astype(dtype, copy=True, errors='call down', **kwargs)

This is used to cast a pandas object to a specified dtype. This function also provides the capacity to convert any suitable existing column to categorical type.

Example 1: Converting one column from float to string.

Python3

              import              pandas as pd            
              data                            =              [[              'Harvey'              ,                            10              ,                            45.25              ], [              'Carson'              ,                            15              ,                            54.85              ],            
                            [              'juli'              ,                            14              ,                            87.21              ], [              'Ricky'              ,                            20              ,                            45.23              ],            
                            [              'Gregory'              ,                            21              ,                            77.25              ], [              'Jessie'              ,                            16              ,                            95.21              ]]            
              df                            =              pd.DataFrame(data, columns                            =              [              'Name'              ,                            'Age'              ,                            'Marks'              ],            
                            index                            =              [              'a'              ,                            'b'              ,                            'c'              ,                            'd'              ,                            'e'              ,                            'f'              ])            
              print              (df.dtypes)            

Output:

Now, we change the information character of column 'Marks' from 'float64' to 'object'.

Python3

              df[              'Marks'              ]                            =              df[              'Marks'              ].astype(              str              )            
              print              ()            
              print              (df.dtypes)            
              df            

Output:

Lesson 2: Converting more than ane column from float to string.

Python3

              import              pandas as pd            
              data                            =              [[              'Harvey.'              ,                            10.5              ,                            45.25              ,                            95.2              ], [              'Carson'              ,                            15.2              ,                            54.85              ,                            50.8              ],            
                            [              'juli'              ,                            14.9              ,                            87.21              ,                            60.4              ], [              'Ricky'              ,                            20.3              ,                            45.23              ,                            99.5              ],            
                            [              'Gregory'              ,                            21.1              ,                            77.25              ,                            90.9              ], [              'Jessie'              ,                            16.4              ,                            95.21              ,                            10.85              ]]            
              df                            =              pd.DataFrame(information, columns                            =              [              'Name'              ,                            'Geezerhoo'              ,                            'Marks'              ,                            'Accuracy'              ],            
                            index                            =              [              'a'              ,                            'b'              ,                            'c'              ,                            'd'              ,                            'e'              ,                            'f'              ])            
              print              (df.dtypes)            

Output:

Now, we change the data type of columns 'Accuracy' and 'Age' from 'float64' to 'object'.

Python3

              df                            =              df.astype({              "Age"              :                            'str'              ,                            "Accuracy"              :                            'str'              })            
              print              ()            
              print              (df.dtypes)            
              df            

Yield:

Method 2: Using Series.apply().

Syntax :

DataFrame.apply(func, axis=0, raw=Delusive, result_type=None, args=(), **kwds)

This method acting allows the users to make it a function and apply it on every single value of the Pandas series.

Deterrent example: Converting column of a Dataframe from swim to string.

Python3

              import              pandas as pd            
              data                            =              [[              'Harvey.'              ,                            10.5              ,                            45.25              ,                            95.2              ], [              'Carson'              ,                            15.2              ,                            54.85              ,                            50.8              ],            
                            [              'juli'              ,                            14.9              ,                            87.21              ,                            60.4              ], [              'Ricky'              ,                            20.3              ,                            45.23              ,                            99.5              ],            
                            [              'Gregory'              ,                            21.1              ,                            77.25              ,                            90.9              ], [              'Jessie'              ,                            16.4              ,                            95.21              ,                            10.85              ]]            
              df                            =              pd.DataFrame(information, columns                            =              [              'Diagnose'              ,                            'Age'              ,                            'Portion'              ,                            'Accuracy'              ],            
                            index                            =              [              'a'              ,                            'b'              ,                            'c'              ,                            'd'              ,                            'e'              ,                            'f'              ])            
              print              (df.dtypes)            

Output:

Now, we change the data type of column 'Part' from 'float64′ to "object'.

Python3

              df[              'Share'              ]                            =              df[              'Percentage'              ].              put on              (              str              )            
              print              ()            
              print              (df.dtypes)            
              df            

Output:

Method 3: Using Series.map().

Syntax:

Series.map(arg, na_action=None)

This method is used to map values from two series having one column same.

Example: Converting column of a dataframe from blow to string.

Python3

              import              pandas as palladium            
              data                            =              [[              'Harvey.'              ,                            10.5              ,                            45.25              ,                            95.2              ], [              'Carson'              ,                            15.2              ,                            54.85              ,                            50.8              ],            
                            [              'juli'              ,                            14.9              ,                            87.21              ,                            60.4              ], [              'Ricky'              ,                            20.3              ,                            45.23              ,                            99.5              ],            
                            [              'Gregory'              ,                            21.1              ,                            77.25              ,                            90.9              ], [              'Jessie'              ,                            16.4              ,                            95.21              ,                            10.85              ]]            
              df                            =              pd.DataFrame(information, columns                            =              [              'Name'              ,                            'Age'              ,                            'Percentage'              ,                            'Truth'              ],            
                            index                            =              [              'a'              ,                            'b'              ,                            'c'              ,                            'd'              ,                            'e'              ,                            'f'              ])            
              print              (df.dtypes)            

Output signal:

Now, we change the data type of pillar 'Age' from 'float64′ to "object'.

Python3

              df[              'Age'              ]                            =              df[              'Age'              ].              map              (              str              )            
              print              ()            
              print              (df.dtypes)            
              df            

Output: