In this post, we’ll see different ways to Convert Floats to Strings in Pandas Dataframe? Pandas Dataframe provides the freedom to change the data type of column values. We can change them from Integers to Float type, Integer to String, String to Integer, Float to String, etc.
There are three methods to convert Float to String:
Method 1: Using DataFrame.astype().
Syntax :
DataFrame.astype(dtype, copy=True, errors=’raise’, **kwargs)
This is used to cast a pandas object to a specified dtype. This function also provides the capability to convert any suitable existing column to categorical type.
Example 1: Converting one column from float to string.
Python3
# Import pandas library import pandas as pd # initialize list of lists 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]] # Create the pandas DataFrame df = pd.DataFrame(data, columns = ['Name', 'Age', 'Marks'], index = ['a', 'b', 'c', 'd', 'e', 'f']) # lets find out the data type # of 'Marks' columnprint (df.dtypes) |
Output:
Now, we change the data type of column ‘Marks’ from ‘float64’ to ‘object’.
Python3
# Now we will convert it from # 'float' to 'String' type. df['Marks'] = df['Marks'].astype(str) print() # lets find out the data# type after changingprint(df.dtypes) # print dataframe. df |
Output:
Example 2: Converting more than one column from float to string.
Python3
# Import pandas library import pandas as pd # initialize list of lists 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]] # Create the pandas DataFrame df = pd.DataFrame(data, columns = ['Name', 'Age', 'Marks', 'Accuracy'], index = ['a', 'b', 'c', 'd', 'e', 'f']) # lets find out the data type # of 'Age' and 'Accuracy' columnsprint (df.dtypes) |
Output:
Now, we change the data type of columns ‘Accuracy‘ and ‘Age‘ from ‘float64’ to ‘object’.
Python3
# Now Pass a dictionary to # astype() function which contains # two columns and hence convert them# from float to string typedf = df.astype({"Age": 'str', "Accuracy": 'str'})print() # lets find out the data # type after changingprint(df.dtypes) # print dataframe. df |
Output:
Method 2: Using Series.apply().
Syntax :
DataFrame.apply(func, axis=0, raw=False, result_type=None, args=(), **kwds)
This method allows the users to pass a function and apply it on every single value of the Pandas series.
Example: Converting column of a Dataframe from float to string.
Python3
# Import pandas library import pandas as pd # initialize list of lists 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]] # Create the pandas DataFrame df = pd.DataFrame(data, columns = ['Name', 'Age', 'Percentage', 'Accuracy'], index = ['a', 'b', 'c', 'd', 'e', 'f']) # lets find out the data # type of 'Percentage' columnprint (df.dtypes) |
Output:
Now, we change the data type of column ‘Percentage‘ from ‘float64′ to ”object’.
Python3
# Now we will convert it from # 'float' to 'string' type. df['Percentage'] = df['Percentage'].apply(str) print() # lets find out the data# type after changingprint(df.dtypes) # print dataframe. 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 float to string.
Python3
# Import pandas library import pandas as pd # initialize list of lists 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]] # Create the pandas DataFrame df = pd.DataFrame(data, columns = ['Name', 'Age', 'Percentage', 'Accuracy'], index = ['a', 'b', 'c', 'd', 'e', 'f']) # lets find out the data# type of 'Age' columnprint (df.dtypes) |
Output:
Now, we change the data type of column ‘Age‘ from ‘float64′ to ”object’.
Python3
# Now we will convert it from 'float' to 'string' type. # using DataFrame.map(str) functiondf['Age'] = df['Age'].map(str) print() # lets find out the data type after changingprint(df.dtypes) # print dataframe. df |
Output:
