In this article, we’ll look at different ways in which we can convert a string to a float in a pandas dataframe. Now, let’s create a Dataframe with ‘Year’ and ‘Inflation Rate’ as a column.
Python3
# importing pandas library import pandas as pd # dictionary Data = {'Year': ['2016', '2017', '2018', '2019'], 'Inflation Rate': ['4.47', '5', '5.98', '4.1']} # create a dataframe df = pd.DataFrame(Data) # show the dataframe print (df) # show the datatypes print(df.dtypes) |
Output:
Method 1: Using DataFrame.astype().
The method is used to cast a pandas object to a specified dtype.
Syntax: DataFrame.astype(self: ~ FrameOrSeries, dtype, copy: bool = True, errors: str = ‘raise’)
Returns: casted: type of caller
Example: In this example, we’ll convert each value of ‘Inflation Rate’ column to float.
Python3
# importing pandas library import pandas as pd # dictionary Data = {'Year': ['2016', '2017', '2018', '2019'], 'Inflation Rate': ['4.47', '5', '5.98', '4.1']} # create a dataframe df = pd.DataFrame(Data) # converting each value # of column to a string df['Inflation Rate'] = df['Inflation Rate'].astype(float) # show the dataframe print(df) # show the datatypes print (df.dtypes) |
Output:
Method 2: Using pandas.to_numeric() function.
The function is used to convert the argument to a numeric type.
Syntax: pandas.to_numeric(arg, errors=’raise’, downcast=None)
Returns: numeric if parsing succeeded. Note that the return type depends on the input. Series if Series, otherwise ndarray.
Example 1: In this example, we’ll convert each value of ‘Inflation Rate’ column to float.
Code:
Python3
# importing pandas library import pandas as pd # creating a dictionary Data = {'Year': ['2016', '2017', '2018', '2019'], 'Inflation Rate': ['4.47', '5', '5.98', '4.1']} # create a dataframe df = pd.DataFrame(Data) # converting each value of column to a string df['Inflation Rate'] = pd.to_numeric(df['Inflation Rate']) # show the dataframe print(df) # show the data types print (df.dtypes) |
Output:
Example 2: Sometimes, we may not have a float value represented as a string. So, pd.to_numeric() function will show an error. To remove this error, we can use errors=’coerce’, to convert the value at this position to be converted to NaN.
Code:
Python3
# importing pandas as pd import pandas as pd # dictionary Data = {'Year': ['2016', '2017', '2018', '2019'], 'Inflation Rate': ['4.47', '5', 'No data', '4.1']} # create a dataframe df = pd.DataFrame(Data) # converting each value of column to a string df['Inflation Rate'] = pd.to_numeric(df['Inflation Rate'], errors = 'coerce') # show the dataframe print(df) # show the data types print (df.dtypes) |
Output:
Note: String data type shows as an object.
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