Apprendre Removing Characters: Method 1 | Preprocessing Data: Part I

There are at least two different ways to solve the problem of redundant symbols. The first method is to treat the column values as strings and then apply the necessary string method to remove the redundant characters.

Note

To treat column values as strings, use the .str accessor.

After deleting symbols, we can convert the columns into numerical format.

There are at least two ways to do this:

The first method is to use the .astype(type) method on a column, where type is either int for integers or float for real numbers. For instance, df['column'] = df['column'].astype(int);
The second method is to use the .to_numeric() method of pd (pandas), passing the column as the parameter. For instance, df['column'] = pd.to_numeric(df['column']).

Tâche

Swipe to start coding

Import the pandas library with the pd alias.
Read the csv file and save it as a dataframe in the df variable.
Remove the redundant symbols from prices and convert them to float type:
- Select the 'Fuel_Price' column;
- Use the .str accessor;
- Remove the '$' characters from the left using the .lstrip() function;
- Convert the resulting values to numerical format (float) using the .astype() method;
- Assign the result to the 'Fuel_Price' column of df.
Remove the % symbols from the 'Unemployment' column (which are located on the right side), using the .rstrip() method, and convert the values to float format using the same algorithm as in step 3.
Remove the °C symbols from the 'Temperature' column (which are located on the right side), using the .rstrip() method, and convert the values to float format using the same algorithm as in step 3.
Display the first row of the df dataframe and the data types of the df dataframe.

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Section 1. Chapitre 4

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