Dropping a Column:

• To drop a column from a DataFrame, we can use the drop() method.
  • The drop() method accepts an argument that specifies the column to drop.
  • The inplace parameter can be set to True to indicate that the DataFrame should be modified in place.
• In the following example, we drop the Security column from the DataFrame df.

1import pandas as pd
2
3# Read the CSV file into a DataFrame
4df = pd.read_csv("Data/Demo_3.8_P1_SP500_Stocks.csv")
5
6# Drop the 'Security' column
7df.drop(columns=['Security'],  inplace=True)
8
9# Display the DataFrame
10print(df.shape)
11df.head()

Output:

(503, 7)

Renaming a Column:

• The rename() method can be used to rename a column in a DataFrame.
  • The method accepts an argument that specifies the column to rename.
  • The columns parameter can be set to a dictionary that maps the old column name to the new column name.
  • The inplace parameter can be set to True to indicate that the DataFrame should be modified in place.
• In the following example, we rename the GICS Sub-Industry column to Sub-Industry in the DataFrame df.

12df.rename(columns={'GICS Sub-Industry': 'Sub-Industry'}, inplace=True)
13print(df.shape)
14df.head()

Output:

(503, 7)

Removing Leading and Trailing Spaces with strip()

• Suppose some of the values in the columns have uninteded leading or trailing spaces. We can use the strip() method to clean these values.
  • The strip() method removes leading and trailing spaces from a string.

15df.columns = df.columns.str.strip()

replace()

• The replace() method can be used with a regular expression to remove errant characters.
  • As shown below, we can use the replace() method to remove certain characters. First, we’ll introduce some junk data for demonstration purposes.

16# Introduce some junk data for demonstration
17df.loc[0, 'Sector'] = 'Industrials!'
18df.loc[1, 'Sector'] = '#Industrials'
19
20# Display the values
21print("Values in 'Sector' column:")
22print(df['Sector'].head())

Output

1Values in 'Sector' column:
20              Industrials!
31              #Industrials
42               Health Care
53               Health Care
64    Information Technology

• Notice that some of the values in the Sector column now have some unwanted characters. We can use the replace() method to remove these characters.
  • In the following example, we remove the characters # and ! from the Sector column.
    • The replace() method accepts a dictionary that maps the characters to be replaced to the replacement characters.
    • The regex parameter can be set to True to indicate that the characters to be replaced are regular expressions.
      • In regular expressions, the dollar sign ($) has a special meaning: it represents the end of a line. To specify that we are referring to the literal dollar sign character rather than its special meaning, we need to escape it with two backslashes.

23df['Sector'] = df['Sector'].replace({'#': '', '!': ''}, regex=True) 
24# .replace({'\\$': '',',': ''}, regex=True) Would remove the dollar sign and comma characters from a string, for example.
25print(df.shape)
26df.head()

(503, 7)