Page 210 - Touhpad Ai

# Add a new column 'Salary' to the DataFrame
df['Salary'] = [50000, 45000, 55000, 60000]

# Deleting a Row
df.drop(index=1, inplace=True) # Deleting the row with index 1 (Ekam)
print("\nDataFrame after deleting a row:")
print(df)

# Deleting a Column
df.drop(columns='Address', inplace=True) # Deleting the 'Address' column

print("DataFrame after deleting a column:")
print(df)
Output:
DataFrame after deleting a row:
Name Age Address Qualification Salary
0 Adit 27 Delhi M.Sc. 50000
2 Sakshi 25 Meerut MCA 55000
3 Anu 30 Indore Ph.D. 60000

DataFrame after deleting a column:
Name Age Qualification Salary
0 Adit 27 M.Sc. 50000
2 Sakshi 25 MCA 55000
3 Anu 30 Ph.D. 60000
To delete multiple columns with specific labels, you can modify the drop method to include a list of column labels.
DataFrame.drop() method can also be used to remove the duplicate rows.

# Deleting Columns with specific labels
labels_to_delete = ['Age', 'Address'] #specify in a list
df.drop(columns=labels_to_delete, inplace=True)

AI REBOOT

1. Fill in the blanks.
a. The two data structures that are supported by Pandas are and .

b. The library in Python excels in creating N-dimension data objects.
c. The statement to install NumPy is .
d. You can check the shape of an array by using the method in NumPy.
2. Answer the following questions:

a. What is a DataFrame in Pandas?

b. Give one advantage of using NumPy arrays over lists.

208 Touchpad Artificial Intelligence - XI