Page 204 - Touhpad Ai

Output:
Name Age Address Qualification
0 Adit 27 Delhi M.Sc.
1 Ekam 24 Kanpur MA
2 Sakshi 25 Meerut MCA
3 Anu 30 Indore Ph.D.

Selecting a Column from the DataFrame
You can select specific columns from a DataFrame using their labels (column names). This allows you to focus on
relevant data for analysis.

Program 10: To select a column from the DataFrame

# Import pandas library
import pandas as pd

# Define a dictionary containing employee data
data = {'Name':['Adit', 'Ekam', 'Sakshi', 'Anu'],
'Age':[27, 24, 25, 30],
'Address':['Delhi', 'Kanpur', 'Meerut', 'Indore'],

'Qualification':['M.Sc.', 'MA', 'MCA', 'Ph.D.']}
# Convert the dictionary into DataFrame
df = pd.DataFrame(data)
# Displaying DataFrame with Selected Column

print(df[['Name', 'Age', 'Qualification']])
Output:

Name Age Qualification
0 Adit 27 M.Sc.
1 Ekam 24 MA

2 Sakshi 25 MCA
3 Anu 30 Ph.D.

Selecting a Row from the DataFrame
You can select specific rows based on their indices or use boolean indexing based on conditions. This enables you to
filter the data based on certain criteria, such as values meeting a particular condition. To select a particular row from
the DataFrame, you can use the .iloc[] method. This method is used for integer-location based indexing, which means
you select rows and columns by their position (index).
Program 11: To select a row using .iloc[] method

# Import pandas library
import pandas as pd

# Define a dictionary containing employee data
data = {'Name':['Adit', 'Ekam', 'Sakshi', 'Anu'],
'Age':[27, 24, 25, 30],
'Address':['Delhi', 'Kanpur', 'Meerut', 'Indore'],

202 Touchpad Artificial Intelligence - XI