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4. Consider the DataFrame mentioned in the previous question and write the code snippet for the following queries:
(i) Retrieve the columns 'Name' and 'Gender'.
(ii) Retrieve rows with 'Age' greater than 25.
(iii) Retrieve rows where 'Gender' is Male.
(iv) Use the iloc function to select the rows from index 2 to index 4.
(v) Set row labels to ['A', 'B', 'C', 'D']. What is the updated DataFrame.
Ans. (i) df[['Name', 'Gender']]
(ii) df[df['Age'] > 25]
(iii) df[df['Gender'] == 'M']
(iv) df.iloc[2:5]
(v) df.index = ['A', 'B', 'C', 'D']
5. Consider the following Pandas DataFrame df:
Name Age Gender Height Weight
0 Rohan 25 M 175 70
1 Jasmine 30 F 160 55
2 Mohit 28 M 180 80
3 Anshika 32 M 165 60
Determine the output of the following statements:
(i) print(df.groupby('Gender').count())
(ii) print(df['Height'].mean())
(iii) print(df['Age'].std())
(iv) print(df['Height'].max())
(v) print(df.iloc[2]['Name'])
(vi) print(df.loc[0:2,'Name':'Height'])
(vii) print(df['Gender'].value_counts())
(viii) print(df['Gender'].unique())
(ix) print(df.groupby('Gender')['Age'].mean())
(x) print(pd.concat([df, df], axis=1))
(xi) print(df.rename(columns={'Name': 'Full Name', 'Age': 'Years'}))
Ans. (i) print(df.groupby('Gender').count())
Name Age Height Weight
Gender
F 1 1 1 1
M 3 3 3 3
(ii) print(df['Height'].mean())
170.0
(iii) print(df['Age'].std())
2.9860788111948193
(iv) print(df['Height'].max())
180
(v) print(df.iloc[2]['Name'])
Mohit
(vi) print(df.loc[0:2,'Name':'Height'])
Name Age Gender Height
0 Rohan 25 M 175
1 Jasmine 30 F 160
2 Mohit 28 M 180
70 Touchpad Informatics Practices-XII

