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(vii) print(df['Gender'].value_counts())
M 3
F 1
Name: Gender, dtype: int64
(viii) print(df['Gender'].unique())
['M' 'F']
(ix) print(df.groupby('Gender')['Age'].mean())
Gender
F 30.000000
M 28.333333
Name: Age, dtype: float64
(x) print(pd.concat([df, df], axis=1))
Name Age Gender Height Weight Name Age Gender Height Weight
0 Rohan 25 M 175 70 Rohan 25 M 175 70
1 Jasmine 30 F 160 55 Jasmine 30 F 160 55
2 Mohit 28 M 180 80 Mohit 28 M 180 80
3 Anshika 32 M 165 60 Anshika 32 M 165 60
(xi) print(df.rename(columns={'Name': 'Full Name', 'Age': 'Years'}))
Full Name Years 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
6. Consider the DataFrame df mentioned in the previous question and write the code snippet for the following queries:
(i) Retrieve the summary statistics of the DataFrame
(ii) Determine the minimum age.
(iii) Retrieve the number of occurrences for each unique value in the Gender column
(iv) Add a new column BMI to the DataFrame to be calculated as weight/height^2
(v) Concatenate the DataFrame df with the following DataFrame in row-wise manner:
df2 = pd.DataFrame({'Name': ['John'], 'Age': [27], 'Gender': ['M'], 'Weight': [75]})
(vi) Write the contents of the DataFrame df to a CSV file named 'details.csv'?
(vii) Group the DataFrame by Gender and calculate the average values for each group?
(viii) Drop the Height column from the DataFrame
(ix) Rename the column Age to Years
Ans. (i) summary = df.describe()
(ii) min_age = df['Age'].min()
(iii) gender_counts = df['Gender'].value_counts()
(iv) df['BMI'] = df['Weight'] / (df['Height'] ** 2)
(v) concatenated_row = pd.concat([df, df2], axis=0)
(vi) df.to_csv('data.csv', index=False)
(vii) grouped_data = df.groupby('Gender').mean()
(viii) df = df.drop('Height', axis=1)
(ix) df = df.rename(columns={'Age': 'Years'})
Data Handling using Pandas DataFrame 71

