Page 338 - Informatics_Practices_Fliipbook_Class12
P. 338
(x) Concatenate the DataFrame 'weatherDF' with a similar DataFrame weatherDF2 in a row-wise
manner.
(xi) Write the contents of the DataFrame weatherDF to a CSV file named weather_data.csv.
(xii) Group the DataFrame by Temperature and calculate the average Humidity for each group.
(xiii) Drop the Wind_Speed column from the DataFrame.
(xiv) Rename the Humidity column to 'Relative Humidity'.
30. Consider the DataFrame weatherDF created in the Practice Task 9 and determine the output of the following
statements:
(i) weatherDF.groupby('Humidity').count()
(ii) weatherDF['Wind_Speed'].mean()
(iii) weatherDF['Temperature'].std()
(iv) weatherDF['Humidity'].max()
(v) weatherDF.iloc[3]['Date']
(vi) weatherDF.loc[1:3, 'Date':'Temperature']
(vii) weatherDF['Temperature'].value_counts()
(viii) weatherDF['Temperature'].unique()
(ix) weatherDF.groupby('Wind_Speed')['Humidity'].mean()
(x) pd.concat([weatherDF, weatherDF], axis=1)
(xi) weatherDF.rename(columns={'Date': 'Day', 'Temperature': 'Temp'})
31. Consider the following Pandas DataFrame transportDF representing data related to different modes of
transport:
import pandas as pd
data = {
'Mode': ['Car', 'Bus', 'Train', 'Bicycle', 'Walking'],
'Speed (km/h)': [100, 60, 120, 20, 5],
'Capacity': [4, 60, 500, 1, 1],
'Fuel Efficiency (km/l)': [12, 4, 0, 0, 0],
}
transportDF = pd.DataFrame(data)
Determine the output of the following Python statements:
(i) print(transportDF.ndim)
(ii) print(transportDF.shape)
(iii) print(transportDF.index)
(iv) print(transportDF.columns)
(v) print(transportDF.head(3))
(vi) print(transportDF.tail(2))
(vii) print(transportDF.iloc[1])
(viii) print(transportDF.loc[3, 'Mode'])
(ix) print(transportDF.set_index('Mode'))
(x) print(transportDF[transportDF['Speed (km/h)'] > 50])
(xi) print(transportDF[transportDF['Capacity'] < 10])
324 Touchpad Informatics Practices-XII

