Page 334 - Informatics_Practices_Fliipbook_Class12
P. 334
(viii) Retrieve the number of occurrences for each unique product in the Product column.
(ix) Add a new column Total to the DataFrame to be calculated as Quantity * Price.
(x) Concatenate the DataFrame purchaseDF with another DataFrame (purchaseDF2) in a row-wise
manner.
(xi) Write the contents of the DataFrame purchaseDF to a CSV file named purchases.csv.
(xii) Group the DataFrame by Product and calculate the total values for each product.
(xiii) Drop the Date column from the DataFrame.
(xiv) Rename the column PurchaseID to ID.
12. Consider the DataFrame purchaseDF created in the previous question and determine the output of the
following statements:
(i) purchaseDF.groupby('Product').count()
(ii) purchaseDF['Price'].mean()
(iii) purchaseDF['Quantity'].std()
(iv) purchaseDF['Price'].max()
(v) purchaseDF.iloc[3]['Product']
(vi) purchaseDF.loc[1:3, 'Date':'Quantity']
(vii) purchaseDF['Product'].value_counts()
(viii) purchaseDF['Product'].unique()
(ix) purchaseDF.groupby('Product')['Quantity'].mean()
(x) pd.concat([purchaseDF, purchaseDF], axis=1)
(xi) purchaseDF.rename(columns={'Quantity': 'Units'})
13. Consider the following Pandas DataFrame plantDF representing data related to different types
of plants:
import pandas as pd
data = {
'Plant Name': ['Rose', 'Tulip', 'Cactus', 'Fern', 'Daisy'],
'Height (cm)': [60, 25, 15, 30, 20],
'Bloom Season': ['Spring', 'Spring', 'Year-round', 'Summer', 'Spring'],
'Indoor/Outdoor': ['Outdoor', 'Outdoor', 'Indoor', 'Indoor', 'Outdoor'],
}
plantDF= pd.DataFrame(data)
Determine the output on the execution of the following Python statements:
(i) print(plantDF.ndim)
(ii) print(plantDF.shape)
(iii) print(plantDF.index)
(iv) print(plantDF.columns)
(v) print(plantDF.head(3))
(vi) print(plantDF.tail(2))
(vii) print(plantDF.iloc[2])
320 Touchpad Informatics Practices-XII

