Answer to Question #275472 in Python for Rushikesh

Question

Create a dataframe by using the dataset [tipsnew.csv] and perform the

following operations

a) Print the day value when the value of total_bill is maximum and

minimum.

b) Retrieve all the rows from dataframe when the value of tip is more

than 3.0 and less than 5.0

c) Print the time of the meal and gender of the person who paid the tip

more than 5.0

d) Select and display last 10 rows from the dataframe when customer

smoking habbit is ‘yes’

e) Create the newdataframe(ndf) from original dataframe(df)

containing all rows and the following columns [total_bill tip, size].

Sort and display all values of column tip in ascendending order.

Answer 1

import pandas as pd


df = pd.read_csv(r"tipsnew.csv")
dataframe = pd.DataFrame(df)

# Print the day value when the value of total_bill is maximum and minimum.

total_bill = dataframe['total_bill']

total_bill_max = total_bill.max()
total_bill_min = total_bill.min()

index_max = dataframe.index[dataframe['total_bill'] == total_bill_max].tolist()
index_min = dataframe.index[dataframe['total_bill'] == total_bill_min].tolist()

print('\n a)')
print('Day maximum: ', dataframe.loc[index_max[0]]['day'])
print('Day minimum: ', dataframe.loc[index_min[0]]['day'])

# All the rows from dataframe when the value of tip is more
# than 3.0 and less than 5.0

b = dataframe[(dataframe.tip < 5.0) & (dataframe.tip > 3.0)]
print('\n b)')
print(b[['total_bill', 'tip', 'sex', 'smoker', 'day', 'time', 'size']])

# The time of the meal and gender of the person who paid the tip more than 5.0\

c = dataframe[dataframe.tip > 5.0]
print('\n c)')
print(c[['sex', 'tip']])

# Last 10 rows from the dataframe when customersmoking habbit is ‘yes’

d = dataframe[dataframe.smoker == 'Yes']
print('\n d)')
print(d[-10:])

# newdataframe from original dataframe
# containing all rows and the following columns [total_bill tip, size].
# Sort and display all values of column tip in ascendending order.

newdataframe = dataframe[['total_bill', 'tip', 'size']]
print('\n e)')
print(newdataframe)