Using Python Panda aggregates operation-CodePudding

I have a table like this-

Hotel         Earning

Abu            1000

Zain            400

Show            500

Zint            300

Abu             500

Zain            700

Abu             500

Abu             500

Abu             800

Abu             1600

Show            1300

Zint            600

Using Panda, How to group by hotel and calculate the min, median and max of the earning on each hotel. And at the end print the aggregates values Hotel name "Abu".

Output:

[500.0, 650.0, 1600.0]

CodePudding user response：

Pandas DataFrame aggregate() Method The aggregate() method allows you to apply a function or a list of function names to be executed along one of the axis of the DataFrame, default 0, which is the index (row) axis. Note: the agg() method is an alias of the aggregate() method.

CodePudding user response：

import pandas as pd

# Read the data into a Pandas DataFrame
df = pd.read_csv('hotel_earnings.csv')

# Group the data by hotel
hotels = df.groupby('Hotel')

# Calculate the min, median, and max of the earning for each hotel
earnings = hotels['Earning'].agg(['min', 'median', 'max'])

# Print the aggregated values for the hotel named "Abu"
print(earnings.loc['Abu'])

This code reads the data from the hotel_earnings.csv file into a Pandas DataFrame, groups the data by hotel, and calculates the minimum, median, and maximum earning for each hotel. It then prints the aggregated values for the hotel named "Abu"

CodePudding user response：

Use agg:

df.groupby('Hotel').agg([('Min' , 'min'), ('Max', 'max'), ('Median', 'median')])
# Out:
#       Earning             
#           Min   Max Median
# Hotel                     
# Abu       500  1600  650.0
# Show      500  1300  900.0
# Zain      400   700  550.0
# Zint      300   600  450.0

For more statistics you can also use describe()

df.groupby('Hotel').describe()
# Out: 
#       Earning                                                             
#         count        mean         std    min    25%    50%     75%     max
# Hotel                                                                     
# Abu       6.0  816.666667  435.507367  500.0  500.0  650.0   950.0  1600.0
# Show      2.0  900.000000  565.685425  500.0  700.0  900.0  1100.0  1300.0
# Zain      2.0  550.000000  212.132034  400.0  475.0  550.0   625.0   700.0
# Zint      2.0  450.000000  212.132034  300.0  375.0  450.0   525.0   600.0