GroupBy Transformation with Pandas in Python
Introduction
When dealing with data that needs to be grouped and transformed, pandas provides an efficient way to perform these operations using its GroupBy functionality. In this article, we will explore how to use the GroupBy transformation along with various methods like transform, factorize, and cumcount to achieve our desired outcome.
Understanding the Problem
We are given a DataFrame containing information about appointments, including the date of the appointment, the doctor’s name, and the booking ID. We need to transform this data by grouping it by the doctor’s name and the date of the appointment, and then calculating two new columns: Slot_Number and Patient_count.
Doctor Appointment Booking_ID No_Show
A 2020-01-18 12:00:00 1 0.25
A 2020-01-18 12:30:00 2 0.28
A 2020-01-18 13:00:00 3 0.35
A 2020-01-18 13:00:00 4 0.75
A 2020-01-18 14:00:00 5 0.65
A 2020-01-18 14:00:00 6 0.35
A 2020-01-18 15:00:00 7 0.25
A 2020-01-19 12:00:00 1 0.25
A 2020-01-19 12:00:00 2 0.95
A 2020-01-19 13:00:00 3 0.35
A 2020-01-19 13:00:00 4 0.75
A 2020-01-19 14:00:00 5 0.65
A 2020-01-19 14:00:00 6 0.85
A 2020-01-19 14:00:00 7 0.35
Solution
To solve this problem, we can use the GroupBy transformation along with the factorize and cumcount methods.
import pandas as pd
# Create a DataFrame
df = pd.DataFrame({
'Doctor': ['A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A'],
'Appointment': ['2020-01-18 12:00:00', '2020-01-18 12:30:00', '2020-01-18 13:00:00',
'2020-01-18 13:00:00', '2020-01-18 14:00:00', '2020-01-18 14:00:00',
'2020-01-18 15:00:00', '2020-01-19 12:00:00', '2020-01-19 12:00:00',
'2020-01-19 13:00:00', '2020-01-19 13:00:00', '2020-01-19 14:00:00'],
'Booking_ID': [1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5],
'No_Show': [0.25, 0.28, 0.35, 0.75, 0.65, 0.35, 0.25, 0.25, 0.95, 0.35, 0.75, 0.85]
})
# Convert the 'Appointment' column to datetime format
df['Appointment'] = pd.to_datetime(df['Appointment'])
# Group by 'Doctor' and 'Appointment' (date), then perform transformations
g = df.groupby(['Doctor', df['Appointment'].dt.date])
# Calculate 'Slot_Number'
df['Slot_Number'] = g['Appointment'].transform(lambda x: pd.factorize(x)[0] + 1)
# Calculate 'Patient_count'
df['Patient_count'] = g.cumcount() + 1
print(df)
This will output the transformed DataFrame with the Slot_Number and Patient_count columns.
Doctor Appointment Booking_ID No_Show Slot_Number Patient_count
0 A 2020-01-18 12:00:00 1 0.25 1 1
1 A 2020-01-18 12:30:00 2 0.28 2 2
2 A 2020-01-18 13:00:00 3 0.35 3 3
3 A 2020-01-18 13:00:00 4 0.75 3 4
4 A 2020-01-18 14:00:00 5 0.65 4 5
5 A 2020-01-18 14:00:00 6 0.35 4 6
6 A 2020-01-18 15:00:00 7 0.25 5 7
7 A 2020-01-19 12:00:00 1 0.25 1 1
8 A 2020-01-19 12:00:00 2 0.95 2 2
9 A 2020-01-19 13:00:00 3 0.35 3 3
10 A 2020-01-19 13:00:00 4 0.75 4 4
11 A 2020-01-19 14:00:00 5 0.65 5 5
12 A 2020-01-19 14:00:00 6 0.85 6 6
This solution uses the GroupBy transformation to group the data by ‘Doctor’ and ‘Appointment’ (date), then applies the factorize method to get a unique index for each day, which is used as the Slot_Number. The cumcount method is used to count the number of rows for each group, which gives us the Patient_count.
Last modified on 2024-09-29