Calculating Duration by Rotating Array from Group Dataset

This blog post will walk you through the process of calculating the duration of trips by rotating an array of departure times within each group. The problem presents a dataset where we have information about the arrival and departure times for each trip, grouped by their respective groups.

Problem Statement

Given a dataframe df with columns group_id, id, departure_time, and arrival_time, calculate the duration of trips by rotating the array of departure times within each group. The rotation is done such that if the minimum arrival time is less than the maximum departure time, it should rotate towards the minimum arrival time.

Solution

To solve this problem, we will use Python with the Pandas library for data manipulation and analysis.

Step 1: Grouping by group_id

We start by grouping the dataframe df by the group_id column. This is done using the groupby function provided by Pandas.

# Group by group_id
df_grouped = df.groupby('group_id')

Step 2: Applying Transformation to departure_time

Next, we apply a transformation to the departure_time column within each group. The transformation involves reversing the order of the departure times for each group. This is done using the transform function provided by Pandas.

# Apply transformation to departure_time
df['departure_rotate'] = (
    df.groupby('group_id')['departure_time'].transform(lambda s: s[::-1])
)

In this step, we create a new column called departure_rotate which contains the reversed departure times for each group.

Step 3: Calculating Duration

Now that we have the rotated departure times, we can calculate the duration of trips by subtracting the arrival time from the rotated departure time. However, if the result is negative (i.e., the minimum arrival time is greater than the maximum departure time), we need to add 24 hours to the result.

This is done using the np.where function provided by NumPy.

import numpy as np

# Calculate duration
diff = df['arrival_time'].sub(df['departure_rotate'])
df['duration'] = np.where(diff > 0, diff, diff.add(24*3600))

In this step, we calculate the difference between the arrival time and the rotated departure time. If the result is positive (i.e., the minimum arrival time is less than or equal to the maximum departure time), we assign the result to the duration column. Otherwise, we add 24 hours to the result and assign it to the duration column.

Example Usage

To demonstrate how to use this code, let’s consider a sample dataframe df:

# Sample dataframe df
import pandas as pd

df = pd.DataFrame({
    'group_id': [1, 1, 2, 2, 2],
    'id': [1, 2, 1, 2, 3],
    'departure_time': [61200, 18000, 32400, 64800, 77400],
    'arrival_time': [61800, 18600, 34200, 66600, 81000]
})

# Apply transformation to departure_time
df['departure_rotate'] = (
    df.groupby('group_id')['departure_time'].transform(lambda s: s[::-1])
)

# Calculate duration
diff = df['arrival_time'].sub(df['departure_rotate'])
df['duration'] = np.where(diff > 0, diff, diff.add(24*3600))

print(df)

This code applies the transformation to the departure_time column and calculates the duration of trips. The resulting dataframe is then printed to the console.

Result

The final result is a dataframe with the additional columns departure_rotate and duration. The departure_rotate column contains the rotated departure times, while the duration column contains the calculated durations of trips.

   group_id  id  departure_time  arrival_time  departure_rotate  duration
0         1   1           61200         61800             18000     43800
1         1   2           18000         18600             61200     43800
2         2   1           32400         34200             77400     43200
3         2   2           64800         66600             64800      1800
4         2   3           77400         81000             32400     48600

This result can be used to analyze the duration of trips within each group and identify any patterns or trends.

Last modified on 2025-02-08