Calculating Average for a Previous Column Condition

In this article, we will explore how to calculate the average of a column in pandas DataFrame where the value is only considered positive if it’s from a previous load number.

Understanding the Problem

The problem statement involves calculating an average based on a specific condition. We have a dataset with columns such as Date-Time, Diff, Load_number, and Load. The goal is to calculate the absolute average of the Diff column for each unique value in the Load_number column, but only considering positive values from previous load numbers.

Background

To solve this problem, we will use pandas library which provides data structures and functions to efficiently handle structured data. We’ll start by importing necessary libraries, loading the dataset, and understanding how to manipulate it using pandas.

import pandas as pd

Loading the Dataset

Let’s assume we have a CSV file named data.csv with the following structure:

Date-Time	Diff	Load_number	Load
10/22/2019	-386	0	0
10/23/2019	-380	0	0
10/24/2019	-370	0	0
10/25/2019	5000	1	Yes
10/26/2019	-490	1	1
10/27/2019	-480	1	1
10/28/2019	-470	1	1
10/22/2019	5000	2	Yes
10/23/2019	-380	2	2
10/24/2019	-370	2	2
10/25/2019	5000	3	Yes
10/26/2019	-490	3	3
10/27/2019	5800	4	Yes
10/28/2019	-550	4	4
10/29/2019	-500	4	4

Data Loading and Preparation

# Load the dataset into a pandas DataFrame
df = pd.read_csv('data.csv')

# Convert 'Date-Time' column to datetime format
df['Date-Time'] = pd.to_datetime(df['Date-Time'])

# Extract year from 'Date-Time'
df['Year'] = df['Date-Time'].dt.year

# Map 'Load' values to numerical values for easier manipulation
load_map = {'Yes': 1, 'No': 0}
df['Load'] = df['Load'].map(load_map)

# Convert 'Diff' column to numeric format
df['Diff'] = pd.to_numeric(df['Diff'])

Calculating the Average

We will calculate the average of Diff values based on a specific condition using numpy.

import numpy as np

# Function to get the absolute sum of Diff where load is less than current_load_number and value > 0
def absolute_sum_diff(df, current_load_number):
    # Filter rows where load_number < current_load_number
    filtered_df = df[df['Load_number'] < current_load_number]
    
    # Calculate the absolute sum of 'Diff' column in the filtered DataFrame
    abs_sum_diff = np.sum(np.abs(filtered_df['Diff']))
    
    return abs_sum_diff

# Apply the function for each unique load number and calculate their average
average_abs_sum_diffs = df.groupby('Load_number')['Diff'].apply(lambda x: absolute_sum_diff(df, x.iloc[-1]))

Data Manipulation

We will manipulate the data to achieve the desired result.

# Create a new column 'average_abs_sum_diff' with the calculated average values
df['average_abs_sum_diff'] = 0
for i in range(len(average_abs_sum_diffs)):
    df.loc[i, 'average_abs_sum_diff'] = average_abs_sum_diffs.iloc[i]

Final Result

# Print the final result
print(df)

In this article, we have explored how to calculate the average of a column in pandas DataFrame where the value is only considered positive if it’s from a previous load number. We used numpy library for numerical computations and pandas library for data manipulation.

Last modified on 2024-09-07