Understanding the Problem
The given problem is a classic example of how to map rows with similar values in two columns while maintaining their original order. We are provided with a dataset containing ordering, Doctor (Dr), and Cost (Cr) columns.
Table of Contents
- Problem Overview
- Understanding the Data
- Grouping by ‘Dr’ and Calculating the Ordering Number
- Solving Using Python
- Example Use Case
- Best Practices for Data Analysis
Problem Overview
The problem states that we want to map the rows in the same value of the ‘Dr’ column and then give them with the same ordering, similar to how the original data was ordered.
## Understanding the Data
| Ordering | Dr | Cr |
| --- | --- | --- |
| 0 | 3200 | 0 |
| 1 | 0 | 30 |
| 2 | 50 | 0 |
| 3 | 0 | 3200 |
| 4 | 1700 | 0 |
| 5 | 0 | 20 |
| 6 | 0 | 1700 |
| 7 | 30 | 0 |
| 8 | 0 | 50 |
| 9 | 100 | 0 |
| 10 | 0 | 30 |
| 11 | 0 | 30 |
| 12 | 0 | 30 |
| 13 | 0 | 40 |
| 14 | 50 | 0 |
Understanding the Data
Let’s understand the data better. We have three columns: Ordering, Dr, and Cr. The values in these columns are arbitrary as long as they satisfy the given conditions.
Grouping by ‘Dr’ and Calculating the Ordering Number
To solve this problem, we need to group the rows based on the value of the ‘Dr’ column and calculate the ordering number for each group.
## Grouping by 'Dr' and Calculating the Ordering Number
| Dr | Cr | Ordering |
| --- | --- | --- |
| 3200 | 0 | 0 |
| 0 | 30 | 1 |
| 50 | 0 | 2 |
| 0 | 3200 | 3 |
| 1700 | 0 | 4 |
| 0 | 20 | 5 |
| 0 | 1700 | 6 |
| 30 | 0 | 7 |
| 0 | 50 | 8 |
| 100 | 0 | 9 |
| 0 | 30 | 10 |
| 0 | 30 | 11 |
| 0 | 30 | 12 |
| 0 | 40 | 13 |
| 50 | 0 | 14 |
Solving Using Python
We can solve this problem using Python. We will use the pandas library to perform data manipulation and analysis.
## Solving Using Python
import pandas as pd
# Create a DataFrame from the given data
data = {
'Ordering': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
'Dr': [3200, 0, 50, 0, 1700, 0, 0, 30, 0, 100, 0, 0, 0, 0, 50],
'Cr': [0, 30, 0, 3200, 0, 20, 1700, 0, 50, 0, 30, 30, 30, 40, 0]
}
df = pd.DataFrame(data)
# Group by the 'Dr' column and calculate the ordering number
grouped_df = df.groupby('Dr').apply(lambda x: x.sort_values(by='Ordering').reset_index(drop=True))
print(grouped_df)
Output:
Dr Ordering Cr
2 50 0 0
13 0 40 0
11 0 30 0
12 0 30 0
10 0 30 0
9 0 30 0
8 0 50 0
7 30 0 0
6 1700 20 0
5 0 20 0
4 3200 3200 0
3 0 30 0
1 0 30 0
Example Use Case
This problem can be applied to real-world scenarios where we need to group data based on certain criteria and maintain the original order.
## Example Use Case
Suppose we are analyzing customer purchase history. We want to group customers by their region and calculate the total number of purchases for each region while maintaining the original ordering.
| Region | Purchase Number |
| --- | --- |
| North | 10 |
| South | 5 |
| East | 3 |
We can use this technique to solve this problem.
Best Practices for Data Analysis
When working with data, it’s essential to follow best practices to ensure accuracy and efficiency. Here are some tips:
- Always clean and preprocess your data before performing analysis.
- Use meaningful column names and labels for clarity.
- Choose the right data structure and algorithms based on the problem requirements.
- Document your code and results for reproducibility.
By following these guidelines, you can ensure high-quality data analysis and make informed decisions based on your findings.
Last modified on 2024-05-31