Removing Values from Data Frame Based on Percentage Change

As a data analyst or programmer, dealing with large datasets can be a daunting task. One common requirement when working with financial or economic data is to remove values that fall outside a certain percentage range. In this article, we will explore how to achieve this using Python and the popular pandas library.

Introduction

The problem at hand involves calculating the percentage change in price for a series of data points and then removing any value that falls outside a specific threshold (in this case, 10% changes). The original code provided by the user attempts to accomplish this using a formula involving absolute values and lambda functions. However, as we will explore later, there is an even more efficient method to achieve this goal.

Understanding Percentage Change

To calculate the percentage change in price, we need to first understand what it means. The percentage change is calculated by taking the difference between two consecutive values and dividing it by the original value. For example, if we have a value of 100 at one point in time and 102 at another point in time, the percentage change would be (102 - 100) / 100 = 0.02 or 2%.

Original Approach

The original code provided by the user attempts to accomplish this using the following formula:

df2=df1_remove.loc[lambda df1_remove:abs(df1_remove.percnt_change)<=.1]

This formula creates a new data frame (df2) that includes only the rows from df1_remove where the absolute value of percnt_change is less than 0.10.

However, this approach has some limitations. Firstly, it requires us to perform multiple operations on the data frame, which can be inefficient for large datasets. Secondly, it relies on a lambda function, which can make the code harder to read and understand.

Optimized Approach

A more efficient method to achieve this goal is to use a while loop that continually updates the data frame until there are no outliers remaining. Here’s how you can do it:

def check_outliers(df, threshold=0.10):
    return df['Price'].pct_change().abs().gt(threshold).any()

while True:
    data['percnt_change'] = data['Price'].pct_change()
    mask = (data['percnt_change'].abs() < 0.10) | (data['percnt_change'] == 0)
    data = data.loc[mask]
    if not check_outliers(data):
        break

This code defines a function check_outliers that checks whether any of the percentage changes exceed the threshold. The while loop then continually updates the data frame by calculating the percentage change, creating a mask to filter out outliers, and updating the data frame until there are no more outliers remaining.

How it Works

1. We define a function check_outliers that takes in a data frame (df) and an optional threshold (threshold=0.10). The function calculates the percentage change by taking the difference between consecutive values and dividing it by the original value.
2. The while loop continually updates the data frame until there are no outliers remaining.
3. Inside the loop, we calculate the percentage change using data['Price'].pct_change().
4. We create a mask to filter out outliers by checking if the absolute value of the percentage change is less than the threshold (< 0.10). We also include values with no change (i.e., percnt_change == 0) in the mask, as they should not be considered outliers.
5. We update the data frame using the mask to exclude outliers.

Example Use Case

Here’s an example of how you can use this code:

import pandas as pd

# Create a sample data frame
data = pd.DataFrame({
    'product': ['ACB', 'ACB', 'ACB', 'ACB', 'ACB', 'ACB'],
    'time': ['2017-01', '2017-02', '2017-03', '2017-04', '2017-05', '2017-06'],
    'Price': [100, 102, 101, 140, 130, 105]
})

# Print the original data frame
print("Original Data Frame:")
print(data)

# Remove outliers using the optimized approach
def check_outliers(df, threshold=0.10):
    return df['Price'].pct_change().abs().gt(threshold).any()

while True:
    data['percnt_change'] = data['Price'].pct_change()
    mask = (data['percnt_change'].abs() < 0.10) | (data['percnt_change'] == 0)
    data = data.loc[mask]
    if not check_outliers(data):
        break

# Print the updated data frame
print("\nUpdated Data Frame:")
print(data)

This code creates a sample data frame with prices for different products at various points in time. It then uses the optimized approach to remove outliers based on a threshold of 10%. The resulting updated data frame is printed out, showing only the values that fall within the acceptable range.

Conclusion

Removing values from a data frame based on percentage change can be an important step in data analysis and processing. While there are several approaches to achieve this goal, the optimized method using a while loop and percentage changes offers a more efficient and effective solution for large datasets. By understanding how to calculate percentage changes and filter out outliers, you can extract valuable insights from your data and make informed decisions.

Last modified on 2023-06-09