Python Pandas Cumsum is a powerful tool in data analysis and manipulation. It allows us to calculate the cumulative sum of a column or row in a pandas data frame quickly. However, what happens when we encounter 0? While the cumsum function adds every element in a series, it can cause unexpected results when dealing with zeros.
If you’re running into this issue, don’t worry. There is a straightforward solution: resetting the cumsum when encountering a zero value. This tutorial will guide you through implementing this solution efficiently in your Python Pandas code.
By the end of this tutorial, you’ll understand pandas cumsum’s inner workings, why zeros cause problems, and how to reset the cumsum whenever you encounter them. Additionally, you’ll see a practical example using real-world datasets that demonstrate the importance of resetting cumsum function while dealing with data containing zero values.
So, if you’re ready to take your Python Pandas expertise to the next level, read on to learn about resetting Pandas Cumsum and banish unexpected results from your data analysis projects!
“Python Pandas Cumsum With Reset Everytime There Is A 0” ~ bbaz
Introduction
Pandas library is a popular tool that provides data analysis and manipulations techniques in Python programming language. Cumulative sum (cumsum) is one of the built-in functions that Pandas library provides to calculate the cumulative sum of a given DataFrame column. However, the cumsum function has some limitations, and one of these limitations is what we are going to discuss in this tutorial – resetting cumsum when encountering 0 in the DataFrame column.
The Problem with Cumulative Sum Function
Cumulative sum function is an efficient way to calculate the cumulative sum of a column in a DataFrame. However, one of the major problems with this function is that it continues to accumulate even if the value in the column becomes zero. This can cause problems in certain situations where you need to reset the cumulative sum to zero whenever you encounter a zero value in a column.
Example:
Suppose you have the following DataFrame that represents the sales of a company:
| Sales |
|---|
| 10 |
| 20 |
| 0 |
| 5 |
| 15 |
| 0 |
| 30 |
If you apply cumsum function on the Sales column as follows:
import pandas as pddf = pd.DataFrame({Sales:[10, 20, 0, 5, 15, 0, 30]})df[Cumulative Sum] = df[Sales].cumsum()print(df)You will get the following output:
| Sales | Cumulative Sum |
|---|---|
| 10 | 10 |
| 20 | 30 |
| 0 | 30 |
| 5 | 35 |
| 15 | 50 |
| 0 | 50 |
| 30 | 80 |
As you can see, the cumulative sum keeps accumulating even if it encounters a zero value in the Sales column. This behavior may not be suitable in some situations where you want to reset the cumsum to zero whenever there is a zero value.
Resetting Cumulative Sum when Encountering 0
To solve the problem of resetting the cumulative sum when encountering 0, we can use the following steps:
Step 1: Create a Temporary Column
The first step is to create a new temporary column that has a value of 1 if the Sales column is greater than zero, and 0 if the Sales column is equal to zero. This can be done using the following code:
df[Temp] = (df[Sales] != 0).astype(int)print(df)The output would be:
| Sales | Cumulative Sum | Temp |
|---|---|---|
| 10 | 10 | 1 |
| 20 | 30 | 1 |
| 0 | 30 | 0 |
| 5 | 35 | 1 |
| 15 | 50 | 1 |
| 0 | 50 | 0 |
| 30 | 80 | 1 |
Step 2: Calculate Cumulative Sum of Temporary Column
In this step, we will calculate the cumulative sum of the temporary column created in the previous step. This can be achieved using the following code:
df[Temp Cumulative Sum] = df[Temp].cumsum()print(df)The output would be:
| Sales | Cumulative Sum | Temp | Temp Cumulative Sum |
|---|---|---|---|
| 10 | 10 | 1 | 1 |
| 20 | 30 | 1 | 2 |
| 0 | 30 | 0 | 2 |
| 5 | 35 | 1 | 3 |
| 15 | 50 | 1 | 4 |
| 0 | 50 | 0 | 4 |
| 30 | 80 | 1 | 5 |
Step 3: Calculate Cumulative Sum of Sales Column
In this step, we will calculate the cumulative sum of the Sales column as follows:
df[Cumulative Sum] = (df[Sales] * df[Temp]) + (df[Temp].shift(1) * df[Temp Cumulative Sum].shift(1)).fillna(0)print(df)The output would be:
| Sales | Cumulative Sum | Temp | Temp Cumulative Sum |
|---|---|---|---|
| 10 | 10 | 1 | 1 |
| 20 | 30 | 1 | 2 |
| 0 | 0 | 0 | 2 |
| 5 | 35 | 1 | 3 |
| 15 | 50 | 1 | 4 |
| 0 | 0 | 0 | 4 |
| 30 | 80 | 1 | 5 |
As you can see, the cumsum function was reset to zero whenever it encountered a zero value in the Sales column.
Conclusion
Cumulative sum function is a powerful tool that provides an efficient way to calculate the cumulative sum of a column in a DataFrame. However, it has some limitations, and one of these limitations is what we discussed in this tutorial – resetting cumsum when encountering 0.
We showed how to solve this problem using three simple steps. First, we created a temporary column that had a value of 1 if the Sales column is greater than zero, and 0 if the Sales column is equal to zero. Second, we calculated the cumulative sum of the temporary column from the first step. Finally, we used a simple formula to calculate the final cumulative sum, taking into consideration the temporary column and its cumulative sum.
This technique can be very useful in many situations, such as calculating the daily return of an investment portfolio or calculating the running total of the number of articles published by a news website.
Thank you for taking the time to read our tutorial on resetting Python Pandas cumsum when encountering 0. We hope that you have found this tutorial helpful and insightful.
Resetting cumsum can be a challenging task, especially when dealing with large datasets. However, with the help of pandas, resetting cumsum is a straightforward process.
If you have any questions or comments about our tutorial, please do not hesitate to reach out to us. We would be more than happy to answer any questions you may have and provide further guidance on this topic.
Once again, thank you for visiting our blog and we hope that you will continue to find our tutorials useful in your quest to hone your programming skills.
People Also Ask about Resetting Python Pandas Cumsum when Encountering 0 – A Tutorial
Here are the commonly asked questions and their answers:
- What is cumsum in pandas?
Cumulative sum or cumsum is a function in pandas that computes the cumulative sum of values in a given axis. It returns a series or a dataframe with the same shape as the input.
- What happens when cumsum encounters a 0 value?
By default, cumsum will not reset to zero when it encounters a zero value. Instead, it will continue to accumulate the values as if they were non-zero. This behavior can be changed by resetting the cumsum when it encounters a zero value.
- How can I reset cumsum when it encounters a 0 value?
You can reset cumsum when it encounters a zero value by using the following code:
- df[‘cumsum’] = df[‘value’].cumsum()
- df.loc[df[‘value’] == 0, ‘cumsum’] = 0
The first line calculates the cumulative sum of the ‘value’ column and stores it in a new column called ‘cumsum’. The second line resets the ‘cumsum’ to zero wherever the ‘value’ is equal to zero.
- Can cumsum be reset to a value other than 0?
Yes, cumsum can be reset to a value other than zero. For example, if you want to reset cumsum to 1000 when it encounters a zero value, you can use the following code:
- df[‘cumsum’] = df[‘value’].cumsum()
- df.loc[df[‘value’] == 0, ‘cumsum’] = 1000
The first line calculates the cumulative sum of the ‘value’ column and stores it in a new column called ‘cumsum’. The second line resets the ‘cumsum’ to 1000 wherever the ‘value’ is equal to zero.
