Conditional Reassignment of Values in a Pandas DataFrame
This article will explore the process of reassigning values in a Pandas DataFrame based on conditions. We’ll examine the use of masks and the loc method to achieve this, using a real-world example as our starting point.
Understanding the Problem
The question at hand involves reassigning values from Company A’s A1000 to Company A’s B2000 for years between 2010-2013. We’ll start by examining how we can generate the desired DataFrame and then discuss the various methods available for performing this conditional reassignment.
Generating the Sample DataFrame
The original DataFrame is created using the following code:
df = pd.DataFrame({'Year': [2010, 2010, 2010, 2010, 2010, 2011, 2011, 2011, 2012, 2012, 2012, 2012, 2012, 2013, 2013, 2013, 2014],
'Company': ['A', 'A', 'A', 'B', 'B', 'A', 'B', 'C', 'A', 'B', 'B', 'B', 'C', 'A', 'B', 'C', 'D'],
'Code': ['A1000', 'B2000', 'C3000', 'A1000', 'B2000', 'B2000', 'B2000', 'B2000', 'A1000', 'A1000', 'B2000', 'C3000', 'A1000', 'B2000', 'C3000', 'A1000', 'A1000'],
'values': [1000, 2000, 3000, 500, 1000, 2000, 4000, 4000, 1500, 4000, 2000, 6000, 1000, 5000, 2000, 1500, 2000]})
This DataFrame contains four columns: Year, Company, Code, and values.
Understanding the Desired Output
We want to reassign values from Company A’s A1000 to Company A’s B2000 for years between 2010-2013. The desired output would look like this:
| Year | Company | Code | values |
|---|---|---|---|
| 2010 | A | B2000 | 1000 |
| 2010 | A | B2000 | 2000 |
| 2010 | A | C3000 | 3000 |
| 2010 | B | A1000 | 500 |
| 2010 | B | B2000 | 1000 |
| 2011 | A | B2000 | 2000 |
| 2011 | B | B2000 | 4000 |
| 2011 | C | B2000 | 4000 |
| 2012 | A | B2000 | 1500 |
| 2012 | B | A1000 | 4000 |
| 2012 | B | B2000 | 2000 |
| 2012 | B | C3000 | 6000 |
| 2012 | C | A1000 | 1000 |
| 2013 | A | B2000 | 5000 |
| 2013 | B | C3000 | 2000 |
| 2013 | C | A1000 | 1500 |
| 2014 | D | A1000 | 2000 |
Method 1: Using Masks and loc
The first method we’ll explore involves creating a mask to select the rows that meet our conditions. We’ll then use the loc method to assign new values.
mask = ((2010 <= df.Year) & (df.Year <= 2013)) & df.Company.eq('A') & df.Code.eq('A1000')
df.loc[mask, 'Code'] = 'B2000'
print(df)
This code creates a mask that includes rows where the year is between 2010-2013, Company A, and Code A1000. It then uses loc to assign the value B2000 to the specified values.
Method 2: Using conditional assignments
We can also use Python’s built-in if-else statement to achieve this:
for index, row in df.iterrows():
if (2010 <= row['Year'] and row['Year'] <= 2013) and row['Company'] == 'A' and row['Code'] == 'A1000':
df.loc[index, 'Code'] = 'B2000'
However, this approach is less efficient than using masks and loc, as it involves iterating over each row in the DataFrame.
Conclusion
In this article, we explored how to reassign values in a Pandas DataFrame based on conditions. We discussed two methods: using masks and loc for more efficient performance, and Python’s built-in conditional assignments. The choice of method depends on the specific requirements and performance considerations of your project.
Whether you’re working with large datasets or need to perform frequent updates, utilizing these techniques can help streamline your data manipulation process.
Last modified on 2023-10-29