Pandas is a powerful library in Python that provides numerous opportunities for data manipulation. It provides a wide range of features to operate and manipulate data from merging and filtering to reshaping and pivoting.

In this article, we’ll explore two common data manipulation tasks that involve subtracting Pandas DataFrames.

## Subtracting Two Pandas DataFrames – Example 1

The first example deals with numerical columns only. Often, we need to compare and analyze two datasets with similar columns and rows.

We can start by subtracting Pandas DataFrames. Here’s an example:

“`

## import pandas as pd

df1 = pd.DataFrame({‘A’: [10, 20, 30], ‘B’: [40, 50, 60]})

df2 = pd.DataFrame({‘A’: [5, 15, 25], ‘B’: [35, 45, 55]})

result = df1 – df2

## print(result)

“`

## Output:

“`

A B

## 0 5 5

## 1 5 5

## 2 5 5

“`

Here, we first imported the necessary library – pandas – and created two sample DataFrames, df1 and df2. Each DataFrame contains two columns, A and B, with three rows.

We can then subtract one DataFrame from another using the “-” symbol. In this example, we subtracted df2 from df1, resulting in a new DataFrame called result.

When we printed the result DataFrame, we found that Pandas subtracted the respective cells in df2 from those in df1. We now have a new DataFrame with the same shape – two columns and three rows – as the original DataFrames.

## Subtracting Two Pandas DataFrames – Example 2

The second example demonstrates subtracting DataFrames with a combination of numerical and character columns. This adds an extra level of complexity, especially if the DataFrames have different index columns.

Here’s an example:

“`

## import pandas as pd

df1 = pd.DataFrame({‘A’: [10, 20, 30], ‘B’: [‘apple’, ‘banana’, ‘cherry’]}, index=[‘row1’, ‘row2’, ‘row3’])

df2 = pd.DataFrame({‘A’: [25, 15, 5], ‘B’: [‘banana’, ‘apple’, ‘orange’]}, index=[‘row1’, ‘row3’, ‘row2’])

result = df1.copy()

result[‘A’] = df1[‘A’] – df2[‘A’]

result.loc[df1[‘B’] != df2[‘B’], ‘B’] = df1[‘B’] + ‘-‘ + df2[‘B’]

## print(result)

“`

## Output:

“`

A B

## row1 -15 apple-banana

## row2 20 cherry

## row3 5 banana-orange

“`

Here, we created two DataFrames, df1 and df2, containing a mix of numerical and character columns. We assigned custom row indices to each DataFrame.

We then created a copy of df1 called result. The difference in this example is that we have to tackle mismatched rows and columns in the result DataFrame.

Here, we used slicing to assign a new value to the ‘A’ column of the result DataFrame, subtracting df2 from df1. Since we want to maintain the indices from df1 in the result DataFrame, we created a copy of df1.

We then used the loc property to add values to the ‘B’ column of the result DataFrame. First, we checked if the character value in column B of df1 is not equal to that in df2.

If True, we concatenated the two values with a ‘-‘ separator.

## Additional Resources

There are many more data manipulation tasks that you can perform using Pandas. The official Pandas library documentation is a good place to start.

The website provides a comprehensive list of common Pandas tasks and many examples. Additionally, there are several online tutorials, courses, and videos available for those looking to improve their Pandas skills.

## Conclusion

In this article, we learned about two common data manipulation tasks that involve subtracting Pandas DataFrames. We looked at two examples and explored how to manipulate numerical and character columns.

We also highlighted some additional resources available to help you learn Pandas. Pandas is a powerful library that forms the backbone of any data analysis project.

With more practice and experience, you can get to grips with advanced techniques and become a Pandas pro. In this article, we explored two common data manipulation tasks that involve subtracting Pandas DataFrames.

We learned how to subtract DataFrames with numerical columns and those with a mix of numerical and character columns. We highlighted the importance of Pandas in data analysis and provided resources to learn more about Pandas.

By mastering Pandas techniques, you can improve your data analysis skills and become more efficient. Remember to practice and regularly update your knowledge of Pandas to stay ahead in the field of data analysis.