## Finding the Difference between Rows in Pandas DataFrame

Pandas DataFrame is a powerful tool in data analysis as it allows easy manipulation of data and performs various operations on data effectively. The difference between rows in a Pandas DataFrame is a common task in data analysis, and there are many ways to achieve it.

### Using the DataFrame.diff() Function

The DataFrame.diff() function calculates the difference between the current row and the previous row.

The syntax of DataFrame.diff() function is straightforward. It takes an argument, `period`

, which specifies the number of rows to shift for the calculation of the difference.

By default, the period is 1, which means that the function calculates the difference between the current row and the previous row.

### Example 1: Finding the Difference between Every Current Row and the Previous Row

Suppose we have a DataFrame with sales data, and we want to find the difference between every current row and the previous row.

We can use the DataFrame.diff() function to achieve this task. The following code snippet demonstrates how to do this:

```
sales_data = {'Sales': [10, 15, 20, 25, 30]}
df = pd.DataFrame(sales_data)
# Calculate the difference between every row and the previous row
sales_diff = df.diff()
# Print the resulting DataFrame
print(sales_diff)
```

### Output:

```
Sales
0 NaN
1 5.0
2 5.0
3 5.0
4 5.0
```

In the above code snippet, we first created a DataFrame with sales data. Then we used the DataFrame.diff() function to calculate the difference between every current row and the previous row.

Finally, we printed the resulting DataFrame. As you can see, the resulting DataFrame has NaN in the first row because there is no previous row to calculate the difference.

### Example 2: Finding Difference Based on Condition

Suppose we have a DataFrame with sales data, and we want to find the difference between every current row and the previous row based on a condition. We can achieve this task by first filtering the DataFrame based on the condition and then using the DataFrame.diff() function.

### The following code snippet demonstrates how to do this:

```
sales_data = {'Sales': [10, 15, 20, 25, 30]}
df = pd.DataFrame(sales_data)
# Filter the DataFrame based on condition
filtered_df = df[df['Sales'] > 15]
# Calculate the difference between every row and the previous row
sales_diff = filtered_df.diff()
# Print the resulting DataFrame
print(sales_diff)
```

### Output:

```
Sales
1 NaN
2 5.0
3 5.0
```

In the above code snippet, we first created a DataFrame with sales data. Then we filtered the DataFrame based on the condition where Sales is greater than 15.

Next, we used the DataFrame.diff() function to calculate the difference between every current row and the previous row. Finally, we printed the resulting DataFrame.

As you can see, the resulting DataFrame has NaN in the first row because there is no previous row to calculate the difference.

## Additional Resources

Pandas documentation provides an extensive list of functions, methods, and properties of Pandas DataFrame. There are also many online resources, such as books and tutorials, that provide a comprehensive guide to using Pandas DataFrame for data manipulation and analysis.

### Some of the popular online resources include:

- Official Pandas Documentation
- Learn Pandas Tutorial
- Pandas Cheat Sheet
- Data School Pandas Tutorials

## Conclusion

In conclusion, finding the difference between rows in a Pandas DataFrame is a common task in data analysis. This article demonstrated how to use the DataFrame.diff() function to calculate the difference between every current row and the previous row in a Pandas DataFrame.

It also provided an example of finding the difference based on a condition. Overall, Pandas DataFrame provides a powerful tool for data manipulation and analysis, and there are many resources available to learn more about it.

In summary, finding the difference between rows in a Pandas DataFrame is an essential task in data analysis. Using the DataFrame.diff() function, we can easily calculate the difference between every current row and the previous row.

The article provided two examples to demonstrate this task and highlighted some additional resources for learning more about Pandas DataFrame. Understanding how to find the difference between rows in a DataFrame can help data analysts manipulate data more effectively and draw accurate conclusions.

By mastering this skill, analysts can improve their data analysis and make better decisions based on data insights.