## Boosting Data Analysis Skills with Pandas DataFrame

Data analysis is a crucial part of business operations. Since the emergence of the internet, companies are collecting vast amounts of data, and analyzing them has become more manageable with the advent of data analysis tools.

One such tool is Pandas, a Python library designed to work with structured data, allowing users to manipulate, filter, and transform data to extract actionable insights. In this article, we will focus on one of the critical functions of Pandas – finding the difference between columns in a DataFrame.

### Example 1: Finding the difference between two columns

Say, you have data about sales in different regions and want to know the difference between the total sales in two regions over a specific period. To achieve this, you need to find the difference between the columns with sales data of the respective regions.

In Pandas, the process is called “column arithmetic”. Here’s how you can achieve it:

```
# import pandas
import pandas as pd
# create a pandas DataFrame
data = {'Region': ['North', 'South', 'East', 'West'],
'Sales Q1': [250000, 200000, 300000, 320000],
'Sales Q2': [320000, 180000, 280000, 310000],
'Sales Q3': [290000, 250000, 340000, 330000],
'Sales Q4': [280000, 220000, 260000, 310000]
}
df = pd.DataFrame(data)
# calculate the difference between Sales Q2 and Sales Q3
df['Diff Q2-Q3'] = df['Sales Q2'] - df['Sales Q3']
```

In the code block above, we create a DataFrame with four columns (‘Region’, ‘Sales Q1’, ‘Sales Q2’, and ‘Sales Q3’). We then calculate the difference between the second and third columns using the ‘+’ operator.

The result is stored in a new column called ‘Diff Q2-Q3’. You can apply mathematical operations such as addition (+), subtraction (-), multiplication (*), or division (/).

### Example 2: Finding the difference between columns based on a condition

Another common use case is to filter rows based on a condition and then find the difference between certain columns. To illustrate this, let’s continue with the sales data example but assume you only want to examine the sales figures for regions with sales above a certain threshold.

The code block below shows how you can apply a condition and then calculate the difference between columns.

```
# filter rows based on condition
df_filtered = df[df['Sales Q1'] > 200000]
# calculate the difference between Sales Q1 and Sales Q2 for filtered rows
df_filtered['Diff Q1-Q2'] = df_filtered['Sales Q1'] - df_filtered['Sales Q2']
```

In the example above, we filter the DataFrame based on a condition (‘Sales Q1’ greater than 200000) and store the result in a new DataFrame called ‘df_filtered’.

We then calculate the difference between ‘Sales Q1’ and ‘Sales Q2’ for the rows in the filtered DataFrame and save the result in a new column called ‘Diff Q1-Q2’.

### Example 3: Finding the difference between consecutive sales periods

You can also use Pandas to calculate the difference in sales between consecutive periods.

This is a common use case in sales forecasting, where analysts want to know the trend in sales over time. Here’s how to do it:

```
# create a new DataFrame with only the sales columns
sales_df = df[['Sales Q1', 'Sales Q2', 'Sales Q3', 'Sales Q4']]
# calculate the difference for consecutive columns
diff_df = sales_df.diff(axis=1)
# calculate the absolute difference
abs_diff_df = diff_df.abs()
# add the absolute difference to the original DataFrame
df_new = pd.concat([df, abs_diff_df], axis=1)
```

In the example above, we create a new DataFrame (‘sales_df’) with only the sales columns.

This makes it easier to apply functions to only the necessary columns. We then use the ‘diff()’ function to calculate the difference between consecutive columns.

Since we are looking at periods, we set the ‘axis’ argument to 1. To obtain the absolute difference, we apply the ‘abs()’ function.

Finally, we add the absolute difference to the original DataFrame by concatenating the two DataFrames along the ‘columns’ axis.

## Conclusion

In this article, we learned how to find the difference between columns in a Pandas DataFrame. We covered three examples – finding the difference between two columns, finding the difference based on a condition, and finding the difference between consecutive sales periods.

Pandas is an essential data analysis tool that has simplified data manipulation and made it more accessible to analysts. By mastering the different functions, you can extract actionable insights that drive business growth.

### Example 2: Finding the difference between columns based on a condition

Calculating the difference between columns in a Pandas DataFrame is a crucial data analysis task that helps us to derive valuable insights. Example 2 focuses on how to find the difference between columns based on a specific condition.

Suppose that you have sales data, and you want to find the difference between the sales in two regions, but only when the sales for a third region exceed a certain threshold. In other words, you want to compare the sales of North and South regions, but only when the sales of East region are greater than 300,000 for the given sales period.

We can solve this problem in multiple ways using different techniques provided by Pandas. Filter rows based on a condition:

The first step is to filter the rows in the DataFrame based on the condition.

To do this, we will use boolean indexing, which means selecting rows where a particular condition holds. Boolean indexing returns an array of True/False values.

```
# import Pandas
import pandas as pd
# create a sample DataFrame
data = {'Region': ['North', 'South', 'East', 'West'],
'Sales 2020': [250000, 200000, 300000, 320000],
'Sales 2021': [320000, 180000, 280000, 310000],
'Sales 2022': [290000, 250000, 340000, 330000] }
df = pd.DataFrame(data)
# filter the rows based on the condition
condition = df['Sales 2022'] > 300000
filtered_df = df[condition]
print(filtered_df)
```

### The output of the code above will be:

```
Region Sales 2020 Sales 2021 Sales 2022
2 East 300000 280000 340000
3 West 320000 310000 330000
```

We can see that only the regions ‘East’ and ‘West’ meet the specified condition. Thus, we will only compare the sales of North and South regions against these two regions.

### Calculate the difference:

To find the difference between the sales data of North and South regions, we need to subtract the values of the two columns containing this data. Since we have filtered the DataFrame, we will add another step to specify the columns which we need to compare.

```
# specify the columns to compare
columns_to_compare = ['Sales 2021', 'Sales 2022']
# filter the rows based on the condition
condition = df['Sales 2022'] > 300000
filtered_df = df[condition]
# calculate the difference
difference = filtered_df['Sales 2022'] - filtered_df['Sales 2021']
filtered_df['Difference'] = difference.abs()
print(filtered_df)
```

### The output of the code above will be:

```
Region Sales 2020 Sales 2021 Sales 2022 Difference
2 East 300000 280000 340000 60000
3 West 320000 310000 330000 20000
```

We can see that the difference in sales for region East is 60,000 and region West is 20,000. Since we only wanted the absolute values of these differences, we used the ‘abs()’ function to have positive values of the differences.

In conclusion, calculating the difference between columns based on a specific condition is a useful skill that helps us in deriving valuable data insights. Pandas provide multiple ways to do this, using different methods like Boolean indexing, filtering, and slicing data.

You can also use functions like ‘abs()’ to calculate the absolute difference. With these techniques, you can easily extract actionable insights from your sales data and make informed decisions about your business strategies.

In conclusion, calculating the difference between columns in a Pandas DataFrame is an essential data analysis task that helps us in deriving valuable insights. In the article, we covered three different examples – finding the difference between two columns, finding the difference based on a condition, and finding the difference between consecutive sales periods.

We explored how to filter rows based on a condition, extract specific columns, and calculate the differences using mathematical operations like subtraction. The Pandas library provides multiple techniques to perform these tasks with ease.

By mastering these techniques, you can extract valuable insights from your data and make informed business decisions.