# Unlocking Data Insights: Getting Averages in Pandas DataFrame

## Getting the Average of each Column and Row in Pandas DataFrame

Are you struggling to analyze data in Pandas DataFrame? One technique that can make this task easier is calculating the average of each column and row in your DataFrame.

The average is a measure that gives you an idea of the central tendency of your data. By calculating the average of each column and row, you can gain insights into the distribution of your data, identify trends, and make data-driven decisions.

In this article, we will explore how to get the average of each column and row in Pandas DataFrame, and give you examples of how to use this technique in real-life scenarios.

## Preparing Data

Before we dive into the process of calculating the average of each column and row, it’s important to have some data to work with. For the purposes of this article, we will use a sample dataset of commission earned by three people over a period of six months.

Let’s assume you have the following data:

| Month | Person A | Person B | Person C |

|——-|———-|———-|———-|

| Jan | \$5,000 | \$3,000 | \$4,500 |

| Feb | \$3,500 | \$2,500 | \$4,000 |

| Mar | \$2,500 | \$4,000 | \$6,000 |

| Apr | \$4,000 | \$3,500 | \$5,500 |

| May | \$3,000 | \$2,500 | \$3,500 |

| Jun | \$3,500 | \$4,500 | \$5,500 |

## Creating a DataFrame

To work with this data in Pandas, you first need to create a DataFrame. You can accomplish this by importing the pandas library and using the DataFrame() method like so:

“`

## import pandas as pd

data = {‘Month’: [‘Jan’, ‘Feb’, ‘Mar’, ‘Apr’, ‘May’, ‘Jun’],

‘Person A’: [5000, 3500, 2500, 4000, 3000, 3500],

‘Person B’: [3000, 2500, 4000, 3500, 2500, 4500],

‘Person C’: [4500, 4000, 6000, 5500, 3500, 5500]}

df = pd.DataFrame(data)

“`

## The resulting DataFrame looks like this:

“`

Month Person A Person B Person C

“`

## Getting the Average of each Column and Row

Now that we have our DataFrame, we can use the `.mean()` method to calculate the average of each column and row. To calculate the average of each column, you can use the following syntax:

“`

df.mean()

“`

## This will give you the average commission earned across all people for each month:

“`

Person A 3700.0

Person B 3250.0

Person C 5000.0

dtype: float64

“`

To calculate the average of each row, you need to specify the `axis` parameter when calling `.mean()`.

Here’s how you would calculate the average commission per person for each month:

“`

df.mean(axis=1)

“`

## This will give you the following output:

“`

0 4166.666667

1 3333.333333

2 4166.666667

3 4333.333333

4 3000.000000

5 4500.000000

dtype: float64

“`

In this case, the output tells us that the average commission earned per person, per month, is \$4,166.67 for the first and third month, and \$3,333.33 for the second month, and so on.

## Examples of Using the Average of each Column and Row in Pandas DataFrame

Let’s take a look at some examples of how to use the average of each column and row in real-life scenarios. Example 1:

## Calculating the Average Commission per Person Over 6 Months

Suppose you want to know the average commission earned per person over the entire six month period.

## You can calculate this by taking the average of each row:

“`

df.mean(axis=0).mean()

“`

This will give you the average commission earned per person, over the six month period, which is \$3983.33. Example 2:

## Calculating the Average Commission per Month Across all People

Suppose you want to know the average commission earned for each month, across all people.

## You can calculate this by taking the average of each column:

“`

df.mean(axis=1).mean()

“`

This will give you the average commission earned per month, across all people, which is \$3833.33.

## Conclusion

In this article, we have explored how to get the average of each column and row in Pandas DataFrame, and given you examples of how to use this technique in real-life scenarios. By calculating averages, you can gain insights into the distribution of your data, identify trends, and make data-driven decisions.

With this knowledge, you can analyze data more easily and efficiently, giving you a competitive edge in today’s data-driven world. Pandas DataFrame is a powerful tool for managing, analyzing, and visualizing data.

One important technique that can be used when working with this type of data is calculating the average of each column and row. In this article, we explored how to get the average of each column and row in Pandas DataFrame, and some examples of how to use this technique for real-life scenarios.

## Syntax for Calculating the Average of each Column and Row

To calculate the average of each column and row in a Pandas DataFrame, we can use the `mean()` method. The `mean()` method calculates the average across a specified axis.

By default, the `mean()` method calculates the average of each column, but we can specify the `axis` parameter to calculate the average of each row instead. The syntax for calculating the average of each column and row is:

“`

# Average of each column

df.mean()

# Average of each row

df.mean(axis=1)

“`

Where `df` is the name of the DataFrame we want to analyze.

## Calculating the Average Commission per Person Over 6 Months

One example of using the average of each column and row in a Pandas DataFrame is to calculate the average commission earned per person over the entire six-month period. To do this, we can take the average of each row, or axis=1.

“`

df.mean(axis=1)

“`

This will give us the average commission earned per person, for each month, across six months. We can then take the mean of these values to get the average commission earned per person over the six-month period.

## Calculating the Average Commission per Month Across all People

Another example of using the average of each column and row in a Pandas DataFrame is to calculate the average commission earned for each month, across all people. To do this, we can take the average of each column, or axis=0.

“`

df.mean(axis=0)

“`

This will give us the average commission earned for each month, across all people. We can then take the mean of these values to get the overall average commission earned per month.

## Benefits of Using the Average of each Column and Row in Pandas DataFrame

By calculating the average of each column and row in a Pandas DataFrame, we can gain insights into the distribution of our data, identify trends, and make data-driven decisions. For example, we can use the average commission earned per person over the six-month period to determine the most productive salesperson.

We can then use this information to reward top performers and motivate other salespeople to improve their performance. We can also use the average commission earned per month across all people to identify trends in sales performance.

For example, if the average commission earned per month increases over time, we can infer that sales performance is improving. On the other hand, if the average commission earned per month decreases over time, we can infer that sales performance is declining.

## Conclusion

In conclusion, calculating the average of each column and row in Pandas DataFrame is a useful technique for analyzing data. By understanding the syntax for calculating the average, and examples of how to use this technique in real-life scenarios, we can gain insights into the distribution of our data, identify trends, and make data-driven decisions.

In summary, getting the average of each column and row in Pandas DataFrame is an essential technique that provides insights into data distribution, identifies trends, and supports data-driven decision-making. By understanding the syntax for calculating averages and utilizing examples in real-life scenarios, we can easily analyze data and identify top performers.

By taking the average commission earned per month across all people, we can identify trends in sales performance and determine areas of improvement. In today’s data-driven world, understanding how to calculate the average commission in Pandas DataFrame is a valuable skill that can provide a significant competitive edge.