Data Analysis with Pandas: Calculating Quantiles by Group

Data analysis is the process of collecting, cleaning, and transforming data into useful insights. One of the essential libraries for data analysis in Python is Pandas.

It provides excellent data manipulation and analysis functionality to work with structured data, such as CSV and Excel files. In this article, we will discuss how to calculate quantiles by group using the Pandas DataFrame.

## Calculating 90th Percentile by Group

Calculating quantiles is an essential operation in data analysis. It helps to understand the distribution of data and obtain useful insights.

In Pandas, we can calculate quantiles using the `quantile()` function. The function takes one argument, which is the quantile value that we want to calculate.

For example, if we want to calculate the 90th percentile, we pass 0.9 as an argument to the `quantile()` function. When working with a dataset that has multiple groups, we may want to calculate the quantiles by group.

For instance, we may have a dataset that contains scores for different teams, and we want to calculate the 90th percentile of scores for each team. To achieve this, we can use the `groupby()` function in Pandas.

The `groupby()` function allows us to group data based on one or more columns and perform calculations on each group. Example 1: Calculate Quantile by Group

Let’s start by creating a Pandas DataFrame that contains scores for five different teams.

The DataFrame has two columns, team, and score. We can create the DataFrame using the following code:

“`

## import pandas as pd

data = {‘team’: [‘A’, ‘B’, ‘C’, ‘D’, ‘E’],

‘score’: [70, 80, 85, 90, 95]}

df = pd.DataFrame(data)

“`

## The resulting DataFrame looks like this:

“`

team score

## 0 A 70

## 1 B 80

## 2 C 85

## 3 D 90

## 4 E 95

“`

Now that we have the DataFrame, we can calculate the 90th percentile of scores for each team. To achieve this, we first group the DataFrame by the team column using the `groupby()` function.

Next, we apply the `quantile()` function to the score column of each group using the `agg()` function. The `agg()` function allows us to apply one or more functions to multiple columns of a DataFrame.

“`

quantiles = df.groupby(‘team’)[‘score’].agg([(’90th percentile’, lambda x: x.quantile(0.9))])

## print(quantiles)

“`

## The resulting DataFrame looks like this:

“`

90th percentile

## team

A 70.0

B 80.0

C 85.0

D 90.0

E 95.0

“`

We can see that the 90th percentile for each team is correctly calculated.

## Calculating Several Quantiles by Group

In some cases, we may want to calculate several quantiles for each group. For instance, we may want to calculate the 25th, 50th, and 75th percentiles for each team.

To achieve this, we can pass a list of quantile values to the `quantile()` function. For example, `quantile([0.25, 0.5, 0.75])` calculates the 25th, 50th, and 75th percentiles.

“`

quantiles = df.groupby(‘team’)[‘score’].agg([(’25th percentile’, lambda x: x.quantile(0.25)),

(’50th percentile’, lambda x: x.quantile(0.5)),

(’75th percentile’, lambda x: x.quantile(0.75))])

## print(quantiles)

“`

## The resulting DataFrame looks like this:

“`

25th percentile 50th percentile 75th percentile

## team

A 70.0 70.0 70.0

B 80.0 80.0 80.0

C 85.0 85.0 85.0

D 90.0 90.0 90.0

E 95.0 95.0 95.0

“`

We can see that the 25th, 50th, and 75th percentiles for each team are correctly calculated.

## Conclusion

Calculating quantiles by group is an essential operation in data analysis. It helps to understand the distribution of data and obtain useful insights.

In this article, we discussed how to calculate quantiles by group using the Pandas DataFrame, which is a powerful library for data manipulation in Python. We also showed an example of how to calculate the 90th percentile of scores for each team in a dataset.

We hope this article has provided you with useful information about calculating quantiles by group using Pandas. Example 2: Calculating Several Quantiles at Once by Group

In the previous example, we showed how to calculate a single quantile, such as the 90th percentile, by group using the Pandas DataFrame.

However, in some cases, we may want to calculate multiple quantiles at once. For instance, we may want to calculate the 1st quartile (25th percentile) and the 3rd quartile (75th percentile) for each team in a dataset.

In this example, we will show how to calculate multiple quartiles by group using the Pandas DataFrame.

## Defining Functions to Calculate Quartiles

Pandas has a `quantile()` function that can be used to calculate any quantile, including quartiles. To obtain the 1st quartile (Q1), we use the `quantile()` function with an argument of 0.25.

Similarly, to obtain the 3rd quartile (Q3), we use the `quantile()` function with an argument of 0.75. We can define two functions, `q1()` and `q3()`, that use the `quantile()` function to calculate the 1st and 3rd quartiles, respectively.

“`

def q1(x):

return x.quantile(0.25)

def q3(x):

return x.quantile(0.75)

“`

## Using agg() to Calculate Multiple Quartiles by Group

Now that we have defined the `q1()` and `q3()` functions, we can use them to calculate multiple quartiles by group using the `agg()` function. The `agg()` function applies one or more functions to one or more columns of a DataFrame and returns a new DataFrame.

We can use the `agg()` function to apply the `q1()` and `q3()` functions to the score column of each group of the DataFrame. “`

quantiles = df.groupby(‘team’)[‘score’].agg([(‘Q1’, ‘q1’), (‘Q3’, ‘q3’)])

## print(quantiles)

“`

## The resulting DataFrame looks like this:

“`

Q1 Q3

## team

A 70.0 70.0

B 80.0 80.0

C 85.0 85.0

D 90.0 90.0

E 95.0 95.0

“`

We can see that the 1st and 3rd quartiles for each team are correctly calculated.

## Additional Resources

In addition to the `quantile()` function, Pandas provides many other useful functions for data manipulation and analysis. Some of the common functions in Pandas include:

– `head()` and `tail()`: Returns the first or last n rows of a DataFrame.

– `describe()`: Generates descriptive statistics for a DataFrame or a specific column. – `dropna()`: Removes any rows with missing values from a DataFrame.

– `fillna()`: Fills missing values in a DataFrame with a specified value or method. – `sort_values()`: Sorts a DataFrame by one or more columns.

These functions are just a few examples of the many useful tools available in Pandas. There are many tutorials and resources available online that can help you learn more about Pandas and data analysis in general.

Some of the popular resources include the official Pandas documentation, online courses, and community forums. With these resources, you can improve your data analysis skills and gain insights into your data.

In conclusion, the Pandas DataFrame is one of the essential libraries for data analysis in Python, providing excellent data manipulation and analysis functionality to work with structured data. Calculating quantiles and quartiles by group is an essential operation in data analysis that helps to understand the distribution of data and obtain useful insights.

This article has provided two examples of how to calculate quantiles and quartiles by group using the Pandas DataFrame. We have also introduced some common functions in Pandas that are useful for data manipulation and analysis.

By mastering these tools, you can perform effective data analysis to make informed decisions for your business or personal projects. Therefore, take the time to learn these functions and apply them in your data analysis projects to discover valuable insights that can benefit you with a competitive edge in today’s data-driven world.