Pandas is a powerful and popular data manipulation library for Python. It provides a lot of functionalities for data analysis, including grouping data by one or more variables and then performing some computations on each group. One common computation that we want to perform on a group is the quantile analysis, which helps us understand the distribution of data in each group. In this article, we will explore how to use the groupby
method in pandas to perform quantile analysis on data with some code examples.
Groupby Method
Before we dive into the quantile analysis, let us first understand the groupby
method in pandas. It is a powerful method that allows us to group data based on one or more variables and then apply some computation on each group. The basic syntax for groupby
method is as follows:
groupby(variable).aggregate(computation)
Here, variable
is the variable or variables that we want to group our data by. It can be a column name, or a list of column names if we want to group by multiple variables. The computation
is the computation that we want to apply on each group. It can be a single aggregation function, such as sum
, mean
, max
, min
, count
, or a combination of aggregation functions. The groupby
method returns a DataFrame
object, with the group variables set as the index.
Quantile Analysis
Quantile analysis is a statistical technique that allows us to understand the distribution of data values in each group. It divides the data into equalsized segments and calculates the value of each segment based on some measure, such as the mean, median, or sum. The most commonly used quantiles are the quartiles, which divide the data into four segments, and the percentiles, which divide the data into 100 segments.
To perform quantile analysis on data using pandas, we can use the quantile
method, which returns the value of the specified quantile for each group. The quantile
method takes a quantile value as input, which is a float between 0 and 1, and returns the value of that quantile for each group. The basic syntax for quantile
method is as follows:
groupby(variable).quantile(quantile_value)
Here, variable
is the variable or variables that we want to group our data by. It can be a column name or a list of column names if we want to group by multiple variables. The quantile_value
is the value of the quantile that we want to calculate, which is a float between 0 and 1. The quantile
method returns a DataFrame
object, with the group variables set as the index and the quantile values as the column names.
Code Examples
Let us now look at some code examples of how to use the groupby
method in pandas to perform quantile analysis on data.
Example 1: Groupby and Quantile
In this example, we will use the groupby
method to group the titanic
dataset by the Sex
variable and then calculate the 25th and 75th percentiles for the Age
variable in each group. We will use the quantile
method to perform this analysis.
import pandas as pd
titanic = pd.read_csv('titanic.csv')
# Groupby method to group by Sex variable and calculate 25th and 75th percentiles for Age variable in each group.
titanic.groupby('Sex')['Age'].quantile([0.25, 0.75])
The output of this code will be a DataFrame
object with the group variable Sex
set as the index and the quantile values 0.25 and 0.75 as the column names.
Example 2: MultiGroupby and Quantile
In this example, we will use the groupby
method to group the titanic
dataset by the Sex
and Pclass
variables and then calculate the 10th, 50th, and 90th percentiles for the Age
variable in each group. We will use the quantile
method to perform this analysis.
import pandas as pd
titanic = pd.read_csv('titanic.csv')
# Groupby method to group by Sex and Pclass variables and calculate 10th, 50th, and 90th percentiles for Age variable in each group.
titanic.groupby(['Sex', 'Pclass'])['Age'].quantile([0.1, 0.5, 0.9])
The output of this code will be a DataFrame
object with the group variables Sex
and Pclass
set as the index and the quantile values 0.1, 0.5, and 0.9 as the column names.
Example 3: Custom Aggregation Functions and Quantile
In this example, we will use the groupby
method to group the titanic
dataset by the Sex
variable and then calculate the 25th, 50th, and 75th percentiles for the Age
variable and the mean
and count
of the Fare
variable in each group. We will use the agg
method to perform this analysis.
import pandas as pd
titanic = pd.read_csv('titanic.csv')
# Custom aggregation function to calculate the median value of a series.
def median(s):
return s.quantile(0.5)
# Groupby method to group by Sex variable and calculate 25th, 50th, and 75th percentiles for Age variable and mean and count of Fare variable in each group.
titanic.groupby('Sex').agg({'Age': [median, lambda x: x.quantile(0.25), lambda x: x.quantile(0.75)], 'Fare': ['mean', 'count']})
The output of this code will be a DataFrame
object with the group variable Sex
set as the index and the quantile values 0.25, 0.5, and 0.75, as well as the mean and count of the Fare
variable, as the column names.
Conclusion
In this article, we explored how to use the groupby
method in pandas to perform quantile analysis on data. We learned that we can use the quantile
method to calculate the value of a specific quantile for each group. We saw some code examples of how to perform this analysis on the titanic
dataset by grouping the data based on one or more variables and then calculating the value of one or more quantiles for each group. We also saw how to use custom aggregation functions to perform more complex analyses. Pandas' groupby
functionality is very powerful and can be used to gain deeper insights into our data.
let me expand on the previous topics mentioned in the article.
Pandas Groupby Method
The groupby
method in pandas is an essential data manipulation tool that splits a DataFrame into multiple groups based on one or more variables and then performs some computation on each group. The basic syntax of the groupby
method is:
groupby(variable).aggregate(computation)
Here, variable
is the variable or variables based on which the DataFrame is grouped. It can be a single column or multiple columns. The computation
can be a single aggregation function or multiple aggregation functions separated by a comma. The aggregation functions can be any of the following:
sum()
: returns the sum of all values in the groupmean()
: returns the mean of all values in the groupmedian()
: returns the median of all values in the groupmax()
: returns the maximum value in the groupmin()
: returns the minimum value in the groupcount()
: returns the count of all values in the groupstd()
: returns the standard deviation of all values in the groupvar()
: returns the variance of all values in the group
The groupby
method returns a GroupBy
object, which can be used to perform various operations on the grouped data.
Quantile Analysis
Quantile analysis is a statistical technique that helps us understand the distribution of data in a group or population. It divides the data into equalsized segments based on percentiles and calculates the value of each segment based on some measure such as the mean, median, or sum. For example, the 25th percentile of a dataset divides the data into four equalsized segments, with the lowest 25% of values being in the first segment, the next 25% of values being in the second segment, and so on.
The most commonly used quantiles are quartiles (25th, 50th, and 75th percentiles) and percentiles (10th, 25th, 50th, 75th, and 90th percentiles). Quantile analysis can help us understand how the data is distributed and whether it has any outliers or extreme values.
In pandas, we can use the quantile
method to calculate the value of a specific quantile for each group. The quantile
method takes a quantile value as input, which is a float between 0 and 1, and returns the value of that quantile for each group.
Custom Aggregation Functions
In addition to the builtin aggregation functions, we can also create custom aggregation functions to perform more complex analyses on grouped data. To create a custom aggregation function, we can define a function that takes a Series object as input and returns a value as output. We can then pass this function to the agg
method of the GroupBy
object to use it as an aggregation function. For example, suppose we want to calculate the range of values in each group. We can define a custom function as follows:
def range(s):
return s.max()  s.min()
We can then use this function in the agg
method as follows:
grouped_data.agg({'column_name': range})
Here, grouped_data
is a GroupBy
object, and column_name
is the name of the column on which the DataFrame is grouped.
In conclusion, the groupby
method in pandas is a powerful tool for data analysis, and it can be used together with quantile analysis and custom aggregation functions to gain deeper insights into our data.
Popular questions
Sure, here are five questions related to the article:
 What is the syntax of the
groupby
method in pandas?
Answer: The syntax of thegroupby
method in pandas is:
groupby(variable).aggregate(computation)

What are some examples of aggregation functions that can be used with
groupby
?
Answer: Some examples of aggregation functions that can be used withgroupby
includesum()
,mean()
,median()
,max()
,min()
,count()
,std()
, andvar()
. 
What is quantile analysis?
Answer: Quantile analysis divides the data into equalsized segments based on percentiles and calculates the value of each segment based on some measure such as the mean, median, or sum. It helps us understand how the data is distributed and whether it has any outliers or extreme values. 
How can we calculate the value of a specific quantile for each group in pandas?
Answer: We can use thequantile
method in pandas to calculate the value of a specific quantile for each group. The method takes a quantile value as input, which is a float between 0 and 1. 
How can we create custom aggregation functions in pandas?
Answer: To create a custom aggregation function in pandas, we can define a function that takes a Series object as input and returns a value as output. We can then pass this function to theagg
method of theGroupBy
object.
Tag
"Quantiles"
Example code:
df.groupby('Category')['Sales'].quantile(0.25)
This code calculates the 25th percentile (lower quartile) of sales for each category using the pandas groupby function and the quantile method.