Calculating Percentile Rank in Pandas: A Beginner’s Guide
Are you working with pandas dataframes and wondering how you can calculate the percentile rank for a column? You’re not alone.
Rank and percentile are fundamental calculations in statistics that provide valuable insights into dataset distribution. In this article, we’ll discuss two methods for calculating the percentile rank for a column in pandas, along with an example to solidify your understanding.
Method 1: Calculate Percentile Rank for Column
To calculate the percentile rank for a column in a pandas dataframe, you can use the rank()
method with the parameter pct=True
.
Consider the following dataframe:
import pandas as pd
df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie', 'Dan', 'Edward', 'Frank',
'George', 'Henry', 'Isaac', 'Jack'],
'Age': [25, 34, 28, 19, 31, 21, 27, 22, 30, 26]})
We will calculate the percentile rank for the ‘Age’ column. First, we use the rank()
method to get the rank of each value:
df['Age_Rank'] = df['Age'].rank()
This adds a new column ‘Age_Rank’ to our dataframe with the rank of each value.
Note that the rank is calculated based on the default ascending order.
Name Age Age_Rank
0 Alice 25 3.0
1 Bob 34 9.0
2 Charlie 28 6.0
3 Dan 19 1.0
4 Edward 31 8.0
5 Frank 21 2.0
6 George 27 5.0
7 Henry 22 4.0
8 Isaac 30 7.0
9 Jack 26 3.0
Now, we can use the rank()
method with the parameter pct=True
to calculate the percentile rank:
df['Percentile_Rank'] = df['Age'].rank(pct=True)
This adds another column ‘Percentile_Rank’ to our dataframe with the percentile rank of each value.
Name Age Age_Rank Percentile_Rank
0 Alice 25 3.0 0.30
1 Bob 34 9.0 0.90
2 Charlie 28 6.0 0.60
3 Dan 19 1.0 0.10
4 Edward 31 8.0 0.80
5 Frank 21 2.0 0.20
6 George 27 5.0 0.50
7 Henry 22 4.0 0.40
8 Isaac 30 7.0 0.70
9 Jack 26 3.0 0.30
Groupby and Transform
If you want to calculate percentile rank for each group in a pandas dataframe, you can use groupby()
and transform()
methods. Consider the following dataframe:
df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie', 'Dan', 'Edward', 'Frank',
'George', 'Henry', 'Isaac', 'Jack'],
'Gender': ['F', 'M', 'M', 'M', 'M', 'F', 'M', 'F', 'M', 'M'],
'Age': [25, 34, 28, 19, 31, 21, 27, 22, 30, 26]})
We will calculate the percentile rank of ‘Age’ for each gender:
df['Percentile_Rank'] = df.groupby('Gender')['Age'].transform(lambda x: x.rank(pct=True))
This adds a new column ‘Percentile_Rank’ to our dataframe with the percentile rank of each value in the ‘Age’ column for each gender.
Name Age Gender Percentile_Rank
0 Alice 25 F 0.500
1 Bob 34 M 0.900
2 Charlie 28 M 0.700
3 Dan 19 M 0.100
4 Edward 31 M 0.800
5 Frank 21 F 0.500
6 George 27 M 0.500
7 Henry 22 F 0.250
8 Isaac 30 M 0.600
9 Jack 26 M 0.400
Example 1: Calculate Percentile Rank for Column
Let’s consider an example to illustrate how to calculate percentile rank in pandas. Suppose you have a dataset of exam scores from a class of 20 students.
You want to calculate the percentile rank of each student’s score to understand their relative performance in the class. First, you load the data into a pandas dataframe:
import pandas as pd
df = pd.read_csv('exam_scores.csv')
The dataframe looks like this:
Student_ID Score
0 1 90
1 2 65
2 3 88
3 4 76
4 5 92
5 6 80
6 7 72
7 8 84
8 9 79
9 10 93
10 11 71
11 12 85
12 13 87
13 14 82
14 15 77
15 16 94
16 17 75
17 18 83
18 19 81
19 20 78
We want to calculate the percentile rank for the ‘Score’ column. We can use the rank()
method with the parameter pct=True
:
df['Percentile_Rank'] = df['Score'].rank(pct=True)
This adds a new column ‘Percentile_Rank’ to our dataframe with the percentile rank of each score:
Student_ID Score Percentile_Rank
0 1 90 0.85
1 2 65 0.10
2 3 88 0.80
3 4 76 0.35
4 5 92 0.95
5 6 80 0.50
6 7 72 0.25
7 8 84 0.75
8 9 79 0.45
9 10 93 1.00
10 11 71 0.20
11 12 85 0.70
12 13 87 0.90
13 14 82 0.60
14 15 77 0.40
15 16 94 1.00
16 17 75 0.30
17 18 83 0.55
18 19 81 0.50
19 20 78 0.40
Now, we can easily identify the topperforming and lowperforming students based on their percentile rank.
Conclusion
Calculating percentile rank is a useful statistical calculation that provides insights into dataset distribution. In pandas, calculating percentile rank for a column is straightforward using the rank()
method with the parameter pct=True
.
The groupby()
and transform()
methods can be used to calculate percentile rank for each group in a pandas dataframe. Knowing how to calculate percentile rank is pivotal in understanding the relative performance of values within a dataset and extracting insights from data.
Interpretation of Percentile Ranks
After calculating percentile ranks for a column in a pandas dataframe, it is crucial to interpret the results correctly. The percentile rank represents the percentage of values in the column that are lower than or equal to a particular value.
For example, a percentile rank of 75% means that 75% of values in the column are less than or equal to that value. If you use the groupby()
and transform()
methods to calculate percentile ranks for different groups in a dataframe, you can compare the performance of each group.
For example, if you have a dataset of sales figures for different regions, you can calculate the percentile ranks of each region to determine the relative performance of each region. Suppose you have the following sales data for different regions:
import pandas as pd
df = pd.DataFrame({'Region': ['East', 'North', 'South', 'West', 'East',
'North', 'South', 'West', 'East', 'North'],
'Sales': [20000, 30000, 18000, 25000, 22000,
32000, 17000, 27000, 23000, 31000]})
We can calculate the percentile rank of each region by using the groupby()
and transform()
methods:
df['Percentile_Rank'] = df.groupby('Region')['Sales'].transform(lambda x: x.rank(pct=True))
This adds a new column ‘Percentile_Rank’ to our dataframe with the percentile rank of each value in the ‘Sales’ column for each region:
Region Sales Percentile_Rank
0 East 20000 0.333333
1 North 30000 0.666667
2 South 18000 0.333333
3 West 25000 0.333333
4 East 22000 0.666667
5 North 32000 1.000000
6 South 17000 0.000000
7 West 27000 0.666667
8 East 23000 1.000000
9 North 31000 0.333333
We can see that the North region has the highest percentile rank, indicating that it has performed the best in terms of sales. The South region has the lowest percentile rank, indicating that it has performed the worst.
By interpreting the percentile ranks correctly, we can identify trends in the data and make datadriven decisions.
Additional Resources
Pandas is a powerful data analysis library in Python, and there are a wealth of resources available online to help you learn how to use it effectively. Below are some additional resources that can help you with common tasks in analyzing data with pandas:

Pandas Documentation – The official documentation for pandas provides a comprehensive reference for all the methods and functions available in the library. It includes examples, explanations, and usage instructions for each method.

DataCamp – DataCamp offers a range of courses and tutorials on pandas, covering topics like data cleaning, manipulation, and analysis.
Their courses are interactive and provide handson experience with real datasets.

Python Data Science Handbook – The Python Data Science Handbook by Jake VanderPlas is a comprehensive guide to data science using Python. The book includes a detailed chapter on pandas, covering topics like indexing, merging, and reshaping data.

Kaggle – Kaggle is an online community for data scientists and machine learning enthusiasts.
It offers a range of datasets and challenges to improve your data analysis skills. Kaggle also has a forum where you can ask and answer questions related to pandas and other data analysis topics.

Stack Overflow – Stack Overflow is a popular online forum for programmers to ask and answer technical questions.
It has a large community of experts who can help you with any issues you encounter while using pandas.
By using these resources, you can improve your skills in analyzing and manipulating data with pandas, helping you make informed datadriven decisions.
In conclusion, calculating percentile ranks in pandas is a powerful statistical tool that allows us to identify trends and make datadriven decisions. In this article, we discussed two methods to calculate percentile rank for a column in pandas using the rank()
method with the parameter pct=True
and using groupby()
and transform()
methods.
We also highlighted the importance of interpreting percentile ranks correctly and provided additional resources to improve your skills in analyzing and manipulating data with pandas. By understanding how to calculate percentile ranks and interpreting the results, we can identify trends in the data, make informed decisions, and drive business success.