# Calculating Percentile Rank in Pandas: A Beginner’s Guide

## 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 top-performing and low-performing 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 data-driven 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:

1. 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.

2. 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 hands-on experience with real datasets.

3. 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.

4. 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.

5. 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 data-driven decisions.

In conclusion, calculating percentile ranks in pandas is a powerful statistical tool that allows us to identify trends and make data-driven 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.