How to Find Percentile Stats of a Given Column Using Pandas | Saturn Cloud Blog (2024)

In this blog, we will learn how to leverage Pandas, the preferred Python library for data manipulation and analysis, when faced with the task of analyzing dataset distribution and extracting percentile statistics for a specific column. As a data scientist or software engineer, encountering scenarios demanding precise percentile insights in a dataset is common, and Pandas provides the optimal toolkit for such tasks. Throughout this post, we will delve into the process of extracting percentile statistics from a designated column using Pandas.

By Saturn Cloud |Monday, June 19, 2023| Miscellaneous| Updated: Wednesday, December 27, 2023

As a data scientist or software engineer, you might come across a situation where you need to analyze the distribution of a dataset and find the percentile statistics of a specific column. In such cases, Pandas is the go-to library for data manipulation and analysis in Python. In this post, we will discuss how to find percentile statistics of a given column using Pandas.

Table of Contents

1. What are Percentile Statistics?
2. Step-by-Step Guide to Finding Percentile Statistics Using Pandas
3. Common Errors
4. Best Practices
5. Conclusion

What are Percentile Statistics?

Percentiles are used to divide a dataset into equal parts based on the value of a specific column. For example, the 50th percentile (also known as the median) is the value that divides the dataset into two equal parts. Similarly, the 25th percentile (also known as the first quartile) is the value that divides the dataset into four equal parts. Percentile statistics are useful in understanding the distribution of a dataset and identifying outliers.

Let’s consider the following DataFrame:

 name age salary0 Alice 25 957671 Bob 30 509672 Charlie 52 520423 David 46 981174 Eva 46 967195 Frank 51 867646 Grace 50 624437 Henry 46 586868 Ivy 30 951219 Jack 58 5927110 Katie 38 7026011 Liam 47 9761812 Mia 48 6833213 Nathan 47 5463414 Olivia 37 8943915 Paul 28 8880616 Quinn 51 6925617 Rachel 31 6405318 Sam 52 8530619 Tyler 59 68671

Step-by-Step Guide to Finding Percentile Statistics Using Pandas

To find percentile statistics of a given column using Pandas, we will follow these steps:

1. Import the Pandas library and read the dataset into a Pandas DataFrame.
2. Identify the column for which you want to find percentile statistics.
3. Use the quantile() function to find the percentile statistics.

Let’s dive into each step in detail.

Step 1: Import the Pandas Library and Read the Dataset into a Pandas DataFrame

To use Pandas, we first need to import the library. We can do this using the following code:

import pandas as pd

Next, we need to read the dataset into a Pandas DataFrame. We can use the read_csv() function to read a CSV file into a DataFrame. For example, if our dataset is stored in a file called data.csv, we can read it into a DataFrame using the following code:

df = pd.read_csv('data.csv')

Step 2: Identify the Column for Which You Want to Find Percentile Statistics

Once we have the dataset loaded into a DataFrame, we need to identify the column for which we want to find percentile statistics. We can do this by referring to the column name. For example, if we want to find percentile statistics for the age column, we can use the following code:

column_name = 'age'

Step 3: Find the Percentile Statistics

Use the quantile() Function

The quantile() function is used to find the percentile statistics of a given column in a Pandas DataFrame. We can use this function to find any percentile, such as the median (50th percentile), first quartile (25th percentile), third quartile (75th percentile), etc.

The quantile() function takes a single argument, which is the percentile value as a decimal. For example, to find the median (50th percentile), we can use the following code:

median = df[column_name].quantile(0.5)print(median)

Output:

46.5

Similarly, to find the first quartile (25th percentile) and third quartile (75th percentile), we can use the following code:

q1 = df[column_name].quantile(0.25)q3 = df[column_name].quantile(0.75)

We can also find any other percentile by specifying the percentile value as a decimal. For example, to find the 90th percentile, we can use the following code:

p90 = df[column_name].quantile(0.9)

Method 2: Using numpy.percentile

import numpy as np# Load the employee data CSV file into a Pandas DataFramedf = pd.read_csv('data.csv')# Extract the salary column for analysissalary_data = df['salary']# Define the desired percentilespercentiles = [25, 50, 75]# Calculate percentiles using numpy.percentilepercentile_values = np.percentile(salary_data, percentiles)print(f"Salary Percentiles {percentiles}: {percentile_values}")

Output:

Salary Percentiles [25, 50, 75]: [68100.5 75557. 88517.25]

Common Errors

Error 1: Missing Data

Handle missing data appropriately using methods like dropna or imputation, especially if your dataset contains missing salary values.

Error 2: Incorrect Percentile Value

Ensure that the specified percentile values are within the valid range (0 to 100 for numpy.percentile and 0 to 1 for Pandas' quantile).

Best Practices

• Handle missing data appropriately using methods like dropna or imputation.
• Verify column names and ensure they match your DataFrame structure.
• Choose the method that best suits your needs; numpy.percentile for more flexibility or Pandas' quantile for simplicity.

Conclusion

In this post, we discussed how to find percentile statistics of a given column using Pandas. We learned that percentile statistics are useful in understanding the distribution of a dataset and identifying outliers. We also went through a step-by-step guide to finding percentile statistics using Pandas. By following these steps, you can easily find the percentile statistics of any column in a Pandas DataFrame.

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