Introduction
In this lab, we will explore the DataFrame.quantile() method in Pandas. The DataFrame.quantile() method calculates the values at a given quantile over the specified axis of a DataFrame. We will learn how to use this method and understand its parameters.
VM Tips
After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.
Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.
If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.
Create a DataFrame
First, let's create a DataFrame using the pd.DataFrame() function from the Pandas library. We will create a DataFrame with three columns: 'Age', 'Height', and 'Weight'. This DataFrame will represent a group of individuals and their corresponding attributes. Each column will contain numerical values.
## Import the pandas library
import pandas as pd
## Create the DataFrame
df = pd.DataFrame({'Age': [12, 14, 11, 12], 'Height': [135, 140, 138, 147], 'Weight': [35, 38, 30, 45]})
Calculate the Quantile
Now, let's calculate the quantile of the DataFrame using the DataFrame.quantile() method. The q parameter represents the desired quantile(s) to compute, where 0 <= q <= 1. In this example, we will calculate the quantile at 0.5, which corresponds to the median.
## Calculate the quantile
quantile_50 = df.quantile(0.5)
Print the Result
Finally, let's print the result to see the calculated quantile.
## Print the result
print(quantile_50)
Summary
In this lab, we learned how to use the DataFrame.quantile() method in Pandas to calculate quantiles of a DataFrame. By specifying the desired quantile(s) using the q parameter, we can obtain the corresponding values. This method is useful for analyzing and summarizing numerical data in a DataFrame.