How to compute quantiles using Pandas?
MACHINE LEARNING RECIPES DATA CLEANING PYTHON DATA MUNGING PANDAS CHEATSHEET     ALL TAGS

How to compute quantiles using Pandas?

How to compute quantiles using Pandas?

This recipe helps you compute quantiles using Pandas

Recipe Objective

quantile() function return values at the given quantile over requested axis, a numpy percentile.

So this recipe is a short example on How to compute quantiles in pandas. Let's get started.

Step 1 - Import the library

import pandas as pd

Let's pause and look at these imports. Pandas is generally used for performing mathematical operation and preferably over arrays.

Step 2 - Setup the Data

df = pd.DataFrame({"A":[0, 1, 2, 3, 5, 9], "B":[11, 5, 8, 6, 7, 8], "C":[2, 5, 10, 11, 9, 8]})

Here we have setup a random dataset with some random values in it.

Step 3 - Finding Quantiles

print(df.quantile(.5,axis=0)) print(df.quantile(.25,axis=0))

Here we are applied quantile() to find out the quantiles. 0.5 signify median and 0.25, first quater quantile. Similarily we can find any values quantiles.

Step 4 - Let's look at our dataset now

Once we run the above code snippet, we will see:

Scroll down to the ipython file to look at the results.

We can see the how quantiles being calculated for each series at our specified value.

Relevant Projects

Time Series LSTM forecasting
In this project, we will use time-series forecasting to predict the values of a sensor using multiple dependent variables. A variety of machine learning models are applied in this task of time series forecasting. We will see a comparison between the LSTM, ARIMA and Regression models. Classical forecasting methods like ARIMA are still popular and powerful but they lack the overall generalizability that memory-based models like LSTM offer. Every model has its own advantages and disadvantages and that will be discussed. The main objective of this article is to lead you through building a working LSTM model and it's different variants such as Vanilla, Stacked, Bidirectional, etc. There will be special focus on customized data preparation for LSTM.

Medical Image Segmentation Deep Learning Project
In this deep learning project, you will learn to implement Unet++ models for medical image segmentation to detect and classify colorectal polyps.

Ecommerce product reviews - Pairwise ranking and sentiment analysis
This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.

Inventory Demand Forecasting using Machine Learning in R
In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.

Build a Face Recognition System in Python using FaceNet
In this deep learning project, you will build your own face recognition system in Python using OpenCV and FaceNet by extracting features from an image of a person's face.

Predict Credit Default | Give Me Some Credit Kaggle
In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.

Customer Churn Prediction Analysis using Ensemble Techniques
In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.

RASA NLU chatbot creation
The project will use rasa NLU for the Intent classifier, spacy for entity tagging, and mongo dB as the DB. The project will incorporate slot filling and context management and will be supporting the following intent and entities. Intents : product_info | ask_price|cancel_order Entities : product_name|location|order id The project will demonstrate how to generate data on the fly, annotate using framework and how to process those for different pieces of training as discussed above .

Predict Churn for a Telecom company using Logistic Regression
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.

Locality Sensitive Hashing Python Code for Look-Alike Modelling
In this deep learning project, you will find similar images (lookalikes) using deep learning and locality sensitive hashing to find customers who are most likely to click on an ad.