What are parallel coordinates and parallel categories in plotly?
MACHINE LEARNING RECIPES DATA CLEANING PYTHON DATA MUNGING PANDAS CHEATSHEET     ALL TAGS

What are parallel coordinates and parallel categories in plotly?

What are parallel coordinates and parallel categories in plotly?

This recipe explains what are parallel coordinates and parallel categories in plotly

0

Recipe Objective

What are parallel coordinates and parallel categories in plotly, explain with example.

Parallel Coordinates in this each row of the dataframe is represented by a polyline mark which traverses a set of parallel axes, one for each of the dimensions. For this we have to use function "px.parallel_coordinates". Parallel Categories this is useful for visualizin multi-dimensional categorical data. Each variable in the data set is represented by a column of rectangles, where each rectangle corresponds to a discrete value taken on by that variable. The relative heights of the rectangles reflect the relative frequency of occurrence of the corresponding value.

Step 1 - Import the necessary libraries

import plotly.express as px import seaborn as sns

Step 2 - load the Sample data

Sample_data = px.data.iris() Sample_data.head()

Step 3 - Plot the Parallel coordinates graph

fig = px.parallel_coordinates(Sample_data, color="species_id", labels={"species_id": "Species", "sepal_width": "Sepal Width", "sepal_length": "Sepal Length", "petal_width": "Petal Width", "petal_length": "Petal Length", }, color_continuous_scale=px.colors.diverging.Tealrose, color_continuous_midpoint=2, ) fig.show()

Here in the above plot the functions used are:

color - It will consist name of a column in the dataframe, values from this are used to assign color to marks.

labels - this can be dictionary with string keys and string values, the column are used here is for axis titles. The keys of the dictionary should correspond to column names, and the values should correspond to the desired label to be displayed.

color_continuos scale - these are list of string, the strings should valid CSS-color. This list is used to build a continuous color scale when the column denoted by color contains numeric data.

color_continuos_midpoint - computes the bounds of the continuous color scale to have the desired midpoint.

Step 4 - Plot the Parallel categories graph

Sample_data2 = px.data.tips() fig = px.parallel_categories(Sample_data2, color="size", color_continuous_scale=px.colors.sequential.Inferno) fig.show()

Relevant Projects

Machine Learning Project to Forecast Rossmann Store Sales
In this machine learning project you will work on creating a robust prediction model of Rossmann's daily sales using store, promotion, and competitor data.

Census Income Data Set Project - Predict Adult Census Income
Use the Adult Income dataset to predict whether income exceeds 50K yr based on census data.

Data Science Project in Python on BigMart Sales Prediction
The goal of this data science project is to build a predictive model and find out the sales of each product at a given Big Mart store.

Machine Learning or Predictive Models in IoT - Energy Prediction Use Case
In this machine learning and IoT project, we are going to test out the experimental data using various predictive models and train the models and break the energy usage.

German Credit Dataset Analysis to Classify Loan Applications
In this data science project, you will work with German credit dataset using classification techniques like Decision Tree, Neural Networks etc to classify loan applications using R.

Customer Market Basket Analysis using Apriori and Fpgrowth algorithms
In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.

Predict Macro Economic Trends using Kaggle Financial Dataset
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.

Data Science Project - Instacart Market Basket Analysis
Data Science Project - Build a recommendation engine which will predict the products to be purchased by an Instacart consumer again.

Natural language processing Chatbot application using NLTK for text classification
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.

Loan Eligibility Prediction using Gradient Boosting Classifier
This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.