How to make waterfall charts in plotly?

This recipe helps you make waterfall charts in plotly

Recipe Objective

How to make waterfall charts in plotly.

Waterfall charts these are the charts used for uderstanding the cumulative effects of sequentially added positive or negative values for a given variable. The charts are 2-dimensional plot commonly used in financial analysis to understand how practical value goes through gains and losses over time.

Step 1 - Import library

import plotly.graph_objects as go

Step 2 - Take Sample data

Months = ['jan','feb','mar','apr','may','jun','jul','aug','sep','oct','nov','dec'] Values = [900,-150,400,-200,100,-800,400,-200,500,-700,200,50]

Step 3 - Plot graph

fig = go.Figure(go.Waterfall(x=Months,y=Values)) fig.show()

What Users are saying..

profile image

Jingwei Li

Graduate Research assistance at Stony Brook University
linkedin profile url

ProjectPro is an awesome platform that helps me learn much hands-on industrial experience with a step-by-step walkthrough of projects. There are two primary paths to learn: Data Science and Big Data.... Read More

Relevant Projects

Stock Price Prediction Project using LSTM and RNN
Learn how to predict stock prices using RNN and LSTM models. Understand deep learning concepts and apply them to real-world financial data for accurate forecasting.

End-to-End Speech Emotion Recognition Project using ANN
Speech Emotion Recognition using RAVDESS Audio Dataset - Build an Artificial Neural Network Model to Classify Audio Data into various Emotions like Sad, Happy, Angry, and Neutral

PyTorch Project to Build a GAN Model on MNIST Dataset
In this deep learning project, you will learn how to build a GAN Model on MNIST Dataset for generating new images of handwritten digits.

Learn How to Build PyTorch Neural Networks from Scratch
In this deep learning project, you will learn how to build PyTorch neural networks from scratch.

MLOps Project on GCP using Kubeflow for Model Deployment
MLOps using Kubeflow on GCP - Build and deploy a deep learning model on Google Cloud Platform using Kubeflow pipelines in Python

Credit Card Default Prediction using Machine learning techniques
In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.

Build CI/CD Pipeline for Machine Learning Projects using Jenkins
In this project, you will learn how to create a CI/CD pipeline for a search engine application using Jenkins.

Multi-Class Text Classification with Deep Learning using BERT
In this deep learning project, you will implement one of the most popular state of the art Transformer models, BERT for Multi-Class Text Classification

Time Series Forecasting with LSTM Neural Network Python
Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.

MLOps AWS Project on Topic Modeling using Gunicorn Flask
In this project we will see the end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable machine learning models by using AWS