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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Interactive Data Visualization Plots with Plotly and Cufflinks

Hardikkumar
Hardikkumar Last Updated : 28 Oct, 2024
6 min read

This article was published as a part of the Data Science Blogathon

Introduction

Data is everywhere in today’s world of data, and we can only benefit from it if we can extract information from data. Data visualization is the most visually appealing aspect of data analysis because it allows us to interact with the data. It’s that magical technique for conveying information to large groups of people in a single glance and creating interesting stories out of data. Pandas is one of the most popular and widely used data analysis tools in Python. It also has a built-in plot function for samples. When it comes to interactive visualization, however, Python users who don’t have front-end engineering skills may have some challenges, like many libraries, such as D3, chart.js, require some javascript knowledge. Plotly and Cufflinks come in handy at this point.

When there is a large amount of data and it becomes difficult for businesses to extract decisive information from it, data visualization plays an important role in making critical business decisions.

Plotly is a charting library built on top of d3.js that can be used directly with Pandas data frames thanks to another library called Cufflinks.

We’ll show you how to use Plotly interactive plots with Pandas data frames in this quick tutorial. To keep things simple, we’ll use Jupyter Notebook (installed using Anaconda Distribution with Python) and the famous Titanic dataset.

Data Visualization in Python

After completing data cleaning and manipulation, the next step in the data analysis process is to extract meaningful insights and conclusions from the data, which can be accomplished using graphs and charts. Python has a number of libraries that can be used for this purpose. We are typically only taught about the two libraries matplotlib and seaborn. These libraries include tools for creating line charts, pie charts, bar plots, box plots, and a variety of other plots. You’re probably wondering why we need other libraries for data visualization if we already have matplotlib and seaborn. When I first heard about plotly and cufflinks, I had the same question in my head.

Plotly

Plotly’s most recent release was 5.1.0, while cufflinks’ was 0.17.5. Because older cufflink versions do not support newly released plotly versions, it is critical to update both packages at the same time or find compatible versions. On Anaconda Prompt, run the following commands to install plotly (or on Terminal if you use OS or Ubuntu)

Plotly is a charting and open-source library that allows for interactive plotting. Python, R, MATLAB, Arduino, and REST, among others, are among the programming languages supported by the library.

Cufflink is a python library that connects plotly and pandas, allowing us to draw charts directly on data frames. It’s essentially a plug-in.
 
Plotly charts are interactive, allowing us to hover overvalues, zoom in and out of graphs, and identifying outliers in the dataset. Matplotlib and seaborn charts, on the other hand, are static; we can’t zoom in or out, and every value on the chart isn’t detailed. Plotly’s most important feature is that it allows us to create dynamic web charts directly from python, which is not possible with matplotlib. We can also make animations and interactive graphs out of geographical, scientific, statistical, and financial data using plotly.

Install “plotly” and “cufflinks“ using an anaconda environment 

conda install -c plotly plotly
conda install -c conda-forge cufflinks-py

or using pip

pip install plotly --upgrade
pip install cufflinks --upgrade

Loading Libraries

The Pandas, Plotly, and Cufflinks libraries will be loaded first. Because plotly is an online platform, it requires a login credential to use it online. We’ll use offline mode in this article, which is sufficient for Jupyter Notebook.

#importing Pandas 
import pandas as pd
#importing plotly and cufflinks in offline mode
import cufflinks as cf
import plotly.offline
cf.go_offline()
cf.set_config_file(offline=False, world_readable=True)

Loading Dataset

We mentioned that we’ll be using the Titanic dataset, which you can get from this kaggle_link. Only the train.csv file will be used.

Python Code:

import pandas as pd
import cufflinks as cf
import plotly.offline

df=pd.read_csv("train(1).csv")
print(df.head())

Histogram

Histograms can be used to inspect the distributions of a feature, such as the “Age” feature in this case. We simply use the (dataframe[“column name”]) syntax to select a column and then add the iplot function. As an example, we can specify bin size, theme, title, and axis names. With the “help(df.iplot)” command, you can see all the parameters for the iplot parameter.

df["Age"].iplot(kind="histogram", bins=20, theme="white", title="Passenger's Ages",xTitle='Ages', yTitle='Count')
histogram | Interactive data visualization

 

You can plot two different distributions as two different columns if you want to compare them. We will, for example, plot the ages of female and male passengers in the same plot.

df["male_age"]=df[df["Sex"]=="male"]["Age"]
df["female_age"]=df[df["Sex"]=="female"]["Age"]df[["male_age","female_age"]].iplot(kind="histogram", bins=20, theme="white", title="Passenger's Ages",
         xTitle='Ages', yTitle='Count')
Multiple plots in single chart | Interactive data visualization

 

Heatmap

Heatmaps can be used for a variety of purposes, but we’ll use them to check the correlation between features in a dataset as an example.

Heatmap

 

Boxplot

Boxplots are extremely useful for quickly interpreting data skewness, outliers, and quartile ranges. We’ll now use a boxplot to display the “Fare” distribution for each Titanic class.

#we will get help from pivot tables to get Fare values in different columns for each class.
df[['Pclass', 'Fare']].pivot(columns='Pclass', values='Fare').iplot(kind='box')
Boxplot

 

Scatter Plots

Scatter plots are commonly used to visualize the relationship between two numerical variables. For the variables “Fare” and “Age,” we’ll use scatter plots. “Categories” allows us to display the variables of a selected feature in various colors (sex of passengers in this case).

df.iplot(kind="scatter", theme="white",x="Age",y="Fare",
            categories="Sex")
Scatterplots | Interactive data visualization

 

a quick reminder: the “categories” parameter must be a string or float64 type column. For example, in the Bubble Chart example, you must convert the integer type “Survived” column to float64 or string.

Bubble Chart

We can use bubble charts to see multiple variable relationships at the same time. With the “categories” and “size” parameters in plotly, we can easily adjust colour and size subcategories. With the “text” parameter, we can also specify the hover text column.

#converting Survived column to float64 to be able to use in plotly
df[['Survived']] = df[['Survived']].astype('float64', copy=False)df.iplot(kind='bubble', x="Fare",y="Age",categories="Survived", size='Pclass', text='Name', xTitle='Fare', yTitle='Age')
Bubblechart |Interactive data visualization

 

Bar Graph

Bar graphs are good to present the data of different groups that are being compared with each other. Plus they can be used stacked to show different variable effects. We will make a bar graph to show survived passenger count by sex.

survived_sex = df[df['Survived']==1]['Sex'].value_counts()
dead_sex = df[df['Survived']==0]['Sex'].value_counts()
df1 = pd.DataFrame([survived_sex,dead_sex])
df1.index = ['Survived','Dead']
df1.iplot(kind='bar',barmode='stack', title='Survival by the Sex')
Bargraph

 

I’ve tried to explain everything as simple as possible. I hope it makes it easier for newcomers to pick up plotly.

Plotly also provides scientific charts, 3-D charts, maps, and animations. You can visit plotly documentation here for more details.

Check out EDA – Exploratory Data Analysis Using Python Pandas and SQL CLICK TO READ

EndNote

Thank you for reading!
I hope you enjoyed the article and increased your knowledge.
Please feel free to contact me on Email
Something not mentioned or want to share your thoughts? Feel free to comment below And I’ll get back to you.

About the Author

Hardikkumar M. Dhaduk
Data Analyst | Digital Data Analysis Specialist | Data Science Learner
Connect with me on Linkedin
Connect with me on Github

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.
Hardikkumar
Hardikkumar

Data Analyst | Digital Data Analysis Specialist | Data Science Learner Currently working in Data Analytics field. I have done my post-graduation. My main focus is growing in the fields of Data Science and Analytics.

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