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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

Data Exploration with the dtale Library in Python

K
Kavish Last Updated : 02 Dec, 2020
6 min read

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

Introduction

Data Analysis is the most essential part of any data science project. Analyzing the data gives us some important and beautiful insights about the data. There are many libraries in Python to perform analysis like Pandas, Matplotlib, Seaborn, etc.

Recently developers introduced a new library ‘dtale’ to perform analysis with fewer lines of code.

dtale

D-Tale is the combination of a Flask backend and a React front-end to bring us an easy way to view & analyze Pandas data structures. It integrates seamlessly with Jupyter notebooks & Python terminals. Currently, this tool supports such Pandas objects as DataFrame, Series, MultiIndex, DatetimeIndex & RangeIndex.

Let’s dive in and see how to work with this dtale library in Python!

 

Setting up the Environment for dtale

conda create -n dtale python
conda activate dtale

Now we first need to install the dtale library in the respective environment.

pip install dtale

 

Exploratory Data Analysis with dtale

Now let’s dive into our data with dtale.

import dtale   ## importing D-Tale library

Importing Seaborn will help us to load data sets. Seaborn has many default Data sets. We will be using the ‘planets’ data set for the below analysis.

import seaborn as sns
df=sns.load_dataset(‘planets’)
dtale.show(df, ignore_duplicate=True)

After running the above code, the user can access below interactive table to perform multiple actions and visualize the dataset.

import dtale

  D-tale interactive table shows the number of columns and rows of the dataset at the top left corner (in this case, 1035 rows and 6 columns)

D-Tale shows us the data in the same way that pandas would. The only difference is the menu in the top left corner that allows us to do many things with the data.

Clicking on any of the column headings opens a drop-down menu that gives us options to sort the data and display it exactly as we want it.

column method dtale

 

The menu has many options. Let’s explore each one by one.

Describe option:

The most interesting options we can find in this menu are Describe that shows us a statistical analysis of the selected column and Column Analysis that shows us a histogram of the selected column.

Describe Option

                                                      Describe Option

After selecting the required column we will be able to see three major options Describe, Histogram, and Categories options in the middle of the screen. The describe option gives us statistical information about selected column data and Histogram gives the corresponding graphical representation. The third option is Categories In that, we will be able to select the X-axis variable from the Category Breakd.

Distance

                                                                          Categories

Another important and awesome feature of this Library is the ‘Code Export’ option at the top right corner. This feature provides the code for a particular output. also, In the Describe menu, we will be able to see all the outliers and Uniques values at the bottom.

python code

                                                                            Python code

Dimensions/Main Menu:

In the D-Tale main menu, you will find all the options available in D-Tale. This is where all the magic of D-Tale is. By clicking the highlighted play button at the top left corner of DataFrame you will be able to view all Options as shown in the below image.

dimensions dtale

                                                                            Main menu

D-Tale provides the ability to perform simple operations such as sorting, filtering, and formatting.

The Duplicates option is the main will help us to remove duplicate values in the Data set and the Custom Filter option has the ability to apply filters to columns. The most important Options used for data visualization are Correlations, Charts, and Heatmaps.

 

Correlations:

Once you click on the Correlations option in the Main menu, it will redirect you to another Correlation Tab. In this, we have to Choose variables in the ‘View correlation for’ dropdown. After selecting values in Dropdown if you click on the Correlation value, you will be able to see a visual representation of the Correlation between two variables in the Scatter plot as shown in the below image.

correlation

                                                                                Correlation

Charts:Charts Option in the Main menu helps us to visualize all different kinds of charts (Line, Bars, ScatterPlot, Circular, Wordcloud, Heatmap, 3D ScatterPlot, and Surface). One of the fantastic features of D-Tale is Interactive plots, it also supports 3d Charts and animations with the group by and aggregation functions also. It is really useful as you can visualize any type of relationship using different charts. The below images show a few examples of charts that we can generate using the Chart option in D-Tale.

charts
                                                                                Scatter plot
word cloud
                                                                                 WordCloud

Finally, one of the fanciest options is Heatmap. It shows the colors in the data table according to the value of each cell. the export CSV option in can b used for exporting the graphical data into a CSV file

heatmap
                                                                         Heatmap

Conclusion

In this paper, we saw how D-tale can save our time and efforts to create visually appealing and insightful EDA reports. This EDA Reporting tool is highly useful in analyzing and visualizing all types of datasets. D-Tale is one of the popular libraries in the Data Science community. There are a lot of options are available in D-Tale which helps us to understand the trend of Data from a statistical perspective, Hence keep exploring.

 

Reference

https://pypi.org/project/dtale/
K
Kavish

I am a freelance Data Science professional with immense passion to solve Data driven problem. I have almost one year of industry experience. I love to write, explore new use cases of data science, and travel. I am open to a collaboration to learn, teach and grow more.

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Abhishek Acharya
Abhishek Acharya

Very helpful article thanks to the author...

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