Exploratory Data Analysis (EDA) is an essential step in the data science project lifecycle. All data scientists have to do this step to get a better understanding of the data they are working on. In this article, I am going to share with you the top 10 Exploratory Data Analysis (EDA) Tools you can try to make this process easier and faster for you. What is Exploratory Data Analysis aka EDA? For those of you who do not know what exploratory data analysis (EDA) is, it is a term that appeared first in 1977 from a statistician name John W. Tukey. He defined it as “detective work – numerical detective work – or counting detective work – or graphical detective work”. If like me, you were confused by the original definition, you can think of EDA as a process in which the data analyst analyses/examines/go through a dataset without having any preconceived idea as to what he/she is going to discover. The goal is to understand what the data is going to tell you about the studied topic. Let the data speak to you. Practically, data scientists use this methodology to analyze, examine and summarize the main characteristics of their dataset. Indeed, it happens through summary information presented as insights and accompanied by various data visualization methods. The results of an EDA help a Data Scientist learn the best way to handle data sources to get the insights you need. Additionally, the whole process makes it easier to spot anomalies, test a hypothesis, discover patterns, or check assumptions. EDA is essentially used to understand what data can show beyond the conventional hypothesis testing task. It gives a better understanding of the data variables and features, along with the relationships between them. It can also help determine if the statistical techniques considered for data analysis are suitable. What do Data Scientist use as Tools for Exploratory Data Analysis (EDA) A trained data scientist often does EDA through standard programming tools such as Python and Pandas. As technology advances, a few libraries were created to ease the process and save a lot of time writing repetitive code. Below are few libraries that may make EDA faster and a bit more intuitive, especially if you are not a code-savvy person. Give them a try in your spare time, and let me know your favorite. 1. SweetViz Sweetviz is one of my, if not my favorite, Exploratory data Analysis library. It is an open-source Python library that generates beautiful, high-density visualizations to kickstart the EDA (Exploratory Data Analysis) process with just two lines of code. The output is a self-contained HTML application. The idea behind the system is quickly and . It aims to help quick analysis of training vs testing, data target characteristics, and other such data characterization tasks. visualizing target values comparing datasets SweetViz Key Features It shows you how a target value relates to other features. Target analysis Automatically detects numerical, categorical, and text features with optional manual override. Type inference : min/max/range, mean, mode, standard deviation, quartiles, sum, median absolute deviation, coefficient of variation, kurtosis, skewness Numerical analysis Distinct datasets (e.g. training vs test data)Intra-set characteristics (e.g. male versus female) Visualization and Comparaison Integration of associations for numerical (Pearson’s correlation), categorical (uncertainty coefficient), and categorical-numerical (correlation ratio) datatypes seamlessly, to provide maximum information for all data types. Mixed-type associations Type, unique values, missing values, duplicate rows, most frequent values Summary information For more information about SweetViz, check . here 2. Pandas Profiling Pandas profiling is yet another EDA platform more often than not, the first one learners learn. It is as well the most popular too. Indeed, Pandas Profiling is relatively easy to use, to set up, and feels like an extension to your standard pandas library rather than a system of its own. It is easily integrable with your favorite tools(Jypyter & Collab), and there is extensive documentation on how to use the library. Additionally, there is a good community around this library that is ready to answer any questions you may have. Pandas Profiling Key Features : detect the of columns in a dataframe. Type inference types : type, unique values, missing values Essentials : minimum value, Q1, median, Q3, maximum, range, interquartile range, mean, mode, standard deviation, sum, median absolute deviation, coefficient of variation, kurtosis, skewness Numerical analysis Most frequent values highlighting of highly correlated variables, Spearman, Pearson, and Kendall matrices HistogramCorrelations matrix, count, heatmap, and dendrogram of missing values Missing values extract file sizes, creation dates, and dimensions and scan for trimmed images or those containing EXIF information. File and Image analysis: learns about classes (Uppercase, Space), scripts (Latin, Cyrillic), and blocks (ASCII) of text data. Text analysis The main problem with Pandas Profiling is that it works very slowly with large datasets. You can solve this problem by generating a partial report that cuts down the heavy steps. Additionally, just like the name, Pandas-Profiling creates a profile of the dataset because Read more about pandas profiling . here 3. Dataprep Dataprep is one of the fastest EDA (Exploratory Data Analysis) tools in Python. It allows you to understand a Pandas/Dask DataFrame with a few lines of code in seconds. Indeed, Dataprep allows the user to explore features/characteristics of a dataset through simple APIs. The awesome part about it is that you can go through a dataset from a high level to a low level, allowing you to test different perspectives. According to the Authors, Data prep has: : DataPrep.EDA is 10-100X faster than Pandas-based profiling tools due to its highly optimized Dask-based computing module. 10-100X Faster : DataPrep.EDA generates interactive visualizations in a report, which makes the report look more appealing to end-users. Interactive Visualization : DataPrep.EDA naturally supports big data stored in a Dask cluster by accepting a Dask dataframe as input. Big Data Support Dataprep Key Features . Descriptive statistics : detect the column type and then output various plots and statistics that are appropriate for the respective type. Columns Distribution Analysis explores the correlation between columns in various ways and using multiple correlation metrics. Correlations: : generate various plots which display the number of missing values for each column and any underlying patterns of the missing values in the dataset. Missing values If you want more information about DataPrep, you can check them out . here 4. D-tale D-Tale is the combination of a Flask backend and a React front-end to bring you an easy way to view & analyze Pandas data structures. It integrates seamlessly with ipython notebooks & python/ipython terminals. Currently, this tool supports such Pandas objects as DataFrame, Series, MultiIndex, DatetimeIndex & RangeIndex. D-Tale is, based in my opinion, one of the best EDA libraries out there. It outshines the other based on the level of customizations that are available within the library. In other words, it fulfills the purpose of Exploratory Data Analysis since you can go deep and explore all the details in your dataset. . It features a code export to regenerate/recreate any plot or analysis made during the exploration. D-Tale Key Features A full exploration navigation of dataset system Overview Code export Custom filters Correlation, Charts, and Heatmaps Highlight datatypes, missing values, ranges The only downside you can have is that there is a small learning curve since the library has many options. Usually, a trained data scientist will be able to use it after an hour or two. If you want to know the full set of features for D-Tale, please check this . link 5. Pandas GUI , as the name suggests, is a graphical user interface for analyzing Pandas’ dataframes. The project is still in the development phase. Ergo, it can be subject to breaking changes, sometimes. However, from an EDA perspective, PandasGUI comes with many useful features. Using it feels like you are doing the . It is very good when you try to illustrate your cleaning steps to someone who is not comfortable reading code. PandasGUI same type of exploration when you are coding, but just through a Graphical Use Interface Pandas GUI Key Features View DataFrames and Series (with MultiIndex support) Filtering through Interactive plotting Query expressions and Statistics summaryData editing copy/paste files with Import CSV drag & drop toolbar Search To get the full set of features  for Pandas GUI, please refer to their official page here 6. Bamboolib Bamboolib allows you to is one of the most intuitive libraries out there and is made of a good set of features for data exploration. analyze data in Python without having to write code. It You can easily illustrate your work to someone who can’t code. Indeed, it allows team members of all skill levels to cooperate within Jupyter and to share the working results as reproducible code. If you are an employer, using this library can reduce employee onboarding time and training costs. As opposed to the other libraries here, Bamboolib has a paid and a community version. The community version is complete so you should not have any trouble using it. Bamboolib Key Features that exports Python code Intuitive GUI Supports all and v common transformations isualizations Provides analyses for data exploration best-practice Can be via simple Python plugins arbitrarily customized Integrate Python library any internal or external To get the full set of features for Bamboolib, please refer to their official page . here 7. AutoViz Even though it is not as feature-rich as the other libraries, AutoViz allows you to . With this library, you can plot all the relevant relationships between the different features with one line of code, no matter the type of dataset you have. perform automatic visualization On a very large dataset, AutoViz will take a from the file. Additionally, if you have too many features (columns), AutoViz can select the features that are the most important and plot them. random sample This library is great if you want to get a quick idea about the . I usually use it first to understand the dynamics and relationships within a dataset, if I am in a hurry. If you have 15 mins to drive quick insights from a dataset, then use AutoVis. relationships between the different features AutoVis Key Features Scatter Plot of each Continuous Variable against Target Variable Pairwise Scatter Plot of each Continuous Variable against other Continuous Variables Plots of all Continuous Variable Histogram Plots of all Continuous Variable Violin Plot of Target Variable Distribution of all Continuous Variables for target Variable Heatmap of Average of each Continuous Variable by Target Variable Bar Plots of Two Continuous Variables against a Date/Time Variable Time Series Plots The main disadvantage of this library is that it is not a full EDA library. Indeed, It does not do anything else other than creating plots quickly, which in some cases saves a lot of time. Checkout the full set of features in their official website by following this . link 8. Dora Dora is a Python library designed to . The library contains helper functions for . The library uses and is intended to be a helpful addition to common Python data analysis tools such as pandas, scikit-learn, and matplotlib. automate the difficult and inconvenient parts of exploratory data analysis data cleaning, feature selection & extraction, data visualization, partitioning data for model validation, and versioning transformations of data This library is not as intuitive as the other ones in this article and you should most definitely know how to code to use it. You can think of this library as a couple of additional repetitive functions that you use to write in every EDA Project. Dora wrote those functions for you. All you have to do is call them and analyze the results. Dora’s Key Features Reading Data & Configuration Cleaning Feature Selection & Extraction Visualization Model Validation Data Versioning You can check out the full set of feature in the Dora’s official documentation available . here 9. Visidata VisiData is an . It combines the clarity of a spreadsheet, the efficiency of the terminal, and the power of Python, into a lightweight utility that can handle millions of rows with ease. interactive multitool for tabular data This library is perfect if you want to do . Analyzing data on the terminal is not the most convenient of things. But, if you have a project that requires it, think of using Visidata for your EDA. EDA on a terminal Visidata key features: Editing contents Grouping data and descriptive statistics Creating sheets, rows, and columns Combining datasets Drawing graphs 10. Scattertext If you want to do EDA on a project, then you may want to use Scattertext. It is a tool for and presenting them in an . The various points relate to terms that are selectively labeled so that they don’t overlap with other labels or points. Natural Language Processing (NLP) finding and distinguishing terms in corpora interactive HTML scatter plot This library again is not a full EDA system, so you will need to know how to code to use it. Even though it is not a full edge EDA platform, the visualization within Scattertext allows you to . They are clean, understandable, well presented, and interactive, allowing you to better present the data you have. give context to your NLP project Scattertext Key Features: Using Scattertext as a : finding characteristic terms and their associations text analysis library Visualizing and term associations phrase associations Visualizing and Empath topics categories Displaying the Moral Foundations 2.0 Dictionary by Corpus Characteristicness Ordering Terms Document-Based Scatterplots or to Using Cohen’s d Hedge’s r visualize effect size You can check the full set of feature that Scattertext has in their official website available . here Conclusion Exploratory data analysis is an iterative cycle with steps including Investigating the data Solving your hypothesis by processing and visualizing the data Refining previous hypothesis after getting a new understanding of the dataset, or new perspective on your hypothesis In one sentence. Let the data speak to you. There is no one-size-fits-all methodology and tools that are suitable for all EDA. It varies from project to project, and it is up to you to figure out what tools allow you to go through the EDA process comfortably. Article refactored from Top 10 Exploratory Data Analysis (EDA) libraries you have to try in 2021.

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