Exploratory Data Analysis (EDA) with NumPy, Pandas, Matplotlib and Seaborn Last Updated : 23 Jul, 2025 Comments Improve Suggest changes Like Article Like Report Exploratory Data Analysis (EDA) serves as the foundation of any data science project. It is an essential step where data scientists investigate datasets to understand their structure, identify patterns, and uncover insights. Data preparation involves several steps, including cleaning, transforming, and exploring data to make it suitable for analysis. Why EDA important in Data Science?To effectively work with data, it’s essential to first understand the nature and structure of data. EDA helps answer critical questions about the dataset and guides the necessary preprocessing steps before applying any algorithms. For instance: What type of data do we have? Are we working with numbers, text, or dates?Are there outliers? These are unusual values that are very different from the rest.Is anything missing? Are some parts of the dataset empty or incomplete?Imagine you’re working with a student performance dataset. If some rows are missing test scores, or the names of subjects are inconsistently spelled (e.g., "Math" and "Mathematics"), you’ll need to address these issues before proceeding. EDA helps to identify such problems and clean the data to ensure reliable analysis. Now, we will understand core packages for exploratory data analysis (EDA), including NumPy, Pandas, Seaborn, and Matplotlib.1. NumPy for Numerical OperationsNumPy is used for working with numerical data in Python. Handles Large Datasets Efficiently: NumPy allows to work with large, multi-dimensional arrays and matrices of numerical data. Provides functions for performing mathematical operations such as linear algebra, statistical analysis.Facilitates Data Transformation: Helps in sorting, reshaping, and aggregating data. Example : Let’s consider a simple example where we analyze the distribution of a dataset containing exam scores for students using numpy: Python import numpy as np # Dataset: Exam scores scores = np.array([45, 50, 55, 60, 65, 70, 75, 80, 200]) # Note: One extreme value (200) # Calculate basic statistics mean_score = np.mean(scores) median_score = np.median(scores) std_dev_score = np.std(scores) print(f"Mean: {mean_score}, Median: {median_score}, Standard Deviation: {std_dev_score}") OutputMean: 77.77777777777777, Median: 65.0, Standard Deviation: 44.541560561838764 This example demonstrates how NumPy can quickly compute statistics. We can also detect anomalies in data using z-score. Now follow below resources for in-depth understanding.Introduction to NumPyBasics of NumPy ArraysData types and type castingAccessing and Modifying Data - Indexing and slicingBroadcasting - Perform operations on arrays with different shapesLinear algebra operations: Solving Mathematical ProblemsSaving and loading NumPy arrays2. Pandas for Data ManipulationBuilt on top of NumPy, Pandas excels at handling tabular data (data organized in rows and columns) through its core data structures: Series (1D) and DataFrame (2D). Pandas simplifies the process of working with structured data by:Easy loading and saving of datasets in formats like CSV, Excel, SQL, or JSON: Read Dataset with PandasSave DataFrame as CSV file for further useReading from JSON files into Pandas DataFrameWorking with Excel filesData Processing with PandasSlicing rows with pandas IndexingData Aggregation and GroupingWorking with Date and Time3. Matplotlib for Data VisualizationMatplotlib brings us data visualizations, it is a powerful and versatile open-source plotting library for Python, designed to help users visualize data in a variety of formats.Introduction to MatplotlibPyplot in MatplotlibMatplotlib – Axes ClassMatplotlib for 3D PlottingExploratory Data Analysis with matplotlib4. Seaborn for Statistical Data VisualizationSeaborn is built on top of Matplotlib and is specifically designed for statistical data visualization. It provides a high-level interface for drawing attractive and informative statistical graphics.Introduction to SeabornTypes Of Seaborn PlotsPairplot function in seabornFacetGrid in SeabornTime Series Visualization with Seaborn : Line PlotComplete EDA Workflow Using NumPy, Pandas, and SeabornLet's implement complete workflow for performing EDA: starting with numerical analysis using NumPy and Pandas, followed by insightful visualizations using Seaborn to make data-driven decisions effectively.Performing EDA with Numpy and Pandas - Set 1 After analysis : Visualizing with seaborn - Set 2For more hands-on implementation - Explore projects below: Titanic Data EDA using SeabornUber Rides Data AnalysisZomato Data Analysis Using PythonGlobal Covid-19 Data Analysis and VisualizationsiPhone Sales AnalysisGoogle Search AnalysisWeb Scraping For EDANow, what is Web-scraping? : It is the automated process of extracting data from websites for later on analysis. How to Extract Weather Data from Google in Python?Movies Review Scraping And AnalysisProduct Price Scraping and AnalysisNews Scraping and AnalysisReal-time Share Price scrapping and analysis Comment More infoAdvertise with us A anurag702 Follow Improve Article Tags : Data Analysis AI-ML-DS With Python Similar Reads Data Analysis (Analytics) Tutorial Data Analytics is a process of examining, cleaning, transforming and interpreting data to discover useful information, draw conclusions and support decision-making. It helps businesses and organizations understand their data better, identify patterns, solve problems and improve overall performance. 4 min read Prerequisites for Data AnalysisExploratory Data Analysis (EDA) with NumPy, Pandas, Matplotlib and SeabornExploratory Data Analysis (EDA) serves as the foundation of any data science project. It is an essential step where data scientists investigate datasets to understand their structure, identify patterns, and uncover insights. 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