Exploratory Data Analysis Ipynb Colab Google Colab

Leo Migdal

-Nov 17, 2025, 7:50 PM

exploratory data analysis ipynb colab google colab

This repository contains a collection of Jupyter Notebooks for conducting Exploratory Data Analysis (EDA) and Statistical Analysis on various datasets. Each notebook is created using Google Colab, making it easy to run and analyze the code interactively. To use these notebooks, simply click on the provided links to open them in Google Colab. You can then run the cells in each notebook interactively to analyze your own datasets or explore the provided examples. These notebooks are provided under the MIT License. .

Here you'll find a series of instructive and educational notebooks organized by topic areas. This notebook contains an example of using the Gemini API to analyze a a product sketch (in this case, a drawing of a Jet Backpack), create a marketing campaign for it, and output taglines... This notebook provides an example of how to prompt Gemini 1.5 Pro using an audio file. In this case, you'll use a sound recording of President John F. Kennedy’s 1961 State of the Union address. System instructions allow you to steer the behavior of the model.

By setting the system instruction, you are giving the model additional context to understand the task, provide more customized responses, and adhere to guidelines over the user interaction. Using function calling allows you to control how the Gemini API acts when tools have been specified. In today’s fast-paced, data-centric world, extracting actionable insights from raw data is not just an advantage — it’s a necessity. Exploratory Data Analysis (EDA) is a vital practice that empowers businesses to uncover hidden patterns, identify anomalies, and formulate hypotheses that drive informed decisions. Google Colab emerges as a powerful ally in this endeavor, offering an accessible and collaborative platform for conducting EDA. Let’s explore how EDA and Google Colab can elevate your data analysis game, turning raw data into strategic gold.

EDA isn’t merely a preliminary step in data analysis; it’s the bedrock upon which data-driven decisions are built. Here’s why EDA is indispensable: By leveraging EDA, businesses can transform data into actionable insights, paving the way for strategic decisions grounded in solid evidence. There was an error while loading. Please reload this page. Colab is a hosted Jupyter Notebook service that requires no setup to use and provides free access to computing resources, including GPUs and TPUs.

Colab is especially well suited to machine learning, data science, and education. Check out our catalog of sample notebooks illustrating the power and flexiblity of Colab. Read about product updates, feature additions, bug fixes and other release details. Check out these resources to learn more about Colab and its ever-expanding ecosystem. We’re working to develop artificial intelligence responsibly in order to benefit people and society. Google Colaboratory (‘Colab’) is a free, cloud-based Jupyter notebook environment that democratizes access to computational resources, including GPUs and TPUs, for machine learning, data science, and general Python development.

Its serverless execution model eliminates the need for local installations, making it accessible from any device with a web browser. The primary file format used in Colab is the .ipynb file, the standard for Jupyter notebooks, encapsulating code, markdown documentation, visualizations, and output. This article will provide a comprehensive guide to opening .ipynb files in Google Colab, covering various methods, benefits, troubleshooting common issues, and best practices. Colab offers several methods for importing and opening .ipynb files, catering to different scenarios and user preferences. Let’s explore each approach: This is the most straightforward method, ideal for quickly accessing files.

Access the Colab Website: Open your preferred web browser and navigate to colab.research.google.com. Initiate a New Notebook: Click on ‘New Notebook.’ This will open a blank notebook. This notebook demonstrates a simple data cleaning and preprocessing process using Python in Google Colab. It includes: Handling missing values Encoding categorical data Normalizing numerical columns Removing outliers using boxplots Step 1: Import Libraries Start by using essential Python libraries like Pandas (for handling data), Matplotlib and Seaborn (for... Step 3: Understand the Dataset Check the number of rows and columns. View the names and data types of each column.

Identify missing or null values. Get summary statistics like mean, median, standard deviation, minimum, and maximum for numeric columns. Step 4: Visualize Distributions Create histograms for each numeric column to: Understand how values are distributed (normal, skewed, etc.) Detect patterns and outliers Step 5: Detect Outliers Use boxplots to: Visualize the spread of... Identify any outliers (extremely high or low values). Step 6: Analyze Correlations Generate a correlation matrix and display it using a heatmap: Helps you understand how strongly features are related to each other. Useful for identifying features that may affect the target variable.

Step 7: Explore Feature Relationships Use pairplots (scatter plots + histograms) for selected features: Shows how features interact with each other. Helps detect clusters, trends, or unusual patterns. Step 8: Create Interactive Visuals (Optional) Use Plotly to build interactive scatter plots: Allows zooming and hovering over data points. Makes it easier to explore complex datasets.

Exploratory Data Analysis Ipynb Colab Google Colab

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