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Python Exploratory Data Analysis Cheat Sheet For Google Colab Github

Leo Migdal
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python exploratory data analysis cheat sheet for google colab github

There was an error while loading. Please reload this page. 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. . EDA reviews, cleans, visualizes, and analyzes data to uncover patterns, spot anomalies, test hypotheses, and prepare for further analysis. When to use: To visualize the distribution of a single numerical variable. When to use: To identify outliers and compare distributions of a single variable. When to use: To visualize the distribution of a single variable and compare across categories.

What are bivariate values? Bivariate values are two values that are compared to each other. For example, the height and weight of a person are bivariate values. 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.

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There Was An Error While Loading. Please Reload This Page.

There was an error while loading. Please reload this page. 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 Cola...

These Notebooks Are Provided Under The MIT License. . EDA

These notebooks are provided under the MIT License. . EDA reviews, cleans, visualizes, and analyzes data to uncover patterns, spot anomalies, test hypotheses, and prepare for further analysis. When to use: To visualize the distribution of a single numerical variable. When to use: To identify outliers and compare distributions of a single variable. When to use: To visualize the distribution of a si...

What Are Bivariate Values? Bivariate Values Are Two Values That

What are bivariate values? Bivariate values are two values that are compared to each other. For example, the height and weight of a person are bivariate values. 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:...

View The Names And Data Types Of Each Column. Identify

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: Visualiz...

Useful For Identifying Features That May Affect The Target Variable.

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...