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Python For Data Science Cheat Sheet

We begin by unpacking the cheat sheets for core Python libraries: NumPy, which lays the groundwork for numerical operations; Pandas, the go-to for data manipulation; and Matplotlib, the bedrock of data plotting in Python. Seamlessly, we transition into libraries like Seaborn, which builds on Matplotlib to provide a higher-level interface for statistical visualizations, and SciPy, which extends the capabilities into scientific computing.

You can download the FULL VERSION of PDF at the end of this article

But the Python ecosystem doesn’t stop there. For those who revel in the cutting-edge interactivity of modern web browsers, Bokeh presents a compelling toolkit for creating dynamic and interactive visualizations. And for machine learning enthusiasts, Scikit-learn’s cheat sheet provides a robust guide to harnessing algorithms for data analysis and modeling. Jupyter Notebook, with its interactive computing environment, allows for these visualizations and analyses to be conducted in a live-code narrative, making the process as educational as it is insightful.

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Python For Data Science Cheat Sheet Python Basics

  1. Python Basics:
  2. Strings:
  3. Lists:
  4. Libraries:
  5. Numpy Arrays:

The cheat sheet is designed to be a quick reference for basic Python operations, with examples for each operation or method. It includes essential Python syntax and functions that are commonly used in data science.

Python For Data Science Cheat Sheet Jupyter Notebook

Here’s a summary of the key sections and points:

  1. Saving/Loading Notebooks:
  2. Writing Code and Text:
  3. Working with Different Programming Languages:
  4. Command Mode Shortcuts:
  5. Edit Mode Shortcuts:
  6. Executing Cells:
  7. Widgets:

The cheat sheet provides a quick reference for common tasks and commands in Jupyter Notebook, aiming to help users work more efficiently with the interface for coding and data science projects.

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Python For Data Science Cheat Sheet NumPy Basics

“Python for Data Science – NumPy Basics”. It covers a range of topics related to NumPy, a core library for scientific computing in Python. The key areas include:

  1. NumPy:
  2. NumPy Arrays:
  3. Creating Arrays:
  4. Initial Placeholders:
  5. I/O:
  6. Data Types:
  7. Inspecting Your Array:
  8. Array Mathematics:
  9. Comparison:
  10. Aggregate Functions:
  11. Copying Arrays:
  12. Sorting Arrays:
  13. Subsetting, Slicing, Indexing:
  14. Array Manipulation:

This cheat sheet is designed to provide quick references and examples for using NumPy’s array functionalities effectively in data science applications.

Python For Data Science Cheat Sheet SciPy – Linear Algebra

“SciPy – Linear Algebra” as part of Python for Data Science. It covers several aspects:

  1. Interacting With NumPy:
  2. Polynomials:
  3. Vectorizing Functions:
  4. Type Handling:
  5. Other Useful Functions:
  6. Linear Algebra:
  7. Creating Sparse Matrices:
  8. Sparse Matrix Routines:
  9. Sparse Matrix Functions:
  10. Matrix Functions:
  11. Decompositions:
  12. Sparse Matrix Decompositions:

This cheat sheet serves as a quick reference guide for common linear algebra operations in the SciPy library, including working with dense and sparse matrices.

Python For Data Science Cheat Sheet Pandas Basics

cheat sheet for “Pandas Basics” and covers several aspects:

  1. Pandas:
  2. Pandas Data Structures:
  3. I/O (Input/Output):
  4. Selection:
  5. Boolean Indexing & Setting:
  6. Asking For Help:
  7. Dropping:
  8. Sort & Rank:
  9. Retrieving Series/DataFrame Information:
  10. Applying Functions:
  11. Data Alignment:

This cheat sheet serves as a quick reference guide for basic operations and functionalities in Pandas, a foundational tool in data analysis with Python.

Python For Data Science Cheat Sheet Scikit-Learn

The key topics covered include:

  1. A Basic Example:
  2. Loading The Data:
  3. Training And Test Data:
  4. Preprocessing The Data:
  5. Create Your Model:
  6. Model Fitting:
  7. Prediction:
  8. Encoding Categorical Features:
  9. Imputing Missing Values:
  10. Generating Polynomial Features:
  11. Evaluate Your Model’s Performance:
  12. Tune Your Model:

This cheat sheet provides quick reference code snippets and explanations for various stages of a machine learning workflow using Scikit-Learn.

Python For Data Science Cheat Sheet Matplotlib

The key topics covered include:

  1. Prepare The Data:
  2. Create Plot:
  3. Plotting Routines:
  4. Customize Plot:
  5. Save Plot:
  6. Show Plot:
  7. Close & Clear:
  8. Plot Anatomy & Workflow:
  9. Workflow:
  10. Limits, Legends & Layouts:
  11. Mathtext:

This cheat sheet provides a quick reference for the key functions and features used in Matplotlib for creating and customizing a wide range of plots and visualizations.

Python For Data Science Cheat Sheet Seaborn

The is a cheat sheet for Seaborn, a statistical data visualization library in Python. It covers several aspects:

  1. Statistical Data Visualization with Seaborn:
  2. Data:
  3. Figure Aesthetics:
  4. Plotting with Seaborn:
  5. Further Customizations:
  6. Show or Save Plot:
  7. Modifying plot context and color palettes.

The cheat sheet serves as a quick reference for creating a variety of plots with Seaborn, detailing the code needed to generate and customize them. It also shows how to manage the data and aesthetics for statistical plotting.

Python For Data Science Cheat Sheet Bokeh

The is a cheat sheet for Bokeh, an interactive visualization library in Python. The key topics covered include:

  1. Plotting with Bokeh:
  2. Data:
  3. Plotting:
  4. Renderers & Visual Customizations:
  5. Layouts:
  6. Output & Export:
  7. Show or Save Your Plots:
  8. The cheat sheet also discusses rows and columns layouts for arranging multiple plots.
  9. Exporting plots to HTML, embedding components, and saving as PNG or SVG formats.

This cheat sheet provides a quick reference for the key functions and features used in Bokeh for creating interactive plots, managing data sources, customizing appearances, and handling the layout and export of visualizations.

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