Matplotlib is the most widely used Python library in the field of data science, machine learning and deep learning for plotting figures and visualizations. Its pyplot module provides a MATLAB-like interface [1] which makes it convenient to use for people familiar with MATLAB. Matplotlib is capable of creating a variety of plots and it is hard to remember the functions which can do those plots.
- Matplotlib Cheat Sheet Dataquest
- Matplotlib Pyplot Cheat Sheet
- Pyplot Cheat Sheets
- Pyplot Cheat Sheet Pdf
Matplotlib is split into two main sections: the Pyplot API(visualization functions for fast production) and the Object Oriented API(more flexible and robust). We will focus on the latter. In order to make a visualization, you need to create 2 objects one right after the other. Julia & IJulia Cheat-sheet (for 18.xxx at MIT, Julia 1.x) Basics: julialang.org — documentation; juliabox.com — run Julia online /julia-mit installation & tutorial start IJulia browser. Execute input cell in IJulia. Using LinearAlgebra. Load functions for blue-highlighted code below. Defining/changing variables: x = 3.
I use the IPython notebook cheat sheet on my Github repository as a quick reference instead of spending time googling for the right function. You can just bookmark this IPython notebook and refer it while working on your projects. The IPython notebook cheat sheet is a collection of commonly use matplotlib functions and provides only a brief description of each function. It is not intended to provide a complete description since google is just a click away once you what plotting function you need.
I will update this sheet when I come across other common functions.
Reference: Datacamp cheat sheet, Matplotlib docs
Fig. 2: “VECTORS”
Fig 4: “DATA DISTRIBUTION” (top row) and “CONTOUR-related” (bottom row)
Fig. 6: “OTHER COMMON PLOTS” part 1
Some frequent needed utilities in Python data scripts —— good to have it by hand when facing puzzle.
ETL
Matplotlib Cheat Sheet Dataquest
Data Loading
Senuti software. Indexing
Sorting
Dropping
Slicing
![Pyplot Pyplot](/uploads/1/1/8/9/118991206/379560310.png)
Dealing with missings
Sampling
Apply function
Dealing with datetime
Categorical to dummy
concat & join
Groupby
Differencing & Cumulation
Sliding Window Apply
Matplotlib Pyplot Cheat Sheet
![Sheet Sheet](/uploads/1/1/8/9/118991206/653373619.jpeg)
Regular Expression
Descriptive Stats
Numerical stats
Correlation
Basic Charts
Feature Engineering
Rescaling
Feature Binarization
Generating Polynomial Features
Feature Selection
Filter methods
Wrapper Methods
Algorithm
Frequent Used Pieces
Linear Regression
Pyplot Cheat Sheets
Kmeans
Random Forest
Time Series
Tuning & Validation
Training/Test split
Cross Validation
Pyplot Cheat Sheet Pdf
Exhaustive Grid Search