- MATLAB–Python–Julia cheatsheet
Dependencies and Setup¶
- January 11th, 2018 A cheat sheet that covers several ways of getting data into Python: from flat files such as.txts and.csv to files native to other software, such as Excel, SAS, or Matlab, and relational databases such as SQLite & PostgreSQL.
- A Cheat Sheet on Probability Cheat Sheet: Data Visualization with R New Machine Learning Cheat Sheet by Emily Barry Matplotlib Cheat Sheet One-page R: a survival guide to data science with R Cheat Sheet: Data Visualization in Python Stata Cheat Sheet Common Probability Distributions: The Data Scientist’s Crib Sheet Data Science Cheat Sheet 24.
Let’s check out the Python Ecosystem using the Ultimate Python Cheat Sheet (note that this is different from the R cheat sheet shown earlier). We see that there’s Pandas for essentially everything related to import, tidying and data wrangling. Call Python from R code in three ways: Use import to import any Python module. Access the attributes of a module with $.import(module, as = NULL, convert = TRUE, delayload = FALSE) Import a Python module. If convert = TRUE, Python objects are converted to their equivalent R types.
In the Python code we assume that you have already run importnumpyasnp
In the Julia, we assume you are using v1.0.2 or later with Compat v1.3.0 or later and have run usingLinearAlgebra,Statistics,Compat
Creating Vectors¶
Operation | MATLAB | Python | Julia |
|---|---|---|---|
Row vector: size (1, n) | |||
Column vector: size (n, 1) | |||
1d array: size (n, ) | Not possible | or | |
Integers from j to n withstep size k | |||
Linearly spaced vectorof k points |
Creating Matrices¶
Operation | MATLAB | Python | Julia |
|---|---|---|---|
Create a matrix | |||
2 x 2 matrix of zeros | |||
2 x 2 matrix of ones | |||
2 x 2 identity matrix | |||
Diagonal matrix | |||
Uniform random numbers | |||
Normal random numbers | |||
Sparse Matrices | |||
Tridiagonal Matrices |
Manipulating Vectors and Matrices¶
Operation | MATLAB | Python | Julia |
|---|---|---|---|
Transpose | |||
Complex conjugate transpose(Adjoint) | |||
Concatenate horizontally | or | or | |
Concatenate vertically | or | or | |
Reshape (to 5 rows, 2 columns) | |||
Convert matrix to vector | |||
Flip left/right | |||
Flip up/down | |||
Repeat matrix (3 times in therow dimension, 4 times in thecolumn dimension) | |||
Preallocating/Similar | N/A similar type | ||
Broadcast a function over acollection/matrix/vector | Functions broadcast directly | Functions broadcast directly |
Accessing Vector/Matrix Elements¶
Operation | MATLAB | Python | Julia |
|---|---|---|---|
Access one element | |||
Access specific rows | |||
Access specific columns | |||
Remove a row | |||
Diagonals of matrix | |||
Get dimensions of matrix |
Mathematical Operations¶
Operation | MATLAB | Python | Julia |
|---|---|---|---|
Dot product | |||
Matrix multiplication | |||
Inplace matrix multiplication | Not possible | ||
Element-wise multiplication | |||
Matrix to a power | |||
Matrix to a power, elementwise | |||
Inverse | or | or | |
Determinant | |||
Eigenvalues and eigenvectors | |||
Euclidean norm | |||
Solve linear system(Ax=b) (when (A)is square) | |||
Solve least squares problem(Ax=b) (when (A)is rectangular) |
Sum / max / min¶
Operation | MATLAB | Python | Julia |
|---|---|---|---|
Sum / max / min ofeach column | |||
Sum / max / min of each row | |||
Sum / max / min ofentire matrix | |||
Cumulative sum / max / minby row | |||
Cumulative sum / max / minby column |
Programming¶
Operation | MATLAB | Python | Julia |
|---|---|---|---|
Comment one line | |||
Comment block | |||
For loop | |||
While loop | |||
If | |||
If / else | |||
Print text and variable | |||
Function: anonymous | |||
Function | |||
Tuples | Can use cells but watch performance | ||
Named Tuples/Anonymous Structures | |||
Closures | |||
Inplace Modification | No consistent or simple syntaxto achieve this |
You can use Python with RStudio professional products to develop and publish interactive applications with Shiny, Dash, Streamlit, or Bokeh; reports with R Markdown or Jupyter Notebooks; and REST APIs with Plumber or Flask.
For an overview of how RStudio helps support Data Science teams using R & Python together, see R & Python: A Love Story.
For more information on administrator workflows for configuring RStudio with Python and Jupyter, refer to the resources on configuring Python with RStudio.
Developing with Python#
Data scientists and analysts can:
- Work with the RStudio IDE, Jupyter Notebook, JupyterLab, or VS Code editors from RStudio Server Pro
Want to learn more about RStudio Server Pro and Python?#
For more information on integrating RStudio Server Pro with Python, refer to the resources on configuring Python with RStudio.
Publishing Python Content#
Data scientists and analysts can publish Python content to RStudio Connect by:
Beginners Python Cheat Sheet Pdf
- Publishing Jupyter Notebooks that can be scheduled and emailed as reports
- Publishing Flask applications and APIs
- Publishing Dash applications
- Publishing Streamlit applications
- Publishing Bokeh applications
Ready to publish Jupyter Notebooks to RStudio Connect?#
View the user documentation for publishing Jupyter Notebooks to RStudio Connect
Ready to share interactive Python content on RStudio Connect?#
Learn more about publishing dash or flask applications and APIs.
Python Regex Cheat Sheet Pdf
View example code as well as samples in the user guide.
Publishing Python and R Content#
Data scientists and analysts can publish mixed Python and R content to RStudio Connect by publishing:
- Shiny applications that call Python scripts
- R Markdown reports that call Python scripts
- Plumber APIs that call Python scripts
Mixed content relies on the reticulate package, which you can read more about on the project's website.
View the user documentation for publishing content that uses Python and R to RStudio Connect
Cheat sheet for using Python with R and reticulate
R Vs Python Cheat Sheet
Managing Python Packages#
RStudio Package Manager supports both R and Python packages. Visit this guide to learn more about how you can securely mirror PyPI.
Additional Resources#
Want to learn more about RStudio Connect and Python?#
Frequently asked questions for using Python with RStudio Connect
Learn about best practices for using Python with RStudio Connect
Want to see examples of using Python with RStudio?#
View code examples on GitHub of Using Python with RStudio
View examples of Flask APIs published to RStudio Connect