- For an Introduction to Data Analysis and a Refresher on Statistics
- For an Introduction to Hypothesis Testing
- For an Introduction to Python
- Handling Common Errors in Python
- Reading Local Files with
pandas
and visualizing datasets withmatplotlib
- Making DataFrames from API requests
- What is wide vs long format data?
- Cleaning data in
pandas
- Handling Data Issues in
pandas
- Introduction to
plotly.express
- Introduction to
matplotlib
- Plotting with
plot()
method forpandas
objects - Introduction to
pandas.plotting()
module
You can install project dependencies either using out-of-the-box conda CLI commands, or installing conda-lock to ensure dependencies are solved no matter the platform you are on.
conda install -c conda-forge conda-lock
conda-lock install --name name-of-your-environment conda-lock.yml
conda activate name-of-your-environment
conda env create -n name-of-your-environment --file environment.yml
conda activate name-of-your-environment
- Install your module using
conda install name-of-module
in your terminal or Anaconda Prompt - Install your module with Anaconda Navigator
- Open Anaconda Navigator
- Click Environments tab
- Select the Environment you want to install a module into
Please don't use python -m pip install name-of-module
when installing packages without activating your conda environment via conda activate name-of-environment
first.
Anaconda's Explanation of conda & pip if you want a more in-depth explanation.
If your list of "Not Installed" packages is blank, I recommend manually updating Anaconda Navigator.
If you are experimenting with your conda base
environment and need to restore a previous version of a conda.
- You can use the
conda list
command with the--revisions
flag to view your conda revision history. - You can use the
conda install
command with the--revision
flag with the number that corresponds to the version you want to rollback to.
conda list --revisions
conda install --revision N
# Replace N with the number that corresponds to the version you want to rollback to.
- Anaconda: Conda is Fast Now. Like, Really Fast
- conda-libmamba-solver: Getting Started
- Replace it with
mamba
; even for existingconda
installations
- Python.org's 'Whetting Your Appetite'
- Python.org's Official Python 3 Tutorial
- Python.org's Glossary of Terms
- Python.org's Style Guide for Python Code
For in-memory analysis of tabular data and introduces the DataFrame for larger-than-memory datasets and want an API similar to pandas.
For in-memory analysis of tabular data and introduces the DataFrame
- Landing page for
pandas
- pandas Compared to Spreadsheets
- pandas Compared to SQL Queries
- pandas Compared to SAS
If you need your DataFrame to be mapped or do geospatial calculations.
If you need more interactivity from your generated map.
If you need more control over your generated static map projection.
If you need your dataset to have more than two dimensions.
Highly configurable visualization library that other libraries build off of.
High-level library for generating statistical graphics, especially for long data format.
Generate interactive graphics, with a focus on exploratory analysis with visuals.
I highly recommend going through the official Python 3 tutorial first. It's a great way to get your feet wet and get a feel for the language. However, here are some books I recommend if you want to go deeper or explore certain topics.
- Automate the Boring Stuff with Python by Al Sweigart
- Data Analysis with Pandas: 2nd Edition by Stefanie Molin
- Data Science at the Command Line: 2nd Edition by Jeoroen Janssens
- Rob Mulla on YouTube for Data Science with Focus on Python
- Conference Talk: So you want to be a Python expert?
- Conference Talk: 1000x faster data manipulation: vectorizing with
pandas
andnumpy
- Conference Talk: No More Sad Pandas: Optimizing Pandas Code for Sped and Efficiency
- Conference Talk: Effective Pandas
- Conference Tutorial: So You Wanna Be a Pandas Expert?
- Installing Anaconda's Package & Environment Manager
conda
(Command Line Interface Tool) and Anaconda-Navigator (Graphical User Interface Tool) (Best practice when it comes to dependency management for Python and R) - Picking an editor of your choice that supports JupyterNotebooks (Visual Studio Code or JupyterLab)
- How do I get data into Python and get descriptive statistics? (reading files with pandas)
- Now paint me a picture with the data. (introduction to Plotly & Holoviz Ecosystem)
- How do I share this?
- How to work with the Terminal
- Basics of version control using Git & pushing to Github
If you have anything you want to cover, I'm open to suggestions. Feel free to checkout the contributing guidelines for ways to offer feedback and contribute. My previous experience with python covers web scraping, cleaning data, statistical analysis, and moving data into and out of databases.