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https://github.com/leifwalsh/data-analysis-problem-sets


https://github.com/leifwalsh/data-analysis-problem-sets

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README 

        
# Data Analysis Problem Sets



This repository contains a sequence of problem sets for practicing working with

data in Python, with Jupyter, pandas, SQL, and a selection of visualization

tools. You can browse the syllabus and jump in wherever feels comfortable or

interesting. If you feel stuck in one area, maybe go back to a previous problem

set to practice.



## Setup



There are three ways to set up an environment to work on these problem sets:



### Jupyter Lite



The easiest way to get started is to open the problem sets in Jupyter Lite,

in your browser. Some Python packages won't come preinstalled, but you can do

most of the initial chapters without installing anything.



Open this project here:

[![lite-badge](https://jupyterlite.rtfd.io/en/latest/_static/badge.svg)](https://leifwalsh.github.io/data-analysis-problem-sets/lab/index.html)



Then, open `README.md` to get started. You can click the links to problem sets

from there.



You can also open any of the problem sets directly from GitHub, by clicking

**"Click here to open this notebook in your browser"** at the top of any

notebook.



### Codespaces



If you have a GitHub account, you can open this project in a Codespace. This

works with VS Code or Jupyter, and will give you a full Python environment

on Linux with all the packages you need preinstalled.



### Devcontainers



If you have VS Code and Docker installed on your computer, you can clone this

repository and open it in a devcontainer. This will give you a full local

Python environment on Linux with all the packages you need preinstalled.



## Syllabus



### Chapter 1: Foundations



Working with command line tools, Jupyter notebooks, and Python.



#### Problem Set 1.1: [Command line tools](./1-foundations/1.1-command-line/1.1-command-line.ipynb)



Learn to run commands and understand their output and errors, navigate

directories, move and copy files.



#### Problem Set 1.2: [Jupyter and Python syntax](./1-foundations/1.2-jupyter-and-python-syntax/1.2-jupyter-and-python-syntax.ipynb)



Work with code cells in a notebook, reason about execution order and state in

Python variables, restarting your kernel.



Python statements, variables, if statements, loops, built in data structures.



#### Problem Set 1.3: [Reading and viewing data](./1-foundations/1.3-reading-and-viewing-data/1.3-reading-and-viewing-data.ipynb)



Read something from a file, without Pandas, and explore it a bit.



#### Problem Set 1.4: Advanced Python



**Note:** You may want to skip this and go straight to Chapter 2 if you're new

to programming. You won't need this until around Chapter 5 or 6.



Defining your own functions, modules, and classes.



### Chapter 2: Pandas basics



Learn the essential pandas operations you'll use frequently.



#### Problem Set 2.1: [numpy arrays, Series, Dataframe](./2-pandas-basics/2.1-numpy-arrays-series-dataframe/2.1-numpy-arrays-series-dataframe.ipynb)



Learn about the core data structures in pandas:

- numpy arrays, which pandas builds on

- The 1-dimensional Series

- The 2-dimensional DataFrame



#### Problem Set 2.2: [Reading CSV and JSON data](./2-pandas-basics/2.2-reading-csv-and-json-data/2.2-reading-csv-and-json-data.ipynb)



Learn how to read data from CSV and JSON files using pandas DataFrames.



#### Problem Set 2.3: [Row and Column selection by location](./2-pandas-basics/2.3-row-and-column-selection-by-location/2.3-row-and-column-selection-by-location.ipynb)



Explore how to select specific rows and columns in a DataFrame.



#### Problem Set 2.4: [Selection by Boolean columns](./2-pandas-basics/2.4-selection-by-boolean-columns/2.4-selection-by-boolean-columns.ipynb)



Explore how to select rows in a DataFrame based on a condition, rather than

their position (or label).



#### Problem Set 2.5: [Indexes: loc[] vs iloc[]](./2-pandas-basics/2.5-indexes-loc-vs-iloc/2.5-indexes-loc-vs-iloc.ipynb)



Explore what "indexes" are in Series and DataFrames.



#### Problem Set 2.6: [Indexes and concatenation](./2-pandas-basics/2.6-indexes-and-concatenation/2.6-indexes-and-concatenation.ipynb)



#### Problem Set 2.7: [Merge and join](./2-pandas-basics/2.7-merge-and-join/2.7-merge-and-join.ipynb)



#### Problem Set 2.8: [Accessors: dates, times, and strings](./2-pandas-basics/2.6-accessors-dates-times-and-strings/2.6-accessors-dates-times-and-strings.ipynb)



#### Problem Set 2.9: [Aggregations](./2-pandas-basics/2.8-aggregations/2.8-aggregations.ipynb)



#### Problem Set 2.10: [Pivot Tables and groupby](./2-pandas-basics/2.9-pivot-tables-and-groupby/2.9-pivot-tables-and-groupby.ipynb)



#### Problem Set 2.11: [Immutable style](./2-pandas-basics/2.10-immutable-style/2.10-immutable-style.ipynb)



#### Problem Set 2.12: [NaN and other gotchas](./2-pandas-basics/2.11-nan-and-other-gotchas/2.11-nan-and-other-gotchas.ipynb)



### Chapter 3: Visualization basics



### Chapter 4: Statistics basics



### Chapter 5: Data oriented thinking



### Chapter 6: Dashboarding



### Chapter 7: Advanced pandas



### Chapter 8: Advanced visualization