To provide 100 datatable exercises over different sections structured as a course or tutorials to teach and learn for beginners, intermediates as well as experts.

Information

Category: Python / Data Analysis
Watchers: 2
Star: 52
Fork: 7
Last update: Jul 13, 2021

Resource links

https://github.com/vopani/datatableton

README
Issues 7

DatatableTon

💯 datatable exercises

Mission 🚀

To provide 100 datatable exercises over different sections structured as a course or tutorials to teach and learn for beginners, intermediates as well as experts.

Datatable

The datatable package in Python is a library for efficient data processing and feature engineering of tabular data. It is synonymous with R's data.table library and heavily inspired by it.

It closely resembles pandas but is more focused on speed and multi-threaded data operations being particularly useful on large datasets.

Exercises 📖

There are a total of 100 datatable exercises divided into 10 sets of Jupyter Notebooks with 10 exercises each. It is recommended to go through the exercises in order but you may start with any set depending on your expertise.

✅ Structured as exercises & tutorials - Choose your style
✅ Suitable for beginners, intermediates & experts - Choose your level
✅ Available on Colab, Kaggle, Binder & GitHub - Choose your platform

The exercises are best experienced using datatable's v1.0.0 (Released on 1st July, 2021) & above but recommended to use the latest available version.

Set 01 • Datatable Introduction • Beginner • Exercises 1-10

Style	Colab	Kaggle	Binder	GitHub
Exercises
Solutions

Set 02 • Files and Formats • Beginner • Exercises 11-20

Style	Colab	Kaggle	Binder	GitHub
Exercises
Solutions

Set 03 • Data Selection • Beginner • Exercises 21-30

Style	Colab	Kaggle	Binder	GitHub
Exercises
Solutions

Set 04 • Frame Operations • Beginner • Exercises 31-40

Style	Colab	Kaggle	Binder	GitHub
Exercises	ETA: 13th July, 2021	ETA: 13th July, 2021	ETA: 13th July, 2021	ETA: 13th July, 2021
Solutions	ETA: 13th July, 2021	ETA: 13th July, 2021	ETA: 13th July, 2021	ETA: 13th July, 2021

Set 05 • Various Aggregations • Beginner • Exercises 41-50

Style	Colab	Kaggle	Binder	GitHub
Exercises	ETA: 16th July, 2021	ETA: 16th July, 2021	ETA: 16th July, 2021	ETA: 16th July, 2021
Solutions	ETA: 16th July, 2021	ETA: 16th July, 2021	ETA: 16th July, 2021	ETA: 16th July, 2021

Set 06 • Grouping Options • Intermediate • Exercises 51-60

Style	Colab	Kaggle	Binder	GitHub
Exercises	ETA: 19th July, 2021	ETA: 19th July, 2021	ETA: 19th July, 2021	ETA: 19th July, 2021
Solutions	ETA: 19th July, 2021	ETA: 19th July, 2021	ETA: 19th July, 2021	ETA: 19th July, 2021

Set 07 • Multiple Frames • Intermediate • Exercises 61-70

Style	Colab	Kaggle	Binder	GitHub
Exercises	ETA: 22nd July, 2021	ETA: 22nd July, 2021	ETA: 22nd July, 2021	ETA: 22nd July, 2021
Solutions	ETA: 22nd July, 2021	ETA: 22nd July, 2021	ETA: 22nd July, 2021	ETA: 22nd July, 2021

Set 08 • Time Series • Intermediate • Exercises 71-80

Style	Colab	Kaggle	Binder	GitHub
Exercises	ETA: 25th July, 2021	ETA: 25th July, 2021	ETA: 25th July, 2021	ETA: 25th July, 2021
Solutions	ETA: 25th July, 2021	ETA: 25th July, 2021	ETA: 25th July, 2021	ETA: 25th July, 2021

Set 09 • Native FTRL • Expert • Exercises 81-90

Style	Colab	Kaggle	Binder	GitHub
Exercises	ETA: 28th July, 2021	ETA: 28th July, 2021	ETA: 28th July, 2021	ETA: 28th July, 2021
Solutions	ETA: 28th July, 2021	ETA: 28th July, 2021	ETA: 28th July, 2021	ETA: 28th July, 2021

Set 10 • Capstone Examples • Expert • Exercises 91-100

Style	Colab	Kaggle	Binder	GitHub
Exercises	ETA: 31st July, 2021	ETA: 31st July, 2021	ETA: 31st July, 2021	ETA: 25th July, 2021
Solutions	ETA: 31st July, 2021	ETA: 31st July, 2021	ETA: 31st July, 2021	ETA: 31st July, 2021

The Jupyter Notebooks can also be run locally by cloning the repo and running on your local jupyter server.

git clone https://github.com/vopani/datatableton.git
python3 -m pip install notebook
jupyter notebook

P.S. The notebooks will be periodically updated to improve the exercises and support the latest version.

Contribution 🛠️

Please create an Issue for any improvements, suggestions or errors in the content.

You can also tag @vopani on Twitter for any other queries or feedback. I have time for anyone wanting to share negative or positive thoughts about this project 🙂

License 📋

This project is licensed under the Apache License 2.0.

0 Open More issues

Closed

Exercise 45 error

Describe Error It is asked in the task to find the mode for several columns. The right answer is: alcohol | proline 12.37 | 520.0 13.05 | 680.0 Because the mode is not necessarily unique to a given discrete distribution, the probability mass function may take the same maximum value at several points. In this case, alcohol values are repeated 6 times and proline values are repeated 5 times.

Exercise / Set Exercise 45 / Set 5

Suggested Fix I don't know - maybe you can specify in the task that only the first value should be returned or the mode function can be improved so it will return multiple values.

documentation

opened Dec 27, 2021 by Extremesarova 1

Closed

Exercise 25 error

Describe Error "Select the element in the 4th row and 2nd column in data and assign it to value_1..." Provided solution: value_1 = data[4, 3]

Exercise / Set Exercise 25 / Set 3

Suggested Fix Shouldn't it be like value_1 = data[3, 1] or I'm missing something? 4th row considering numeration from 0 -> 3 2nd column considering numeration from 0 -> 1

documentation

opened Dec 16, 2021 by Extremesarova 1

Closed

Use classification evaluation metric

Describe Issue Using MAE for classification is non-standard.

Exercise / Set Set 9

Suggested Fix Use Logloss or AUC instead?

enhancement

opened Jul 29, 2021 by vopani 1

Closed

Updated set 6 notebooks to fix the typo issue

Closes Issue Typo in Set 6

opened Jul 19, 2021 by shrinidhin 1

Closed

Added the excel dataset for S 02

Added an excel workbook sample.xlsx for the Set 2 module,

opened Jul 6, 2021 by parulnith 1

Closed

datatable version 1.0.0 fails in Google Colab

The latest version of datatable, i.e., 1.0.0, fails to install in the Google Colaboratory environment. It throws the following error:

ERROR: Exception:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/pip/_internal/cli/base_command.py", line 153, in _main
    status = self.run(options, args)
  File "/usr/local/lib/python3.7/dist-packages/pip/_internal/commands/install.py", line 382, in run
    resolver.resolve(requirement_set)
  File "/usr/local/lib/python3.7/dist-packages/pip/_internal/legacy_resolve.py", line 201, in resolve
    self._resolve_one(requirement_set, req)
  File "/usr/local/lib/python3.7/dist-packages/pip/_internal/legacy_resolve.py", line 365, in _resolve_one
    abstract_dist = self._get_abstract_dist_for(req_to_install)
  File "/usr/local/lib/python3.7/dist-packages/pip/_internal/legacy_resolve.py", line 313, in _get_abstract_dist_for
    req, self.session, self.finder, self.require_hashes
  File "/usr/local/lib/python3.7/dist-packages/pip/_internal/operations/prepare.py", line 224, in prepare_linked_requirement
    req, self.req_tracker, finder, self.build_isolation,
  File "/usr/local/lib/python3.7/dist-packages/pip/_internal/operations/prepare.py", line 49, in _get_prepared_distribution
    abstract_dist.prepare_distribution_metadata(finder, build_isolation)
  File "/usr/local/lib/python3.7/dist-packages/pip/_internal/distributions/source/legacy.py", line 37, in prepare_distribution_metadata
    self._setup_isolation(finder)
  File "/usr/local/lib/python3.7/dist-packages/pip/_internal/distributions/source/legacy.py", line 90, in _setup_isolation
    reqs = backend.get_requires_for_build_wheel()
  File "/usr/local/lib/python3.7/dist-packages/pip/_vendor/pep517/wrappers.py", line 152, in get_requires_for_build_wheel
    'config_settings': config_settings
  File "/usr/local/lib/python3.7/dist-packages/pip/_vendor/pep517/wrappers.py", line 255, in _call_hook
    raise BackendUnavailable(data.get('traceback', ''))
pip._vendor.pep517.wrappers.BackendUnavailable: Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/pip/_vendor/pep517/_in_process.py", line 63, in _build_backend
    obj = import_module(mod_path)
  File "/usr/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
  File "<frozen importlib._bootstrap>", line 983, in _find_and_load
  File "<frozen importlib._bootstrap>", line 965, in _find_and_load_unlocked
ModuleNotFoundError: No module named 'ext'

bug

opened Jul 6, 2021 by parulnith 1

Open

Include an exercise for qcut()

Describe Topic Include an exercise to implement the qcut() function in datatables which is a function for binning columns

Suggested Set It can possibly be included in Set 5: Column Aggregations

Sample Exercise Exercise : Create a new column B in frame data which contains deciles of column A Solution : data['B'] = dt.qcut([dt.f.A])

enhancement

opened Jul 28, 2021 by shrinidhin 1

A simple, modern and interactive datatable library for the web

vue-materialize-datatable - VueJS datatable for Materialize CSS

jQuery Datatable's serverside usage example with Golang

Package filippo.io/intermediates embeds a bundle of known unexpired, unrevoked intermediate certificates chaining to roots in the Mozilla Root Program

Jupyter Notebook exercises for k-means clustering with Python 3 and scikit-learn

cpp fundamentals and questions for beginners and intermediates

Exercises for hierarchical clustering with Python 3 and scipy as Jupyter Notebooks

MomentumTable is material based rich DataTable component for Angular.

A React and Redux-based table for server-processed data.

A fancy Materialize CSS datatable VueJS component.

This course will teach you only the relevant topics in Python for starting your career in Data Analytics. There are also a bunch of tips and tricks throughout for resume writing, solving case studies, interviews etc. The idea is to help you land a job in analytics and not just teach you Python.

Stanford Machine Learning course exercises implemented with scikit-learn

Collection of JavaScript modern interview code challenges for beginners to experts

A set of curated exercises to help you prepare for the CKS exam

PyTorch tutorials demonstrating modern techniques with readable code

📜 Course plan, video lectures, slides

SunEngine – site engine with blog, forum and articles sections features support.

AutoNormalize is a Python library for automated datatable normalization.

✨ A feature-rich yet lightweight data-table crafted for Angular

TinySQL a course designed to teach you how to implement a distributed relational database in Go.

Vue Datatable - VueJS powered Datatable with Laravel server-side loading and JSON template setup

Share this repo

Related Repos

Data Analysis

Serverless Scientific Computing

numpywren Scientific Computing with Infinite Ram What is this? numpywren is scientific computing framework built on top of the serverless execution fr

Data Analysis

tidytext: Text mining using tidy tools

Data Analysis

493

Efficient Python Tricks and Tools for Data Scientists

Data Analysis

Framework for creating efficient data processing pipelines

Framework for creating efficient data processing pipelines.

Data Analysis

Set of Mindmaps providing a detailed overview of the different #Windows auditing capacitie...

Set of Mindmaps providing a detailed overview of the different #Windows auditing capacities and event log files.

Data Analysis

numberwang will convert floating point numbers (and integers) to their word representation...

numberwang will convert floating point numbers (and integers) to their word representations, and vice versa.

Data Analysis

7.8k

🦔 PostHog provides open-source product analytics that you can self host.

PostHog is an open-source product analytics suite, built for developers. Automate the collection of every event on your website or app, with no need to send data to 3rd parties.

Data Analysis

Mapillary Street-level Sequences (MSLS) is a large-scale long-term place recognition datas...

Mapillary Street-level Sequences (MSLS) is a large-scale long-term place recognition dataset that contains 1.6M street-level images.

Data Analysis

DenseClus is a Python module for clustering mixed type data using UMAP and HDBSCAN.

DenseClus is a Python module for clustering mixed type data using UMAP and HDBSCAN. Allowing for both categorical and numerical data, DenseClus makes it possible to incorporate all features in clustering.

Data Analysis

PreallocationTools.jl is a set of tools for helping build non-allocating pre-cached functi...

PreallocationTools.jl is a set of tools for helping build non-allocating pre-cached functions for high-performance computing in Julia.

Data Analysis

Pyspark Spotify ETL

This is my first Data Engineering project, it extracts data from the user's recently played tracks using Spotify's API, transforms data and then loads it into Postgresql using SQLAlchemy engine. Data is shown as a Spark Dataframe before loading and the whole ETL job is scheduled with crontab.

Data Analysis

454

Official data on the COVID-19 epidemic in Malaysia.

Official data on the COVID-19 epidemic in Malaysia. Powered by CPRC, CPRC Hospital System, MKAK, and MySejahtera.

Data Analysis

Data exploration done quick.

Implementation of Stata's tabulate command in Pandas for extremely easy to type one-way and two-way tabulations.

Data Analysis

To provide 100 datatable exercises over different sections structured as a course or tutor...

To provide 100 datatable exercises over different sections structured as a course or tutorials to teach and learn for beginners, intermediates as well as experts.

Data Analysis

Implementation in R of the Black Scholes formula and some greeks.