Pandas for Data Analysis

About Pandas

What is Pandas?

The Pandas library is built on NumPy and provides easy-to-use data structures and data analysis tools for the Python programming language.

Pandas Series

A one-dimensional labeled array a capable of holding any data type

Pandas DataFrame

A two-dimensional labeled data structure with columns of potentially different types

Advantages of Pandas

Data representation
Less writing and more work done
An extensive set of features
Efficiently handles large data
Makes data flexible and customizable
Made for Python

Datasets

Filename	Description	Raw File	Original Source	Other
chipotle.tsv	Online orders from the Chipotle restaurant chain	bit.ly/chiporders	The Upshot	Upshot article
drinks.csv	Alcohol consumption by country	bit.ly/drinksbycountry	FiveThirtyEight	FiveThirtyEight article
imdb_1000.csv	Top rated movies from IMDb	bit.ly/imdbratings	IMDb	Web scraping script
stocks.csv	Small dataset of stock prices	bit.ly/smallstocks	DataCamp
titanic_test.csv	Testing set from Kaggle's Titanic competition	bit.ly/kaggletest	Kaggle	Data dictionary
titanic_train.csv	Training set from Kaggle's Titanic competition	bit.ly/kaggletrain	Kaggle	Data dictionary
u.user	Demographic information about MovieLens users	bit.ly/movieusers	GroupLens	Data dictionary
ufo.csv	Reports of UFO sightings from 1930-2000	bit.ly/uforeports	National UFO Reporting Center	Web scraping script

How to create your own Jupyter Book

conda env create -f environment.yml
conda activate dsn-template

Building a Jupyter Book

Run the following command in your terminal: jb build book/. If you would like to work with a clean build, you can empty the build folder by running jb clean book/. If the jupyter execution is cached, this command will not delete the cached folder. To remove the build folder, you can run jb clean --all book/.

Publishing this Jupyter Book

Run ghp-import -n -p -f book/_build/html

hossainlab/pandas