Tag Archives: pandas

Top 15 functions for Analytics in Python #python #rstats #analytics

Here is a list of top ten  fifteen functions for analysis in Python import (imports a particular package library in Python) getcwd (from os library) – get current working directory chdir (from os) -change directory listdir (from os ) -list files in the specified directory read_csv(from pandas) reads in a csv file objectname.info (like proc contents […]


Routes Python for Data Analysis Wrangling with Pandas NumPy

Python, a high level language developed by Guido van Rossum, is known for its easy readability. The core philosophies of the language are simple – simplicity over complexity; beauty over ugliness, explicit over implicit and other similar aphorisms. The most important philosophy of the language is “Readability Counts”, which means that the syntaxes and codes […]


Enthought Canopy Python Package Index

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An Enthought Canopy subscription provides Python 2.7.6, easy installation and updates of over 250 pre-built and tested scientific and analytic Python packages such as NumPy, Pandas, SciPy, Matplotlib, and IPython, PLUS an integrated analysis environment, graphical debugger, and online Python Essentials and Python Development Tools training courses. Academic users may request a free Canopy Academic license.

Canopy Express provides over 100 of these core packages free to all users. See the table below for a list of packages available in Canopy and Canopy Express (via the Canopy Package Manager). Both versions are available for Windows, Mac, and Linux users.

Video: Translating SQL to pandas. And back.

View slide presentation here:

PyData NYC 2014
SQL is still the bread-and-butter of the data world, and data analysts/scientists/engineers need to have some familiarity with it as the world runs on relational databases.

When first learning pandas (and coming from a database background), I found myself wanting to be able to compare equivalent pandas and SQL statements side-by-side, knowing that it would allow me to pick up the library quickly, but most importantly, apply it to my workflow.

This tutorial will provide an introduction to both syntaxes, allowing those inexperienced with either SQL or pandas to learn a bit of both, while also bridging the gap between the two, so that practitioners of one can learn the other from their perspective. Additionally, I’ll discuss the tradeoffs between each and why one might be better suited for some tasks than the other.