Pandas

Pandas is a Python library for Panel Data manipulation and analysis, e.g. multidimensional time series and cross-sectional data sets commonly found in statistics, experimental science results, econometrics, or finance.

pandas

Elasticsearch for Data Science just got way easier

Eland is a brand new python module that bridges the gap between Elasticsearch and the data science ecosystem.

Must-have-on-finger-tips Datetime functions for python

Must-have-on-finger-tips Datetime functions for Python: From python and pandas. Python offers a whole bunch of functions and libraries for working with DateTime data.

Do you know how to import these 6 file types in Python?

Learn how to: Import Pickle, import Excel, import SAS, import Stata, import HDF5, ... Do you know how to import these 6 file types in Python?

A Quick Way to Reformat Columns in a Pandas DataFrame

Using df.melt to compress multiple columns into one.

KDnuggets™ News 20:n26, Jul 8: Speed up Your Numpy and Pandas

Speed up your Numpy and Pandas with NumExpr Package; A Layman's Guide to Data Science. Part 3: Data Science Workflow; Getting Started with TensorFlow 2; Feature Engineering in SQL and Python: A Hybrid Approach; Deploy Machine Learning Pipeline on AWS Fargate.

Pull and Analyze Financial Data Using a Simple Python Package

We demonstrate a simple Python script/package to help you pull financial data (all the important metrics and ratios that you can think of) and plot them. Pull and Analyze Financial Data Using a Simple Python Package. Stock market analysis and good investing (for long-term growth) require careful examination of the financial data. Various metrics and ratios are often used in such analysis i.e. to assess the inherent quality of a stock. You may have heard about some of them in the talk from financial and investment experts.

Moving averages with Python

Simple, cumulative, and exponential moving averages with Pandas

Accessing user data via the Strava API using stravalib

Accessing Strava user data using the the stravalib python library and Strava API, with initial analysis using pandas and seaborn.

Unleash the Power of Pandas ‘category’ Dtype: Encode Categorical Data in a Smarter Way

Tutorials on using Pandas’ category’ data type in Python

How to Scrape LinkedIn

It’s become rather difficult to scrape some of the larger tech websites, such as LinkedIn. Likely due to the amount of personal information at stake. But I’m here to mess up everything they worked so hard to prevent. Also, For the record, this is for educational purposes only.

Introducing pandagg: pandas-inspired library

In this article, I’ll show you how to effectively explore indices and compute deeply nested aggregations on data indexed in ElasticSearch.How to effectively explore indices and compute deeply nested aggregations on data indexed in ElasticSearch, using the pandagg library.

Pandas DataFrame Group by Consecutive Certain Values

Pandas DataFrame Group by Consecutive Certain Values.Grouping Pandas DataFrame by consecutive certain values appear in arbitrary rows

How to get free historical and live stock prices and FX rates using Python

How to get free historical and live stock prices and FX rates using Python. This is a guide that shows how to get historical and live stock prices and FX rates using Python from either Yahoo Finance or Alpha Vantage. Both APIs are free, however Alpha Vantage standard API limits to 5 requests per minute and 500 requests per day. This should be enough for a simple portfolio stock monitoring tool.

How Waffle Charts Can Help With Your Next Data Migration Project

Your team is executing a big scale file migration between two systems. The files are grouped into separate entities each consisting of approximately 5000 to 1 0000 files.

Rendering Images inside a Pandas DataFrame

Ever thought about rendering images inside a dataframe? I’m glad you did!

Exploratory Data Analysis — Passport Numbers in Pandas

Exploring the leading and trailing zeros, distribution of letters and numbers, common prefixes, regular expressions, and randomization of the data set.

Loading binary data to NumPy/Pandas

How to efficiently load your data and get back to analysis. I’ll show you how to use a combination of built-in functions, the C-API, and Cython to quickly and easily put together your own super-fast custom data loader for NumPy/Pandas.

Pandas DataFrame Group by Consecutive Same Values

Grouping Pandas DataFrame by consecutive same values repeated multiple times.It is very common that we want to segment a Pandas DataFrame by consecutive values.

Exploratory Data Analysis on Steroids - KDnuggets

This is a central aspect of Data Science, which sometimes gets overlooked. The first step of anything you do should be to know your data: understand it, get familiar with it.

Speed up your Numpy and Pandas with NumExpr Package - KDnuggets

Speed up your Numpy and Pandas with NumExpr Package