background preloader

Python Data Analysis Library — pandas: Python Data Analysis Library

Python Data Analysis Library — pandas: Python Data Analysis Library
pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. 0.13.1 released (February 3, 2014) This is a scheduled bugfix release for 0.13.0, with multiple issues and regressions addressed, and several back-compatible enhancements introduced. See the Release Notes to read all about it. For binaries and source archives of v0.13.1 see the Download page. What problem does pandas solve?

http://pandas.pydata.org/

Related:  Big Data

Cage Match Greg Jackson, the single most successful trainer in the multi-billion-dollar sport of professional mixed martial arts fighting, works out of a musty old gym in Albuquerque, New Mexico, not far from the base of the Sandia Mountains. On a recent morning, the 38-year-old Jackson, who has the cauliflowered ears and bulbous nose of a career fighter, watched two of his students square off inside the chain-link walls of a blood-splattered ring called the Octagon. One of them was Jon Jones, the light heavyweight champion of the Ultimate Fighting Championship (UFC), the premier MMA league. In four weeks, Jones would be defending his title against Rashad Evans, an expert fighter and his former training partner. StatsModels: Statistics in Python — statsmodels 0.8.0 documentation statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open source Modified BSD (3-clause) license. The online documentation is hosted at statsmodels.org. Since version 0.5.0 of statsmodels, you can use R-style formulas together with pandas data frames to fit your models.

10 Minutes to Pandas — pandas 0.12.0 documentation This is a short introduction to pandas, geared mainly for new users. You can see more complex recipes in the Cookbook Customarily, we import as follows: In [1]: import pandas as pd In [2]: import numpy as np In [3]: import matplotlib.pyplot as plt Object Creation Toolkits Toolkits are collections of application-specific functions that extend matplotlib. Basemap (Not distributed with matplotlib) Plots data on map projections, with continental and political boundaries, see basemap docs. Cartopy (Not distributed with matplotlib) An alternative mapping library written for matplotlib v1.2 and beyond. Cartopy builds on top of matplotlib to provide object oriented map projection definitions and close integration with Shapely for powerful yet easy-to-use vector data processing tools.

The Principles of VBD Revisited Want to Dominate your League? Then Dominate your Draft. This article will show you how to do this with the draft system that serious Fantasy Owners across the country use. It's called Value Based Drafting or VBD. Why listen to us about it? Non-Linear Least-Squares Minimization and Curve-Fitting for Python — Non-Linear Least-Squares Minimization and Curve-Fitting for Python Lmfit provides a high-level interface to non-linear optimization and curve fitting problems for Python. It builds on and extends many of the optimization methods of scipy.optimize. Initially inspired by (and named for) extending the Levenberg-Marquardt method from scipy.optimize.leastsq, lmfit now provides a number of useful enhancements to optimization and data fitting problems, including: Using Parameter objects instead of plain floats as variables.

Alex Gaudio - Monary+Mongo+Pandas = :) A lot of people such as myself waste time getting mongo data into numpy or pandas data structures. You could do it using pymongo. The general process would be to initialize the pymongo driver and make a query, wait for pymongo to convert stuff into lists of son (bson) objects (aka dictionaries), parse the data into arrays, and then copy it into some numpy array. But work’s been done for you already, so why do it again? Thanks to djcbeach, we have a nifty little module that utilizes mongo’s C driver, the bson C library and python’s ctypes module to load data directly into numpy arrays. Its fast and easy!

Screenshots Here you’ll find a host of example plots with the code that generated them. mplot3d The mplot3d toolkit (see mplot3d tutorial and mplot3d Examples) has support for simple 3d graphs including surface, wireframe, scatter, and bar charts. (Source code, png, hires.png, pdf) What a Big-Data Business Model Looks Like - R “Ray” Wang by R “Ray” Wang | 10:00 AM December 6, 2012 The rise of big data is an exciting — if in some cases scary — development for business. Together with the complementary technology forces of social, mobile, the cloud, and unified communications, big data brings countless new opportunities for learning about customers and their wants and needs. It also brings the potential for disruption, and realignment.

Related:  pythonPythonPython for Data Science