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World Population Prospects - Population Division - United Nations. The 2015 Revision of World Population Prospects is the twenty-fourth round of official United Nations population estimates and projections that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat.

World Population Prospects - Population Division - United Nations

The main results are presented in a series of Excel files displaying key demographic indicators for each development group, income group, major area, region and country for selected periods or dates within 1950-2100. A publication labelled Key findings and advance tables, which provide insights on the results of this latest revision, is also made available here. Quick Navigation NOTE: An error occurred while generating the population estimates by age and sex for Malaysia based on the 2010 census.

The data referring to the male and female populations were reversed. Disclaimer: This web site contains data tables, figures, maps, analyses and technical notes from the current revision of the World Population Prospects. Olympics at Sports-Reference.com - Olympics Statistics and History. Summer Games medalists and more1936 Berlin, 1984 Los Angeles, 2000 Sydney, 1972 Swimming, 1976 Men's Decathlon … Winter Games summaries and more1932 Lake Placid, 1952 Oslo, 1998 Nagano, 1980 Speed Skating, 1988 Women's Downhill … Athletes bios, results, and moreNadia Comăneci, Torben Grael, Tom Jager, Jerzy Pawłowski, Jim Lightbody, … Countries participants and moreCameroon, Paraguay, Brazil, Afghanistan, Luxembourg, North Korea, Ukraine, Burundi, …

Olympics at Sports-Reference.com - Olympics Statistics and History

Python Data Visualization Cookbook. Today, data visualization is a hot topic as a direct result of the vast amount of data created every second.

Python Data Visualization Cookbook

Transforming that data into information is a complex task for data visualization professionals, who, at the same time, try to understand the data and objectively transfer that understanding to others. This book is a set of practical recipes that strive to help the reader get a firm grasp of the area of data visualization using Python and its popular visualization and data libraries. Python Data Visualization Cookbook will progress the reader from the point of installing and setting up a Python environment for data manipulation and visualization all the way to 3D animations using Python libraries.

Readers will benefit from over 60 precise and reproducible recipes that guide the reader towards a better understanding of data concepts and the building blocks for subsequent and sometimes more advanced concepts. The Data Visualisation Catalogue. Data Analysis in Python with Pandas. Ever wonder how you can best analyze data in python?

Data Analysis in Python with Pandas

Wondering how you can advance your career beyond doing basic analysis in excel? Want to take the skills you already have from the R language and learn how to do the same thing in python and pandas? By taking the course, you will master the fundamental data analysis methods in python and pandas! You’ll also get access to all the code for future reference, new updated videos, and future additions for FREE! You'll Learn the most popular Python Data Analysis Technologies! By the end of this course: - Understand the data analysis ecosystem in Python. - Learn how to use the pandas data analysis library to analyze data sets. Colors Tutorial. Flat UI Colors. Modèles de couleurs. Coolors.co - The super fast color schemes generator.

Color Emotion Guide. DELL JPMorgan DIVERSITY Nikon ups IMDB BOL SUBWAY NBC DIVERSITY SUBWAY ebay BEST BUY DHL Hertz amazon Sprint TRUST IKEA PENNZOIL NBC monster - Your calling is calling - Google NATIONAL GEOGRAPHIC CHANNEL CAT Denny's HOOTERS NICKELODEON Fanta Lego Kellogg's Nintendo KMART YouTube ORACLE Coca-Cola Syfy Virgin Lowe's CNN starz Cadbury bp vimeo hp intel NASA flicr LAND ROVER Girl Scouts Big Brothers Big Sisters NETFLIX HARLEY-DAVIDSON MOTOR CYCLES AUSSIE Crush Orange Welch's Ford Wordpress Barbie American Express T-Mobile Animal Planet EXXON Walmart Lays ACE The helpful people FritoLay KFC NABISCO Heinz Gulf IBM pfizer publix HESS Apple Nike Hallmark Mercedes Hertz Yahoo!

Color Emotion Guide

Orkut tropicaña Spotlfy LYNX payless facebook Canon Walmart OREO puma WHOLE FOODS SunChips McDonalds GOOD YEAR shutterfIy Blogger boost Ferraro AVIS TACOBELL Oral-B CN Cartoon Network. Common Excel Tasks Demonstrated in Pandas - Practical Business Python. Introduction The purpose of this article is to show some common Excel tasks and how you would execute similar tasks in pandas.

Common Excel Tasks Demonstrated in Pandas - Practical Business Python

Some of the examples are somewhat trivial but I think it is important to show the simple as well as the more complex functions you can find elsewhere. As an added bonus, I’m going to do some fuzzy string matching to show a little twist to the process and show how pandas can utilize the full python system of modules to do something simply in python that would be complex in Excel. Make sense? Let’s get started. Adding a Sum to a Row The first task I’ll cover is summing some columns to add a total column. We will start by importing our excel data into a pandas dataframe. import pandas as pdimport numpy as npdf = pd.read_excel("excel-comp-data.xlsx")df.head() We want to add a total column to show total sales for Jan, Feb and Mar.

This is straightforward in Excel and in pandas. Next, here is how we do it in pandas: df["total"] = df["Jan"] + df["Feb"] + df["Mar"]df.head()