Tag: Pandas (41)

• Top KDnuggets tweets, Jan 30 – Feb 05: state-of-the-art in #AI, #MachineLearning - Feb 6, 2019.
  
  Also Brilliant tour-de-force! Reinforcement Learning to solve Rubiks Cube; Dask, Pandas, and GPUs: first steps; Neural network AI is simple. So Stop pretending you are a genius.

  Tags: Dask, GPU, Pandas, Python, Reinforcement Learning, Top tweets

• Top Python Libraries in 2018 in Data Science, Deep Learning, Machine Learning - Dec 19, 2018.
  
  Here are the top 15 Python libraries across Data Science, Data Visualization. Deep Learning, and Machine Learning.

  Tags: Data Science, Deep Learning, Machine Learning, Pandas, Python, PyTorch, TensorFlow

• Top 10 Python Data Science Libraries - Nov 16, 2018.
  
  The third part of our series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.

  Tags: Data Science, GitHub, numpy, Pandas, Python, StatsModels

• Healthcare Analytics Made Simple - Nov 12, 2018.
  
  Finally, a book on Python healthcare machine learning techniques is here! Healthcare Analytics Made Simple does just what the title says: it makes healthcare data science simple and approachable for everyone.

  Tags: Analytics, Book, Healthcare, Pandas, Python

• Beginner Data Visualization & Exploration Using Pandas - Oct 22, 2018.
  
  This tutorial will offer a beginner guide into how to get around with Pandas for data wrangling and visualization.

  Pages: 1 2

  Tags: Data Exploration, Data Visualization, Pandas, Python

• Optimus v2: Agile Data Science Workflows Made Easy - Aug 30, 2018.
  
  Looking for a library to skyrocket your productivity as Data Scientist? Check this out!

  Tags: Apache Spark, Machine Learning, Pandas, Python

• Programming Best Practices For Data Science - Aug 7, 2018.
  
  In this post, I'll go over the two mindsets most people switch between when doing programming work specifically for data science: the prototype mindset and the production mindset.

  Tags: Best Practices, Data Science, Pandas, Programming, Python

• Top 20 Python Libraries for Data Science in 2018 - Jun 27, 2018.
  
  Our selection actually contains more than 20 libraries, as some of them are alternatives to each other and solve the same problem. Therefore we have grouped them as it's difficult to distinguish one particular leader at the moment.

  Pages: 1 2

  Tags: Bokeh, Data Science, Keras, Matplotlib, NLTK, numpy, Pandas, Plotly, Python, PyTorch, scikit-learn, SciPy, Seaborn, TensorFlow, XGBoost

• Swiftapply  – Automatically efficient pandas apply operations - Apr 24, 2018.
  
  Using Swiftapply, easily apply any function to a pandas dataframe in the fastest available manner.

  Tags: Pandas, Python

• Quick Feature Engineering with Dates Using fast.ai - Mar 16, 2018.
  
  The fast.ai library is a collection of supplementary wrappers for a host of popular machine learning libraries, designed to remove the necessity of writing your own functions to take care of some repetitive tasks in a machine learning workflow.

  Tags: fast.ai, Feature Engineering, Machine Learning, Pandas, Python, Time Series

• Using Excel with Pandas - Jan 23, 2018.
  
  In this tutorial, we are going to show you how to work with Excel files in pandas, covering computer setup, reading in data from Excel files into pandas, data exploration in pandas, and more.

  Pages: 1 2

  Tags: Excel, Histogram, Pandas, Python

• Top KDnuggets tweets, Jan 3-9: A collection of Jupyter notebooks NumPy, Pandas, matplotlib, basic #Python #MachineLearning - Jan 10, 2018.
  
  Artificial General Intelligence (AGI) in less than 50 years; Top KDnuggets tweets: 10 Free Must-Read Books for #MachineLearning and #DataScience; The Art of Learning #DataScience; Supercharging Visualization with Apache Arrow; Docker for #DataScience

  Tags: Jupyter, numpy, Pandas, Top tweets

• Python Data Preparation Case Files: Group-based Imputation - Sep 25, 2017.
  
  The second part in this series addresses group-based imputation for dealing with missing data values. Check out why finding group means can be a more formidable action than overall means, and see how to accomplish it in Python.

  Tags: Data Preparation, Pandas, Python

• Top KDnuggets tweets, Sep 13-19: Top Books on NLP; What Else Can AI Guess From Your Face? - Sep 20, 2017.
  
  Also: The Ten Fallacies of Data Science; #Python #Pandas tips and tricks; Geoff Hinton says we need to start all over.

  Tags: Geoff Hinton, NLP, Pandas, Python, Top tweets

• Python Data Preparation Case Files: Removing Instances & Basic Imputation - Sep 14, 2017.
  
  This is the first of 3 posts to cover imputing missing values in Python using Pandas. The slowest-moving of the series (out of necessity), this first installment lays out the task and data at the risk of boring you. The next 2 posts cover group- and regression-based imputation.

  Tags: Data Preparation, Pandas, Python

• Top KDnuggets tweets, Aug 30 – Sep 5: Python overtakes R, becomes the leader in #DataScience; Humble Book Bundle: #DataScience - Sep 6, 2017.
  
  Also: Pandas tips and tricks #Python #DataScience; How I replicated an $86 million project in 57 lines of code; Future #MachineLearning Class.

  Tags: Machine Learning, Pandas, Python, Top tweets

• 6 Interesting Things You Can Do with Python on Facebook Data - Jun 6, 2017.
  
  Facebook has a huge amount of data that is available for you to explore, you can do many things with this data. I will be sharing my experience with you on how you can use the Facebook Graph API for analysis with Python.

  Tags: Facebook, Pandas, Python

• 7 Steps to Mastering Data Preparation with Python - Jun 2, 2017.
  
  Follow these 7 steps for mastering data preparation, covering the concepts, the individual tasks, as well as different approaches to tackling the entire process from within the Python ecosystem.

  Pages: 1 2

  Tags: 7 Steps, Data Preparation, Data Preprocessing, Data Science, Data Wrangling, Machine Learning, Pandas, Python

• Data Science for Newbies: An Introductory Tutorial Series for Software Engineers - May 31, 2017.
  
  This post summarizes and links to the individual tutorials which make up this introductory look at data science for newbies, mainly focusing on the tools, with a practical bent, written by a software engineer from the perspective of a software engineering approach.

  Tags: Apache Spark, Data Science, Jupyter, Machine Learning, Pandas, Python, Reddit, Scala, SQL

• 5 Machine Learning Projects You Can No Longer Overlook, May - May 10, 2017.
  
  In this month's installment of Machine Learning Projects You Can No Longer Overlook, we find some data preparation and exploration tools, a (the?) reinforcement learning "framework," a new automated machine learning library, and yet another distributed deep learning library.

  Tags: Automated Machine Learning, Data Exploration, Deep Learning, Distributed Systems, Machine Learning, Overlook, Pandas, Reinforcement Learning

• Top KDnuggets tweets, Apr 26 – May 02: Face Recognition with Python, in under 25 lines of code - May 3, 2017.
  
  Face Recognition with Python, in under 25 lines of code; Try #DeepLearning in #Python w. a fully pre-configured VM; Homo Bayesians #MachineLearning #humor #cartoon; The Most Popular Language For #MachineLearning, #DataScience Is ...

  Tags: Deep Learning, Face Recognition, Pandas, Python, Top tweets

• The Guerrilla Guide to Machine Learning with Python - May 1, 2017.
  
  Here is a bare bones take on learning machine learning with Python, a complete course for the quick study hacker with no time (or patience) to spare.

  Tags: Deep Learning, Machine Learning, Pandas, Python, scikit-learn, Sebastian Raschka

• Dask and Pandas and XGBoost: Playing nicely between distributed systems - Apr 27, 2017.
  
  This blogpost gives a quick example using Dask.dataframe to do distributed Pandas data wrangling, then using a new dask-xgboost package to setup an XGBoost cluster inside the Dask cluster and perform the handoff.

  Tags: Dask, Distributed Systems, Pandas, Python, XGBoost

• Data Science Dividends – A Gentle Introduction to Financial Data Analysis - Apr 24, 2017.
  
  This post outlines some very basic methods for performing financial data analysis using Python, Pandas, and Matplotlib, focusing mainly on stock price data. A good place for beginners to start.

  Pages: 1 2

  Tags: Finance, Pandas, Python

• KDnuggets™ News 17:n13, Apr 5: What makes a great data scientist? Best R Packages for Machine Learning - Apr 5, 2017.
  
  Also Best R Packages for Machine Learning; Deep Stubborn Networks - A Breakthrough Advance Towards Adversarial Machine Learning; A Short Guide to Navigating the Jupyter Ecosystem.

  Tags: Data Science Skills, Jupyter, Machine Learning, Pandas, R

• A Beginner’s Guide to Tweet Analytics with Pandas - Mar 29, 2017.
  
  Unlike a lot of other tutorials which often pull from the real-time Twitter API, we will be using the downloadable Twitter Analytics data, and most of what we do will be done in Pandas.

  Tags: Pandas, Python, Twitter

• Moving from R to Python: The Libraries You Need to Know - Feb 24, 2017.
  
  Are you considering making a move from R to Python? Here are the libraries you need to know, how they stack up to their R contemporaries, and why you should learn them.

  Tags: Jupyter, Pandas, Programming, Python, R, scikit-learn, Yhat

• Introduction to Correlation - Feb 22, 2017.
  
  Correlation is one of the most widely used (and widely misunderstood) statistical concepts. We provide the definitions and intuition behind several types of correlation and illustrate how to calculate correlation using the Python pandas library.

  Tags: Beginners, Correlation, Datascience.com, Pandas, Python, Statistics

• Making Python Speak SQL with pandasql - Feb 8, 2017.
  
  Want to wrangle Pandas data like you would SQL using Python? This post serves as an introduction to pandasql, and details how to get it up and running inside of Rodeo.

  Tags: Pandas, Python, SQL, Yhat

• KDnuggets™ News 17:n04, Feb 1: Data Science and Python Wrangling: Pandas Cheat Sheet; Great Collection of Machine Learning Algorithms - Feb 1, 2017.
  
  Also Great Collection of Minimal and Clean Implementations of Machine Learning Algorithms; Bad Data + Good Models = Bad Results; Data Scientist - best job in America, again.

  Tags: AI, Algorithms, Chatbot, Cheat Sheet, Machine Learning, Pandas

• Pandas Cheat Sheet: Data Science and Data Wrangling in Python - Jan 27, 2017.
  
  The Pandas library can seem very elaborate and it might be hard to find a single point of entry to the material: with other learning materials focusing on different aspects of this library, you can definitely use a reference sheet to help you get the hang of it.

  Tags: Cheat Sheet, Data Preparation, DataCamp, Pandas, Python

• Tidying Data in Python - Jan 4, 2017.
  
  This post summarizes some tidying examples Hadley Wickham used in his 2014 paper on Tidy Data in R, but will demonstrate how to do so using the Python pandas library.

  Tags: Data Cleaning, Data Preparation, Pandas, Python

• 5 Machine Learning Projects You Can No Longer Overlook, January - Jan 2, 2017.
  
  There are a lot of popular machine learning projects out there, but many more that are not. Which of these are actively developed and worth checking out? Here is an offering of 5 such projects, the most recent in an ongoing series.
   
   

  Tags: Boosting, C++, Data Preparation, Decision Trees, Machine Learning, Neural Networks, Optimization, Overlook, Pandas, Python, scikit-learn

• Introduction to Machine Learning for Developers - Nov 28, 2016.
  
  Whether you are integrating a recommendation system into your app or building a chat bot, this guide will help you get started in understanding the basics of machine learning.

  Pages: 1 2

  Tags: Beginners, Classification, Clustering, Machine Learning, Pandas, Python, R, scikit-learn, Software Developer

• Statistical Data Analysis in Python - Jul 18, 2016.
  
  This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects, taking the form of a set of IPython notebooks.

  Tags: IPython, Jupyter, Pandas, Python, Statistical Analysis

• Top KDnuggets tweets, Jul 6 – Jul 12: Statistical Data Analysis #Python #Jupyter Notebooks; Modern Pandas Notebooks - Jul 13, 2016.
  
  Statistical Data Analysis in #Python (#Jupyter Notebooks); Modern Pandas: idiomatic Pandas notebook collection; New (free) book by @rdpeng: #rstats Programming for #DataScience

  Tags: Data Analysis, Jupyter, Pandas, Python, Statistical Analysis, Top tweets

• 5 Machine Learning Projects You Can No Longer Overlook - May 19, 2016.
  
  We all know the big machine learning projects out there: Scikit-learn, TensorFlow, Theano, etc. But what about the smaller niche projects that are actively developed, providing useful services to users? Here are 5 such projects.

  Tags: Data Cleaning, Deep Learning, Machine Learning, Open Source, Overlook, Pandas, Python, scikit-learn, Theano

• Doing Data Science: A Kaggle Walkthrough – Cleaning Data - Mar 23, 2016.
  
  Gain insight into the process of cleaning data for a specific Kaggle competition, including a step by step overview.

  Pages: 1 2

  Tags: Data Cleaning, Data Preparation, Kaggle, Pandas, Python

• Python Data Science with Pandas vs Spark DataFrame: Key Differences - Jan 29, 2016.
  
  A post describing the key differences between Pandas and Spark's DataFrame format, including specifics on important regular processing features, with code samples.

  Tags: Apache Spark, Pandas, Python

• Overview of Python Visualization Tools - Nov 3, 2015.
  
  An overview and comparison of the leading data visualization packages and tools for Python, including Pandas, Seaborn, ggplot, Bokeh, pygal, and Plotly.

  Pages: 1 2

  Tags: Data Visualization, ggplot2, Pandas, Plotly, Python

• Top KDnuggets tweets, Mar 30 – Apr 01: Very useful! Data Visualization with ggplot2 CheatSheet - Apr 2, 2015.
  
  Very useful! Data Visualization with ggplot2 Cheat Sheet; Great Data Science resource: Intro to Statistics using Python, Pandas; 14 Best Python Pandas Features; Data Science shows why taxis can never compete.

  Tags: Andrew Ng, Cheat Sheet, ggplot2, Lionel Messi, Pandas, Python, Soccer, Uber