Tag: pandas

Pandas Cheat Sheet – Python for Data Science

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“It’s common when first learning pandas to have trouble remembering all the functions and methods that you need, and while we at Dataquest advocate getting used to consulting the pandas documentation, sometimes it’s nice to have a handy reference, so we’ve put together this cheat sheet to help you out!” This cheat sheet covers the following topics: Key and Imports Importing Data Exporting Data Create Test Objects Veiwing/Inspecting Data Selection … Continue Reading

Comprehensive learning path – Data Science in Python

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From Analytics Vidhya: “So, you want to become a data scientist or may be you are already one and want to expand your tool repository. You have landed at the right place. The aim of this page is to provide a comprehensive learning path to people new to python for data analysis. This path provides a comprehensive overview of steps you need to learn to use Python for data analysis. … Continue Reading

Python Data Science Handbook: Essential Tools for Working with Data

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From Amazon: “For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this … Continue Reading

DataCamp – Merging DataFrames with pandas

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From DataCamp: “As a Data Scientist, you’ll often find that the data you need is not in a single file. It may be spread across a number of text files, spreadsheets, or databases. You want to be able to import the data of interest as a collection of DataFrames and figure out how to combine them to answer your central questions. This course is all about the act of combining, or … Continue Reading

DataCamp – Manipulating DataFrames with pandas

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From DataCamp: “In this course, you’ll learn how to leverage pandas’ extremely powerful data manipulation engine to get the most out of your data. It is important to be able to extract, filter, and transform data from DataFrames in order to drill into the data that really matters. The pandas library has many techniques that make this process efficient and intuitive. You will learn how to tidy, rearrange, and restructure your … Continue Reading

DataCamp – pandas Foundations

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From DataCamp: “Many real-world data sets include strings, integers, time-stamps and unstructured data. How do you store data like this so that you can manipulate it and easily retrieve important information? The answer is in a pandas DataFrame! In this course, you’ll learn how to use the industry-standard pandas library to import, build, and manipulate DataFrames. With pandas, you’ll always be able to convert your data into a form that permits … Continue Reading

Udemy – Data Analysis with Pandas and Python

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From Udemy: “The most comprehensive Pandas course available on Udemy! An excellent choice for both beginners and experts looking to refresh their technical knowledge on one of the most popular Python libraries in the world! Data Analysis with Pandas and Python offers 19+ hours of in-depth tutorials on one of the most powerful data analysis toolkits available today. Lessons include: installing sorting filtering grouping aggregating de-duplicating pivoting munging deleting merging text cleaning visualizing If you’ve spent time in … Continue Reading

DataCamp – Intermediate Python for Data Science

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From DataCamp: “The intermediate python course is crucial to your data science curriculum. Learn to visualize real data with matplotlib’s functions and get to know new data structures such as the dictionary and the Pandas DataFrame. After covering key concepts such as boolean logic, control flow and loops in Python, you’re ready to blend together everything you’ve learned to solve a case study using hacker statistics.” This course consists of 5 … Continue Reading

DataQuest – Handling Large Data Sets in Python

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“Learn how to build data pipelines to work with large datasets.” Consists of one step: Handling Large Data Sets in Python. This step includes: Processing Large Datasets in Pandas Optimizing Dataframe Memory Footprint Processing Dataframes in Chunks Augmenting Pandas with SQLite Guided projects Optimizing Code Performance on Large Datasets CPU Bound Programs I/O Bound Programs Overcoming the Limitations of Threads Quickly Analyzing Data with Parallel Processing Guided project

DataQuest – Intermediate Python and Pandas

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This course is step 2 of 6 in the DataQuest Data Analyst Path and step 2 of 11 in the DataQuest Data Scientist Path. The curriculum for this step includes the following topics: Getting Started with NumPy Computation with NumPy Introduction to Pandas Data Manipulation with Pandas Working with Missing Data Pandas Internals: Series & Dataframes Exploratory Data Visualization Line Charts Multiple Plots Bar and Scatter Plots Histograms and Box Plots … Continue Reading