Tag Archives: Pandas

Cheat Sheets: Pandas, the Python Data Analysis Library

Download all 8 Pandas Cheat Sheets

Learn more about the Python for Data Analysis and Pandas Mastery Workshop training courses

Pandas (the Python Data Analysis library) provides a powerful and comprehensive toolset for working with data. Fundamentally, Pandas provides a data structure, the DataFrame, that closely matches real world data, such as experimental results, SQL tables, and Excel spreadsheets, that no other mainstream Python package provides. In addition to that, it includes tools for reading and writing diverse files, data cleaning and reshaping, analysis and modeling, and visualization. Using Pandas effectively can give you super powers, regardless of whether you’re working in data science, finance, neuroscience, economics, advertising, web analytics, statistics, social science, or engineering.

However, learning Pandas can be a daunting task because the API is so rich and large. This is why we created a set of cheat sheets built around the data analysis workflow illustrated below. Each cheat sheet focuses on a given task. It shows you the 20% of functions you will be using 80% of the time, accompanied by simple and clear illustrations of the different concepts. Use them to speed up your learning, or as a quick reference to refresh your mind.

Here’s the summary of the content of each cheat sheet:

  1. Reading and Writing Data with Pandas: This cheat sheet presents common usage patterns when reading data from text files with read_table, from Excel documents with read_excel, from databases with read_sql, or when scraping web pages with read_html. It also introduces how to write data to disk as text files, into an HDF5 file, or into a database.
  2. Pandas Data Structures: Series and DataFrames: It presents the two main data structures, the DataFrame, and the Series. It explain how to think about them in terms of common Python data structure and how to create them. It gives guidelines about how to select subsets of rows and columns, with clear explanations of the difference between label-based indexing, with .loc, and position-based indexing, with .iloc.
  3. Plotting with Series and DataFrames: This cheat sheet presents some of the most common kinds of plots together with their arguments. It also explains the relationship between Pandas and matplotlib and how to use them effectively. It highlights the similarities and difference of plotting data stored in Series or DataFrames.
  4. Computation with Series and DataFrames: This one codifies the behavior of DataFrames and Series as following 3 rules: alignment first, element-by-element mathematical operations, and column-based reduction operations. It covers the built-in methods for most common statistical operations, such as mean or sum. It also covers how missing values are handled by Pandas.
  5. Manipulating Dates and Times Using Pandas: The first part of this cheatsheet describes how to create and manipulate time series data, one of Pandas’ most celebrated features. Having a Series or DataFrame with a Datetime index allows for easy time-based indexing and slicing, as well as for powerful resampling and data alignment. The second part covers “vectorized” string operations, which is the ability to apply string transformations on each element of a column, while automatically excluding missing values.
  6. Combining Pandas DataFrames: The sixth cheat sheet presents the tools for combining Series and DataFrames together, with SQL-type joins and concatenation. It then goes on to explain how to clean data with missing values, using different strategies to locate, remove, or replace them.
  7. Split/Apply/Combine with DataFrames: “Group by” operations involve splitting the data based on some criteria, applying a function to each group to aggregate, transform, or filter them and then combining the results. It’s an incredibly powerful and expressive tool. The cheat sheet also highlights the similarity between “group by” operations and window functions, such as resample, rolling and ewm (exponentially weighted functions).
  8. Reshaping Pandas DataFrames and Pivot Tables: The last cheatsheet introduces the concept of “tidy data”, where each observation, or sample, is a row, and each variable is a column. Tidy data is the optimal layout when working with Pandas. It illustrates various tools, such as stack, unstack, melt, and pivot_table, to reshape data into a tidy form or to a “wide” form.

Download all 8 Pandas Cheat Sheets

Data Analysis Workflow

Ready to accelerate your skills with Pandas?

Enthought’s Pandas Mastery Workshop (for experienced Python users) and Python for Data Analysis (for those newer to Python) classes are ideal for those who work heavily with data. Contact us to learn more about onsite corporate or open class sessions.

 

What’s New in the Canopy Data Import Tool Version 1.1

New features in the Canopy Data Import Tool Version 1.1:
Support for Pandas v. 20, Excel / CSV export capabilities, and more

Enthought Canopy Data Import ToolWe’re pleased to announce a significant new feature release of the Canopy Data Import Tool, version 1.1. The Data Import Tool allows users to quickly and easily import CSVs and other structured text files into Pandas DataFrames through a graphical interface, manipulate the data, and create reusable Python scripts to speed future data wrangling. Here are some of the notable updates in version 1.1:

1. Support for PyQt
The Data Import Tool now supports both PyQt and PySide backends. Python 3 support will also be available shortly.

2. Exporting DataFrames to csv/xlsx file formats
We understand that data exploration and manipulation are only one part of your data analysis process, which is why the Data Import Tool now provides a way for you to save the DataFrame as a CSV/XLSX file. This way, you can share processed data with your colleagues or feed this processed file to the next step in your data analysis pipeline.

3. Column Sort Indicators
In earlier versions of the Data Import Tool, it was not obvious that clicking on the right-end of the column header sorted the columns. With this release, we added sort indicators on every column, which can be pressed to sort the column in an ascending or descending fashion. And given the complex nature of the data we get, we know sorting the data based on single column is never enough, so we also made sorting columns using the Data Import Tool stable (ie, sorting preserves any existing order in the DataFrame).

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Webinar: Python for Data Science: A Tour of Enthought’s Professional Training Course

View Python for Data Science Webinar
What: A guided walkthrough and Q&A about Enthought’s technical training course “Python for Data Science and Machine Learning” with VP of Training Solutions, Dr. Michael Connell

Who Should Watch: individuals, team leaders, and learning & development coordinators who are looking to better understand the options to increase professional capabilities in Python for data science and machine learning applications

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Enthought’s Python for Data Science training course is designed to accelerate the development of skill and confidence in using Python’s core data science tools — including the standard Python language, the fast array programming package NumPy, and the Pandas data analysis package, as well as tools for database access (DBAPI2, SQLAlchemy), machine learning (scikit-learn), and visual exploration (Matplotlib, Seaborn).

In this webinar, we give you the key information and insight you need to evaluate whether Enthought’s Python for Data Science course is the right solution to advance your professional data science skills in Python, including:

  • Who will benefit most from the course
  • A guided tour through the course topics
  • What skills you’ll take away from the course, how the instructional design supports that
  • What the experience is like, and why it is different from other training alternatives (with a sneak peek at actual course materials)
  • What previous course attendees say about the course

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michael_connell-enthought-vp-trainingPresenter: Dr. Michael Connell, VP, Enthought Training Solutions

Ed.D, Education, Harvard University
M.S., Electrical Engineering and Computer Science, MIT


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Handling Missing Values in Pandas DataFrames: the Hard Way, and the Easy Way

The Data Import Tool can highlight missing value cells, helping you easily identify columns or rows containing NaN valuesThis is the second blog in a series. See the first blog here: Loading Data Into a Pandas DataFrame: The Hard Way, and The Easy Way

No dataset is perfect and most datasets that we have to deal with on a day-to-day basis have values missing, often represented by “NA” or “NaN”. One of the reasons why the Pandas library is as popular as it is in the data science community is because of its capabilities in handling data that contains NaN values.

But spending time looking up the relevant Pandas commands might be cumbersome when you are exploring raw data or prototyping your data analysis pipeline. This is one of the places where the Canopy Data Import Tool helps make data munging faster and easier, by simplifying the task of identifying missing values in your raw data and removing/replacing them.

Why are missing values a problem you ask? We can answer that question in the context of machine learning. scikit-learn and TensorFlow are popular and widely used libraries for machine learning in Python. Both of them caution the user about missing values in their datasets. Various machine learning algorithms expect all the input values to be numerical and to hold meaning. Both of the libraries suggest removing rows and/or columns that contain missing values.

If removing the missing values is not an option, given the size of your dataset, then they suggest replacing the missing values. The scikit-learn library provides an Imputer class, which can be used to replace missing values. See the sci-kit learn documentation for an example of how the Imputer class is used. Similarly, the decode_csv function in the TensorFlow library can be passed a record_defaults argument, which will replace missing values in the dataset. See the TensorFlow documentation for specifics.

The Data Import Tool provides capabilities to handle missing values in your dataset because we strongly believe that discovering and handling missing values in your dataset is a part of the data import and cleaning phase and not the analysis phase of the data science process.

Digging into the specifics, here we’ll compare how you can go about handling missing values with three typical scenarios, first using the Pandas library, then contrasting with the Data Import Tool:

  1. Identifying missing values in data
  2. Replacing missing values in data, and
  3. Removing missing values from data.

Note : Pandas’ internal representation of your data is called a DataFrame. A DataFrame is simply a tabular data structure, similar to a spreadsheet or a SQL table.

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Webinar: An Exclusive Peek “Under the Hood” of Enthought Training and the Pandas Mastery Workshop

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Enthought’s Pandas Mastery Workshop is designed to accelerate the development of skill and confidence with Python’s Pandas data analysis package — in just three days, you’ll look like an old pro! This course was created ground up by our training experts based on insights from the science of human learning, as well as what we’ve learned from over a decade of extensive practical experience of teaching thousands of scientists, engineers, and analysts to use Python effectively in their everyday work.

In this webinar, we’ll give you the key information and insight you need to evaluate whether the Pandas Mastery Workshop is the right solution to advance your data analysis skills in Python, including:

  • Who will benefit most from the course
  • A guided tour through the course topics
  • What skills you’ll take away from the course, how the instructional design supports that
  • What the experience is like, and why it is different from other training alternatives (with a sneak peek at actual course materials)
  • What previous workshop attendees say about the course

See the Webinar


michael_connell-enthought-vp-trainingPresenter: Dr. Michael Connell, VP, Enthought Training Solutions

Ed.D, Education, Harvard University
M.S., Electrical Engineering and Computer Science, MIT


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Loading Data Into a Pandas DataFrame: The Hard Way, and The Easy Way

This is the first blog in a series. See the second blog here: Handling Missing Values in Pandas DataFrames: the Hard Way, and the Easy Way

Importing files or data into Pandas with the Canopy Data Import ToolData exploration, manipulation, and visualization start with loading data, be it from files or from a URL. Pandas has become the go-to library for all things data analysis in Python, but if your intention is to jump straight into data exploration and manipulation, the Canopy Data Import Tool can help, instead of having to learn the details of programming with the Pandas library.

The Data Import Tool leverages the power of Pandas while providing an interactive UI, allowing you to visually explore and experiment with the DataFrame (the Pandas equivalent of a spreadsheet or a SQL table), without having to know the details of the Pandas-specific function calls and arguments. The Data Import Tool keeps track of all of the changes you make (in the form of Python code). That way, when you are done finding the right workflow for your data set, the Tool has a record of the series of actions you performed on the DataFrame, and you can apply them to future data sets for even faster data wrangling in the future.

At the same time, the Tool can help you pick up how to use the Pandas library, while still getting work done. For every action you perform in the graphical interface, the Tool generates the appropriate Pandas/Python code, allowing you to see and relate the tasks to the corresponding Pandas code.

With the Data Import Tool, loading data is as simple as choosing a file or pasting a URL. If a file is chosen, it automatically determines the format of the file, whether or not the file is compressed, and intelligently loads the contents of the file into a Pandas DataFrame. It does so while taking into account various possibilities that often throw a monkey wrench into initial data loading: that the file might contain lines that are comments, it might contain a header row, the values in different columns could be of different types e.g. DateTime or Boolean, and many more possibilities as well.

Importing files or data into Pandas with the Canopy Data Import Tool

The Data Import Tool makes loading data into a Pandas DataFrame as simple as choosing a file or pasting a URL.

A Glimpse into Loading Data into Pandas DataFrames (The Hard Way)

The following 4 “inconvenience” examples show typical problems (and the manual solutions) that might arise if you are writing Pandas code to load data, which are automatically solved by the Data Import Tool, saving you time and frustration, and allowing you to get to the important work of data analysis more quickly.

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Webinar: Fast Forward Through the “Dirty Work” of Data Analysis: New Python Data Import and Manipulation Tool Makes Short Work of Data Munging Drudgery

Python Import & Manipulation Tool Intro Webinar

Whether you are a data scientist, quantitative analyst, or an engineer, or if you are evaluating consumer purchase behavior, stock portfolios, or design simulation results, your data analysis workflow probably looks a lot like this:

Acquire > Wrangle > Analyze and Model > Share and Refine > Publish

The problem is that often 50 to 80 percent of time is spent wading through the tedium of the first two stepsacquiring and wrangling data – before even getting to the real work of analysis and insight. (See The New York Times, For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights)

WHAT YOU’LL LEARN:

Enthought Canopy Data Import Tool

Try the Data Import Tool with your own data. Download here.

In this webinar we’ll demonstrate how the new Canopy Data Import Tool can significantly reduce the time you spend on data analysis “dirty work,” by helping you:

  • Load various data file types and URLs containing embedded tables into Pandas DataFrames
  • Perform common data munging tasks that improve raw data
  • Handle complicated and/or messy data
  • Extend the work done with the tool to other data files

WEBINAR RECORDING:
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