Plot time series of sunspots xtset id year declare national longitudinal data to be a panel generate lagspot = L1.spot create a new variable of annual lags of sun spots tsreport report time series aspects of a dataset xtdescribe report panel aspects of a dataset xtsum hours summarize hours worked, decomposing standard deviation into between. Classical time series methods (+cheat sheet) Autoregression (AR): The autoregression (AR) method models the next step in the sequence as a linear function of the observations at prior time steps. The method is suitable for univariate time series without trend and seasonal components.
Pandas is an open-source Python library that is powerful and flexible for data analysis. If there is something you want to do with data, the chances are it will be possible in pandas. There are a vast number of possibilities within pandas, but most users find themselves using the same methods time after time. In this article, we compiled the best cheat sheets from across the web, which show you these core methods at a glance.
The primary data structure in pandas is the DataFrame used to store two-dimensional data, along with a label for each corresponding column and row. If you are familiar with Excel spreadsheets or SQL databases, you can think of the DataFrame as being the pandas equivalent. If we take a single column from a DataFrame, we have one-dimensional data. In pandas, this is called a Series. DataFrames can be created from scratch in your code, or loaded into Python from some external location, such as a CSV. This is often the first stage in any data analysis task. We can then do any number of things with our DataFrame in Pandas, including removing or editing values, filtering our data, or combining this DataFrame with another DataFrame. Each line of code in these cheat sheets lets you do something different with a DataFrame. Also, if you are coming from an Excel background, you will enjoy the performance pandas has to offer. After you get over the learning curve, you will be even more impressed with the functionality.
Whether you are already familiar with pandas and are looking for a handy reference you can print out, or you have never used pandas and are looking for a resource to help you get a feel for the library- there is a cheat sheet here for you!
1. The Most Comprehensive Cheat Sheet
This one is from the pandas guys, so it makes sense that this is a comprehensive and inclusive cheat sheet. It covers the vast majority of what most pandas users will ever need to do to a DataFrame. Have you already used pandas for a little while? And are you looking to up your game? This is your cheat sheet! However, if you are newer to pandas and this cheat sheet is a bit overwhelming, don’t worry! You definitely don’t need to understand everything in this cheat sheet to get started. Instead, check out the next cheat sheet on this list.
2. The Beginner’s Cheat Sheet
Dataquest is an online platform that teaches Data Science using interactive coding challenges. I love this cheat sheet they have put together. It has everything the pandas beginner needs to start using pandas right away in a friendly, neat list format. It covers the bare essentials of each stage in the data analysis process:
- Importing and exporting your data from an Excel file, CSV, HTML table or SQL database
- Cleaning your data of any empty rows, changing data formats to allow for further analysis or renaming columns
- Filtering your data or removing anomalous values
- Different ways to view the data and see it’s dimensions
- Selecting any combination of columns and rows within the DataFrame using loc and iloc
- Using the .apply method to apply a formula to a particular column in the DataFrame
- Creating summary statistics for columns in the DataFrame. This includes the median, mean and standard deviation
- Combining DataFrames
3. The Excel User’s Cheat Sheet
Ok, this isn’t quite a cheat sheet, it’s more of an entire manifesto on the pandas DataFrame! If you have a little time on your hands, this will help you get your head around some of the theory behind DataFrames. It will take you all the way from loading in your trusty CSV from Microsoft Excel to viewing your data in Jupyter and handling the basics. The article finishes off by using the DataFrame to create a histogram and bar chart. For migrating your spreadsheet work from Excel to pandas, this is a fantastic guide. It will teach you how to perform many of the Excel basics in pandas. If you are also looking for how to perform the pandas equivalent of a VLOOKUP in Excel, check out Shane’s article on the merge method.
4. The Most Beautiful Cheat Sheet
If you’re more of a visual learner, try this cheat sheet! Many common pandas tasks have intricate, color-coded illustrations showing how the operation works. On page 3, there is a fantastic section called ‘Computation with Series and DataFrames’, which provides an intuitive explanation for how DataFrames work and shows how the index is used to align data when DataFrames are combined and how element-wise operations work in contrast to operations which work on each row or column. At 8 pages long, it’s more of a booklet than a cheat sheet, but it can still make for a great resource!
5. The Best Machine Learning Cheat Sheet
Much like the other cheat sheets, there is comprehensive coverage of the pandas basic in here. So, that includes filtering, sorting, importing, exploring, and combining DataFrames. However, where this Cheat Sheet differs is that it finishes off with an excellent section on scikit-learn, Python’s machine learning library. In this section, the DataFrame is used to train a machine learning model. This cheat sheet will be perfect for anybody who is already familiar with machine learning and is transitioning from a different technology, such as R.
6. The Most Compact Cheat Sheet
Data Camp is an online platform that teaches Data Science with videos and coding exercises. They have made cheat sheets on a bunch of the most popular Python libraries, which you can also check out here. This cheat sheet nicely introduces the DataFrame, and then gives a quick overview of the basics. Unfortunately, it doesn’t provide any information on the various ways you can combine DataFrames, but it does all fit on one page and looks great. So, if you are looking to stick a pandas cheat sheet on your bedroom wall and nail home the basics, this one might be for you! The cheat sheet finishes with a small section introducing NaN values, which come from NumPy. These indicate a null value and arise when the indices of two Series don’t quite match up in this case.
7. The Best Statistics Cheat Sheet
While there aren’t any pictures to be found in this sheet, it is an incredibly detailed set of notes on the pandas DataFrame. This cheat shines with its complete section on time series and statistics. There are methods for calculating covariance, correlation, and regression here. So, if you are using pandas for some advanced statistics or any kind of scientific work, this is going to be your cheat sheet.
Where to go from here?
For just automating a few tedious tasks at work, or using pandas to replace your crashing Excel spreadsheet, everything covered in these cheat sheets should be entirely sufficient for your purposes.
If you are looking to use pandas for Data Science, then you are only going to be limited by your knowledge of statistics and probability. This is the area that most people lack when they try to enter this field. I highly recommend checking out Think Stats by Allen B Downey, which provides an introduction to statistics using Python.
For those a little more advanced, looking to do some machine learning, you will want to start taking a look at the scikit-learn library. Data Camp has a great cheat sheet for this. You will also want to pick up a linear algebra textbook to understand the theory of machine learning. For something more practical, perhaps give the famous Kaggle Titanic machine learning competition.
Learning about pandas has many uses, and can be interesting simply for its own sake. However, Python is massively in demand right now, and for that reason, it is a high-income skill. At any given time, there are thousands of people searching for somebody to solve their problems with Python. So, if you are looking to use Python to work as a freelancer, then check out the Finxter Python Freelancer Course. This provides the step by step path to go from nothing to earning a full-time income with Python in a few months, and gives you the tools to become a six-figure developer!
Related Posts
This post updates a previous very popular post 50+ Data Science, Machine Learning Cheat Sheets by Bhavya Geethika. If we missed some popular cheat sheets, add them in the comments below.
Cheatsheets on Python, R and Numpy, Scipy, Pandas
Data science is a multi-disciplinary field. Thus, there are thousands of packages and hundreds of programming functions out there in the data science world! An aspiring data enthusiast need not know all. A cheat sheet or reference card is a compilation of mostly used commands to help you learn that language’s syntax at a faster rate. Here are the most important ones that have been brainstormed and captured in a few compact pages.
Mastering Data science involves understanding of statistics, mathematics, programming knowledge especially in R, Python & SQL and then deploying a combination of all these to derive insights using the business understanding & a human instinct—that drives decisions.
Here are the cheat sheets by category:
Cheat sheets for Python:
Python is a popular choice for beginners, yet still powerful enough to back some of the world’s most popular products and applications. It's design makes the programming experience feel almost as natural as writing in English. Python basics or Python Debugger cheat sheets for beginners covers important syntax to get started. Community-provided libraries such as numpy, scipy, sci-kit and pandas are highly relied on and the NumPy/SciPy/Pandas Cheat Sheet provides a quick refresher to these.
- Python Cheat Sheet by DaveChild via cheatography.com
- Python Basics Reference sheet via cogsci.rpi.edu
- OverAPI.com Python cheatsheet
- Python 3 Cheat Sheet by Laurent Pointal
Cheat sheets for R:
The R's ecosystem has been expanding so much that a lot of referencing is needed. The R Reference Card covers most of the R world in few pages. The Rstudio has also published a series of cheat sheets to make it easier for the R community. The data visualization with ggplot2 seems to be a favorite as it helps when you are working on creating graphs of your results.
At cran.r-project.org:
At Rstudio.com:
- R markdown cheatsheet, part 2
Others:
- DataCamp’s Data Analysis the data.table way
Cheat sheets for MySQL & SQL:
For a data scientist basics of SQL are as important as any other language as well. Both PIG and Hive Query Language are closely associated with SQL- the original Structured Query Language. SQL cheatsheets provide a 5 minute quick guide to learning it and then you may explore Hive & MySQL!
- SQL for dummies cheat sheet
Cheat sheets for Spark, Scala, Java:
Apache Spark is an engine for large-scale data processing. For certain applications, such as iterative machine learning, Spark can be up to 100x faster than Hadoop (using MapReduce). The essentials of Apache Spark cheatsheet explains its place in the big data ecosystem, walks through setup and creation of a basic Spark application, and explains commonly used actions and operations.
- Dzone.com’s Apache Spark reference card
- DZone.com’s Scala reference card
- Openkd.info’s Scala on Spark cheat sheet
- Java cheat sheet at MIT.edu
- Cheat Sheets for Java at Princeton.edu
Cheat sheets for Hadoop & Hive:
Hadoop emerged as an untraditional tool to solve what was thought to be unsolvable by providing an open source software framework for the parallel processing of massive amounts of data. Explore the Hadoop cheatsheets to find out Useful commands when using Hadoop on the command line. A combination of SQL & Hive functions is another one to check out.
Cheat sheets for web application framework Django:
Django is a free and open source web application framework, written in Python. If you are new to Django, you can go over these cheatsheets and brainstorm quick concepts and dive in each one to a deeper level.
- Django cheat sheet part 1, part 2, part 3, part 4
Pandas Time Series Cheat Sheet
Cheat sheets for Machine learning:
We often find ourselves spending time thinking which algorithm is best? And then go back to our big books for reference! These cheat sheets gives an idea about both the nature of your data and the problem you're working to address, and then suggests an algorithm for you to try.
- Machine Learning cheat sheet at scikit-learn.org
- Scikit-Learn Cheat Sheet: Python Machine Learning from yhat (added by GP)
- Patterns for Predictive Learning cheat sheet at Dzone.com
- Equations and tricks Machine Learning cheat sheet at Github.com
- Supervised learning superstitions cheatsheet at Github.com
Cheat sheets for Matlab/Octave
MATLAB (MATrix LABoratory) was developed by MathWorks in 1984. Matlab d has been the most popular language for numeric computation used in academia. It is suitable for tackling basically every possible science and engineering task with several highly optimized toolboxes. MATLAB is not an open-sourced tool however there is an alternative free GNU Octave re-implementation that follows the same syntactic rules so that most of coding is compatible to MATLAB.
Sequences And Series Cheat Sheet
Cheat sheets for Cross Reference between languages
Time Series Cheat Sheet R
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