At DataCamp, we always look out for ways to help our students, who are all eager to become more data savvy, reach their objectives even faster. That’s why we recently created a series of Python cheat sheets that target people who are using it for data analysis. The ongoing series already covers some of the most important and fundamental topics in data science and are must-haves for anyone that wants to get started with Python for data science.
The Pandas cheat sheet will guide you through some more advanced indexing techniques, DataFrame iteration, handling missing values or duplicate data, grouping and combining data, data. 🔥 Datacamp cheat sheets. Datacamp provides 3 cheatsheets describing the basics of matplotlib, seaborn and pandas: the 3 most commonly used library for data analysis with python.
At DataCamp, we always look out for ways to help our students, who are all eager to become more data savvy, reach their objectives even faster. That’s why we recently created a series of Python cheat sheets that target people who are using it for data analysis. The ongoing series already covers some of the most important and fundamental topics in data science and are must-haves for anyone that wants to get started with Python for data science.
And if you haven’t yet, you should consider learning this programming language. Year after year, Python’s popularity is increasing in the data science industry. The use of Python as a data science tool has been on the rise over the past few years: 54% of the respondents of the latest O'Reilly Data Science Salary Survey indicated that they used Python. The results of the 2015 survey showed that 51% of the respondents used Python.
Nobody can deny that Python has been on the rise in the data science industry and it certainly seems that it's here to stay.
So why not start now and make sure that the first steps you take count?
Get a copy of Python for data science cheat sheet and go through DataCamp’s Intro to Python for Data Science course. You’ll cover topics such as variables and data types, strings, lists, the basics of NumPy arrays, and much more. Complete your Python basics with an interactive Python List tutorial, to practice using this built-in data structure in Python for data analysis.
After, it’s time to lay the foundation for learning other data science libraries and dig deeper into (part of) the fundaments of the Pandas and Scikit-Learn libraries: take a look at NumPy, the Python scientific computing library that is excellent for data analysis. You’ll see that this library provides you with an array data structure that is a great alternative to Python lists: it is more compact, allows faster access when you’re reading and writing items, and is more convenient and more efficient overall.
The NumPy cheat sheet will introduce you to array creation, array mathematics, selecting elements (through subsetting, slicing and indexing), array manipulation and much more!
Make sure to use the reference sheet when you’re practicing arrays with DataCamp’s Python NumPy Tutorial or when you go through the Intro to Python for Data Science course. Undoubtedly, you’ll take your first steps with NumPy with confidence!
When you have mastered the basics, it’s time to get your hands dirty and analyze some real-life data. But you cannot start without the Pandas library: it’s all you ever need and want to use if you want to do data manipulation and analysis in Python.
But don’t go in unprepared: take DataCamp’s Pandas Foundations and Manipulating DataFrames with Pandas courses and make sure to keep the Pandas cheat sheet handy when you’re starting the Pandas DataFrame tutorial, where you can get extra practice to use this fast, flexible and expressive data structure.
Just like the tutorial, the cheat sheet not only gives basic information about the Pandas data structures and how to select values or basic statistics from them, but also shows you how inputting and outputting of data, sorting and ranking the data in your DataFrame or Series and data alignment works.
After you have already explored your data with some summary statistics on your DataFrame and manipulated your data in such a way that it’s ready for further analysis, it’s time to visualize your data!
The Bokeh library is the one that you need quickly and easily create interactive plots, dashboards, and data applications. What’s more, Bokeh enables high-performance visual presentations of large data sets in modern web browsers!
This Python visualization library is a powerful tool for your data science toolbox, so why not get started straight away?
First, get a copy of our Bokeh cheat sheet: it will make you familiar with the steps you need to go through to plotting and creating statistical charts. It summarizes how you can prepare your data, create a new plot, add renderers for your data with custom visualizations, output your plot and save or show it. Also, the creation of basic statistical charts will hold no secrets for you any longer.
But don’t just sit around and look at the cheat sheet: take the Interactive Data Visualization with Bokeh course and get the practice you need to become a data viz wizard in no time!
After exploring your data, you’ll have even more detailed research questions. Here’s where modeling your data gets important if you want to find a solid answer for them.
Machine learning is essential to data science; And everybody that says “machine learning” and “Python” in the same sentence, knows that Scikit-Learn is the way to go for machine learning in Python. This library implements a wide variety of machine learning, preprocessing, cross-validation and visualization algorithms with the help of a unified interface.
However, starting to tackle machine learning problems can be a pain: you don’t necessarily know where to start and how to go about it. That’s why the Scikit-Learn cheat sheet is a perfect companion to your first steps with Scikit-Learn: you'll not only see how to load in your data and how to preprocess it, but you’ll also see how to create your own model to which you can fit your data and predict target labels. Validation and tuning of your models to improve performance are also included in the reference sheet. Keep it handy while you’re going through our Scikit-Learn tutorial with character recognition as a topic.
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Why Organizations Should Invest in a Chief Data OfficerPandas 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.
Datacamp Pandas Cheat Sheet
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.
Datacamp Pandas Cheat Sheet 2020
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!
Datacamp Pandas Cheat Sheet Template
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