Are you looking for examples of using Python for data analysis? This article is for you. We will show you how to accomplish the most common data analysis tasks with Python, from the features of Python itself to using modules like Pandas to a simple machine learning example with TensorFlow. Let’s dive in.
- Python Pandas Cheat Sheet Data Wrangling
- Python For Data Science Cheat Sheet
- Python For Data Science Cheat Sheet Pdf
Pandas Cheat Sheet One of the first things that you need to do to make use of this library is importing it. What might come unnaturally to people who are just starting with Python and/or programming is the import convention. Pandas is a python library used in data manipulation ( create, delete, and update the data). It is one of the most commonly used libraries for data analysis in python. Pandas offer data structures and operations for manipulating numerical and time-series data. Pandas First Steps. Install and import. Pandas is an easy package to install. NumPy / SciPy / Pandas Cheat Sheet Select column. Select row by label. Return DataFrame index. Delete given row or column. Pass axis=1 for columns. Reindex df1 with index of df2. Reset index, putting old index in column named index. Change DataFrame index, new indecies set to NaN. Show first n rows. Show last n rows.
A Note About Python Versions
All examples in this cheat sheet use Python 3. We recommend using the latest stable version of Python, for example, Python 3.8. You can check which version you have installed on your machine by running the following command in the system shell:
Sometimes, a development machine will have Python 2 and Python 3 installed side by side. Having two Python versions available is common on macOS. If that is the case for you, you can use the python3 command to run Python 3 even if Python 2 is the default in your environment:
If you don’t have Python 3 installed yet, visit the Python Downloads page for instructions on installing it.
Launch a Python interpreter by running the python3 command in your shell:
Libraries and Imports
The easiest way to install Python modules that are needed for data analysis is to use pip. Installing NumPy and Pandas takes only a few seconds:
Once you’ve installed the modules, use the import statement to make the modules available in your program:
Getting Help With Python Data Analysis Functions
If you get stuck, the built-in Python docs are a great place to check for tips and ways to solve the problem. The Python help() function displays the help article for a method or a class:
The help function uses the system text pagination program, also known as the pager, to display the documentation. Many systems use less as the default text pager, just in case you aren’t familiar with the Vi shortcuts here are the basics:
- j and k navigate up and down line by line.
- / searches for content in a documentation page.
- After pressing / type in the search query, press Enter to go to the first occurrence.
- Press n and N to go forward and back through the search results.
- Ctrl+d and Ctrl+u move the cursor one page down and one page up, respectively.
Another useful place to check out for help articles is the online documentation for Python data analysis modules like Pandas and NumPy. For example, the Pandas user guides cover all the Pandas functionality with explanations and examples.
Basic language features
A quick tour through the Python basics:
There are many more useful string methods in Python, find out more about them in the Python string docs.
Working with data sources
Pandas provides a number of easy-to-use data import methods, including CSV and TSV import, copying from the system clipboard, and reading and writing JSON files. This is sufficient for most Python data analysis tasks:
Find all other Pandas data import functions in the Pandas docs.
Working with Pandas Data Frames
Pandas data frames are a great way to explore, clean, tweak, and filter your data sets while doing data analysis in Python. This section covers a few of the things you can do with your Pandas data frames.
Here are a few functions that allow you to easily know more about the data set you are working on:
All standard statistical operations like minimums, maximums, and custom quantiles are present in Pandas:
Cleaning the Data
It is quite common to have not-a-number (NaN) values in your data set. To be able to operate on a data set with statistical methods, you’ll first need to clean up the data. The fillna and dropna Pandas functions are a convenient way to replace the NaN values with something more representative for your data set, for example, a zero, or to remove the rows with NaN values from the data frame.
Python Pandas Cheat Sheet Data Wrangling
Filtering and sorting
Here are some basic commands for filtering and sorting the data in your data frames.
While machine learning algorithms can be incredibly complex, Python’s popular modules make creating a machine learning program straightforward. Below is an example of a simple ML algorithm that uses Python and its data analysis and machine learning modules, namely NumPy, TensorFlow, Keras, and SciKit-Learn.
In this program, we generate a sample data set with pizza diameters and their respective prices, train the model on this data set, and then use the model to predict the price of a pizza of a diameter that we choose.
Once the model is set up we can use it to predict a result:
Python For Data Science Cheat Sheet
In this article, we’ve taken a look at the basics of using Python for data analysis. For more details on the functionality available in Pandas, visit the Pandas user guides. For more powerful math with NumPy (it can be used together with Pandas), check out the NumPy getting started guide.
To learn more about Python for data analysis, enroll in our Data Analysis Nanodegree program today.
For working with data in python, Pandas is an essential tool you must use. This is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
But even when you’ve learned pandas in python, it’s easy to forget the specific syntax for doing something. That’s why today I am giving you a cheat sheet to help you easily reference the most common pandas tasks.
It’s also a good idea to check to the official pandas documentation from time to time, even if you can find what you need in the cheat sheet. Reading documentation is a skill every data professional needs, and the documentation goes into a lot more detail than we can fit in a single sheet anyway!
Python For Data Science Cheat Sheet Pdf
Use these commands to import data from a variety of different sources and formats.
Use these commands to export a DataFrame to CSV, .xlsx, SQL, or JSON.
Use these commands to take a look at specific sections of your pandas DataFrame or Series.
Use these commands to select a specific subset of your data.
Use these commands to perform a variety of data cleaning tasks.
Filter, Sort, and Groupby:
Use these commands to filter, sort, and group your data. Install ios simulator xcode.
Use these commands to combine multiple dataframes into a single one.
These commands perform various statistical tests. (They can be applied to a series as well)
I hope this cheat sheet will be useful to you no matter you are new to python who is learning python for data science or a data professional. Happy Programming.
You can alsodownload the printable PDF file from here.