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.
Read and Write to CSV. pd.readcsv('file.csv', header=None, nrows=5). Today I was doing some pandas exercises on Kaggle and I found this cheat sheet that can be really useful on daily work. I don't know if this is an old news or something but I thought that will be good to share it, especially for beginners as me. Pandas Cheat Sheet: Link. UPDATE: Here are others cheat sheet resources provided by users. Explore and run machine learning code with Kaggle Notebooks Using data from multiple data sources. Kaggle相关资料; jupyter相关资料; MachinLearningOnSpark; 实践代码; cheat sheet ML. Machine learning cheat sheet. Numpy cheat sheet. Pandas cheat sheet. 实操 内存占用减少高达90%，还不用升级硬件？没错，这篇文章教你妙用Pandas轻松处理大规模数据. Scikit cheat sheet. Pandas Cheat Sheet for Everyone! Pandas Cheat Sheet for Everyone! Skip to content. Skip to content. We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. By using Kaggle, you agree to our.
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
Being able to look up and use functions fast allows us to achieve a certain flow when writing code. So I’ve created this cheatsheet of functions from python pandas.
This is not a comprehensive list but contains the functions I use most, an example, and my insights as to when it’s most useful.
Load CSV
If you want to run these examples yourself, download the Anime recommendation dataset from Kaggle, unzip and drop it in the same folder as your jupyter notebook.
Next Run these commands and you should be able to replicate my results for any of the below functions.
Convert a CSV directly into a data frame. Sometimes loading data from a CSV also requires specifying an encoding (ie:encoding='ISO-8859–1'). It’s the first thing you should try if your data frame contains unreadable characters.
Another similar function also exists called pd.read_excel for excel files.
Build data frame from inputted data
Useful when you want to manually instantiate simple data so that you can see how it changes as it flows through a pipeline.
Copy a data frame
Useful when you want to make changes to a data frame while maintaining a copy of the original. It’s good practise to copy all data frames immediately after loading them.
Save to CSV
This dumps to the same directory as the notebook. I’m only saving the 1st 5 rows below but you don’t need to do that. Again, df.to_excel() also exists and functions basically the same for excel files.
Get top or bottom n records
Display the first n records from a data frame. I often print the top record of a data frame somewhere in my notebook so I can refer back to it if I forget what’s inside.
Count rows
This is not a pandas function, but len() Sdl trados studio 2011 advanced pdf. counts rows and can be saved to a variable and used elsewhere.
Count unique rows
Count unique values in a column:
Get data frame info
Useful for getting some general information like header, number of values and datatype by column. A similar but less useful function is df.dtypes which just gives column data types.
Get statistics
Really useful if the data frame has a lot of numeric values. Knowing the mean, min and max of the rating column give us a sense of how the data frame looks overall.
Get counts of values
Get a list or series of values for a column
This works if you need to pull the values in columns into x and y variables so you can fit a machine learning model.
Get a list of index values
Create a list of values from index.
Get a list of column values
Append new column with a set value
I do this on occasion when I have test and train sets in 2 separate data frames and want to mark which rows are related to what set before combining them.
Kaggle Pandas Cheat Sheet Printable
Create new data frame from a subset of columns
Useful when you only want to keep a few columns from a giant data frame and don’t want to specify each that you want to drop.
Drop specified columns
Useful when you only need to drop a few columns. Otherwise, it can be tedious to write them all out and I prefer the previous option.
Add a row with sum of other rows
We’ll manually create a small data frame here because it’s easier to look at. The interesting part here is df.sum(axis=0) which adds the values across rows. Alternatively df.sum(axis=0) adds values across columns.
Kaggle Pandas Cheat Sheets
The same logic applies when calculating counts or means, ie: df.mean(axis=0). The 87th award download.
Concatenate 2 dataframes
Use this if you have 2 data frames with the same columns and want to combine them.
Here we split a data frame in 2 them add them back together.
Merge dataframes
This functions like a SQL left join, when you have 2 data frames and want to join on a column.
Retrieve rows with matching index values
The index values in anime_modified are the names of the anime. Notice how we’ve used those names to grab specific columns.
Retrieve rows by numbered index values
This differs from the previous function. Using iloc, the 1st row has an index of 0, the 2nd row has an index of 1, and so on… even if you’ve modified the data frame and are now using string values in the index column.
Use this is you want the first 3 rows in a data frame.
Get rows
Retrieve rows where a column’s value is in a given list. anime[anime['type'] 'TV'] also works when matching on a single value. Lpn to adn programs in missouri download free.
Slice a dataframe
This is just like slicing a list. Slice a data frame to get all rows before/between/after specified indices.
Filter by value
Filter data frame for rows that meet a condition. Note this maintains existing index values.
sort_values
Sort data frame by values in a column.
Groupby and count
Count number of records for each distinct value in a column.
Groupby and aggregate columns in different ways
Note I added reset_index() otherwise the type column becomes the index column — I recommend doing the same in most cases.
Create a pivot table
Nothing better than a pivot table for pulling a subset of data from a data frame.
Note I’ve heavily filtered the data frame so it’s quicker to build the pivot table.
Set NaN cells to some value
Set cells with NaN value to 0 . In the example we create the same pivot table as before but without fill_value=0 then use fillna(0) to fill them in afterwards.
Sample a data frame
I use this all the time taking a small sample from a larger data frame. It allows randomly rearranging rows while maintaining indices if frac=1
Iterate over row indices
Iterate over index and rows in data frame.