The “Cheat Sheets” are still a work in progress. These diagrams were kindly provided by user dotherwise. The latest versions are available from this web page. When they are done (and checked for bugs) they will be added to this manual page. Everyone is invited to look for bugs and make suggestions. The source file (SVG) is also available. Python Pandas Cheat Sheet Last updated on Feb 16, 2021 Import required packages import pandas as pd import numpy as np Create a Series from a list With default index.
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!
Importing Data:
Use these commands to import data from a variety of different sources and formats.
Exporting Data:
Use these commands to export a DataFrame to CSV, .xlsx, SQL, or JSON.
Viewing/Inspecting Data:
Use these commands to take a look at specific sections of your pandas DataFrame or Series.
Selection:
Use these commands to select a specific subset of your data.
Data Cleaning:
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.
Join/Combine:
Use these commands to combine multiple dataframes into a single one.
Statistics:
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.
.
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() 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.
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.
The same logic applies when calculating counts or means, ie: df.mean(axis=0).
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
Python Spark Dataframe Cheat Sheet
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.
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.
Python Numpy Cheat Sheet
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.