Data can be messy: it often comes from various sources, doesn’t have structure or contains errors and missing fields. Working with data requires to clean, refine and filter the dataset before making use of it.

JUPYTER NOTEBOOK CHEAT SHEET Learn PYTHON from experts at Keyboard Shortcuts Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. It is used for data cleaning and transformation, numerical simulation, statistical. Selecting List Elements Import libraries import numpy import numpy as np Selective import from math import pi help(str) Python For Data Science Cheat Sheet.

Pandas is one of the most popular tools to perform such data transformations. It is an open source library for Python offering a simple way to aggregate, filter and analyze data. The library is often used together with Jupyter notebooks to empower data exploration in various research and data visualization projects.

This Jupyter Notebook cheat sheet will help you to find your way around the well-known Notebook App, a subproject of Project Jupyter. You'll probably know the Jupyter notebooks pretty well - it's one of the most well-known parts of the Jupyter ecosystem!

Pandas introduces the concept of a DataFrame – a table-like data structure similar to a spreadsheet. You can import data in a data frame, join frames together, filter rows and columns and export the results in various file formats. Here is a pandas cheat sheet of the most common data operations:

Getting Started

Import Pandas & Numpy

Get the first 5 rows in a dataframe:

Get the last 5 rows in a dataframe:

Import Data

Create DataFrame from dictionary:

Import data from a CSV file:

Import data from an Excel Spreadsheet:

Import data from an Excel Spreadsheet without the header:

Export Data

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Export to a CSV file:

Convert Data Types

Convert column data to string:

Convert column data to integer (nan values are set to -1):

Convert column data to numeric type:

Get / Set Values

Import Pandas Jupyter Notebook

Get the value of a column on a row with index idx:

Set column value on a given row:

Count

Number of rows in a DataFrame:

Count rows where column is equal to a value:

Count unique values in a column:

Count rows based on a value:

Filter Data

Filter rows based on a value:

Filter rows based on multiple values:

Filter rows that contain a string:

Filter rows containing some of the strings:

Filter rows where value is in a list:

Filter rows where value is _not_ in a list:

Filter all rows that have valid values (not null):

Sort Data

Sort rows by value:

Sort Columns By Name:

Rename columns

Rename particular columns:

Rename all columns:

Make all columns lowercase:

Drop data

Drop column named col

Drop all rows with null index:

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Drop rows that have missing values in some columns:

Drop duplicate rows:

Create columns

Create a new column based on row data:

Create a new column based on another column:

Create multiple new columns based on row data:

Match id to label:

Data Joins

Update Pandas In Jupyter Notebook

Join data frames by columns:

Concatenate two data frames (one after the other):

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Utilities

Increase the number of table rows & columns shown:

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Pandas Cheat Sheet Jupiter Notebooks

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