Python Data Science Cheat Sheet

Flashcards to help beginners and intermediate users learn Python for data science.

Python

A popular programming language in data science known for its ease of learning and powerful libraries for data analysis.

Packages

Collections of useful tools developed by the open-source community that extend the capabilities of the Python language.

pip install pandas

Command prompt command to install a new Python package (example: pandas).

Working Directory

The default file path that Python reads or saves files into.

os library

Needed to set and get the working directory in Python.

Arithmetic Operators

• (addition), - (subtraction), * (multiplication), / (division), // (integer division), ^ (raise to the power), % (remainder after division).

Assignment Operators

Used to assign values to variables (e.g., a = 5) or change the value of an item in a list (e.g., x[0] = 1).

Numeric Comparison Operators

== (equal to), != (not equal to), > (greater than), >= (greater than or equal to), < (less than), <= (less than or equal to).

Logical Operators

not (logical NOT), and (logical AND), or (logical OR).

List

An ordered and changeable sequence of elements that can hold integers, characters, floats, strings, and objects.

Zero-indexed

Python lists are zero-indexed, meaning the first element has index 0.

Dictionary

Stores data values in key-value pairs, indexed by unique keys (names).

NumPy

A Python package for scientific computing that provides multidimensional array objects and efficient operations on them.

np.array([1, 2, 3])

Convert a python list to a NumPy array

String

A sequence of characters, created with double or single quotes.

str[0]

Get the character at a specific position

Concatenate strings (+)

Combines strings together

Pandas

A fast and powerful package for data analysis and manipulation in Python.

DataFrame

A Pandas structure that contains two-dimensional data stored as rows and columns.

Series

A Pandas structure that contains one-dimensional data.

pd.concat([df, df])

Concatenate DataFrames vertically

str.upper()

Convert a string to uppercase

str.lower()

Convert a string to lowercase

df.mean()

Calculate the mean of each column