Python Cheat Sheet

STRINGS

A sequence of unicode (Python3) or ASCII (Python2) characters
myStr = "Hello World" Strings are specified with double or single quotes
newStr = str(variable) Convert the given variable to a string
newLst = myString.split(sep) Split a string into a list with elements divided by the given separator (the default separator is space)
myStr.startswith(substring) Check whether a string begins with a given substring. Check end of string with endswith(). Returns either True or False
stripStr = myStr.lstrip(substring) Strip the given substring from the start (left) of myStr. Use rstrip() to strip from the end of the string
upStr = myStr.upper() Convert all characters in myStr to uppercase. Use .lower() for converting to lowercase

FLOATS & INTEGERS

Floating point numbers and integers are two separate numeric types in Python. Integers are whole numbers while floats contain a decimal.
myInt = 6 Integer values have no quotations and do not contain a decimal point
myFloat = 6.0 Float values have no quotations and must contain a decimal point
newFloat = float(int) Converts the given int to a float. Also converts numerical strings e.g. “6”
newInt = int(float) Converts the given float to an integer. Also converts numerical strings
Numerical Operations (Warning: Performing arithmetic with ints will result in outputs rounded to the nearest integer)
x + y Sum of x and y
x - y Difference of x and y
x * y Product of x and y
x / y Quotient of x and y
x % y Remainder of x and y
x ** y x to the power of y
abs(x) Absolute value of x

LISTS

A one dimensional ordered array of Python objects that can be modified (mutable)
myList = ['a', 'b', 'c'] Create a new list called ‘myList’. Use [] to create an empty list
myList.append('new entry') Append a new entry to the end of a list
list1.extend(list2) Concatenate lists
newLst = list(variable) Convert variable into a list
myList[index] Retrieve an item from myList at the given index. You can change the item at the given index by setting it equal to a new value. An index of -1 will return the last item in the list
myList.insert(index, item) Insert an item into myList at the given index
del myList[index] Delete an item from myList at the given index
slicedList = myList[start:stop] Create a new list from a slice of myList. Either start or stop can be ommitted (e.g. myList[:3] will give you a slice of myList up to index 3)
myList.sort(key=..., reverse=...) Sort myList in a specific order
newStr = ''.join(myList) Combine all the elements of a list into a single string. Each element will be separated by whatever value is within the quotation marks

SETS

An unordered collection of unique elements that cannot be modified (immutable)
mySet = set(['a','b','c'] Create a new set named ‘mySet’
unionSet = set1 | set2 Union operation. Returns a set containing all elements of set1 and set2
interSet = set1 & set2 Intersection operation. Returns a set containing only the elements present in both set1 and set2
diffSet = set1 - set2 Difference operation. Returns a set containing only the elements that are different between set1 and set2

DICTIONARIES

A data structure consisting of a set of keys that each map to some value. Keys must be unique and immutable but values can be practically any data structure and repeat themselves
myDict = {'key1':'value1', 'key2':'value2'} Create a dictionary named myDict. {} will create an empty dictionary
myDict[key] Access the value associated with the given key in myDict
myDict['key'] = 'newValue' Create a new key:value pair in myDict or update an old key with a new value
myDict.keys() Returns all the keys within myDict. Use .values() to return all values and .items() to return all key:value pairs

PYTHON CONDITIONS AND IF STATEMENTS

Python conditions evaluate to either True or False. If statements allow for control over the execution of a block of code once a certain condition is met
x == y Returns True if x and y are equal to each other
x is y Returns True if x and y are identical to each other (i.e. x and y are pointing to the same object)
x != y Returns True if x and y are not equal to each other
x < y Returns True if x is less than y. Check if x is greater than y with x > y
x <= y Returns True if x is less than or equal to y. Check if x is greater than or equal to y with x >= y
if statements Check whether a condition is True and if so, execute the associated block of code
  if x == y:
    print('x equals y')
elif statements After an initial if statement evaluates as False another condition can be checked with an elif (else if) statement
  if x==y:
    print('x equals y')
elif x > y:
    print('x is greater than y')
else statements If no previous if or elif statements evaluate to True, another block of text can be executed under control of an else statment
  if x == y:
    print('x equals y')
else:
    print('x does not equal y')

IMPORTING MODULES

Modules are files containing Python code. After installation Python functions, classes, and variables can be imported from these files and used in new projects. There are many useful Python Modules out there such as numpy and matplotlib and you can even write your own modules! Commands for importing modules are written at the start of a Python file
import moduleName Grants access to the entirety of a modules contents. Functions, variables, and classes from the module must be preceeded by the module name (i.e. moduleName.functionName()
import moduleName as alias Grants access to the entirety of a modules contents under a user defined alias. Functions, variables, and classes from the module must be preceeded by the alias (i.e. alias.functionName())
from moduleName import functionName Grants access to a specific part of a module. A function, class, or variable imported in this manner does not have to be preceded by the moduleName (i.e. functionName())
from moduleName import * Grants access to the entirety of a modules contents. Functions, classes, and variables imported in this manner do not have to be preceded by the moduleName. Warning: best practice is to be specific with import statements and the wildcard import should be avoided

LOOPS

A way of repeating a block of code over an iterable object, a range of numbers, or until a certain condition is met
for i in range(0, 4):
    print(i, i*100))

Out:
 (0, 0)
 (1, 100)
 (2, 200)
 (3, 300)		 For loop through range of numbers
myList = ['a', 'b', 'c']
for entry in myList:
    print(entry.upper())
Out:
 A
 B
 C		 For loop through entries in a list. You can use a for loop with any iterable object
count = 0
while count < 4:
    print(count)
    count += 1
Out:
 0
 1
 2
 3		 While loop that stops when condition is no longer met

FUNCTIONS

Functions are blocks of code that accept arguments as input and return some value. After defining a function it can be called repeatedly with new arguments. This makes them great for tasks that are performed multiple times. They’re also handy for organizing a script by breaking it up into smaller pieces that each complete a defined task
def simpleFxn():
	"""
	Example of a simple function with no arguments and a return statement. 
	If no return statement is specified the function will return None. 
	Text within triple quotations is called a docstring and is used for 
	documenting code (like hashtags)
	"""
	return 'Hello World'

# After defining a function it must be called to run
print(simpleFxn())

Out:
	'Hello World'

def subtract(x, y):
	"""
	Example of a subtraction function that takes two numbers as arguments and 
	finds the difference between them. This function returns None
	"""
	print('%s minus %s equals...' % (x, y))
	print(x - y)

newVal = subtract(5, 3)
# Since there is no return statement in subtract() this will print None
print(newVal)

Out:
	5 minus 3 equals...
	2
	None
Some useful functions built-in to Python
objType = type(object) Returns the class type of the given object
listLen = len(myList) Returns the length (number of items) of the object. This object can be a sequence such as a string or a collection such as a list
listSum = sum(myList) Returns the sum of all numeric entries in a given list

FILE I/O

Python has some built in tools for reading in and writing output to files
f = open(filepath, mode) Creates a file object in Python. You must create this object before reading from the file. Mode describes the way the file will be used and is read only (‘r’) by default
for line in f:
    print(line)		 Loops through each line in a file object. Looping is often the most efficient way to read files in Python
fileString = f.read() Read the file object and return the file’s contents as a string
lineString = f.readline() Reads a single line of the file and returns that line as a string. Also progresses you forward one line in the file so the next time readline() is called the next line will returned
f.write(string) Writes the contents of the string to the open file and returns the number of characters written. This will only work if the file was opened in a write capable mode
f.close() After you’re done with a file you should always close it to prevent it from taking up system resources
with open(filepath) as f:
    read_data = f.read()		 Opening a file using the with keyword is good practice and will automatically close the file once the indented code block has completed

PANDAS

import pandas as pd To use pandas you have to import it at the beginning of your Python file. It’s common practice to import pandas with the alias of pd
Creating dataframes
df = pd.read_csv(filepath, sep=',') Create a dataframe from a csv file. Since csv files are comma separated the default sep is ‘,’ but you can change this to any delimiter you want (e.g. use sep=’\t’ for tab separated files). There are other functions available in pandas for reading in other filetypes that go by the naming scheme pd.read_filetype()
pd.DataFrame(array, headers=['my', 'headers']) Create a pandas dataframe from a numpy array or list of lists
df.columns = ['my', 'desired', 'column', 'headers'] Set or change the dataframe column headers to the specified labels
Combining and adding to dataframes
df['newColumn'] = ['new', 'column', 'data'] Add a list as a new column to the dataframe
df.concat([df1, df2], axis=1) Combine two dataframes by rows (axis=0) or columns (axis=1)
Subsetting
df[['my', 'headers']] Returns a df containing only the specified columns
df[df[column] != 0] Filter a dataframe by row based on whether a condition is met in the specified column
df.head(n) Takes the first ‘n’ rows of the df. You can use df.tail(n) to take the last ‘n’ rows
Data selection
df.iloc[rowNumber, columnNumber] An integer position based way of selecting entries in a dataframe
df.loc[rowName] A label based way of selecting rows from a dataframe
Operations and inspecting data
df.sort_values(column) Sort a dataframe by values in the specified column
df.mean() Returns the mean for all columns in the dataframe
df.max() Returns the max value for all columns in the dataframe. Use min() for the minimum value
df.std() Returns the standard deviation of each column in the dataframe
df.shape Gives the number of rows and columns in the dataframe
Saving dataframes to a file
df.to_csv(outputFilename, sep=',') Writes the contents of a dataframe to a file. Again, the default delimiter here is a comma for csv but you can specify whatever delimiter you wish with the sep argument