Cheatsheet -- Python & Pandas

Python Syntax & Operators

Category	Operator	Description	Example
Arithmetic	+ - * /	Basic Math	10 / 2 = 5.0
	//	Floor Division (removes decimal)	10 // 3 = 3
	%	Modulus (remainder)	10 % 3 = 1
	**	Exponentiation (Power)	2 ** 3 = 8
Assignment	=	Assign value	x = 5
	+= -=	Increment/Decrement	x += 1
	:=	Walrus (assign inside expression)	if (n := len(a)) > 10:
Comparison	== !=	Equal / Not Equal	x == 5
	> <	Greater / Less than	x > 10
Logical	and	Both must be True	True and False -> False
	or	One must be True	True or False -> True
	not	Inverts Boolean	not True -> False
Identity	is	Same object in memory?	x is None
Membership	in	Exists in sequence?	'a' in 'apple'

Data Structures

Type	Syntax	Mutable?	Access
List	[1, 2, "a"]	Yes	x[0]
Tuple	(1, 2, "a")	No	x[0]
Dict	{'k': 'v'}	Yes	x['k']
Set	{1, 2, 3}	Yes	N/A (Unordered)

Pandas Cheat Sheet

1. Inspection

df.head(n)          # First n rows
df.shape            # (rows, columns)
df.info()           # Index, Datatype, Memory info
df.describe()       # Statistical summary
df.columns          # List of column names

2. Selection

df['col']           # Returns Series
df[['c1', 'c2']]    # Returns DataFrame
df.iloc[0]          # Selection by position (Integer)
df.loc['index_val'] # Selection by Label
df[df['col'] > 5]   # Boolean conditional selection

3. Cleaning & Manipulation

df.isnull().sum()            # Count nulls per column
df.dropna()                  # Drop rows with nulls
df.fillna(value)             # Fill nulls
df.astype(dtype)             # Change data type
df.sort_values(by='col')     # Sort
df.groupby('col').mean()     # Group and aggregate

4. Combining Data

Method	Use Case
pd.concat([df1, df2])	Stacking dataframes vertically or horizontally.
df1.merge(df2)	Database-style join on a common column.
df1.join(df2)	Joining based on the Index.

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Zen of Python (abridged)

Explicit is better than implicit. Simple is better than complex. Readability counts.