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Pandas 3.0 (released January 2026) made copy-on-write the default and turned the old chained-assignment warning into a hard error. The patterns below use .loc[], which stays safe either way.

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Core Python

Function What it does
len() Returns the number of items in a list, string or other collection
range() Generates a sequence of numbers, most often used in a for loop
enumerate() Loops over a collection while also giving you the index
zip() Pairs up items from two or more collections
sorted() Returns a sorted copy of a collection (sorted(x, reverse=True) for descending)
sum() Adds up a collection of numbers
map() Applies a function to every item in a collection
filter() Keeps only the items that pass a condition
[x for x in y if ...] List comprehension: builds a list in one line
type() Shows the type of a value
isinstance() Checks whether a value is a particular type

Working with strings

Method What it does
.split() Breaks a string into a list, on whitespace or a given separator
.join() Joins a list of strings into one, using the string it's called on as the separator
.strip() Removes leading and trailing whitespace
.replace() Swaps one substring for another
f"{variable}" f-string: embeds a variable directly inside a string

Pandas essentials

Method What it does
pd.read_csv() Loads a CSV file into a DataFrame (read_parquet, read_json also exist)
.head() / .info() / .describe() Previews the data, its structure, and summary stats
.loc[] Selects rows and columns by label, and the safe way to assign values
.groupby() Groups rows to run an aggregate function per group
.merge() Joins two DataFrames together, similar to a SQL join
.pivot_table() Reshapes data into a summary table
.fillna() / .dropna() Fills or removes missing values
.apply() Runs a function across a column or row