python

set( )

{ }

a collection that stores unique values (no duplicates) and is unordered

use .add( ) and .remove( )

range(3)

0, 1, 2

4%3

1 remainder

15 // 4

there are 3 4s in 15

HashSet

a data structure that stores unique elements in no particular order. No duplicate item, cannot access elements by an index, allow null

// for Sudoku

• // = "which group?" (top/middle/bottom)

  row = (nsquare//n)*x+i # how many

n*n box,

x num box per row,

nsquare is 第几个box

% for sudoku

% = "which position inside the group?" (left/middle/right)

col = (nsquare% n)* x +j # remainder

n*n box,

x num box per row,

nsquare is 第几个box

s[::-1]

s[::-1] means:

start : end : step
-1 = go backwards

\

line continuation character. It means this statement continues on the next line.

world[name] what type does this output

Returns one column as a Series.

world[['name']] what type does this output

dataframe

~

not

filter those name starting with A

df[df['name'].str.startswith('A')]

filter name containing John

df[df['name'].str.contains('John')]

df.sort_values('column_name')

Ascending (default) 少到多

rename

df.rename(columns={'old_name': 'new_name'})

unique

drop_duplicates()

df['name'].str[0:3]

substring

string spaces

.str.strip()

df['name'].str.split(' ')

split by space

df['name'].str.len() how to use this

df[df['name'].str.len() > 5]

replace certain string

.str.replace(old, new)

lambda

means a small function you write in one line.

lambda arguments: expression

def bonus(x):

return x['salary']

becomes lambda x: x['salary']

what does axis 1 mean in apply

axis=1 mean apply function to each row

how to use assign ? what does it do ?

df.assign(new_column=value)

create or modify columns in a DataFrame.

how to use apply ?

df.apply(function, axis=1)

def get_bonus(x): return x['salary'] * 2 df['bonus'] = df.apply(get_bonus, axis=1)

df['bonus'] = df.apply(lambda x: x['salary'] * 2, axis=1)

.assign() syntax

df.assign(new_column=value)

df.assign( bonus=df['salary'] * 0.1 )

what does assign do

.assign() lets you create or modify columns and returns a new DataFrame.

df.assign(new_column=value)

change data

astype(int)

fill in the missing value as 0 in the quantity column

.fillna(0, inplace=True)