Section 0

print commands

1. print(): printing
2. \n: next line
3. +: print combined string
4. input(): get prompt. passing user's input data to the sentence, and user will see the sentence you input in the ()
5. len(): calculate the length of the str

save variables

Once use "input", the variables input will be saved by the functions, by hold it with a variable, we can access it after.

variables naming rules

1. make sense to you
2. 1 single unit
3. use "_" to separate
4. can not use number in the first character

data types

strings

a str of characters str[n] prints out the character in the n+1 place

integer

whole actual number 1_234-->1,234

float

decimal number

boolean

True/False

type converting

type(): output the type of the input

str(): convert into string data type

int(): convert into int data type

float(): convert into float data type

operations

+: combine strings;

+,-,*,/:math operations

**: power.

all the output is float data type

order: (PEMDAS) ()--> *_ --> _,/ --> +,-

number manipulation

print(round(8/3.the number you want to round into))

print(8//3) round to 2 decimals

a = a + 1 equals a+=1

F strings

use when we want to correctly work with different types of data, instead of converting every variables, we can use f str, and python does the converting for correctly output.

x=1
y=2.123
print(f"your score is {x} and {y}")

key concepts

if else elif

if condition1 and/or condition2 :

if not condition :

random module

import random module, askpython.com

module is something to do specific function

create own module is also accessible, just create new python file

list

list_0 = [sth1, sth2], sth can also be a list.

list[n], n starts from 0 to length

.append("end"): add item to the list

loop

for item in list_of_items:
  actions

assign the variable to the items in the list in order

range

for item in range(a, b, *step):

function

def function():

def function(x, y):
# do sth with x, y

indentation

similar with the file and folder in the PC

while loop

while sth-is-true:

dictionary

group together data and tag

think it as a table. every dictionary has 2 parts: key & value.

dic = {
"key_1": "value_1",
"key_2": "value_2",
123: "value_3"
}

# retrieving items
dic["key_1"]

# editing & adding new items
dic["new_item"] = "new_value"

# initialization & wiping
empty_dic = {}
dic = {}

# looping through, get the keys in dic
for things in dic:
    print(things)

nesting

function with output

def function():
    do with something
    return something

# call function
output = function()

"return" statement implies the end of the function

docstrings

a way for us to create documentation as going along in the function

def function():
    """docstrings """

scope

things inside the function are blocked.

def fc_1():
    def fc_2():

fc_2() # can'† call, because fc_2 is in the scope of fc_1()

game_level = 3
enemies = ["Skeleton", "Zombie", "Alien"]
if game_level < 5:
    new_enemy = enemies[0]
print (new_enemy)
# var. inside the if loop or any other loops, it will not be scoped, it will be counted as global.

game_level = 3
def create_enemy () :
    enemies = ["Skeleton", "Zombie", "Alien"]
    if game_level < 5:
        new_enemy = enemies[0]
print (new_enemy)
# this time var. is created inside the function, so it will be scoped

how to debug

1. use print()
2. use debugger python visualizer

Section 1.

importing modules

way 1:

import module

This can be very annoying if constantly using 1 thing in module, such as module.thing

way 2:

from module import Thing

In this way we only need to type Thing directly to use it.

way 3:

from module import *

If everything in module will be frequently used, import this way. But please avoid.

way 4:

import module as m

quite useful way to import, if module has a long name.

If find a module can not be imported, install first.

read and write file

with open('my_file.txt', 'w') as f:
   f.write('Hello, world!')

absolute file path

relative file path

working directory: the project folder we are now at. use "." to represent the current file path

read CSV files

CSV (comma separate values) is a very common way to visualize the data into tables like a spreadsheet

# open weather_data.csv

import csv
import pandas as pd

with open('./US-map/weather_data.csv') as data_file:
   data = data_file.readlines()
   print(data)
   temperatures = []
   for row in data:
      if row[1] != 'temp':
         temperatures.append(int(row[1]))
   print(temperatures)

# open weather_data.csv

import csv
import pandas as pd

with open('./US-map/weather_data.csv') as data_file:
   data = csv.reader(data_file) # can be looped through
   print(data)
   for row in data:
      print(row)

# open weather_data.csv

import csv
import pandas as pd

with open('./US-map/weather_data.csv') as data_file:
   data = csv.reader(data_file) # can be looped through
   temperatures = []
   for row in data:
      if row[1] != 'temp':
         temperatures.append(row[1])
   print(temperatures)

pandas library

# open weather_data.csv

import csv
import pandas as pd

# read the csv file

pandas_data = pd.read_csv('./US-map/weather_data.csv') # simple 1 line code kills the 3 lines of code in the previous file
print(pandas_data)
print(pandas_data['day'])

There are 2 primary data structures of pandas, series(1-d) and DataFrame (2-d)

type(pandas_data['day'])
type(pandas_data) # DataFrame
# this is to check the type of the data

basically, we can convert these data structures in pandas to any other formats(dictionary, list,...)

# get data in the column
temp_list = data['temp'].to_list()

# get data in the row
row_index = 0  # Replace 0 with the desired row index
row_data = pandas_data.iloc[row_index]
print(row_data)

# open weather_data.csv

import csv
import pandas as pd

# create a dataframe from scratch

data_dict = {
"students": ["Amy", "James", "Angela"],
"scores": [76, 56, 65]
}

data = pd.DataFrame(data_dict) # convert the dictionary to a dataframe

print(data)

# convert data to csv

data.to_csv("./US-map/new_data.csv") # save the dataframe to a csv file

list comprehension

create a list from a previous list. (we use for loop to do this so far)

create list comprehension:

new_list = [new_item for item in list]

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0]
new_numbers = []

for n in numbers:
   new_numbers.append(n + 1)

print(new_numbers)

# use list comprehension
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0]
new_numbers = [n+1 for n in numbers]
print(new_numbers)

new_list = [new_item for item in list if test]

dictionary comprehension

new_dic = { new_key:new_value for item in list }