Python

Contents

Project Setup - venv

  • Why to use :
    • to install modules independently
    • to manage modules independently
  • How

    • setup virtual environment

        python -m venv venv

    • activate virtual environment

        # In Windows 
  venv\Scripts\activate

  # In Mac or Linux
  source venv/bin/activate

    • manage modules

        # install
  pip install {module_name}

  # check the list of modules
  pip list

  # get the list within txt file
  pip freeze > requirements.txt

  # install module list using txt file
  pip install -r requirements.txt

    • deactivate virtunal environment

        deactivate

Docstring Convention

  • function

      def function_name(param1: int, param2: str) -> bool:
      """
      한 줄 요약: 함수가 수행하는 작업을 간단히 설명합니다.

      여러 줄 설명: 함수의 동작, 사용법, 특별한 조건 등을 설명합니다.
      예를 들어, param1은 X를 의미하며, param2는 Y의 역할을 합니다.

      Args:
          param1 (int): 설명1.
          param2 (str): 설명2.

      Returns:
          bool: 반환값에 대한 설명.
      """
      return True

  • class

      class ClassName:
      """
      클래스 요약: 클래스의 역할이나 목적을 간단히 설명합니다.

      클래스의 동작, 속성, 주요 메서드 등을 상세히 기술합니다.
      예를 들어, 이 클래스는 X 작업을 수행하며 Y 속성을 관리합니다.

      Attributes:
          attr1 (type): 속성1의 설명.
          attr2 (type): 속성2의 설명.
      """

      def __init__(self, param1: int, param2: str) -> None:
          """
          클래스의 생성자: 인스턴스를 초기화합니다.

          Args:
              param1 (int): 설명1.
              param2 (str): 설명2.
          """
          self.param1 = param1
          self.param2 = param2

Absolute Import vs Relative Import

  • Two terms are for where does code start to track
  • Absolute Import
    • start from root directory of project
  • Relative Import
    • start from current script file
  • In python2, there was a confliction issue between original module name and created module name. So for the sake of avoiding the confliction, need to use from __future__ import absolute_import
  • In python3, this condition or setup become default, so this is not necessary

Module

__future__

Deferred evaluation by storing module as string

  • This is for preventing the codes from circular import

      from typing import TYPE_CHECKING

  if TYPE_CHECKING:
      from mod1 import School

  class Student:
      def register_school(school: 'School'):
          pass

typing

Union

  • This represents that parameter type and return type can be one of several specific types

  • | could be used instead of Union from 3.10 onwards

      from typing import Union

  def parse_data(data: Union[str, list]) -> list:
      if isinstance(data, str):
          return data.split(",")  # 문자열을 리스트로 변환
      return data

  def get_data() -> Union[str, dict]:
      # 조건에 따라 반환 타입이 달라질 수 있음
      if some_condition:
          return "data as string"
      return {"key": "value"}

pandas

  • CRUD
    • C : create
      • create empty dataframe
      • create dataframe from list of dict
    • R : read
      • search row by condition ( column name cell value )
      • selecting and indexing from Dataframe to sub dataframe ( filtering )
      • read and access exact cell
    • U : update
      • add row with dictionary
    • D : delete
  • Utility
    • DataFrame.copy()
    • DataFrame.to_dict(“index”)
    • DataFrame.to_dict(orient=”records”)

Create

  • Create table 
      def create_table(default_info :dict) -> pd.DataFrame:
      return pd.DataFrame(default_info)

  default_header = {"task_name":[], "task_id":[], "assignee":[], "entity_id":[]}
  table = create_table(default_header)
  print(table)

  # =============
  # Print results
  # =============
  # Empty DataFrame
  # Columns: [task_name, task_id, assignee, entity_id]
  # Index: []

Update

  • Add rows 
      def add_row_to_table(tar_table :pd.DataFrame, input_info :dict) -> None:
      tar_table.loc[len(tar_table)] = input_info

  assignee = [{"name":"송태영"}]
  assignee_str = dumps(assignee)

  input_infos = [
                  {"task_name":"fx01", "task_id":123, "assignee":assignee_str, "entity_id":456},
                  {"task_name":"fx02", "task_id":111, "assignee":assignee_str, "entity_id":333},
                  {"task_name":"fx03", "task_id":122, "assignee":assignee_str, "entity_id":555},
              ]
  for _info in input_infos:
      add_row_to_table(table, _info)

  print(table)

  # =============
  # Print results
  # =============
  #     task_name  task_id                          assignee  entity_id
  # 0      fx01      123  [{"name": "\uc1a1\ud0dc\uc601"}]        456
  # 1      fx02      111  [{"name": "\uc1a1\ud0dc\uc601"}]        333
  # 2      fx03      122  [{"name": "\uc1a1\ud0dc\uc601"}]        555

Read

  • Search row by condition ( filtering )

      def search_in_table(tar_table :pd.DataFrame, col_name :str, input_info :str) -> pd.DataFrame:
      return tar_table.loc[tar_table[col_name] == input_info]

  res = search_in_table(table, "task_id", 123)
  print(res)

  • Select and Index from Dataframe to sub dataframe ( filtering )

      # 한개의 column
  print(table["task_name"] )

  # 선택한 column 들
  print(table[["task_name", "entity_id"]] )


  # 한개의 row
  print(table.loc[[1]] )

  # 선택한 row들
  print(table.loc[[0,2]])

  • Access exact cell

      print(table.iloc[0]["task_name"])
  print(loads(table.iloc[0]["assignee"]))
  print(table.to_dict("index"))
  print(table.to_dict(orient='records'))

  # =============
  # Print results
  # =============
  # fx01
  # [{'name': '송태영'}]
  # {0: {'task_name': 'fx01', 'task_id': 123, 'assignee': '[{"name": "\\uc1a1\\ud0dc\\uc601"}]', 'entity_id': 456}}
  # [{'task_name': 'fx01', 'task_id': 123, 'assignee': '[{"name": "\\uc1a1\\ud0dc\\uc601"}]', 'entity_id': 456}]

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