BackEnd/Automation Program with Python
2023.01.25
import pymysql conn = pymysql.connect( host='localhost', user='root', password='password', db='python_app_dbtest', charset='utf8' ) try: with conn.cursor() as c: #딕셔너리 반환 : conn.cursor(pymysql.cursors.DictCursor) c.execute("SELECT * FROM users") #1개 로우 선택 print(c.fetchone()) #지정 로우 선택 print(c.fetchmany(3)) #전체 로우 선택 print(c.fetchall()) #순회1 c.execute("SELECT * FROM users ORDER BY id ASC") rows =..
BackEnd/Automation Program with Python
2023.01.25
import pymysql import json import datetime #MySQL Connection conn = pymysql.connect( host='localhost', user='root', password='password', # 자신이 설정한 비밀번호 입력 db='python_app_dbtest', # 자신이 설정한 database charset='utf8' ) #pyMysql 버전확인 print('pymysql.version', pymysql.__version__) #데이터베이스 선택 conn.select_db('python_app_dbtest') #Cursor 연결 c = conn.cursor() print(type(c)) now = datetime.datetime.now() no..
BackEnd/Automation Program with Python
2023.01.19
2023.1.1 ~ 1.15 까지 주식 최고가 조회하기 import matplotlib.pyplot as plt import pandas_datareader as web import datetime import FinanceDataReader as fdr #조회 시작 및 종료 날짜 start = datetime.datetime(2023,1,1) end = datetime.datetime(2023,1,15) finance_naver = fdr.DataReader('035420', start, end) # print(finance_naver) finance_mando = fdr.DataReader('204320', start, end) # print(finance_mando) #윈도우 제목 fig = plt..
BackEnd/Automation Program with Python
2023.01.19
from pandas import Series #matplotlib, pandas_datareader #Series1 선언 series1 = Series([92600, 92400, 92100, 94300, 92300]) #출력 print(series1) #총 개수 print('count',series1.count()) #요약 print('count',series1.describe()) #인덱스 접근 print(series1[0]) #Series2 선언 series2 = Series([92600, 92400, 92100, 94300, 92300], index=['2018-02-19','2018-02-18','2018-02-17','2018-02-16','2018-02-15']) #출력2 print(seri..
BackEnd/Automation Program with Python
2023.01.19
Pandas 이용해서 읽기import pandas as pd df = pd.read_excel('excel_s1.xlsx', header=0) print(df) #컬럼 값 수정 df['State'] = df['State'].str.replace(' ', '|') print(df) #평균 컬럼 추가 df['Avg'] = df[['2003', '2004', '2005']].mean(axis=1).round(2) print(df) #합 컬럼 추가 df['Sum'] = df[['2003', '2004', '2005']].sum(axis=1).round(2) print(df) #최대값 출력 print(df[['2003', '2004', '2005']].max(axis=0)) #최소값 출력 print(df[['20..
BackEnd/Automation Program with Python
2023.01.19
import pandas as pd import csv #기본 읽기 df = pd.read_csv('csv_s1.csv') print(df) #행 스킵 df = pd.read_csv('csv_s1.csv', skiprows=[0]) print(df) #행 스킵, 헤더 생략 df = pd.read_csv('csv_s1.csv', skiprows=[0],header=None) print(df) #헤더 정의 df = pd.read_csv('csv_s1.csv', skiprows=[0],header=None, names=["Month",1958,1959,1960]) print(df) #인덱스 컬럼 정의 df = pd.read_csv('csv_s1.csv', skiprows=[0],header=None, name..
BackEnd/Automation Program with Python
2023.01.19
import urllib.request as req import simplejson as json import os.path #url url = 'https://api.github.com/repositories' # 경로 & 파일명 savename = 'C:/repo.json' if not os.path.exists(url): req.urlretrieve(url, savename) items = json.load(open(savename, 'r', encoding='utf-8')) # items = json.loads(open(savename, 'r', encoding='utf-8').read()) # 출력 for item in items: print(item['full_name'] + ' - ' + i..
BackEnd/Automation Program with Python
2023.01.19
import simplejson as json # import json #Dict(json) 선언 data = {} data['people'] = [] data['people'].append({ 'name':'KUK', 'website':'naver.com', 'from':'seoul' }) data['people'].append({ 'name':'Lee', 'website':'google.com', 'from':'kyungki' }) data['people'].append({ 'name':'park', 'website':'daum.net', 'from':'pusan' }) print(data) # data = {'people': [{'name': 'KUK', 'website': 'naver.com', ..
BackEnd/Automation Program with Python
2023.01.18
기상청 xml 데이터 지역별 시간대별 최고 최저 온도 수집 import sys import io import urllib.request as req from bs4 import BeautifulSoup import os.path # 다운로드 url url = 'https://www.kma.go.kr/weather/forecast/mid-term-rss3.jsp?stnId=108' savename = 'C:/forecast.xml' if not os.path.exists(savename): req.urlretrieve(url, savename) # Beautifulsoup 파싱 xml = open(savename, 'r', encoding="utf-8").read() soup = BeautifulSoup(..
BackEnd/Automation Program with Python
2023.01.18
import pickle #(객체, 텍스트) 직렬화, 역직렬화 # 파일이름과 데이터 bfilename = 'C:/test.bin' tfilename = 'C:/test.txt' data1 = 77 data2 = 'hello world' data3 = ['car', 'apple', 'house'] # 바이너리 쓰기 with open(bfilename, 'wb') as f: pickle.dump(data1,f) #dumps(문자열 직렬화) pickle.dump(data2,f) pickle.dump(data3,f) # 텍스트 쓰기 with open(tfilename, 'wt') as f: f.write(str(data1)) f.write('\n') f.write(data2) f.write('\n') f.wri..
