yaojenkuo

from sqlalchemy import create_engine
from urllib.parse import quote
import sqlite3
import pandas as pd

password = "YOURPASSWORD"
db_name = "YOURDBNAME"
table_name = "YOURTABLENAME"
connection_url = "mysql+pymysql://root:{0}@localhost:3306/{1}".format(quote(password), db_name)
engine = create_engine(connection_url)

yaojenkuo

約維安計畫：第二週

初嘗命令列的滋味。

基礎命令一覽

首先，我們將這篇文章會介紹的基礎命令全數列出：

pwd # print working directory

yaojenkuo

(base)~$ conda install mamba -n base -c conda-forge
(base)~$ source activate xeus-sql
(xeus-sql)~$ mamba install xeus-sql jupyterlab -c conda-forge
(xeus-sql)~$ jupyter notebook

yaojenkuo

(base)~$ R
> install.packages('IRkernel') 
> IRkernel::installspec(user = FALSE) 
> q()

yaojenkuo

(base)~$ conda install jupyter 
(base)~$ jupyter --version

yaojenkuo

pwd                      # print working directory
mkdir folder_name        # make directory
ls                       # list directory and file
cd folder_name           # changing directory
touch file_name          # create file
mv file_name file_name   # move file
cp file_name file_name   # copy file
rm file_name file_name   # remove file
clear                    # clear command line, ctrl-L
# up-arrow / down-arrow for history commands

yaojenkuo

import pandas as pd
from urllib.parse import quote_plus
from string import ascii_uppercase
import re

class TaiwanElection2020:
    def __init__(self):
        self._counties = ['宜蘭縣', '彰化縣', '金門縣', '桃園市', '苗栗縣', '臺南市', '雲林縣', '南投縣', '高雄市', '臺北市', '新北市', '花蓮縣', '新竹市', '新竹縣', '基隆市', '連江縣', '嘉義縣', '嘉義市', '屏東縣', '澎湖縣', '臺東縣', '臺中市']
    def tidy_dataframe(self, df):
        # updating columns attributes 

yaojenkuo

import pandas as pd
import datetime as dt

today = dt.date.today()
day_delta = dt.timedelta(1)
while True:
    try:
        today_str = today.strftime('%m-%d-%Y')
        print("嘗試載入 {} 的資料...".format(today_str))
        csv_url = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/{}.csv'.format(today_str)

yaojenkuo

yaojenkuo

get_actual_historic_prices <- function(adjusted_prices, split_date_periods, split_ratios) {
  n_splits <- length(split_ratios)
  #xts_list <- list()
  actual_historic_prices <- adjusted_prices[split_date_periods[n_splits + 1]]
  for (i in n_splits:1) {
    single_xts <- adjusted_prices[split_date_periods[[i]]] / prod(split_ratios[n_splits:i])
    #actual_historic_prices <- rbind.xts(actual_historic_prices, single_xts)
    actual_historic_prices <- rbind.xts(actual_historic_prices, single_xts)
  }
  return(actual_historic_prices)