rcsmit

def get_data_eurostat():
    """Get from Eurostat : Deaths by week, sex and 5-year age group
    Data from https://ec.europa.eu/eurostat/databrowser/product/view/demo_r_mwk_05?lang=en
    https://ec.europa.eu/eurostat/databrowser/bookmark/fbd80cd8-7b96-4ad9-98be-1358dd80f191?lang=en
    https://ec.europa.eu/eurostat/api/dissemination/sdmx/2.1/dataflow/ESTAT/DEMO_R_MWK_05/1.0?references=descendants&detail=referencepartial&format=sdmx_2.1_generic&compressed=true
   
    Returns:
        df: dataframe with weekly mortality in 5 year tranches

                                DATAFLOW        LAST UPDATE freq     age sex  ... OBS_VALUE OBS_FLAG   age_sex  jaar weeknr

rcsmit

import pandas as pd
import streamlit as st
import plotly.graph_objects as go
import numpy as np
import statsmodels.api as sm

// output: https://twitter.com/rcsmit/status/1800539585841344854

def test():
 

rcsmit

# recreating https://twitter.com/NateB_Panic/status/1636811443612860417/photo/1

import numpy as np
import plotly
import plotly.graph_objects as go
import numpy as np

p_values = [0.01, 0.05,0.1,0.2, 0.3, 0.5]  # set the values of p
x = np.arange(0, 30, 1)  # generate an array of x values from 0 to 10

rcsmit

""" A script to calculate the loss or gain after x months working in a year with a montly salary of y
    This calculation is a simplification

    Goal is to learn to work with OOP and make the script pythonic as much as possible.  

    TODO : calculate the total capital after z years, with taking inflation in account

Returns:
    A dataframe like this:

rcsmit

# For each accotype: The first number is the name of the acco, second number is the first row and third number is the last row
to_do_2022 = [
    [1, 4, 9],  # ALPHA
    [11, 14, 23],  # BRAVO
] 


def make_complete_df(columns_to_use, year):
    """Generate the dataframe
        Columns: ['acco_type', 'number', 'date',"month","year", "new_arrival","departure_no_clean", "departure_clean", "back_to_back", "yellow"])

rcsmit

def find_fill_color(cell):
    """Find fill color of a cell.
    Hulproutine, wordt niet aangeroepen in het script

    # dirty solution to find the fill color.
    # as solutions given here https://stackoverflow.com/questions/58429823/getting-excel-cell-background-themed-color-as-hex-with-openpyxl
    # dont work

    Args:
        cell (string): The cell you want to find the color from

rcsmit

import pandas as pd
from openpyxl import load_workbook
import urllib.request

local = False
if local:
    excel_file = r"C:\Users\rcxsm\Documents\python_scripts\streamlit_scripts\input\dummy_occupation.xlsx"
    wb = load_workbook(excel_file, data_only=True)
else:
    excel_file = r"https://github.com/rcsmit/streamlit_scripts/blob/main/input/dummy_occupation.xlsx?raw=true"

rcsmit

# adaption of the solutioon given by Derek O to make it easy to reuse
# https://stackoverflow.com/questions/70129355/value-annotations-around-plotly-sunburst-diagram

import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from math import sin,cos,pi

import plotly.io as pio

rcsmit

for (m in 1:10){
    x = m/10 #new in R so I dont know how to make steps of .1 in loops ;)
    monsterlist <- c() #This will hold the number of disabled in each sim run
    for (h in 1:1){ #Repeat simulation 1 times
        disabled <- c() #This will hold the disabled within each sim run
        population <- as.vector(c(rep(0,10000))) #The population of 10,000
        for(j in 1:30) { # Number of Years
            infected <- sample(1:length(population),floor(x*length(population))) # infect x% of the population every year
            population[infected] <- population[infected]-1 #infected get an increasingly-negative counter
            # The next 2 paragraphs control who gets LC. One should be commented out.

rcsmit

import sqlite3 as sl
import pandas as pd
import streamlit as st
import os
from PIL import Image

def delete_records_from_db(dir):
    sql_statement = f"DELETE FROM txt_from_images WHERE directory = '{dir}'"
    db_name = dir + os.sep + "my_test.db"
    con = sl.connect(db_name)