salary.py

import ast # run code from string for example ast.literal_eval("1+1") 

salaries = df.salary.dropna() # remove all NA's from dataframe
currencies = [ast.literal_eval(salaries.iloc[i])['currency'] for i in range(len(salaries))]
curr = set(currencies) #{'EUR', 'RUR', 'USD'}

#divide dataframe salararies by currency
rur = [ast.literal_eval(salaries.iloc[i]) for i in range(len(salaries)) if ast.literal_eval(salaries.iloc[i])['currency']=='RUR']
eur = [ast.literal_eval(salaries.iloc[i]) for i in range(len(salaries)) if ast.literal_eval(salaries.iloc[i])['currency']=='EUR']
usd = [ast.literal_eval(salaries.iloc[i]) for i in range(len(salaries)) if ast.literal_eval(salaries.iloc[i])['currency']=='USD']


fr = [x['from'] for x in rur] # lower range of salary
fr = list(filter(lambda x: x is not None, fr)) # remove NA's from lower range [0, 100, 200,...]

to = [x['to'] for x in rur] #upper range of salary
to = list(filter(lambda x: x is not None, to)) #remove NA's from lower range [100, 200, 300,...]

import numpy as np
salary_range = list(zip(fr, to)) # concatenate upper and lower range  [(0,100), (100, 200), (200, 300)...]
av = map(np.mean, salary_range) # convert [(0,100), (100, 200), (200, 300)...] to [50, 150, 250,...]
av = round(np.mean(list(av)),1) # average value from [50, 150, 250,...]


print("average salary as Data Scientist ", av, "rubles")