NeverTooLateToAutomate

city_list = df.values.tolist()

geo_list = []
lat_list = []
lon_list = []

for i in city_list:
     location = geolocator.geocode(i[0], i[1])
     lat_list.append(location.latitude)
     lon_list.append(location.longitude)

NeverTooLateToAutomate

from geopy.geocoders import Nominatim
geolocator = Nominatim(user_agent="yourmail@mail.com")
import pandas as pd

url="https://pkgstore.datahub.io/core/world-cities/world-cities_csv/data/6cc66692f0e82b18216a48443b6b95da/world-cities_csv.csv"
df=pd.read_csv(url)
df=df[['name', 'country']]

df = df.loc[df['country'] == 'Poland']
df = df.rename(columns = {'name':'city'})

NeverTooLateToAutomate

from datetime import datetime
start_time = datetime.now()

from itertools import combinations

input_txt = 'aabchit'
# input_sort = ''.join(sorted(input_txt))

import string
alphabet = list('aąbcćdeęfghijklłmnńoóprsśtuwyzźż')

NeverTooLateToAutomate

from datetime import datetime
import pickle

# load pickle
start_time = datetime.now()

with gzip.open(r'C:\your_path_here\slowa_dict.pickle', 'rb') as f:
     word_dict = pickle.load(f)

time_elapsed = datetime.now() - start_time

NeverTooLateToAutomate

import pickle
import gzip

df_sort = pd.read_csv(r'C:\your_path_here\slowa_sort.txt.gz')
merged = pd.merge(df_sort, df, left_index=True, right_index=True)
grouped = merged.groupby('sorted').agg(list)
grouped = grouped.reset_index()
word_dict = grouped.set_index('sorted')['words'].to_dict()

with gzip.open(r'C:\your_path_here\slowa_dict.pickle', 'wb') as f:

NeverTooLateToAutomate

import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import numpy as np

x_labels = single_yr_list
y_temp_year = temp_year

plt.plot(x_labels, y_temp_year, color = 'crimson')
for a,b in zip(x_labels, y_temp_year):
     plt.text(a, b, str(b))

NeverTooLateToAutomate

single_yr_list = df_dynow["Rok"].drop_duplicates().tolist()
single_yr_list = single_yr_list[:-1]
print(single_yr_list)

NeverTooLateToAutomate

temp_year = df_dynow['Średnia dobowa temperatura'].tolist()
temp_year = temp_year[:-1] # no 2019 cause no Dec data for 2019
print(temp_year)

NeverTooLateToAutomate

file_name = r'C:\your_path_here\imgw_grouped.xlsx'
df_grouped = pd.read_excel(file_name, encoding='utf-8')
df_dynow = df_grouped[df_grouped['Nazwa stacji'] == 'DYNÓW']
df_dynow = df_dynow.reset_index(drop=True)
print(df_dynow.shape)
df_dynow.head()

NeverTooLateToAutomate

file_name = r'C:\your_path_here\imgw_grouped.xlsx'
df_grouped.to_excel(file_name, encoding='utf-8', index=False)