How to use multiple inputs in function and append results?

Question

Below is my function to grab the weather data for a specific city and year, but I was wondering how would I modify the function so I could grab more than one city and more than one year at a time and append it to the same dataset?

import pandas as pd

def calgary_weather(city, year):
    d = dict(calgary = "https://climate.weather.gc.ca/climate_data/bulk_data_e.html?format=csv&stationID=27211&Year="+year+"&Month=5&Day=1&timeframe=2&submit=Download+Data",
             edmonton = "https://climate.weather.gc.ca/climate_data/bulk_data_e.html?format=csv&stationID=27793&Year="+year+"&Month=5&Day=1&timeframe=2&submit=Download+Data")
    url = d[city]
    data = pd.read_csv(url)
    return data

calgary_weather('edmonton', '2017')

You don't seem to be doing anything with the data you are getting now. — Scott Hunter
– Scott Hunter, Commented May 27, 2020 at 17:32
define more than one city and more than one year. Do you want to pass multiple cities and years and get information for each city at all years? Or take them as pairs of city/year in matching indexes? — Tomerikoo
– Tomerikoo, Commented May 27, 2020 at 18:04
Yes input multiple cities and multiple years and get all of the information in one place. — Xin
– Xin, Commented May 27, 2020 at 18:05

Trenton McKinney · Accepted Answer · 2020-05-27 19:29:16Z

There shouldn't be more than one url in the function, because it's not efficient coding. Just think if you had 30+ locations.
Use a dictionary with station and station id
- stations can have as many key-value pairs as you like
The function will take a list of city names and a list of years
url will use f-strings to insert stationID and Year values
Create a list of dataframes for city and year
Concat them together
A dataframe will be returned
Call the function with a list of only the desired cities and years.
The function also uses type hints for clarity
- List[str]: list of strings
- List[int]: list of integers

import pandas as pd
from typing import List

def get_weather_data(cities: List[str], years: List[int]) -> pd.DataFrame:
    stations = {'calgary': 27211, 'edmonton': 27793}
    df_list = list()
    for city in cities:
        for year in years:
            url = f'https://climate.weather.gc.ca/climate_data/bulk_data_e.html?format=csv&stationID={stations[city]}&Year={year}&Month=5&Day=1&timeframe=2&submit=Download+Data'
            df_list.append(pd.read_csv(url))

    return pd.concat(df_list)


cities = ['calgary', 'edmonton']
years = [2017, 2018, 2019]

df = get_weather_data(cities=cities, years=years)

Top 3 rows and first 5 columns

print(df.iloc[:3, :5].to_markdown())

|    |   Longitude (x) |   Latitude (y) | Station Name     |   Climate ID | Date/Time   |
|---:|----------------:|---------------:|:-----------------|-------------:|:------------|
|  0 |            -114 |          51.11 | CALGARY INT'L CS |      3031094 | 2017-01-01  |
|  1 |            -114 |          51.11 | CALGARY INT'L CS |      3031094 | 2017-01-02  |
|  2 |            -114 |          51.11 | CALGARY INT'L CS |      3031094 | 2017-01-03  |

Bottom 3 rows and first 5 columns

print(df.iloc[-3:, :5].to_markdown())

|     |   Longitude (x) |   Latitude (y) | Station Name              |   Climate ID | Date/Time   |
|----:|----------------:|---------------:|:--------------------------|-------------:|:------------|
| 362 |         -113.61 |          53.31 | EDMONTON INTERNATIONAL CS |      3012206 | 2019-12-29  |
| 363 |         -113.61 |          53.31 | EDMONTON INTERNATIONAL CS |      3012206 | 2019-12-30  |
| 364 |         -113.61 |          53.31 | EDMONTON INTERNATIONAL CS |      3012206 | 2019-12-31  |

Naphat Amundsen · Accepted Answer · 2020-05-27 18:16:36Z

You could do something like the code below. Altough, the cities will "share" the years. I don't know how efficient df.append is, but it works. Note how i changed the strings in the dictionary.

import pandas as pd

def calgary_weather(cities: (list, str), years: (list, str)) -> pd.DataFrame:
    d = dict(calgary = "https://climate.weather.gc.ca/climate_data/bulk_data_e.html?format=csv&stationID=27211&Year={year}&Month=5&Day=1&timeframe=2&submit=Download+Data",
             edmonton = "https://climate.weather.gc.ca/climate_data/bulk_data_e.html?format=csv&stationID=27793&Year={year}&Month=5&Day=1&timeframe=2&submit=Download+Data")

    df = pd.DataFrame()

    for city in cities:
        for year in years:
            url = d[city].format(year=year)
            df = df.append(pd.read_csv(url))

    return df

calgary_weather(['edmonton','calgary'], ['2016','2017','2018'])

But if you want to be able to pick the years more flexibly (although the years have to be a list of lists):

def calgary_weather2(cities: (list, str), years: (list, list, str)) -> pd.DataFrame:
    d = dict(calgary = "https://climate.weather.gc.ca/climate_data/bulk_data_e.html?format=csv&stationID=27211&Year={year}&Month=5&Day=1&timeframe=2&submit=Download+Data",
             edmonton = "https://climate.weather.gc.ca/climate_data/bulk_data_e.html?format=csv&stationID=27793&Year={year}&Month=5&Day=1&timeframe=2&submit=Download+Data")

    df = pd.DataFrame()

    for city, cityyears in zip(cities, years):
        for year in cityyears:
            url = d[city].format(year=year)
            df = df.append(pd.read_csv(url))

    return df

calgary_weather2(['edmonton','calgary'], [['2016','2017','2018'],['2012','2013']])

I like your second function of passing separate years for each city. Nice touch. +10 rep

Sam · Accepted Answer · 2020-05-27 18:14:01Z

1

def weather(*args: str):
years = []
cities = []
for arg in args:
    if arg.isdigit():
        years.append(arg)
    else: cities.append(arg)

I prefer just passing multiple arguments over inputting a list because if you're trying to create a user-friendly program, you'll have to make each of their inputs a list anyway. I think this is probably a fine way of doing things, but you can also just use your original code with a for loop and input lists in place of city and year.

edited May 27, 2020 at 18:14

answered May 27, 2020 at 18:07

Sam

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