将数据从API保存到Python Pandas中的csv

Question

I need help with saving data I read with API key to csv. The code I have is below:

import requests
import pandas as pd
def get_precip(gooddate):
    urlstart = 'http://api.wunderground.com/api/API_KEY/history_'
    urlend = '/q/Switzerland/Zurich.json'
    url = urlstart + str(gooddate) + urlend
    data = requests.get(url).json()

    for summary in data['history']['dailysummary']:
        abc = ','.join((gooddate,summary['date']['year'],summary['date']['mon'],summary['date']['mday'],summary['precipm'], summary['maxtempm'], summary['meantempm'],summary['mintempm']))
        df = pd.DataFrame(data=abc)
        df.to_csv('/home/user/Desktop/2013_weather.csv', index=False)

if __name__ == "__main__":
    from datetime import date
    from dateutil.rrule import rrule, DAILY

    a = date(2013, 1, 1)
    b = date(2013, 12, 31)

    for dt in rrule(DAILY, dtstart=a, until=b):
        get_precip(dt.strftime("%Y%m%d"))

I'm sure that can't work this way, because it need to be saved into some list or dictionary before transform into dataframe, but not sure how to do that this time. If save it to the list it will give me just one row? Any help is welcomed. Thanks.

Answer 1

I think you can return tuples from get_precip , append them to list and use DataFrame constructor:

def get_precip(gooddate):
    urlstart = 'http://api.wunderground.com/api/API_KEY/history_'
    urlend = '/q/Switzerland/Zurich.json'
    url = urlstart + str(gooddate) + urlend
    data = requests.get(url).json()

    for summary in data['history']['dailysummary']:
        return (gooddate,summary['date']['year'],summary['date']['mon'],summary['date']['mday'],summary['precipm'], summary['maxtempm'], summary['meantempm'],summary['mintempm'])

---

if __name__ == "__main__":
    from datetime import date
    from dateutil.rrule import rrule, DAILY

    a = date(2013, 1, 1)
    b = date(2013, 12, 31)

    L = []
    for dt in rrule(DAILY, dtstart=a, until=b):
        tup = get_precip(dt.strftime("%Y%m%d"))
        L.append(tup)

what is same as:

    L = [get_precip(dt.strftime("%Y%m%d")) for dt in rrule(DAILY, dtstart=a, until=b)]

---

    cols = ['date','date.year','date.mon','date.mday','precipm','maxtempm', 
            'meantempm','mintempm']     
    df = pd.DataFrame(L, columns=cols)
    print (df.head())

           date date.year date.mon date.mday precipm maxtempm meantempm mintempm
    0  20130101      2013       01        01     0.0        7         2       -2
    1  20130102      2013       01        02     0.0        5         2       -3
    2  20130103      2013       01        03     0.0        4         0       -3
    3  20130104      2013       01        04     0.0        7         5        3

   df.to_csv('/home/user/Desktop/2013_weather.csv', index=False)