我所拥有的：
1）GPS坐标列表：纬度、经度和ID。
2）一个定义的函数来刮取过去24小时的每小时的温度和湿度数据。它返回3列的数据帧：温度、湿度、ID和每小时数据作为日期时间索引。该函数接受3个参数：拉特、lon、ID。
我想要的是：
以下是适用于一个lat/lon/ID集的函数：
# grab only weather of interest
attributes = [u'temperature', u'humidity']
# 24 hours ago #round to closest hour
date = dt.datetime.now().replace(microsecond=0,second=0,minute=0) -
dt.timedelta(hours=24)
#initalize
times = []
data = {}
for attr in attributes:
data[attr] = []
def scrape_weather(LAT, LON, Id):
for offset in range(1,2): #i.e 1 day
forecast = forecastio.load_forecast(api_key, LAT, LON,
time=date+dt.timedelta(offset), units = 'ca' )
h = forecast.hourly()
d = h.data
for p in d:
times.append(p.time)
try:
for i in attributes:
data[i].append(p.d[i])
except:
print(KeyError)
df2 = pd.DataFrame(data)
df1 = pd.DataFrame(times)
df1.reset_index(drop=True, inplace=True)
df2.reset_index(drop=True, inplace=True)
dfweather = pd.concat([df1, df2], axis=1)
dfweather['ID'] = Id
dfweather = dfweather.set_index(pd.DatetimeIndex(dfweather[0]))
dfweather = dfweather.drop([0], axis=1)
return dfweather
当使用lat/lon/Ids传递数据帧的单列时，此操作正常
scrape_weather(df.at[0,'latitude'],df.at[0,'longitude'], df.at[0,'Id'])
但是当我经过的时候
for index, row in dummy_gps.iterrows():
test = scrape_weather(row['longitude'],row['latitude'], row['Id'])
预期的结果看起来像这样：
temperature humidity ID
2019-05-14 07:00:00 22.58 0.34 1
2019-05-14 08:00:00 20.50 0.42 1
....
2019-05-14 07:00:00 22.58 0.34 2
2019-05-14 08:00:00 20.50 0.42 2
....
但是ID是错误的，只有一个ID被复制粘贴到每个人身上，如下所示：
temperature humidity ID
2019-05-14 07:00:00 22.58 0.34 2
2019-05-14 08:00:00 20.50 0.42 2
....
2019-05-14 07:00:00 22.58 0.34 2
2019-05-14 08:00:00 20.50 0.42 2
....
因此，我不确定在天气刮板功能中的何处添加ID逻辑，以确保每个ID与每个预测关联
新答案
import pandas as pd
import forecastio
import datetime as dt
def scrape_weather(row):
forecast = forecastio.load_forecast(api_key,
lat = row['latitude'],
lng = row['longitude'],
time = date,
units = 'ca' )
h = forecast.hourly()
d = h.data
dfweather = pd.DataFrame({'times': [p.time for p in d],
'temps': [p.temperature for p in d],
'humidity': [p.humidity for p in d],
'gatewayID': row['Id']
})
return dfweather
# Sample dataframe
id_col = [1, 2, 3, 4, 5, 6, 7]
lng = ['86.44511', '-121.13295', '-162.74005', '22.34765', '-152.18709', '-152.18709', '-107.65340']
lat = ['-18.67825', '-20.84215', '57.31227', '6.15070', '-27.72616', '-27.72616', '6.15863']
df = pd.DataFrame({'Id':id_col, 'latitude':lat, 'longitude':lng})
api_key = ###############################
# 24 hours ago #round to closest hour
date = dt.datetime.now().replace(microsecond=0,second=0,minute=0) - dt.timedelta(hours=24)
out = df.apply(scrape_weather, axis=1)
out = pd.concat([df for df in out])
旧答案
如果我没弄错的话，你能这样做吗？
df = pd.DataFrame({'LAT':[1,2,3],'LON':[1,2,3],'ID':[1,2,3]})
def scrape_weather(row):
temperature = row['LAT'] # change this to what you need to do
humidity = row['LON'] # change this to what you need to do
id = row['ID'] # change this to what you need to do
return temperature, humidity, id
new_df = pd.DataFrame(columns=['temp', 'hum', 'id'])
new_df['temp'], new_df['hum'], new_df['id'] = df.apply(scrape_weather, axis=1)
这让我
temp hum id
0 1 2 3
1 1 2 3
2 1 2 3
