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Python 解析并扩展当前嵌入在数据帧中的JSON数据

python json pandas dataframe

Python 解析并扩展当前嵌入在数据帧中的JSON数据,python,json,pandas,dataframe,Python,Json,Pandas,Dataframe,基本上，我有从某个天气API中提取的原始数据。通过SQL查询，数据被格式化为一个数据框，其中包含以下列：纬度（lats）、经度（lngs）、日期和“blob” blob是嵌套在两层中的JSON数据。您将在下面看到的数据以第一层的全天“每日”摘要和第二层的“每小时”下的24小时摘要开始 lats lngs date blob -46 168 2015-01-31 {"daily": {"apparentTemperatureMaxTime

基本上，我有从某个天气API中提取的原始数据。通过SQL查询，数据被格式化为一个数据框，其中包含以下列：纬度（lats）、经度（lngs）、日期和“blob”

blob是嵌套在两层中的JSON数据。您将在下面看到的数据以第一层的全天“每日”摘要和第二层的“每小时”下的24小时摘要开始

lats    lngs    date            blob
-46     168     2015-01-31      {"daily": {"apparentTemperatureMaxTime": 1422680400, "temperatureMax": 21.33, "temperatureMinTime": 1422615600, "temperatureMin": 16.06, "icon": "clear-day", "apparentTemperatureMax": 21.33, "summary": "Clear throughout the day.", "pressure": 1010.91, "temperatureMaxTime": 1422680400, "humidity": 0.81, "dewPoint": 15.14, "sunsetTime": 1422692673, "precipType": "rain", "windSpeed": 3.01, "apparentTemperatureMin": 16.06, "sunriseTime": 1422639631, "apparentTemperatureMinTime": 1422615600, "time": 1422615600, "visibility": 16.09, "windBearing": 75, "moonPhase": 0.38}, "hourly": [{"apparentTemperature": 16.06, "windSpeed": 4.29, "icon": "clear-night", "temperature": 16.06, "summary": "Clear", "pressure": 1015.05, "humidity": 0.89, "dewPoint": 14.23, "precipType": "rain", "time": 1422615600, "visibility": 16.09, "windBearing": 97}, {"apparentTemperature": 16.17, "windSpeed": 4.22, "icon": "clear-night", "temperature": 16.17, "summary": "Clear", "pressure": 1014.91, "humidity": 0.88, "dewPoint": 14.19, "precipType": "rain", "time": 1422619200, "visibility": 16.09, "windBearing": 94}, {"apparentTemperature": 16.27, "windSpeed": 4.09, "icon": "clear-night", "temperature": 16.27, "summary": "Clear", "pressure": 1014.51, "humidity": 0.87, "dewPoint": 14.14, "precipType": "rain", "time": 1422622800, "visibility": 16.09, "windBearing": 87}, {"apparentTemperature": 16.36, "windSpeed": 4, "icon": "clear-night", "temperature": 16.36, "summary": "Clear", "pressure": 1013.94, "humidity": 0.86, "dewPoint": 14.09, "precipType": "rain", "time": 1422626400, "visibility": 16.09, "windBearing": 80}, {"apparentTemperature": 16.4, "windSpeed": 3.9, "icon": "clear-night", "temperature": 16.4, "summary": "Clear", "pressure": 1013.43, "humidity": 0.86, "dewPoint": 14.07, "precipType": "rain", "time": 1422630000, "visibility": 16.09, "windBearing": 75},
下面是一个日期的2组数据的目录

{'lat': {0: -45, 1: -45}, 'lng': {0: 169, 1: 170}, 'date': {0: datetime.date(2015, 1, 1), 1: datetime.date(2015, 1, 1)}, 'blob': {0: {'daily': {'apparentTemperatureMaxTime': 1420088400, 'temperatureMax': 19.06, 'temperatureMinTime': 1420045200, 'temperatureMin': 7.86, 'icon': 'clear-day', 'apparentTemperatureMax': 19.06, 'summary': 'Clear throughout the day.', 'pressure': 1013.08, 'temperatureMaxTime': 1420088400, 'humidity': 0.61, 'dewPoint': 5.49, 'sunsetTime': 1420101288, 'precipType': 'rain', 'windSpeed': 3.18, 'apparentTemperatureMin': 6.76, 'sunriseTime': 1420045310, 'apparentTemperatureMinTime': 1420041600, 'time': 1420023600, 'visibility': 16, 'windBearing': 241, 'moonPhase': 0.36}, 'hourly': [{'apparentTemperature': 6.78, 'windSpeed': 6.98, 'icon': 'clear-night', 'temperature': 9.88, 'summary': 'Clear', 'pressure': 1005.77, 'humidity': 0.81, 'dewPoint': 6.74, 'precipType': 'rain', 'time': 1420023600, 'visibility': 12.59, 'windBearing': 208}, {'apparentTemperature': 7.23, 'windSpeed': 4.95, 'icon': 'clear-night', 'temperature': 9.7, 'summary': 'Clear', 'pressure': 1007.34, 'humidity': 0.81, 'dewPoint': 6.51, 'precipType': 'rain', 'time': 1420027200, 'visibility': 16.09, 'windBearing': 217}, {'apparentTemperature': 7.19, 'windSpeed': 4.13, 'icon': 'clear-night', 'temperature': 9.39, 'summary': 'Clear', 'pressure': 1008.35, 'humidity': 0.81, 'dewPoint': 6.29, 'precipType': 'rain', 'time': 1420030800, 'visibility': 16.09, 'windBearing': 226}, {'apparentTemperature': 6.96, 'windSpeed': 3.77, 'icon': 'clear-night', 'temperature': 9.06, 'summary': 'Clear', 'pressure': 1009.02, 'humidity': 0.82, 'dewPoint': 6.09, 'precipType': 'rain', 'time': 1420034400, 'visibility': 16.09, 'windBearing': 235}, {'apparentTemperature': 6.79, 'windSpeed': 3.38, 'icon': 'clear-night', 'temperature': 8.76, 'summary': 'Clear', 'pressure': 1009.81, 'humidity': 0.82, 'dewPoint': 5.83, 'precipType': 'rain', 'time': 1420038000, 'visibility': 16.09, 'windBearing': 243}, {'apparentTemperature': 6.76, 'windSpeed': 2.56, 'icon': 'clear-night', 'temperature': 8.29, 'summary': 'Clear', 'pressure': 1010.94, 'humidity': 0.82, 'dewPoint': 5.33, 'precipType': 'rain', 'time': 1420041600, 'visibility': 16.09, 'windBearing': 249}, {'apparentTemperature': 7.09, 'windSpeed': 1.6, 'icon': 'clear-night', 'temperature': 7.86, 'summary': 'Clear', 'pressure': 1012.19, 'humidity': 0.81, 'dewPoint': 4.76, 'precipType': 'rain', 'time': 1420045200, 'visibility': 16.09, 'windBearing': 255}, {'apparentTemperature': 8.08, 'windSpeed': 1.15, 'icon': 'clear-day', 'temperature': 8.08, 'summary': 'Clear', 'pressure': 1013.28, 'humidity': 0.78, 'dewPoint': 4.47, 'precipType': 'rain', 'time': 1420048800, 'visibility': 16.09, 'windBearing': 265}, {'apparentTemperature': 8.95, 'windSpeed': 1.62, 'icon': 'clear-day', 'temperature': 9.49, 'summary': 'Clear', 'pressure': 1014.17, 'humidity': 0.71, 'dewPoint': 4.59, 'precipType': 'rain', 'time': 1420052400, 'visibility': 16.09, 'windBearing': 265}, {'apparentTemperature': 11.57, 'windSpeed': 2.55, 'icon': 'clear-day', 'temperature': 11.57, 'summary': 'Clear', 'pressure': 1014.91, 'humidity': 0.63, 'dewPoint': 4.76, 'precipType': 'rain', 'time': 1420056000, 'visibility': 16.09, 'windBearing': 261}, {'apparentTemperature': 13.41, 'windSpeed': 3.3, 'icon': 'clear-day', 'temperature': 13.41, 'summary': 'Clear', 'pressure': 1015.39, 'humidity': 0.56, 'dewPoint': 4.79, 'precipType': 'rain', 'time': 1420059600, 'visibility': 16.09, 'windBearing': 260}, {'apparentTemperature': 14.68, 'windSpeed': 3.65, 'icon': 'clear-day', 'temperature': 14.68, 'summary': 'Clear', 'pressure': 1015.52, 'humidity': 0.52, 'dewPoint': 4.83, 'precipType': 'rain', 'time': 1420063200, 'visibility': 16.09, 'windBearing': 260}, {'apparentTemperature': 15.71, 'windSpeed': 3.81, 'icon': 'clear-day', 'temperature': 15.71, 'summary': 'Clear', 'pressure': 1015.39, 'humidity': 0.49, 'dewPoint': 4.96, 'precipType': 'rain', 'time': 1420066800, 'visibility': 16.09, 'windBearing': 261}, {'apparentTemperature': 16.59, 'windSpeed': 3.77, 'icon': 'clear-day', 'temperature': 16.59, 'summary': 'Clear', 'pressure': 1015.18, 'humidity': 0.47, 'dewPoint': 5.17, 'precipType': 'rain', 'time': 1420070400, 'visibility': 16.09, 'windBearing': 260}, {'apparentTemperature': 17.33, 'windSpeed': 3.4, 'icon': 'clear-day', 'temperature': 17.33, 'summary': 'Clear', 'pressure': 1014.95, 'humidity': 0.46, 'dewPoint': 5.55, 'precipType': 'rain', 'time': 1420074000, 'visibility': 16.09, 'windBearing': 253}, {'apparentTemperature': 17.92, 'windSpeed': 2.94, 'icon': 'clear-day', 'temperature': 17.92, 'summary': 'Clear', 'pressure': 1014.64, 'humidity': 0.46, 'dewPoint': 6.06, 'precipType': 'rain', 'time': 1420077600, 'visibility': 16.09, 'windBearing': 240}, {'apparentTemperature': 18.36, 'windSpeed': 2.81, 'icon': 'clear-day', 'temperature': 18.36, 'summary': 'Clear', 'pressure': 1014.31, 'humidity': 0.46, 'dewPoint': 6.39, 'precipType': 'rain', 'time': 1420081200, 'visibility': 16.09, 'windBearing': 229}, {'apparentTemperature': 18.78, 'windSpeed': 3.08, 'icon': 'clear-day', 'temperature': 18.78, 'summary': 'Clear', 'pressure': 1013.85, 'humidity': 0.44, 'dewPoint': 6.43, 'precipType': 'rain', 'time': 1420084800, 'visibility': 16.09, 'windBearing': 227}, {'apparentTemperature': 19.06, 'windSpeed': 3.5, 'icon': 'clear-day', 'temperature': 19.06, 'summary': 'Clear', 'pressure': 1013.37, 'humidity': 0.43, 'dewPoint': 6.29, 'precipType': 'rain', 'time': 1420088400, 'visibility': 16.09, 'windBearing': 230}, {'apparentTemperature': 18.78, 'windSpeed': 3.76, 'icon': 'clear-day', 'temperature': 18.78, 'summary': 'Clear', 'pressure': 1013.31, 'humidity': 0.43, 'dewPoint': 6.06, 'precipType': 'rain', 'time': 1420092000, 'visibility': 16.09, 'windBearing': 233}, {'apparentTemperature': 17.53, 'windSpeed': 3.78, 'icon': 'clear-day', 'temperature': 17.53, 'summary': 'Clear', 'pressure': 1014.01, 'humidity': 0.45, 'dewPoint': 5.54, 'precipType': 'rain', 'time': 1420095600, 'visibility': 16.09, 'windBearing': 238}, {'apparentTemperature': 15.72, 'windSpeed': 3.68, 'icon': 'clear-day', 'temperature': 15.72, 'summary': 'Clear', 'pressure': 1015.13, 'humidity': 0.48, 'dewPoint': 4.85, 'precipType': 'rain', 'time': 1420099200, 'visibility': 16.09, 'windBearing': 244}, {'apparentTemperature': 14.18, 'windSpeed': 3.29, 'icon': 'clear-night', 'temperature': 14.18, 'summary': 'Clear', 'pressure': 1016.13, 'humidity': 0.52, 'dewPoint': 4.51, 'precipType': 'rain', 'time': 1420102800, 'visibility': 16.09, 'windBearing': 250}, {'apparentTemperature': 13.23, 'windSpeed': 2.39, 'icon': 'clear-night', 'temperature': 13.23, 'summary': 'Clear', 'pressure': 1016.88, 'humidity': 0.57, 'dewPoint': 4.88, 'precipType': 'rain', 'time': 1420106400, 'visibility': 16.09, 'windBearing': 255}]}, 1: {'daily': {'apparentTemperatureMaxTime': 1420081200, 'temperatureMax': 18.18, 'temperatureMinTime': 1420045200, 'temperatureMin': 8.68, 'icon': 'clear-day', 'apparentTemperatureMax': 18.18, 'summary': 'Clear throughout the day.', 'pressure': 1013.16, 'temperatureMaxTime': 1420081200, 'humidity': 0.63, 'dewPoint': 6.58, 'sunsetTime': 1420101048, 'precipType': 'rain', 'windSpeed': 1.6, 'apparentTemperatureMin': 7.85, 'sunriseTime': 1420045069, 'apparentTemperatureMinTime': 1420041600, 'time': 1420023600, 'visibility': 16.06, 'windBearing': 232, 'moonPhase': 0.36}, 'hourly': [{'apparentTemperature': 11.77, 'windSpeed': 6.44, 'icon': 'clear-night', 'temperature': 11.77, 'summary': 'Clear', 'pressure': 1004.34, 'humidity': 0.77, 'dewPoint': 7.78, 'precipType': 'rain', 'time': 1420023600, 'visibility': 14.73, 'windBearing': 222}, {'apparentTemperature': 11.13, 'windSpeed': 5.33, 'icon': 'clear-night', 'temperature': 11.13, 'summary': 'Clear', 'pressure': 1006.11, 'humidity': 0.78, 'dewPoint': 7.52, 'precipType': 'rain', 'time': 1420027200, 'visibility': 16.09, 'windBearing': 218}, {'apparentTemperature': 10.48, 'windSpeed': 4.48, 'icon': 'clear-night', 'temperature': 10.48, 'summary': 'Clear', 'pressure': 1007.3, 'humidity': 0.79, 'dewPoint': 6.95, 'precipType': 'rain', 'time': 1420030800, 'visibility': 16.09, 'windBearing': 218}, {'apparentTemperature': 8.01, 'windSpeed': 3.65, 'icon': 'clear-night', 'temperature': 9.87, 'summary': 'Clear', 'pressure': 1008.18, 'humidity': 0.78, 'dewPoint': 6.27, 'precipType': 'rain', 'time': 1420034400, 'visibility': 16.09, 'windBearing': 222}, {'apparentTemperature': 7.87, 'windSpeed': 2.9, 'icon': 'clear-night', 'temperature': 9.42, 'summary': 'Clear', 'pressure': 1009.12, 'humidity': 0.78, 'dewPoint': 5.77, 'precipType': 'rain', 'time': 1420038000, 'visibility': 16.09, 'windBearing': 228}, {'apparentTemperature': 7.85, 'windSpeed': 2.18, 'icon': 'clear-night', 'temperature': 8.98, 'summary': 'Clear', 'pressure': 1010.33, 'humidity': 0.79, 'dewPoint': 5.55, 'precipType': 'rain', 'time': 1420041600, 'visibility': 16.09, 'windBearing': 235}, {'apparentTemperature': 8.07, 'windSpeed': 1.56, 'icon': 'clear-night', 'temperature': 8.68, 'summary': 'Clear', 'pressure': 1011.62, 'humidity': 0.8, 'dewPoint': 5.5, 'precipType': 'rain', 'time': 1420045200, 'visibility': 16.09, 'windBearing': 247}, {'apparentTemperature': 9.08, 'windSpeed': 1.31, 'icon': 'clear-day', 'temperature': 9.08, 'summary': 'Clear', 'pressure': 1012.76, 'humidity': 0.79, 'dewPoint': 5.6, 'precipType': 'rain', 'time': 1420048800, 'visibility': 16.09, 'windBearing': 265}, {'apparentTemperature': 10.71, 'windSpeed': 1.57, 'icon': 'clear-day', 'temperature': 10.71, 'summary': 'Clear', 'pressure': 1013.74, 'humidity': 0.72, 'dewPoint': 5.8, 'precipType': 'rain', 'time': 1420052400, 'visibility': 16.09, 'windBearing': 277}, {'apparentTemperature': 13.04, 'windSpeed': 2.04, 'icon': 'clear-day', 'temperature': 13.04, 'summary': 'Clear', 'pressure': 1014.59, 'humidity': 0.62, 'dewPoint': 5.82, 'precipType': 'rain', 'time': 1420056000, 'visibility': 16.09, 'windBearing': 280}, {'apparentTemperature': 15, 'windSpeed': 2.33, 'icon': 'clear-day', 'temperature': 15, 'summary': 'Clear', 'pressure': 1015.2, 'humidity': 0.53, 'dewPoint': 5.62, 'precipType': 'rain', 'time': 1420059600, 'visibility': 16.09, 'windBearing': 280}, {'apparentTemperature': 16.19, 'windSpeed': 2.35, 'icon': 'clear-day', 'temperature': 16.19, 'summary': 'Clear', 'pressure': 1015.48, 'humidity': 0.49, 'dewPoint': 5.54, 'precipType': 'rain', 'time': 1420063200, 'visibility': 16.09, 'windBearing': 277}, {'apparentTemperature': 17.03, 'windSpeed': 2.2, 'icon': 'clear-day', 'temperature': 17.03, 'summary': 'Clear', 'pressure': 1015.53, 'humidity': 0.47, 'dewPoint': 5.64, 'precipType': 'rain', 'time': 1420066800, 'visibility': 16.09, 'windBearing': 271}, {'apparentTemperature': 17.63, 'windSpeed': 1.76, 'icon': 'clear-day', 'temperature': 17.63, 'summary': 'Clear', 'pressure': 1015.51, 'humidity': 0.46, 'dewPoint': 5.91, 'precipType': 'rain', 'time': 1420070400, 'visibility': 16.09, 'windBearing': 263}, {'apparentTemperature': 18.01, 'windSpeed': 0.8, 'icon': 'clear-day', 'temperature': 18.01, 'summary': 'Clear', 'pressure': 1015.5, 'humidity': 0.47, 'dewPoint': 6.51, 'precipType': 'rain', 'time': 1420074000, 'visibility': 16.09, 'windBearing': 236}, {'apparentTemperature': 18.17, 'windSpeed': 1.01, 'icon': 'clear-day', 'temperature': 18.17, 'summary': 'Clear', 'pressure': 1015.42, 'humidity': 0.49, 'dewPoint': 7.28, 'precipType': 'rain', 'time': 1420077600, 'visibility': 16.09, 'windBearing': 134}, {'apparentTemperature': 18.18, 'windSpeed': 1.86, 'icon': 'clear-day', 'temperature': 18.18, 'summary': 'Clear', 'pressure': 1015.32, 'humidity': 0.51, 'dewPoint': 7.79, 'precipType': 'rain', 'time': 1420081200, 'visibility': 16.09, 'windBearing': 118}, {'apparentTemperature': 18.16, 'windSpeed': 1.94, 'icon': 'clear-day', 'temperature': 18.16, 'summary': 'Clear', 'pressure': 1015.08, 'humidity': 0.51, 'dewPoint': 7.83, 'precipType': 'rain', 'time': 1420084800, 'visibility': 16.09, 'windBearing': 118}, {'apparentTemperature': 17.98, 'windSpeed': 1.56, 'icon': 'clear-day', 'temperature': 17.98, 'summary': 'Clear', 'pressure': 1014.82, 'humidity': 0.51, 'dewPoint': 7.63, 'precipType': 'rain', 'time': 1420088400, 'visibility': 16.09, 'windBearing': 123}, {'apparentTemperature': 17.43, 'windSpeed': 1.06, 'icon': 'clear-day', 'temperature': 17.43, 'summary': 'Clear', 'pressure': 1014.82, 'humidity': 0.52, 'dewPoint': 7.46, 'precipType': 'rain', 'time': 1420092000, 'visibility': 16.09, 'windBearing': 136}, {'apparentTemperature': 16.22, 'windSpeed': 0.63, 'icon': 'clear-day', 'temperature': 16.22, 'summary': 'Clear', 'pressure': 1015.3, 'humidity': 0.56, 'dewPoint': 7.32, 'precipType': 'rain', 'time': 1420095600, 'visibility': 16.09, 'windBearing': 191}, {'apparentTemperature': 14.62, 'windSpeed': 1.22, 'icon': 'clear-day', 'temperature': 14.62, 'summary': 'Clear', 'pressure': 1016.05, 'humidity': 0.61, 'dewPoint': 7.07, 'precipType': 'rain', 'time': 1420099200, 'visibility': 16.09, 'windBearing': 249}, {'apparentTemperature': 13.17, 'windSpeed': 1.68, 'icon': 'clear-night', 'temperature': 13.17, 'summary': 'Clear', 'pressure': 1016.68, 'humidity': 0.66, 'dewPoint': 6.86, 'precipType': 'rain', 'time': 1420102800, 'visibility': 16.09, 'windBearing': 262}, {'apparentTemperature': 11.98, 'windSpeed': 1.21, 'icon': 'clear-night', 'temperature': 11.98, 'summary': 'Clear', 'pressure': 1017.11, 'humidity': 0.71, 'dewPoint': 6.86, 'precipType': 'rain', 'time': 1420106400, 'visibility': 16.09, 'windBearing': 271}]}}}
现在，为了最终将其转化为一个函数，我一直在一步一步地进行，希望得到所有的步骤，但我一直被一些嵌套数据的JSON/DICT转换所困扰

目标是分解blob，以便将24小时拉出并分离，同时保持它们与原始LAT、LGN和日期配对

从前面提到的SQL查询中，我已经获得了上面所示的4列数据框中的数据。我可以使用以下方法隔离“斑点”：

并获得以下输出：

id   blob
0    {'daily': {'apparentTemperatureMaxTime': 14215...
然后，我尝试使用以下方法将其正常化：

test_df = pd.DataFrame.from_dict(json_normalize(test_df))
它将它分解为一个包含所有每日条件（垃圾）的数据帧，然后将所有24小时条件分解为另一个bob（没有简单的方法将这个22列的表放在这里）

为了进一步深入，我尝试：

hourly_df = json_normalize(data=test_df, record_path = 'hourly')
但这给了我：

TypeError: string indices must be integers
因此，它将显示一个lat和lng的一个日期的所有24小时数据，然后移动到该lat和lng的下一个日期，直到它通过帧中的所有日期，然后它将增加到lat lng对

import json
from pandas.io.json import json_normalize

#from your data
df = pd.DataFrame(data)

#make the entire df a json file
df_json = df.to_json(orient = 'records', date_format='iso')

#use json_normalize to read in your json file, look at the hourly dict, and attach lat, lng and date.
df2 = json_normalize(json.loads(df_json), record_path = ['blob' , 'hourly'] , meta = ['lat', 'lng', 'date'])

#look at only the columns you want
df3 = df2.reindex(['lat', 'lng', 'date','temperature'], axis = 1)

#repeat 0-23 for the lenght of the df (since the time column in hourly isnt quite right, look at your dict that you posted)
df3['hour'] = (np.arange(0, 24).tolist())*(int(len(df3)/24))

df3.head()

    lat lng            date          temperature     hour
0   -44 169   2015-09-28T00:00:00.000Z   8.62         0
1   -44 169   2015-09-28T00:00:00.000Z   8.34         1
2   -44 169   2015-09-28T00:00:00.000Z   7.30         2
3   -44 169   2015-09-28T00:00:00.000Z   5.94         3
4   -44 169   2015-09-28T00:00:00.000Z   4.88         4
最简单的方法是将数据导出到json，然后用json_normalize读入。dict中的time列不太正确，因此我创建了自己的hour列（但如果它们都是全天，那么这就可以了）

你能把下面的输出发出去吗：
print（test_df.head（）。to_dict（））
？它允许用户轻松地重新创建您的数据帧，并让人们更容易地帮助您！Ben，您希望在哪一步看到该数据帧？我只需要一种方法，使您的数据帧的一部分能够轻松创建自己的数据帧。否则很难进行测试。Ben，理想情况下，您希望看到多少lat/lng？如果我给您一个dict在5个条目的标题中，我超过了帖子的字符限制。添加了一行的dict。在实现中出错…
temps_json=temps_df.to_json（orient='records'，date_format='iso'）temps_hourly_df=json_normalize（json.loads（temps_json），record_path=['blob'，'hourly']，meta='lat'，'lng'，'date']））-------------------------------------------------------------TypeError:字符串索引必须是整数
您的temp_df似乎有错误。当我运行您的代码但用我的df替换temp_df时，效果很好。尝试像我一样用您自己的dict将您自己的数据导入到新的df中，看看会发生什么。我按照您的操作u建议，将我的数据转换为dict，然后再转换回数据帧，但同样的事情发生了。可能是因为我有多个lat和LNG，而我提供的只有一个？为什么不编辑dict，让我留下两行不同的lat/LONG，我可以看到它是否在我的端工作，同时只使用dic运行我的代码你发了帖子，看看这是否有效。你也有同样的错误吗？添加了两行数据。我所做的是将我的数据帧（temps_df）转换成一个dict，以便它模拟我给你的。我运行了你的行，如上所示，得到了同样的错误。我现在再试一次，看看我是否弄错了什么。 
temps_df = db.get_historical_weather(lats, lngs, start_date, end_date)
temps_df.head()

lat     lng     date    blob
0   -45     170     2015-01-18  {'daily': {'apparentTemperatureMaxTime': 14215...
1   -45     170     2015-01-19  {'daily': {'apparentTemperatureMaxTime': 14216...
2   -45     170     2015-01-20  {'daily': {'apparentTemperatureMaxTime': 14217...
3   -45     170     2015-01-21  {'daily': {'apparentTemperatureMaxTime': 14218...
4   -45     170     2015-01-22  {'daily': {'apparentTemperatureMaxTime': 14219...

test_df = temps_df.iloc[:,3]
test_df.head()

0    {'daily': {'apparentTemperatureMaxTime': 14215...
1    {'daily': {'apparentTemperatureMaxTime': 14216...
2    {'daily': {'apparentTemperatureMaxTime': 14217...
3    {'daily': {'apparentTemperatureMaxTime': 14218...
4    {'daily': {'apparentTemperatureMaxTime': 14219...
Name: blob, dtype: object

test_df = pd.DataFrame.from_dict(json_normalize(test_df))
test_df.head()

daily.apparentTemperatureMax    daily.apparentTemperatureMaxTime    daily.apparentTemperatureMin    daily.apparentTemperatureMinTime    daily.dewPoint  daily.humidity  daily.icon  daily.moonPhase     daily.precipType    daily.pressure  ...     daily.sunsetTime    daily.temperatureMax    daily.temperatureMaxTime    daily.temperatureMin    daily.temperatureMinTime    daily.time  daily.visibility    daily.windBearing   daily.windSpeed     hourly
0   21.17   1421542800  12.39   1421514000  10.78   0.74    clear-day   0.90    rain    995.62  ...     1421569528  21.17   1421542800  12.39   1421514000  1421492400  14.27   232     1.13    [{'apparentTemperature': 14.21, 'windSpeed': 0...
1   15.69   1421632800  9.66    1421600400  9.34    0.79    clear-day   0.94    rain    1000.24     ...     1421655887  15.69   1421632800  9.66    1421600400  1421578800  13.74   223     0.53    [{'apparentTemperature': 11.41, 'windSpeed': 1...
2   16.73   1421719200  8.53    1421686800  7.86    0.74    clear-day   0.97    rain    1014.10     ...     1421742244  16.73   1421719200  8.53    1421686800  1421665200  15.85   208     1.94    [{'apparentTemperature': 10.08, 'windSpeed': 1...

hourly_df = json_normalize(data=test_df, record_path = 'hourly')

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-15-dd6793be4e4c> in <module>()
----> 1 hourly_df = json_normalize(data=test_df, record_path = 'hourly')

/opt/conda/lib/python3.6/site-packages/pandas/io/json/normalize.py in json_normalize(data, record_path, meta, meta_prefix, record_prefix, errors, sep)
    260                 records.extend(recs)
    261 
--> 262     _recursive_extract(data, record_path, {}, level=0)
    263 
    264     result = DataFrame(records)

/opt/conda/lib/python3.6/site-packages/pandas/io/json/normalize.py in _recursive_extract(data, path, seen_meta, level)
    236         else:
    237             for obj in data:
--> 238                 recs = _pull_field(obj, path[0])
    239 
    240                 # For repeating the metadata later

/opt/conda/lib/python3.6/site-packages/pandas/io/json/normalize.py in _pull_field(js, spec)
    183                 result = result[field]
    184         else:
--> 185             result = result[spec]
    186 
    187         return result

TypeError: string indices must be integers


    lat     lng    date          hour    temp
0   -45     170    2015-01-28    0       10
1   -45     170    2015-01-28    1       10
2   -45     170    2015-01-28    2       10
3   -45     170    2015-01-28    3       10
4   -45     170    2015-01-28    4       10

import json
from pandas.io.json import json_normalize

#from your data
df = pd.DataFrame(data)

#make the entire df a json file
df_json = df.to_json(orient = 'records', date_format='iso')

#use json_normalize to read in your json file, look at the hourly dict, and attach lat, lng and date.
df2 = json_normalize(json.loads(df_json), record_path = ['blob' , 'hourly'] , meta = ['lat', 'lng', 'date'])

#look at only the columns you want
df3 = df2.reindex(['lat', 'lng', 'date','temperature'], axis = 1)

#repeat 0-23 for the lenght of the df (since the time column in hourly isnt quite right, look at your dict that you posted)
df3['hour'] = (np.arange(0, 24).tolist())*(int(len(df3)/24))

df3.head()

    lat lng            date          temperature     hour
0   -44 169   2015-09-28T00:00:00.000Z   8.62         0
1   -44 169   2015-09-28T00:00:00.000Z   8.34         1
2   -44 169   2015-09-28T00:00:00.000Z   7.30         2
3   -44 169   2015-09-28T00:00:00.000Z   5.94         3
4   -44 169   2015-09-28T00:00:00.000Z   4.88         4

#Output with two rows:

df3.iloc[np.r_[0:5, -5:0]]

    lat lng          date           temperature hour
0   -45 169 2015-01-01T00:00:00.000Z    9.88    0
1   -45 169 2015-01-01T00:00:00.000Z    9.70    1
2   -45 169 2015-01-01T00:00:00.000Z    9.39    2
3   -45 169 2015-01-01T00:00:00.000Z    9.06    3
4   -45 169 2015-01-01T00:00:00.000Z    8.76    4
43  -45 170 2015-01-01T00:00:00.000Z    17.43   19
44  -45 170 2015-01-01T00:00:00.000Z    16.22   20
45  -45 170 2015-01-01T00:00:00.000Z    14.62   21
46  -45 170 2015-01-01T00:00:00.000Z    13.17   22
47  -45 170 2015-01-01T00:00:00.000Z    11.98   23