Python 如何在一瞬间将api调用的修剪输出放入数据帧中？_Python_Pandas

Python 如何在一瞬间将api调用的修剪输出放入数据帧中？

python pandas

Python 如何在一瞬间将api调用的修剪输出放入数据帧中？,python,pandas,Python,Pandas,api返回我需要的此类数据Lat&Long作为DF中的独立列： {“成功”：true，“错误”：false，“数据”：[{“Id”：“JTDKDTB38E1570707”，“名称”：“AE2” 42K，“板”：空，“Lat”：49.2771263，“Lon”：-122.909874，“燃料”： 75，“地址”：“0”}，{“Id”：“JTDKDTB36G1138197”，“名称”：“DN706K”， “板”：空，“横向”：49.27695，“纵向”：-122.91011，“燃料”： 85，“地址

api返回我需要的此类数据Lat&Long作为DF中的独立列：

{“成功”：true，“错误”：false，“数据”：[{“Id”：“JTDKDTB38E1570707”，“名称”：“AE2” 42K，“板”：空，“Lat”：49.2771263，“Lon”：-122.909874，“燃料”： 75，“地址”：“0”}，{“Id”：“JTDKDTB36G1138197”，“名称”：“DN706K”， “板”：空，“横向”：49.27695，“纵向”：-122.91011，“燃料”： 85，“地址”：“0”}，“Id”：“JTDKDTB39G1117652”，“名称”：“CH633A”， “标牌”：空，“Lat”：49.27719，“Lon”：-122.910339，“燃料”：79，“地址”：“0” “}}

但在通过阅读json之后，我将“success”、“error”和“data”作为三列

pd.read_json(requests.get('url').contents)

它有大约1000行，我将不得不处理数据列以获得lat-long

def LatRegex(x):
 Lat = re.findall('"Lat":(.{,10}),',str(x))
def LongRegex(x):
 Long = re.findall('"Lon":(.{,10}),',str(x))
df['Lat'] = df['data'].apply(LatRegex)
df['Long'] = df['data'].apply(LongRegex)

但是它不起作用&花了太多时间&我需要在一秒钟内得到数据。有什么建议吗？？？

使用

。应用
Ex:
import json
import pandas as pd


data = json.loads('''{"success":true,"error":false,"data":[{"Id":"JTDKDTB38E1570707","Name":"AE2 42K", "Plate" :null, "Lat": 49.2771263,"Lon": -122.909874,"Fuel": 75,"Address": "0 "},{"Id" :"JTDKDTB36G1138197", "Name": "DN706K", "Plate" :null,"Lat" : 49.27695,"Lon": -122.91011,"Fuel": 85,"Address":"0 "},{"Id": "JTDKDTB39G1117652", "Name": "CH633A", "Plate" :null,"Lat":49.27719,"Lon":-122.910339,"Fuel":79,"Address":"0 "}]}''')
df = pd.DataFrame(data)

df["Lat"] = df["data"].apply(lambda x: x["Lat"])
df["Lon"] = df["data"].apply(lambda x: x["Lon"])
df.drop("data", inplace=True, axis=1)
print(df)

   error  success        Lat         Lon
0  False     True  49.277126 -122.909874
1  False     True  49.276950 -122.910110
2  False     True  49.277190 -122.910339

import json
from pandas.io.json import json_normalize

data = json.loads('''{"success":true,"error":false,"data":[{"Id":"JTDKDTB38E1570707","Name":"AE2 42K", "Plate" :null, "Lat": 49.2771263,"Lon": -122.909874,"Fuel": 75,"Address": "0 "},{"Id" :"JTDKDTB36G1138197", "Name": "DN706K", "Plate" :null,"Lat" : 49.27695,"Lon": -122.91011,"Fuel": 85,"Address":"0 "},{"Id": "JTDKDTB39G1117652", "Name": "CH633A", "Plate" :null,"Lat":49.27719,"Lon":-122.910339,"Fuel":79,"Address":"0 "}]}''')
df = json_normalize(data, 'data', ['success', 'error'])
print(df)

  Address  Fuel                 Id        Lat         Lon     Name Plate  \
0      0     75  JTDKDTB38E1570707  49.277126 -122.909874  AE2 42K  None   
1      0     85  JTDKDTB36G1138197  49.276950 -122.910110   DN706K  None   
2      0     79  JTDKDTB39G1117652  49.277190 -122.910339   CH633A  None   

   success  error  
0     True  False  
1     True  False  
2     True  False  

输出：
import json
import pandas as pd


data = json.loads('''{"success":true,"error":false,"data":[{"Id":"JTDKDTB38E1570707","Name":"AE2 42K", "Plate" :null, "Lat": 49.2771263,"Lon": -122.909874,"Fuel": 75,"Address": "0 "},{"Id" :"JTDKDTB36G1138197", "Name": "DN706K", "Plate" :null,"Lat" : 49.27695,"Lon": -122.91011,"Fuel": 85,"Address":"0 "},{"Id": "JTDKDTB39G1117652", "Name": "CH633A", "Plate" :null,"Lat":49.27719,"Lon":-122.910339,"Fuel":79,"Address":"0 "}]}''')
df = pd.DataFrame(data)

df["Lat"] = df["data"].apply(lambda x: x["Lat"])
df["Lon"] = df["data"].apply(lambda x: x["Lon"])
df.drop("data", inplace=True, axis=1)
print(df)

   error  success        Lat         Lon
0  False     True  49.277126 -122.909874
1  False     True  49.276950 -122.910110
2  False     True  49.277190 -122.910339

import json
from pandas.io.json import json_normalize

data = json.loads('''{"success":true,"error":false,"data":[{"Id":"JTDKDTB38E1570707","Name":"AE2 42K", "Plate" :null, "Lat": 49.2771263,"Lon": -122.909874,"Fuel": 75,"Address": "0 "},{"Id" :"JTDKDTB36G1138197", "Name": "DN706K", "Plate" :null,"Lat" : 49.27695,"Lon": -122.91011,"Fuel": 85,"Address":"0 "},{"Id": "JTDKDTB39G1117652", "Name": "CH633A", "Plate" :null,"Lat":49.27719,"Lon":-122.910339,"Fuel":79,"Address":"0 "}]}''')
df = json_normalize(data, 'data', ['success', 'error'])
print(df)

  Address  Fuel                 Id        Lat         Lon     Name Plate  \
0      0     75  JTDKDTB38E1570707  49.277126 -122.909874  AE2 42K  None   
1      0     85  JTDKDTB36G1138197  49.276950 -122.910110   DN706K  None   
2      0     79  JTDKDTB39G1117652  49.277190 -122.910339   CH633A  None   

   success  error  
0     True  False  
1     True  False  
2     True  False  


或者使用json\u规范化
Ex:
import json
import pandas as pd


data = json.loads('''{"success":true,"error":false,"data":[{"Id":"JTDKDTB38E1570707","Name":"AE2 42K", "Plate" :null, "Lat": 49.2771263,"Lon": -122.909874,"Fuel": 75,"Address": "0 "},{"Id" :"JTDKDTB36G1138197", "Name": "DN706K", "Plate" :null,"Lat" : 49.27695,"Lon": -122.91011,"Fuel": 85,"Address":"0 "},{"Id": "JTDKDTB39G1117652", "Name": "CH633A", "Plate" :null,"Lat":49.27719,"Lon":-122.910339,"Fuel":79,"Address":"0 "}]}''')
df = pd.DataFrame(data)

df["Lat"] = df["data"].apply(lambda x: x["Lat"])
df["Lon"] = df["data"].apply(lambda x: x["Lon"])
df.drop("data", inplace=True, axis=1)
print(df)

   error  success        Lat         Lon
0  False     True  49.277126 -122.909874
1  False     True  49.276950 -122.910110
2  False     True  49.277190 -122.910339

import json
from pandas.io.json import json_normalize

data = json.loads('''{"success":true,"error":false,"data":[{"Id":"JTDKDTB38E1570707","Name":"AE2 42K", "Plate" :null, "Lat": 49.2771263,"Lon": -122.909874,"Fuel": 75,"Address": "0 "},{"Id" :"JTDKDTB36G1138197", "Name": "DN706K", "Plate" :null,"Lat" : 49.27695,"Lon": -122.91011,"Fuel": 85,"Address":"0 "},{"Id": "JTDKDTB39G1117652", "Name": "CH633A", "Plate" :null,"Lat":49.27719,"Lon":-122.910339,"Fuel":79,"Address":"0 "}]}''')
df = json_normalize(data, 'data', ['success', 'error'])
print(df)

  Address  Fuel                 Id        Lat         Lon     Name Plate  \
0      0     75  JTDKDTB38E1570707  49.277126 -122.909874  AE2 42K  None   
1      0     85  JTDKDTB36G1138197  49.276950 -122.910110   DN706K  None   
2      0     79  JTDKDTB39G1117652  49.277190 -122.910339   CH633A  None   

   success  error  
0     True  False  
1     True  False  
2     True  False  

输出：
import json
import pandas as pd


data = json.loads('''{"success":true,"error":false,"data":[{"Id":"JTDKDTB38E1570707","Name":"AE2 42K", "Plate" :null, "Lat": 49.2771263,"Lon": -122.909874,"Fuel": 75,"Address": "0 "},{"Id" :"JTDKDTB36G1138197", "Name": "DN706K", "Plate" :null,"Lat" : 49.27695,"Lon": -122.91011,"Fuel": 85,"Address":"0 "},{"Id": "JTDKDTB39G1117652", "Name": "CH633A", "Plate" :null,"Lat":49.27719,"Lon":-122.910339,"Fuel":79,"Address":"0 "}]}''')
df = pd.DataFrame(data)

df["Lat"] = df["data"].apply(lambda x: x["Lat"])
df["Lon"] = df["data"].apply(lambda x: x["Lon"])
df.drop("data", inplace=True, axis=1)
print(df)

   error  success        Lat         Lon
0  False     True  49.277126 -122.909874
1  False     True  49.276950 -122.910110
2  False     True  49.277190 -122.910339

import json
from pandas.io.json import json_normalize

data = json.loads('''{"success":true,"error":false,"data":[{"Id":"JTDKDTB38E1570707","Name":"AE2 42K", "Plate" :null, "Lat": 49.2771263,"Lon": -122.909874,"Fuel": 75,"Address": "0 "},{"Id" :"JTDKDTB36G1138197", "Name": "DN706K", "Plate" :null,"Lat" : 49.27695,"Lon": -122.91011,"Fuel": 85,"Address":"0 "},{"Id": "JTDKDTB39G1117652", "Name": "CH633A", "Plate" :null,"Lat":49.27719,"Lon":-122.910339,"Fuel":79,"Address":"0 "}]}''')
df = json_normalize(data, 'data', ['success', 'error'])
print(df)

  Address  Fuel                 Id        Lat         Lon     Name Plate  \
0      0     75  JTDKDTB38E1570707  49.277126 -122.909874  AE2 42K  None   
1      0     85  JTDKDTB36G1138197  49.276950 -122.910110   DN706K  None   
2      0     79  JTDKDTB39G1117652  49.277190 -122.910339   CH633A  None   

   success  error  
0     True  False  
1     True  False  
2     True  False  

使用.apply

Ex:
import json
import pandas as pd


data = json.loads('''{"success":true,"error":false,"data":[{"Id":"JTDKDTB38E1570707","Name":"AE2 42K", "Plate" :null, "Lat": 49.2771263,"Lon": -122.909874,"Fuel": 75,"Address": "0 "},{"Id" :"JTDKDTB36G1138197", "Name": "DN706K", "Plate" :null,"Lat" : 49.27695,"Lon": -122.91011,"Fuel": 85,"Address":"0 "},{"Id": "JTDKDTB39G1117652", "Name": "CH633A", "Plate" :null,"Lat":49.27719,"Lon":-122.910339,"Fuel":79,"Address":"0 "}]}''')
df = pd.DataFrame(data)

df["Lat"] = df["data"].apply(lambda x: x["Lat"])
df["Lon"] = df["data"].apply(lambda x: x["Lon"])
df.drop("data", inplace=True, axis=1)
print(df)

   error  success        Lat         Lon
0  False     True  49.277126 -122.909874
1  False     True  49.276950 -122.910110
2  False     True  49.277190 -122.910339

import json
from pandas.io.json import json_normalize

data = json.loads('''{"success":true,"error":false,"data":[{"Id":"JTDKDTB38E1570707","Name":"AE2 42K", "Plate" :null, "Lat": 49.2771263,"Lon": -122.909874,"Fuel": 75,"Address": "0 "},{"Id" :"JTDKDTB36G1138197", "Name": "DN706K", "Plate" :null,"Lat" : 49.27695,"Lon": -122.91011,"Fuel": 85,"Address":"0 "},{"Id": "JTDKDTB39G1117652", "Name": "CH633A", "Plate" :null,"Lat":49.27719,"Lon":-122.910339,"Fuel":79,"Address":"0 "}]}''')
df = json_normalize(data, 'data', ['success', 'error'])
print(df)

  Address  Fuel                 Id        Lat         Lon     Name Plate  \
0      0     75  JTDKDTB38E1570707  49.277126 -122.909874  AE2 42K  None   
1      0     85  JTDKDTB36G1138197  49.276950 -122.910110   DN706K  None   
2      0     79  JTDKDTB39G1117652  49.277190 -122.910339   CH633A  None   

   success  error  
0     True  False  
1     True  False  
2     True  False  

输出：
import json
import pandas as pd


data = json.loads('''{"success":true,"error":false,"data":[{"Id":"JTDKDTB38E1570707","Name":"AE2 42K", "Plate" :null, "Lat": 49.2771263,"Lon": -122.909874,"Fuel": 75,"Address": "0 "},{"Id" :"JTDKDTB36G1138197", "Name": "DN706K", "Plate" :null,"Lat" : 49.27695,"Lon": -122.91011,"Fuel": 85,"Address":"0 "},{"Id": "JTDKDTB39G1117652", "Name": "CH633A", "Plate" :null,"Lat":49.27719,"Lon":-122.910339,"Fuel":79,"Address":"0 "}]}''')
df = pd.DataFrame(data)

df["Lat"] = df["data"].apply(lambda x: x["Lat"])
df["Lon"] = df["data"].apply(lambda x: x["Lon"])
df.drop("data", inplace=True, axis=1)
print(df)

   error  success        Lat         Lon
0  False     True  49.277126 -122.909874
1  False     True  49.276950 -122.910110
2  False     True  49.277190 -122.910339

import json
from pandas.io.json import json_normalize

data = json.loads('''{"success":true,"error":false,"data":[{"Id":"JTDKDTB38E1570707","Name":"AE2 42K", "Plate" :null, "Lat": 49.2771263,"Lon": -122.909874,"Fuel": 75,"Address": "0 "},{"Id" :"JTDKDTB36G1138197", "Name": "DN706K", "Plate" :null,"Lat" : 49.27695,"Lon": -122.91011,"Fuel": 85,"Address":"0 "},{"Id": "JTDKDTB39G1117652", "Name": "CH633A", "Plate" :null,"Lat":49.27719,"Lon":-122.910339,"Fuel":79,"Address":"0 "}]}''')
df = json_normalize(data, 'data', ['success', 'error'])
print(df)

  Address  Fuel                 Id        Lat         Lon     Name Plate  \
0      0     75  JTDKDTB38E1570707  49.277126 -122.909874  AE2 42K  None   
1      0     85  JTDKDTB36G1138197  49.276950 -122.910110   DN706K  None   
2      0     79  JTDKDTB39G1117652  49.277190 -122.910339   CH633A  None   

   success  error  
0     True  False  
1     True  False  
2     True  False  


或者使用json\u规范化
Ex:
import json
import pandas as pd


data = json.loads('''{"success":true,"error":false,"data":[{"Id":"JTDKDTB38E1570707","Name":"AE2 42K", "Plate" :null, "Lat": 49.2771263,"Lon": -122.909874,"Fuel": 75,"Address": "0 "},{"Id" :"JTDKDTB36G1138197", "Name": "DN706K", "Plate" :null,"Lat" : 49.27695,"Lon": -122.91011,"Fuel": 85,"Address":"0 "},{"Id": "JTDKDTB39G1117652", "Name": "CH633A", "Plate" :null,"Lat":49.27719,"Lon":-122.910339,"Fuel":79,"Address":"0 "}]}''')
df = pd.DataFrame(data)

df["Lat"] = df["data"].apply(lambda x: x["Lat"])
df["Lon"] = df["data"].apply(lambda x: x["Lon"])
df.drop("data", inplace=True, axis=1)
print(df)

   error  success        Lat         Lon
0  False     True  49.277126 -122.909874
1  False     True  49.276950 -122.910110
2  False     True  49.277190 -122.910339

import json
from pandas.io.json import json_normalize

data = json.loads('''{"success":true,"error":false,"data":[{"Id":"JTDKDTB38E1570707","Name":"AE2 42K", "Plate" :null, "Lat": 49.2771263,"Lon": -122.909874,"Fuel": 75,"Address": "0 "},{"Id" :"JTDKDTB36G1138197", "Name": "DN706K", "Plate" :null,"Lat" : 49.27695,"Lon": -122.91011,"Fuel": 85,"Address":"0 "},{"Id": "JTDKDTB39G1117652", "Name": "CH633A", "Plate" :null,"Lat":49.27719,"Lon":-122.910339,"Fuel":79,"Address":"0 "}]}''')
df = json_normalize(data, 'data', ['success', 'error'])
print(df)

  Address  Fuel                 Id        Lat         Lon     Name Plate  \
0      0     75  JTDKDTB38E1570707  49.277126 -122.909874  AE2 42K  None   
1      0     85  JTDKDTB36G1138197  49.276950 -122.910110   DN706K  None   
2      0     79  JTDKDTB39G1117652  49.277190 -122.910339   CH633A  None   

   success  error  
0     True  False  
1     True  False  
2     True  False  

输出：
import json
import pandas as pd


data = json.loads('''{"success":true,"error":false,"data":[{"Id":"JTDKDTB38E1570707","Name":"AE2 42K", "Plate" :null, "Lat": 49.2771263,"Lon": -122.909874,"Fuel": 75,"Address": "0 "},{"Id" :"JTDKDTB36G1138197", "Name": "DN706K", "Plate" :null,"Lat" : 49.27695,"Lon": -122.91011,"Fuel": 85,"Address":"0 "},{"Id": "JTDKDTB39G1117652", "Name": "CH633A", "Plate" :null,"Lat":49.27719,"Lon":-122.910339,"Fuel":79,"Address":"0 "}]}''')
df = pd.DataFrame(data)

df["Lat"] = df["data"].apply(lambda x: x["Lat"])
df["Lon"] = df["data"].apply(lambda x: x["Lon"])
df.drop("data", inplace=True, axis=1)
print(df)

   error  success        Lat         Lon
0  False     True  49.277126 -122.909874
1  False     True  49.276950 -122.910110
2  False     True  49.277190 -122.910339

import json
from pandas.io.json import json_normalize

data = json.loads('''{"success":true,"error":false,"data":[{"Id":"JTDKDTB38E1570707","Name":"AE2 42K", "Plate" :null, "Lat": 49.2771263,"Lon": -122.909874,"Fuel": 75,"Address": "0 "},{"Id" :"JTDKDTB36G1138197", "Name": "DN706K", "Plate" :null,"Lat" : 49.27695,"Lon": -122.91011,"Fuel": 85,"Address":"0 "},{"Id": "JTDKDTB39G1117652", "Name": "CH633A", "Plate" :null,"Lat":49.27719,"Lon":-122.910339,"Fuel":79,"Address":"0 "}]}''')
df = json_normalize(data, 'data', ['success', 'error'])
print(df)

  Address  Fuel                 Id        Lat         Lon     Name Plate  \
0      0     75  JTDKDTB38E1570707  49.277126 -122.909874  AE2 42K  None   
1      0     85  JTDKDTB36G1138197  49.276950 -122.910110   DN706K  None   
2      0     79  JTDKDTB39G1117652  49.277190 -122.910339   CH633A  None   

   success  error  
0     True  False  
1     True  False  
2     True  False