blocks|key|4676268|text|您可以首先在Python字典中导入json数据：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4676269|data+=+json.loads(elevations)|code-block|syntax|javascript|4676270|然后动态修改数据：|4676271|for+result+in+data['results']:
++++result[u'lat']=result[u'location'][u'lat']
++++result[u'lng']=result[u'location'][u'lng']
++++del+result[u'location']|4676272|重建json字符串：|4676273|elevations+=+json.dumps(data)|4676274|最后：|4676275|pd.read_json(elevations)|4676276|您还可以避免将数据转储回字符串，我假设熊猫可以直接从字典创建DataFrame+(我很长时间没有使用它了:p)|4676277|entityMap^0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|X|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Y|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|Z|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|10|8|@]|9|@]|A|$]]|$1|M|3|N|5|D|7|11|8|@]|9|@]|A|$E|F]]|$1|O|3|P|5|6|7|12|8|@]|9|@]|A|$]]|$1|Q|3|R|5|D|7|13|8|@]|9|@]|A|$E|F]]|$1|S|3|T|5|6|7|14|8|@]|9|@]|A|$]]|$1|U|3|-4|5|6|7|15|8|@]|9|@]|A|$]]]|V|$]]

You could first import your json data in a Python dictionnary :

<pre><code>data = json.loads(elevations)
</code></pre>

Then modify data on the fly :

<pre><code>for result in data['results']:
 result[u'lat']=result[u'location'][u'lat']
 result[u'lng']=result[u'location'][u'lng']
 del result[u'location']
</code></pre>

Rebuild json string :

<pre><code>elevations = json.dumps(data)
</code></pre>

Finally :

<pre><code>pd.read_json(elevations)
</code></pre>

You can, also, probably avoid to dump data back to a string, I assume Panda can directly create a DataFrame from a dictionnary (I haven't used it since a long time :p)

blocks|key|4676295|text|我使用pandas+1.01中包含的json_normalize()找到了一个快速而简单的解决方案来满足我的需求。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4676296|from+urllib2+import+Request,+urlopen
import+json

import+pandas+as+pd++++

path1+=+'42.974049,-81.205203%7C42.974298,-81.195755'
request=Request('http://maps.googleapis.com/maps/api/elevation/json?locations='%2Bpath1%2B'&sensor=false')
response+=+urlopen(request)
elevations+=+response.read()
data+=+json.loads(elevations)
df+=+pd.json_normalize(data['results'])|code-block|syntax|javascript|4676297|这提供了一个很好的扁平化数据帧，其中包含我从Google+Maps+API获得的json数据。|4676298|entityMap^0|3|B|I|G|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@$9|P|A|Q|B|C]|$9|R|A|S|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|T|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|U|8|@]|D|@]|E|$]]|$1|M|3|-4|5|6|7|V|8|@]|D|@]|E|$]]]|N|$]]

I found a quick and easy solution to what I wanted using <code>json_normalize()</code> included in <code>pandas 1.01</code>.
<pre><code>from urllib2 import Request, urlopen
import json

import pandas as pd 

path1 = '42.974049,-81.205203|42.974298,-81.195755'
request=Request('http://maps.googleapis.com/maps/api/elevation/json?locations='+path1+'&amp;sensor=false')
response = urlopen(request)
elevations = response.read()
data = json.loads(elevations)
df = pd.json_normalize(data['results'])
</code></pre>
This gives a nice flattened dataframe with the json data that I got from the Google Maps API.

blocks|key|4676326|text|问题是，您在数据框中有几列，其中包含带有较小字典的字典。有用的Json通常是大量嵌套的。我一直在写一些小函数，将我想要的信息拉到一个新的专栏中。这样我就有了我想要使用的格式。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4676327|for+row+in+range(len(data)):
++++#First+I+load+the+dict+(one+at+a+time)
++++n+=+data.loc[row,'dict_column']
++++#Now+I+make+a+new+column+that+pulls+out+the+data+that+I+want.
++++data.loc[row,'new_column']+=+n.get('key')|code-block|syntax|javascript|4676328|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

The problem is that you have several columns in the data frame that contain dicts with smaller dicts inside them. Useful Json is often heavily nested. I have been writing small functions that pull the info I want out into a new column. That way I have it in the format that I want to use. 

<pre><code>for row in range(len(data)):
 #First I load the dict (one at a time)
 n = data.loc[row,'dict_column']
 #Now I make a new column that pulls out the data that I want.
 data.loc[row,'new_column'] = n.get('key')
</code></pre>

blocks|key|170539|text|看看这个片段。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|170540|#+reading+the+JSON+data+using+json.load()
file+=+'data.json'
with+open(file)+as+train_file:
++++dict_train+=+json.load(train_file)

#+converting+json+dataset+from+dictionary+to+dataframe
train+=+pd.DataFrame.from_dict(dict_train,+orient='index')
train.reset_index(level=0,+inplace=True)|code-block|syntax|javascript|170541|希望它能有所帮助:)|170542|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

Check this snip out. 

<pre><code># reading the JSON data using json.load()
file = 'data.json'
with open(file) as train_file:
 dict_train = json.load(train_file)

# converting json dataset from dictionary to dataframe
train = pd.DataFrame.from_dict(dict_train, orient='index')
train.reset_index(level=0, inplace=True)
</code></pre>

Hope it helps :)

blocks|key|4676345|text|billmanH的解决方案对我很有帮助，但直到我从：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4676346|n+=+data.loc[row,'json_column']|code-block|syntax|javascript|4676347|至：|4676348|n+=+data.iloc[[row]]['json_column']|4676349|下面是其余部分，转换为字典对于处理json数据很有帮助。|4676350|import+json

for+row+in+range(len(data)):
++++n+=+data.iloc[[row]]['json_column'].item()
++++jsonDict+=+json.loads(n)
++++if+('mykey'+in+jsonDict):
++++++++display(jsonDict['mykey'])|4676351|entityMap^0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|S|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|U|8|@]|9|@]|A|$]]|$1|M|3|N|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|O|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|P|$]]

billmanH's solution helped me but didn't work until i switched from:

<pre><code>n = data.loc[row,'json_column']
</code></pre>

to:

<pre><code>n = data.iloc[[row]]['json_column']
</code></pre>

here's the rest of it, converting to a dictionary is helpful for working with json data.

<pre><code>import json

for row in range(len(data)):
 n = data.iloc[[row]]['json_column'].item()
 jsonDict = json.loads(n)
 if ('mykey' in jsonDict):
 display(jsonDict['mykey'])
</code></pre>

blocks|key|4676384|text|因为python3.x不支持urllib2，所以只是接受答案的新版本|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4676385|from+requests+import+request
import+json
from+pandas.io.json+import+json_normalize

path1+=+'42.974049,-81.205203%7C42.974298,-81.195755'
response=request(url='http://maps.googleapis.com/maps/api/elevation/json?locations='%2Bpath1%2B'&sensor=false',+method='get')
elevations+=+response.json()
elevations
data+=+json.loads(elevations)
json_normalize(data['results'])|code-block|syntax|javascript|4676386|entityMap^0|2|9|E|7|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]|$9|P|A|Q|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|R|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|S|8|@]|D|@]|E|$]]]|L|$]]

Just a new version of the accepted answer, as <code>python3.x</code> does not support <code>urllib2</code>

<pre><code>from requests import request
import json
from pandas.io.json import json_normalize

path1 = '42.974049,-81.205203|42.974298,-81.195755'
response=request(url='http://maps.googleapis.com/maps/api/elevation/json?locations='+path1+'&amp;sensor=false', method='get')
elevations = response.json()
elevations
data = json.loads(elevations)
json_normalize(data['results'])
</code></pre>

blocks|key|4676400|text|#Use+the+small+trick+to+make+the+data+json+interpret-able
#Since+your+data+is+not+directly+interpreted+by+json.loads()

>>>+import+json
>>>+f=open("sampledata.txt","r%2B")
>>>+data+=+f.read()
>>>+for+x+in+data.split("\n"):
...+++++strlist+=+"["%2Bx%2B"]"
...+++++datalist=json.loads(strlist)
...+++++for+y+in+datalist:
...+++++++++++++print(type(y))
...+++++++++++++print(y)
...
...
<type+'dict'>
{u'0':+[[10.8,+36.0],+{u'10':+0,+u'1':+0,+u'0':+0,+u'3':+0,+u'2':+0,+u'5':+0,+u'4':+0,+u'7':+0,+u'6':+0,+u'9':+0,+u'8':+0}]}
<type+'dict'>
{u'1':+[[10.8,+36.1],+{u'10':+0,+u'1':+0,+u'0':+0,+u'3':+0,+u'2':+0,+u'5':+0,+u'4':+0,+u'7':+0,+u'6':+0,+u'9':+0,+u'8':+0}]}
<type+'dict'>
{u'2':+[[10.8,+36.2],+{u'10':+0,+u'1':+0,+u'0':+0,+u'3':+0,+u'2':+0,+u'5':+0,+u'4':+0,+u'7':+0,+u'6':+0,+u'9':+0,+u'8':+0}]}
<type+'dict'>
{u'3':+[[10.8,+36.300000000000004],+{u'10':+0,+u'1':+0,+u'0':+0,+u'3':+0,+u'2':+0,+u'5':+0,+u'4':+0,+u'7':+0,+u'6':+0,+u'9':+0,+u'8':+0}]}
<type+'dict'>
{u'4':+[[10.8,+36.4],+{u'10':+0,+u'1':+0,+u'0':+0,+u'3':+0,+u'2':+0,+u'5':+0,+u'4':+0,+u'7':+0,+u'6':+0,+u'9':+0,+u'8':+0}]}
<type+'dict'>
{u'5':+[[10.8,+36.5],+{u'10':+0,+u'1':+0,+u'0':+0,+u'3':+0,+u'2':+0,+u'5':+0,+u'4':+0,+u'7':+0,+u'6':+0,+u'9':+0,+u'8':+0}]}
<type+'dict'>
{u'6':+[[10.8,+36.6],+{u'10':+0,+u'1':+0,+u'0':+0,+u'3':+0,+u'2':+0,+u'5':+0,+u'4':+0,+u'7':+0,+u'6':+0,+u'9':+0,+u'8':+0}]}
<type+'dict'>
{u'7':+[[10.8,+36.7],+{u'10':+0,+u'1':+0,+u'0':+0,+u'3':+0,+u'2':+0,+u'5':+0,+u'4':+0,+u'7':+0,+u'6':+0,+u'9':+0,+u'8':+0}]}
<type+'dict'>
{u'8':+[[10.8,+36.800000000000004],+{u'1':+0,+u'0':+0,+u'3':+0,+u'2':+0,+u'5':+0,+u'4':+0,+u'7':+0,+u'6':+0,+u'9':+0,+u'8':+0}]}
<type+'dict'>
{u'9':+[[10.8,+36.9],+{u'1':+0,+u'0':+0,+u'3':+0,+u'2':+0,+u'5':+0,+u'4':+0,+u'7':+0,+u'6':+0,+u'9':+0,+u'8':+0}]}|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|4676401|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>#Use the small trick to make the data json interpret-able
#Since your data is not directly interpreted by json.loads()

&gt;&gt;&gt; import json
&gt;&gt;&gt; f=open("sampledata.txt","r+")
&gt;&gt;&gt; data = f.read()
&gt;&gt;&gt; for x in data.split("\n"):
... strlist = "["+x+"]"
... datalist=json.loads(strlist)
... for y in datalist:
... print(type(y))
... print(y)
...
...
&lt;type 'dict'&gt;
{u'0': [[10.8, 36.0], {u'10': 0, u'1': 0, u'0': 0, u'3': 0, u'2': 0, u'5': 0, u'4': 0, u'7': 0, u'6': 0, u'9': 0, u'8': 0}]}
&lt;type 'dict'&gt;
{u'1': [[10.8, 36.1], {u'10': 0, u'1': 0, u'0': 0, u'3': 0, u'2': 0, u'5': 0, u'4': 0, u'7': 0, u'6': 0, u'9': 0, u'8': 0}]}
&lt;type 'dict'&gt;
{u'2': [[10.8, 36.2], {u'10': 0, u'1': 0, u'0': 0, u'3': 0, u'2': 0, u'5': 0, u'4': 0, u'7': 0, u'6': 0, u'9': 0, u'8': 0}]}
&lt;type 'dict'&gt;
{u'3': [[10.8, 36.300000000000004], {u'10': 0, u'1': 0, u'0': 0, u'3': 0, u'2': 0, u'5': 0, u'4': 0, u'7': 0, u'6': 0, u'9': 0, u'8': 0}]}
&lt;type 'dict'&gt;
{u'4': [[10.8, 36.4], {u'10': 0, u'1': 0, u'0': 0, u'3': 0, u'2': 0, u'5': 0, u'4': 0, u'7': 0, u'6': 0, u'9': 0, u'8': 0}]}
&lt;type 'dict'&gt;
{u'5': [[10.8, 36.5], {u'10': 0, u'1': 0, u'0': 0, u'3': 0, u'2': 0, u'5': 0, u'4': 0, u'7': 0, u'6': 0, u'9': 0, u'8': 0}]}
&lt;type 'dict'&gt;
{u'6': [[10.8, 36.6], {u'10': 0, u'1': 0, u'0': 0, u'3': 0, u'2': 0, u'5': 0, u'4': 0, u'7': 0, u'6': 0, u'9': 0, u'8': 0}]}
&lt;type 'dict'&gt;
{u'7': [[10.8, 36.7], {u'10': 0, u'1': 0, u'0': 0, u'3': 0, u'2': 0, u'5': 0, u'4': 0, u'7': 0, u'6': 0, u'9': 0, u'8': 0}]}
&lt;type 'dict'&gt;
{u'8': [[10.8, 36.800000000000004], {u'1': 0, u'0': 0, u'3': 0, u'2': 0, u'5': 0, u'4': 0, u'7': 0, u'6': 0, u'9': 0, u'8': 0}]}
&lt;type 'dict'&gt;
{u'9': [[10.8, 36.9], {u'1': 0, u'0': 0, u'3': 0, u'2': 0, u'5': 0, u'4': 0, u'7': 0, u'6': 0, u'9': 0, u'8': 0}]}


</code></pre>

blocks|key|170605|text|下面是在JSON和DataFrame之间来回转换的小工具类:希望对您有所帮助。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|170606|#+-*-+coding:+utf-8+-*-
from+pandas.io.json+import+json_normalize

class+DFConverter:

++++#Converts+the+input+JSON+to+a+DataFrame
++++def+convertToDF(self,dfJSON):
++++++++return(json_normalize(dfJSON))

++++#Converts+the+input+DataFrame+to+JSON+
++++def+convertToJSON(self,+df):
++++++++resultJSON+=+df.to_json(orient='records')
++++++++return(resultJSON)|code-block|syntax|javascript|170607|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Here is small utility class that converts JSON to DataFrame and back: Hope you find this helpful.

<pre><code># -*- coding: utf-8 -*-
from pandas.io.json import json_normalize

class DFConverter:

 #Converts the input JSON to a DataFrame
 def convertToDF(self,dfJSON):
 return(json_normalize(dfJSON))

 #Converts the input DataFrame to JSON 
 def convertToJSON(self, df):
 resultJSON = df.to_json(orient='records')
 return(resultJSON)
</code></pre>

blocks|key|170627|text|一旦您有了由被接受的答案获得的扁平化的DataFrame，您就可以使列成为MultiIndex+(“花哨的多行标题”)，如下所示：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|170628|df.columns+=+pd.MultiIndex.from_tuples([tuple(c.split('.'))+for+c+in+df.columns])|code-block|syntax|javascript|170629|entityMap^0|J|9|11|A|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]|$9|P|A|Q|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|R|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|S|8|@]|D|@]|E|$]]]|L|$]]

Once you have the flattened <code>DataFrame</code> obtained by the accepted answer, you can make the columns a <code>MultiIndex</code> ("fancy multiline header") like this: 

<pre><code>df.columns = pd.MultiIndex.from_tuples([tuple(c.split('.')) for c in df.columns])
</code></pre>

blocks|key|4676474|text|我更喜欢一种更通用的方法，其中可能是用户不喜欢给出关键的“结果”。您仍然可以通过使用递归方法来查找具有嵌套数据的键，或者如果您有键但JSON非常嵌套，则仍然可以将其扁平化。它类似于：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4676475|from+pandas+import+json_normalize

def+findnestedlist(js):
++++for+i+in+js.keys():
++++++++if+isinstance(js[i],list):
++++++++++++return+js[i]
++++for+v+in+js.values():
++++++++if+isinstance(v,dict):
++++++++++++return+check_list(v)


def+recursive_lookup(k,+d):
++++if+k+in+d:
++++++++return+d[k]
++++for+v+in+d.values():
++++++++if+isinstance(v,+dict):
++++++++++++return+recursive_lookup(k,+v)
++++return+None

def+flat_json(content,key):
++++nested_list+=+[]
++++js+=+json.loads(content)
++++if+key+is+None+or+key+==+'':
++++++++nested_list+=+findnestedlist(js)
++++else:
++++++++nested_list+=+recursive_lookup(key,+js)
++++return+json_normalize(nested_list,sep="_")

key+=+"results"+#+If+you+don't+have+it,+give+it+None

csv_data+=+flat_json(your_json_string,root_key)
print(csv_data)|code-block|syntax|javascript|4676476|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

I prefer a more generic method in which may be user doesn't prefer to give key 'results'. You can still flatten it by using a recursive approach of finding key having nested data or if you have key but your JSON is very nested. It is something like:
<pre><code>from pandas import json_normalize

def findnestedlist(js):
 for i in js.keys():
 if isinstance(js[i],list):
 return js[i]
 for v in js.values():
 if isinstance(v,dict):
 return check_list(v)


def recursive_lookup(k, d):
 if k in d:
 return d[k]
 for v in d.values():
 if isinstance(v, dict):
 return recursive_lookup(k, v)
 return None

def flat_json(content,key):
 nested_list = []
 js = json.loads(content)
 if key is None or key == '':
 nested_list = findnestedlist(js)
 else:
 nested_list = recursive_lookup(key, js)
 return json_normalize(nested_list,sep=&quot;_&quot;)

key = &quot;results&quot; # If you don't have it, give it None

csv_data = flat_json(your_json_string,root_key)
print(csv_data)
</code></pre>

blocks|key|170666|text|Rumble通过JSONiq原生支持JSON，并在Spark上运行，在内部管理DataFrames，因此您不需要这样做--即使数据不是完全结构化的：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|170667|let+$coords+:=+"42.974049,-81.205203%257C42.974298,-81.195755"
let+$request+:=+json-doc("http://maps.googleapis.com/maps/api/elevation/json?locations="%7C%7C$coords%7C%7C"&sensor=false")
for+$obj+in+$request.results[]
return+{
++"latitude"+:+$obj.location.lat,
++"longitude"+:+$obj.location.lng,
++"elevation"+:+$obj.elevation
}|code-block|syntax|javascript|170668|结果可以导出到CSV，然后以任何其他宿主语言作为DataFrame重新打开。|170669|entityMap|0|LINK|mutability|MUTABLE|url|http://www.rumbledb.org/^0|0|6|0|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@$A|T|B|U|1|V]]|C|$]]|$1|D|3|E|5|F|7|W|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|X|8|@]|9|@]|C|$]]|$1|K|3|-4|5|6|7|Y|8|@]|9|@]|C|$]]]|L|$M|$5|N|O|P|C|$Q|R]]]]

<a href="http://www.rumbledb.org" rel="nofollow noreferrer">Rumble</a> supports JSON natively with JSONiq and runs on Spark, managing DataFrames internally so you don't need to -- even if the data isn't fully structured:
<pre><code>let $coords := &quot;42.974049,-81.205203%7C42.974298,-81.195755&quot;
let $request := json-doc(&quot;http://maps.googleapis.com/maps/api/elevation/json?locations=&quot;||$coords||&quot;&amp;sensor=false&quot;)
for $obj in $request.results[]
return {
 &quot;latitude&quot; : $obj.location.lat,
 &quot;longitude&quot; : $obj.location.lng,
 &quot;elevation&quot; : $obj.elevation
}
</code></pre>
The results can be exported to CSV and then reopened in any other host language as a DataFrame.

blocks|key|4676510|text|使用Json加载文件，并使用DataFrame.from_dict函数将其转换为pandas数据帧|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4676511|import+json
import+pandas+as+pd
json_string+=+'{+"name":"John",+"age":30,+"car":"None"+}'

a_json+=+json.loads(json_string)
print(a_json)

dataframe+=+pd.DataFrame.from_dict(a_json)|code-block|syntax|javascript|4676512|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

use Json to load the file and convert it to a pandas dataframe using DataFrame.from_dict function
<pre><code>import json
import pandas as pd
json_string = '{ &quot;name&quot;:&quot;John&quot;, &quot;age&quot;:30, &quot;car&quot;:&quot;None&quot; }'

a_json = json.loads(json_string)
print(a_json)

dataframe = pd.DataFrame.from_dict(a_json)
</code></pre>

blocks|key|4676521|text|参考MongoDB+documentation，我得到了以下代码：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4676522|from+pandas+import+DataFrame
df+=+DataFrame('Your+json+string')|code-block|syntax|javascript|4676523|entityMap|0|LINK|mutability|MUTABLE|url|https://www.mongodb.com/languages/python^0|2|L|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@$A|R|B|S|1|T]]|C|$]]|$1|D|3|E|5|F|7|U|8|@]|9|@]|C|$G|H]]|$1|I|3|-4|5|6|7|V|8|@]|9|@]|C|$]]]|J|$K|$5|L|M|N|C|$O|P]]]]

Refer <a href="https://www.mongodb.com/languages/python" rel="nofollow noreferrer">MongoDB documentation</a>, I got the following code:
<pre><code>from pandas import DataFrame
df = DataFrame('Your json string')
</code></pre>

What I am trying to do is extract elevation data from a google maps API along a path specified by latitude and longitude coordinates as follows:

<pre><code>from urllib2 import Request, urlopen
import json

path1 = '42.974049,-81.205203|42.974298,-81.195755'
request=Request('http://maps.googleapis.com/maps/api/elevation/json?locations='+path1+'&amp;sensor=false')
response = urlopen(request)
elevations = response.read()
</code></pre>

This gives me a data that looks like this:

<pre><code>elevations.splitlines()

['{',
 ' "results" : [',
 ' {',
 ' "elevation" : 243.3462677001953,',
 ' "location" : {',
 ' "lat" : 42.974049,',
 ' "lng" : -81.205203',
 ' },',
 ' "resolution" : 19.08790397644043',
 ' },',
 ' {',
 ' "elevation" : 244.1318664550781,',
 ' "location" : {',
 ' "lat" : 42.974298,',
 ' "lng" : -81.19575500000001',
 ' },',
 ' "resolution" : 19.08790397644043',
 ' }',
 ' ],',
 ' "status" : "OK"',
 '}']
</code></pre>

when putting into as DataFrame here is what I get:

<img src="https://i.stack.imgur.com/t9aSp.png" alt="enter image description here">

<pre><code>pd.read_json(elevations)
</code></pre>

and here is what I want:

<img src="https://i.stack.imgur.com/4Kdlg.png" alt="enter image description here">

I'm not sure if this is possible, but mainly what I am looking for is a way to be able to put the elevation, latitude and longitude data together in a pandas dataframe (doesn't have to have fancy mutiline headers).

If any one can help or give some advice on working with this data that would be great! If you can't tell I haven't worked much with json data before...

EDIT:

This method isn't all that attractive but seems to work:

<pre><code>data = json.loads(elevations)
lat,lng,el = [],[],[]
for result in data['results']:
 lat.append(result[u'location'][u'lat'])
 lng.append(result[u'location'][u'lng'])
 el.append(result[u'elevation'])
df = pd.DataFrame([lat,lng,el]).T
</code></pre>

ends up dataframe having columns latitude, longitude, elevation

<img src="https://i.stack.imgur.com/seht1.png" alt="enter image description here">

JSON to pandas DataFrame

Python

Spark 

JSON

我要做的是沿着纬度和经度坐标指定的路径从google地图API中提取高程数据，如下所示：from urllib2 import Request, urlopenimport jsonpath1 = '42.974049,-81.205203|42.974298,-81.195755'request=Request('h...

问JSON to pandas DataFrame
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