pandas.read_json — pandas 0.19.2 documentation
pandas.read_json
pandas.read_json(path_or_buf=None, orient=None, typ='frame', dtype=True, convert_axes=True, convert_dates=True, keep_default_dates=True, numpy=False, precise_float=False, date_unit=None, encoding=None, lines=False)
Convert a JSON string to pandas object
Parameters:path_or_buf : a valid JSON string or file-like, default: None
The string could be a URL. Valid URL schemes include http, ftp, s3, and
file. For file URLs, a host is expected. For instance, a local file
could be file://localhost/path/to/table.json
orient : string,
Indication of expected JSON string format.
Compatible JSON strings can be produced by to_json() with a
corresponding orient value.
The set of possible orients is:
'split' : dict like
{index -& [index], columns -& [columns], data -& [values]}
'records' : list like
[{column -& value}, ... , {column -& value}]
'index' : dict like {index -& {column -& value}}
'columns' : dict like {column -& {index -& value}}
'values' : just the values array
The allowed and default values depend on the value
of the typ parameter.
when typ == 'series',
allowed orients are {'split','records','index'}
default is 'index'
The Series index must be unique for orient 'index'.
when typ == 'frame',
allowed orients are {'split','records','index',
'columns','values'}
default is 'columns'
The DataFrame index must be unique for orients 'index' and
'columns'.
The DataFrame columns must be unique for orients 'index',
'columns', and 'records'.
typ : type of object to recover (series or frame), default ‘frame’
dtype : boolean or dict, default True
If True, infer dtypes, if a dict of column to dtype, then use those,
if False, then don’t infer dtypes at all, applies only to the data.
convert_axes : boolean, default True
Try to convert the axes to the proper dtypes.
convert_dates : boolean, default True
List of column If True, then try to parse
datelike columns default is T a column label is datelike if
it ends with '_at',
it ends with '_time',
it begins with 'timestamp',
it is 'modified', or
it is 'date'
keep_default_dates : boolean, default True
If parsing dates, then parse the default datelike columns
numpy : boolean, default False
Direct decoding to numpy arrays. Supports numeric data only, but
non-numeric column and index labels are supported. Note also that the
JSON ordering MUST be the same for each term if numpy=True.
precise_float : boolean, default False
Set to enable usage of higher precision (strtod) function when
decoding string to double values. Default (False) is to use fast but
less precise builtin functionality
date_unit : string, default None
The timestamp unit to detect if converting dates. The default behaviour
is to try and detect the correct precision, but if this is not desired
then pass one of ‘s’, ‘ms’, ‘us’ or ‘ns’ to force parsing only seconds,
milliseconds, microseconds or nanoseconds respectively.
lines : boolean, default False
Read the file as a json object per line.
New in version 0.19.0.
encoding : str, default is ‘utf-8’
The encoding to use to decode py3 bytes.
New in version 0.19.0.
Returns:result : Series or DataFrame, depending on the value of typ.
&&& df = pd.DataFrame([['a', 'b'], ['c', 'd']],
index=['row 1', 'row 2'],
columns=['col 1', 'col 2'])
Encoding/decoding a Dataframe using 'split' formatted JSON:
&&& df.to_json(orient='split')
'{&columns&:[&col 1&,&col 2&],
&index&:[&row 1&,&row 2&],
&data&:[[&a&,&b&],[&c&,&d&]]}'
&&& pd.read_json(_, orient='split')
col 1 col 2
Encoding/decoding a Dataframe using 'index' formatted JSON:
&&& df.to_json(orient='index')
'{&row 1&:{&col 1&:&a&,&col 2&:&b&},&row 2&:{&col 1&:&c&,&col 2&:&d&}}'
&&& pd.read_json(_, orient='index')
col 1 col 2
Encoding/decoding a Dataframe using 'records' formatted JSON.
Note that index labels are not preserved with this encoding.
&&& df.to_json(orient='records')
'[{&col 1&:&a&,&col 2&:&b&},{&col 1&:&c&,&col 2&:&d&}]'
&&& pd.read_json(_, orient='records')
col 1 col 2系统集成（14）
&Let's see how the data is encoded in JSON, for this consider that we have a entity called Customer, which has name, age, country of residence and two telephone numbers. This information if represented in XML could look something like this:&customer&
&name&Mr A&/name&
&age&&33/age&
&country&USA&/country&
&tel&&/tel&
&tel&&/tel&
&/customer&
Here name, age and country are represented as Name Value Pairs and tel is an Array of one or more than one telephone the customer has. This same information can be represented in JSON format as:
{&customer&:{
&name&:&Mr A&,
&age&:&33&,
&country&:&USA&,
&tel&:[&&,&&]
If there were mutliple customers then it could have been represented as:
&customers&
&customer&
&name&Mr A&/name&
&age&33&/age&
&country&USA&/country&
&tel&&/tel&
&tel&&/tel&
&/customer&
&customer&
&name&Mr B&/name&
&age&22&/age&
&country&USA&/country&
&tel&&/tel&
&tel&&/tel&
&/customer&
&/customers&
Now here customers is a array(collection) of customer and can be represented in JSON as:
{&customers&:
{&customer&:[
{&name&:&Mr A&,
&age&:&33&,
&country&:&USA&,
&tel&:[&&,&&]},
{&name&:&Mr B&,
&age&:&22&,
&country&:&USA&,
&tel&:[&&,&&]}
Some more example from JSON.org
{&menu&: {
&id&: &file&,
&value&: &File&,
&popup&: {
&menuitem&: [
{&value&: &New&, &onclick&: &CreateNewDoc()&},
{&value&: &Open&, &onclick&: &OpenDoc()&},
{&value&: &Close&, &onclick&: &CloseDoc()&}
The same text expressed as XML:
&menu id=&file& value=&File&&
&menuitem value=&New& onclick=&CreateNewDoc()& /&
&menuitem value=&Open& onclick=&OpenDoc()& /&
&menuitem value=&Close& onclick=&CloseDoc()& /&
参考知识库
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Sample of JSON Data
Sample JSON Call
/data/getSnapQuotes.json?symbols=GE,MSFT&webmasterId=XXXXX
Sample Output
"copyright":"Copyright (c) 2010 QuoteMedia, Inc.",
"symbolcount":2,
"quotedata":[
"symbol":"GE",
"datatype":"equity",
"entitlement":"DL",
"delaymin":15,
"datetime":"T15:03:40-04:00",
"exchange":"NYE",
"longname":"GENERAL ELECTRIC",
"shortname":"GE",
"pricedata":{
"last":15.3499,
"change":-0.0601,
"changepercent":-0.390006,
"open":15.46,
"high":15.55,
"low":15.05,
"prevclose":15.41,
"bid":15.34,
"ask":15.35,
"bidsize":20100,
"asksize":19100,
"tradevolume":149741,
"sharevolume":,
"lasttradedatetime":"T15:03:40-04:00"
"symbol":"MSFT",
"datatype":"equity",
"entitlement":"DL",
"delaymin":15,
"datetime":"T15:03:40-04:00",
"exchange":"NGS",
"longname":"Microsoft Corp.",
"shortname":"MSFT",
"pricedata":{
"last":24.95,
"change":-0.34,
"changepercent":-1.3444,
"open":25.25,
"high":25.26,
"low":24.65,
"prevclose":25.29,
"bid":24.94,
"ask":24.95,
"bidsize":28900,
"asksize":10400,
"tradevolume":198456,
"sharevolume":,
"lasttradedatetime":"T15:03:38-04:00"
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Copyright & 2016 QuoteMedia, Inc. All Rights Reserved}