yay!

This is pretty awesome.

One thing I think worth being explicit in the docs (am I right in saying this?) only works with valid JSON.

the json routines read/write from strings
this is unlike any of the other io routines that pandas has
which all take a path_or_buf

is this typical of dealing with JSON data?

should we have a kw to do this? always do it?

@jreback That is an excellent point, this should work as all the other read_s. I don't think this is necessarily typical to always have the string but at least it does makes it clear the read_json takes the entire string at once rather than by chunks.

(The first thing I did was open a json_file f.readlines(). f.read())

It'd certainly be a useful feature is we could go pd.from_json(datas_url) and perhaps this would be a fairly standard use case.

We could either:

1. Have have a string kwarg, filepath_or_buffer is first argument (this would be my preference).
2. Check if it's a filepath_or_buffer, if not there, treat as json string (seems like a can of worms)

(Also, to clarify previous point, from_json only reads valid json :) )

do u have a URL that yields JSON?

Yoinked from so:

http://search.twitter.com/search.json?q=blue%20angels&rpp=5&include_entities=true&result_type=mixed

... or http://search.twitter.com/search.json?q=pandas%20python

A more interesting example: https://api.github.com/repos/pydata/pandas/issues?per_page=100

parsed first try!

(Pdb) url_table
<class 'pandas.core.frame.DataFrame'>
Int64Index: 100 entries, 0 to 99
Data columns (total 19 columns):
assignee        6  non-null values
body            100  non-null values
closed_at       0  non-null values
comments        100  non-null values
comments_url    100  non-null values
created_at      100  non-null values
events_url      100  non-null values
html_url        100  non-null values
id              100  non-null values
labels          100  non-null values
labels_url      100  non-null values
milestone       75  non-null values
number          100  non-null values
pull_request    100  non-null values
state           100  non-null values
title           100  non-null values
updated_at      100  non-null values
url             100  non-null values
user            100  non-null values
dtypes: int64(3), object(16)

The one thing that trips is dates (you just have to to_datetime after), but that can be left for another day.

Whoop! :)

yeh....we'll see how this goes....in 0.12 can add infer_types directive, kind of like read_html

i wonder if there are any other similar libraries or systems that have this much io functionality in a single package...

(Does infer_types work for unix time stamps? ...to get the roundtrip working. Anyway....)

doubtful since those are just integers...but i haven't tested

i tried

date +"%s" | python -c 'import sys; from dateutil.parser import parse; parse(sys.stdin.read())'

that doesn't work so i'm going to say no it won't work.

You can do pd.to_datetime on the column after reading.

yes an know....we have an open issue #3540 to make a better API for this, but look at #2015

so if you know they are epoch time stamps (e.g. passing in as an option maybe), then its easy, we can convert them

Timestamp accepts a nanosecond based epoch timestamp (e.g. nanoseconds since 1970; so epoch time stamps are in seconds since 1970, so juse int(1e9) and it will work).....

but there is an issue because sometimes they are not in seconds...so have to disambiguate

Just saying as to_json exports timestamps to unix time.

oh yes Timestamp works...hm maybe should add to read_html...i doubt people are using html tables to store unix timestamp but who the hell knows? maybe i'll wait until the api is sorted out

could add a parse_dates arg that takes a list of fields to try to convert?

read_html much tougher because people are just reading it in ...and there is no standard...@hayd is right...since its a standard, could even try to convert an integer column (if they are in range)?

(obviously far too reckless to just .applymap(pd.to_datetime) lol)

what's a quick way to fix inconcistent space/tabs....something got screwed up...

M-x untabify

on a region i think prolly whole file works too

@hayd actually .apply(pd.to_datetime) will not change the column if all conversions fail, so it is safe sort of

try out: convert_objects(convert_dates='coerce'))

Quite slow though?

convert_objects is ok speed wise it operates on blocks using cython functions so it's gotta be faster than lambdas :)

@jreback correct me if i'm wrong here...

@Komnomnomnom go ahead and paste here
and well get this in

Ok the following patch should make it safe to call Npy_releaseContext multiple times (which is what was causing the problem). Segmentation fault is gone and valgrind output from Python 2.7 debug build is clean. Likewise all tests pass for Python 2.7 and valgrind output for json tests is clean (i.e. there are no warnings for json related code).

diff --git a/pandas/src/ujson/python/JSONtoObj.c b/pandas/src/ujson/python/JSONtoObj.c
index 1db7586..160c30f 100644
--- a/pandas/src/ujson/python/JSONtoObj.c
+++ b/pandas/src/ujson/python/JSONtoObj.c
@@ -10,6 +10,7 @@ typedef struct __PyObjectDecoder
     JSONObjectDecoder dec;

     void* npyarr;       // Numpy context buffer
+    void* npyarr_addr;  // Ref to npyarr ptr to track DECREF calls
     npy_intp curdim;    // Current array dimension

     PyArray_Descr* dtype;
@@ -67,9 +68,7 @@ void Npy_releaseContext(NpyArrContext* npyarr)
         }
         if (npyarr->dec)
         {
-            // Don't set to null, used to make sure we don't Py_DECREF npyarr
-            // in releaseObject
-            // npyarr->dec->npyarr = NULL;
+            npyarr->dec->npyarr = NULL;
             npyarr->dec->curdim = 0;
         }
         Py_XDECREF(npyarr->labels[0]);
@@ -88,6 +87,7 @@ JSOBJ Object_npyNewArray(void* _decoder)
     {
         // start of array - initialise the context buffer
         npyarr = decoder->npyarr = PyObject_Malloc(sizeof(NpyArrContext));
+        decoder->npyarr_addr = npyarr;

         if (!npyarr)
         {
@@ -515,7 +515,7 @@ JSOBJ Object_newDouble(double value)
 static void Object_releaseObject(JSOBJ obj, void* _decoder)
 {
     PyObjectDecoder* decoder = (PyObjectDecoder*) _decoder;
-    if (obj != decoder->npyarr)
+    if (obj != decoder->npyarr_addr)
     {
         Py_XDECREF( ((PyObject *)obj));
     }
@@ -555,6 +555,7 @@ PyObject* JSONToObj(PyObject* self, PyObject *args, PyObject *kwargs)
     pyDecoder.dec = dec;
     pyDecoder.curdim = 0;
     pyDecoder.npyarr = NULL;
+    pyDecoder.npyarr_addr = NULL;

     decoder = (JSONObjectDecoder*) &pyDecoder;

@@ -609,6 +610,7 @@ PyObject* JSONToObj(PyObject* self, PyObject *args, PyObject *kwargs)

     if (PyErr_Occurred())
     {
+        Npy_releaseContext(pyDecoder.npyarr);
         return NULL;
     }

patch applied.....looking good now

@jreback Something like this for requests: hayd@dbd968b

@wesm this is mergable....any objections?

Looks good to me, bombs away

3.2.1.....

Awesome. I'll see about merging in upstream changes. Will send thru a pull request soonish.

oh...you have additional dependencies on this?

Mentioned in #3583, there have been some enhancements / fixes in ultrajson since the pandas json version was originally written. Nothing major (I think) and should be straightforward enough to merge but it'd be a good idea to keep them in sync I think.

ok...sure...

thanks all for making this happen, especially to @Komnomnomnom for authoring this code in the first place =)