Prep.py throws an AssertionError

[Gandhi-uses-nukes]

Hi,

I just wanted to try the tutorial after I found this video on YouTube: https://www.youtube.com/watch?v=5Md_sSsN51k

I use Anaconda on Windows 10, installed all necessary packages and after I tried to execute the first line in the notebook, I get the following error:

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
C:\Users\Name\Documents\Python Scripts\parallel-tutorial-master\parallel-tutorial-master\prep.py in <module>()
     39 
     40 for symbol in stocks:
---> 41     write_stock(symbol)
     42 
     43 

C:\Users\Name\Documents\Python Scripts\parallel-tutorial-master\parallel-tutorial-master\prep.py in write_stock(symbol)
     35         names = [str(ts.date()) for ts in df.divisions]
     36         df.to_csv(os.path.join(here, 'data', 'minute', symbol, '*.csv'),
---> 37                   name_function=names.__getitem__)
     38         print("Finished CSV: %s" % symbol)
     39 

C:\Users\Name\Anaconda3\lib\site-packages\dask\dataframe\core.py in to_csv(self, filename, **kwargs)
    957         """ See dd.to_csv docstring for more information """
    958         from .io import to_csv
--> 959         return to_csv(self, filename, **kwargs)
    960 
    961     def to_delayed(self):

C:\Users\Name\Anaconda3\lib\site-packages\dask\dataframe\io\csv.py in to_csv(df, filename, name_function, compression, compute, get, storage_options, **kwargs)
    503 
    504     if compute:
--> 505         delayed(values).compute(get=get)
    506     else:
    507         return values

C:\Users\Name\Anaconda3\lib\site-packages\dask\base.py in compute(self, **kwargs)
     97             Extra keywords to forward to the scheduler ``get`` function.
     98         """
---> 99         (result,) = compute(self, traverse=False, **kwargs)
    100         return result
    101 

C:\Users\Name\Anaconda3\lib\site-packages\dask\base.py in compute(*args, **kwargs)
    204     dsk = collections_to_dsk(variables, opNameize_graph, **kwargs)
    205     keys = [var._keys() for var in variables]
--> 206     results = get(dsk, keys, **kwargs)
    207 
    208     results_iter = iter(results)

C:\Users\Name\Anaconda3\lib\site-packages\dask\threaded.py in get(dsk, result, cache, num_workers, **kwargs)
     73     results = get_async(pool.apply_async, len(pool._pool), dsk, result,
     74                         cache=cache, get_id=_thread_get_id,
---> 75                         pack_exception=pack_exception, **kwargs)
     76 
     77     # Cleanup pools associated to dead threads

C:\Users\Name\Anaconda3\lib\site-packages\dask\local.py in get_async(apply_async, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, **kwargs)
    519                         _execute_task(task, data)  # Re-execute locally
    520                     else:
--> 521                         raise_exception(exc, tb)
    522                 res, worker_id = loads(res_info)
    523                 state['cache'][key] = res

C:\Users\Name\Anaconda3\lib\site-packages\dask\compatibility.py in reraise(exc, tb)
     58         if exc.__traceback__ is not tb:
     59             raise exc.with_traceback(tb)
---> 60         raise exc
     61 
     62 else:

C:\Users\Name\Anaconda3\lib\site-packages\dask\local.py in execute_task(key, task_info, dumps, loads, get_id, pack_exception)
    288     try:
    289         task, data = loads(task_info)
--> 290         result = _execute_task(task, data)
    291         id = get_id()
    292         result = dumps((result, id))

C:\Users\Name\Anaconda3\lib\site-packages\dask\local.py in _execute_task(arg, cache, dsk)
    269         func, args = arg[0], arg[1:]
    270         args2 = [_execute_task(a, cache) for a in args]
--> 271         return func(*args2)
    272     elif not ishashable(arg):
    273         return arg

C:\Users\Name\Anaconda3\lib\site-packages\dask\compatibility.py in apply(func, args, kwargs)
     45     def apply(func, args, kwargs=None):
     46         if kwargs:
---> 47             return func(*args, **kwargs)
     48         else:
     49             return func(*args)

C:\Users\Name\Anaconda3\lib\site-packages\dask\dataframe\io\demo.py in generate_day(date, open, high, low, close, volume, freq, random_state)
    114         values += np.linspace(open - values[0], close - values[-1],
    115                               len(values))  # endpoints
--> 116         assert np.allclose(open, values[0])
    117         assert np.allclose(close, values[-1])
    118 

AssertionError: 

I also tried it unter MacOS Sierra with Miniconda and an extra enviroment as well as under Ubuntu 17.04 with Miniconda. And now I am out of operating systems :smile:

[mrocklin]

This is because of https://github.com/pydata/pandas-datareader/issues/391

We're working around it. I recommend checking back on Monday

On Fri, Sep 22, 2017 at 8:21 AM, Gandhi-uses-nukes <notifications@github.com

wrote:

Hi,

I just wanted to try the tutorial after I found this video on YouTube: https://www.youtube.com/watch?v=5Md_sSsN51k

I use Anaconda on Windows 10, installed all necessary packages and after I tried to execute the first line in the notebook, I get the following error:

---------------------------------------------------------------------------AssertionError Traceback (most recent call last) C:\Users\Name\Documents\Python Scripts\parallel-tutorial-master\parallel-tutorial-master\prep.py in () 39 40 for symbol in stocks:---> 41 write_stock(symbol) 42 43

C:\Users\Name\Documents\Python Scripts\parallel-tutorial-master\parallel-tutorial-master\prep.py in write_stock(symbol) 35 names = [str(ts.date()) for ts in df.divisions] 36 df.to_csv(os.path.join(here, 'data', 'minute', symbol, '*.csv'),---> 37 name_function=names.getitem) 38 print("Finished CSV: %s" % symbol) 39

C:\Users\Name\Anaconda3\lib\site-packages\dask\dataframe\core.py in to_csv(self, filename, kwargs) 957 """ See dd.to_csv docstring for more information """ 958 from .io import to_csv--> 959 return to_csv(self, filename, kwargs) 960 961 def to_delayed(self):

C:\Users\Name\Anaconda3\lib\site-packages\dask\dataframe\io\csv.py in to_csv(df, filename, name_function, compression, compute, get, storage_options, **kwargs) 503 504 if compute:--> 505 delayed(values).compute(get=get) 506 else: 507 return values

C:\Users\Name\Anaconda3\lib\site-packages\dask\base.py in compute(self, kwargs) 97 Extra keywords to forward to the scheduler get function. 98 """---> 99 (result,) = compute(self, traverse=False, kwargs) 100 return result 101 C:\Users\Name\Anaconda3\lib\site-packages\dask\base.py in compute(args, kwargs) 204 dsk = collections_to_dsk(variables, opNameize_graph, kwargs) 205 keys = [var._keys() for var in variables]--> 206 results = get(dsk, keys, kwargs) 207 208 results_iter = iter(results)C:\Users\Name\Anaconda3\lib\site-packages\dask\threaded.py in get(dsk, result, cache, num_workers, kwargs) 73 results = get_async(pool.apply_async, len(pool._pool), dsk, result, 74 cache=cache, get_id=_thread_get_id,---> 75 pack_exception=pack_exception, kwargs) 76 77 # Cleanup pools associated to dead threadsC:\Users\Name\Anaconda3\lib\site-packages\dask\local.py in get_async(apply_async, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, kwargs) 519 _execute_task(task, data) # Re-execute locally 520 else:--> 521 raise_exception(exc, tb) 522 res, worker_id = loads(res_info) 523 state['cache'][key] = resC:\Users\Name\Anaconda3\lib\site-packages\dask\compatibility.py in reraise(exc, tb) 58 if exc.traceback is not tb: 59 raise exc.with_traceback(tb)---> 60 raise exc 61 62 else:C:\Users\Name\Anaconda3\lib\site-packages\dask\local.py in execute_task(key, task_info, dumps, loads, get_id, pack_exception) 288 try: 289 task, data = loads(task_info)--> 290 result = _execute_task(task, data) 291 id = get_id() 292 result = dumps((result, id))C:\Users\Name\Anaconda3\lib\site-packages\dask\local.py in _execute_task(arg, cache, dsk) 269 func, args = arg[0], arg[1:] 270 args2 = [_execute_task(a, cache) for a in args]--> 271 return func(args2) 272 elif not ishashable(arg): 273 return argC:\Users\Name\Anaconda3\lib\site-packages\dask\compatibility.py in apply(func, args, kwargs) 45 def apply(func, args, kwargs=None): 46 if kwargs:---> 47 return func(*args, *kwargs) 48 else: 49 return func(args)C:\Users\Name\Anaconda3\lib\site-packages\dask\dataframe\io\demo.py in generate_day(date, open, high, low, close, volume, freq, random_state) 114 values += np.linspace(open - values[0], close - values[-1], 115 len(values)) # endpoints--> 116 assert np.allclose(open, values[0]) 117 assert np.allclose(close, values[-1]) 118 AssertionError:

I also tried it unter MacOS Sierra with Miniconda and an extra enviroment as well as under Ubuntu 17.04 with Miniconda. And now I am out of operating systems 😄

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[joegoldbeck]

As per the instructor, seems it's updated in the repo, but if you've already built the environment, you can update just the relevant dependency with conda install dask -c conda-forge

[Gandhi-uses-nukes]

I did a new 'git clone ...' and run python prep.py This time it worked until

C:\Users\Name\Documents\Python Scripts\parallel-tutorial>python prep.py
Finished CSV: afl
Finished CSV: aig
Finished CSV: al
Finished CSV: avy
Finished CSV: bwa
Traceback (most recent call last):
  File "prep.py", line 44, in <module>
    write_stock(symbol)
  File "prep.py", line 37, in write_stock
    data_source='google')
  File "C:\Users\Name\Anaconda3\lib\site-packages\dask\dataframe\io\demo.py", line 187, in daily_stock
    df = data.DataReader(symbol, data_source, start, stop)
  File "C:\Users\Name\Anaconda3\lib\site-packages\pandas_datareader\data.py", line 137, in DataReader
    session=session).read()
  File "C:\Users\Name\Anaconda3\lib\site-packages\pandas_datareader\base.py", line 181, in read
    params=self._get_params(self.symbols))
  File "C:\Users\Name\Anaconda3\lib\site-packages\pandas_datareader\base.py", line 79, in _read_one_data
    out = self._read_url_as_StringIO(url, params=params)
  File "C:\Users\Name\Anaconda3\lib\site-packages\pandas_datareader\base.py", line 90, in _read_url_as_StringIO
    response = self._get_response(url, params=params)
  File "C:\Users\Name\Anaconda3\lib\site-packages\pandas_datareader\base.py", line 139, in _get_response
    raise RemoteDataError('Unable to read URL: {0}'.format(url))
pandas_datareader._utils.RemoteDataError: Unable to read URL: http://www.google.com/finance/historical?q=hal&startdate=Sep+26%2C+2016&enddate=Sep+26%2C+2017&output=csv

But I could easily fix it myself. Apparently Google has no data in the specified date range. I removed hal and ibm, then everything worked.

[mdtdev]

Surprised this is still open, but Google changed their API for historical stock data. So this is sort of unfixable now without migrating to a different data source. See https://github.com/pydata/parallel-tutorial/issues/30 and the discussion at the end of https://github.com/pydata/pandas-datareader/issues/391.