How to fix the 'The columns in the computed data do not match the columns in the provided metadata' error? - pivot-table

Question

I have a table of price updates in the format (timestamp, price, amount).
The timestamp is a datetime, price categorical and amount float64. The timestamp column is set as an index.
My goal is to get the amount available at each price level at each point in time.
First, I use the pivot to spread the prices into columns, and then forward fill.
pivot = price_table.pivot_table(index = 'timestamp',
columns = 'price', values = 'amount')
pivot_ffill = pivot.fillna(method = 'ffill')
I can compute or apply head to pivot_ffill and it works fine.
Clearly, there are still NAs at the beginning of the table where there have been no updates yet.
When I apply
pivot_nullfill = pivot_ffill.fillna(0)
pivot_nullfill.head()
I do get an error
The columns in the computed data do not match the columns in the provided metadata. I tried replacing the zero with 0.0 or float(0), but to no avail. As the previous steps work, I strongly suspect it has something to do with the fillna, but due to the delayed calculations that does not have to be true.
Does someone know what causes this? Thank you!
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-180-f8ab344c7939> in <module>
----> 1 pivot_ffill.fillna(0).head()
C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\dask\dataframe\core.py in head(self, n, npartitions, compute)
896
897 if compute:
--> 898 result = result.compute()
899 return result
900
C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\dask\base.py in compute(self, **kwargs)
154 dask.base.compute
155 """
--> 156 (result,) = compute(self, traverse=False, **kwargs)
157 return result
158
C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\dask\base.py in compute(*args, **kwargs)
396 keys = [x.__dask_keys__() for x in collections]
397 postcomputes = [x.__dask_postcompute__() for x in collections]
--> 398 results = schedule(dsk, keys, **kwargs)
399 return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
400
C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\dask\threaded.py in get(dsk, result, cache, num_workers, pool, **kwargs)
74 results = get_async(pool.apply_async, len(pool._pool), dsk, result,
75 cache=cache, get_id=_thread_get_id,
---> 76 pack_exception=pack_exception, **kwargs)
77
78 # Cleanup pools associated to dead threads
C:\ProgramData\Anaconda3\envs\python36\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)
460 _execute_task(task, data) # Re-execute locally
461 else:
--> 462 raise_exception(exc, tb)
463 res, worker_id = loads(res_info)
464 state['cache'][key] = res
C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\dask\compatibility.py in reraise(exc, tb)
110 if exc.__traceback__ is not tb:
111 raise exc.with_traceback(tb)
--> 112 raise exc
113
114 import pickle as cPickle
C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\dask\local.py in execute_task(key, task_info, dumps, loads, get_id, pack_exception)
228 try:
229 task, data = loads(task_info)
--> 230 result = _execute_task(task, data)
231 id = get_id()
232 result = dumps((result, id))
C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\dask\core.py in _execute_task(arg, cache, dsk)
116 elif istask(arg):
117 func, args = arg[0], arg[1:]
--> 118 args2 = [_execute_task(a, cache) for a in args]
119 return func(*args2)
120 elif not ishashable(arg):
C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\dask\core.py in <listcomp>(.0)
116 elif istask(arg):
117 func, args = arg[0], arg[1:]
--> 118 args2 = [_execute_task(a, cache) for a in args]
119 return func(*args2)
120 elif not ishashable(arg):
C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\dask\core.py in _execute_task(arg, cache, dsk)
117 func, args = arg[0], arg[1:]
118 args2 = [_execute_task(a, cache) for a in args]
--> 119 return func(*args2)
120 elif not ishashable(arg):
121 return arg
C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\dask\optimization.py in __call__(self, *args)
940 % (len(self.inkeys), len(args)))
941 return core.get(self.dsk, self.outkey,
--> 942 dict(zip(self.inkeys, args)))
943
944 def __reduce__(self):
C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\dask\core.py in get(dsk, out, cache)
147 for key in toposort(dsk):
148 task = dsk[key]
--> 149 result = _execute_task(task, cache)
150 cache[key] = result
151 result = _execute_task(out, cache)
C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\dask\core.py in _execute_task(arg, cache, dsk)
117 func, args = arg[0], arg[1:]
118 args2 = [_execute_task(a, cache) for a in args]
--> 119 return func(*args2)
120 elif not ishashable(arg):
121 return arg
C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\dask\compatibility.py in apply(func, args, kwargs)
91 def apply(func, args, kwargs=None):
92 if kwargs:
---> 93 return func(*args, **kwargs)
94 else:
95 return func(*args)
C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\dask\dataframe\core.py in apply_and_enforce(*args, **kwargs)
3800 if not np.array_equal(np.nan_to_num(meta.columns),
3801 np.nan_to_num(df.columns)):
-> 3802 raise ValueError("The columns in the computed data do not match"
3803 " the columns in the provided metadata")
3804 else:
ValueError: The columns in the computed data do not match the columns in the provided metadata

Answer 1

The error message should have give you a suggestion of how to fix the situation. We assume you are loading from CSV (the question doesn't say), so you would probably end up with a line like
df = dd.read_csv(..., dtype={...})
which instructs the pandas reader on the dtypes you want to enforce, since you know more information than pandas does. That ensures that all partitions have the same types for all columns - see the notes part of the docs.