Dask dataframe valueError - python-3.x

Question

I have several parquet files (dataframes), which I load as one dask dataframe graph and sample.
Afterwards I perform some computations based on the original data in the dataframe and append the new columns to my dask dataframe.
Finally, I want to compute the mean() and std() for all columns in and do get a ValueError that I am not sure where it comes from or what I'm doing wrong.
import pandas as pd
import numpy as np
import tensorflow as tf
import os
from os.path import join
import dask
import dask.dataframe as dd
import dask.array as da
# read in the data
data_pq = dd.read_parquet(join(path_to_data,'filter_width_*_DNN_train.parquet'),chunksize='4GB')
print('Convert to single precission and sample')
data_pq = data_pq.astype(np.float32).sample(frac=0.1)
# ## compute the additional quantites (tensors)
# compute tensors R, S mag(U) etc.
mag_U = da.sqrt(data_pq['U_bar'].values**2 + data_pq['V_bar'].values**2 +data_pq['W_bar'].values**2)
mag_grad_c = da.sqrt(data_pq['grad_c_x_LES'].values**2 + data_pq['grad_c_y_LES'].values**2 +data_pq['grad_c_z_LES'].values**2)
sum_U = data_pq['U_bar'].values + data_pq['V_bar']+data_pq['W_bar'].values
sum_c = da.absolute(data_pq['grad_c_x_LES'].values) + da.absolute(data_pq['grad_c_y_LES'].values) +da.absolute(data_pq['grad_c_z_LES'].values)
grad_U = da.sqrt(data_pq['grad_U_x_LES'].values**2 + data_pq['grad_U_y_LES'].values**2 +data_pq['grad_U_z_LES'].values**2)
grad_V = da.sqrt(data_pq['grad_V_x_LES'].values**2 + data_pq['grad_V_y_LES'].values**2 +data_pq['grad_V_z_LES'].values**2)
grad_W = da.sqrt(data_pq['grad_W_x_LES'].values**2 + data_pq['grad_W_y_LES'].values**2 +data_pq['grad_W_z_LES'].values**2)
mag_grad_U = da.sqrt(grad_U**2 + grad_V**2 +grad_W**2)
sum_grad_U = da.absolute(grad_U) + da.absolute(grad_V) +da.absolute(grad_W)
print('Computing gradient_tensor')
gradient_tensor = da.array([
[data_pq['grad_U_x_LES'],data_pq['grad_V_x_LES'],data_pq['grad_W_x_LES']],
[data_pq['grad_U_y_LES'],data_pq['grad_V_y_LES'],data_pq['grad_W_y_LES']],
[data_pq['grad_U_z_LES'],data_pq['grad_V_z_LES'],data_pq['grad_W_z_LES']]
])
print('Computing S and R')
# symetric strain
Strain = 0.5*(gradient_tensor + da.transpose(gradient_tensor,(1,0,2)))
#anti symetric strain
Anti = 0.5*(gradient_tensor - da.transpose(gradient_tensor,(1,0,2)))
print('Computing lambdas')
lambda_1 = da.trace(Strain**2)
lambda_2 = da.trace(Anti**2)
lambda_3 = da.trace(Strain**3)
lambda_4 = da.trace(Anti**2 * Strain)
lambda_5 = da.trace(Anti**2 * Strain**2)
# Add to the dask dataframe
data_pq['mag_grad_c'] = mag_grad_c
data_pq['mag_U'] = mag_U
data_pq['sum_c'] = sum_c
data_pq['sum_U'] = sum_U
data_pq['sum_grad_U'] = sum_grad_U
data_pq['mag_grad_U'] = mag_grad_U
# REPARTITON
data_pq = data_pq.repartition(npartitions=lambda_1.npartitions)
data_pq['lambda_1'] = lambda_1
data_pq['lambda_2'] = lambda_2
data_pq['lambda_3'] = lambda_3
data_pq['lambda_4'] = lambda_4
data_pq['lambda_5'] = lambda_5
print('Done with feature computation')
# reindex and compute mean and std
data_pq = data_pq.reset_index().drop('index',axis=1)
# compute the mean and std
data_mean, data_std = dask.compute(data_pq.mean(),data_pq.std())
Not sure where it comes from. It says the indexes do not match.
This is the error message I get:
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) ~/Python/Data_driven_models/DASK_processing/Dask_parquet.py in <module>
119 data_pq = data_pq.reset_index().drop('index',axis=1)
120
--> 121 data_mean, data_std = dask.compute(data_pq.mean(),data_pq.std())
122
~/.local/lib/python3.6/site-packages/dask/base.py in compute(*args,
**kwargs)
450 postcomputes.append(x.__dask_postcompute__())
451
--> 452 results = schedule(dsk, keys, **kwargs)
453 return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
454
~/.local/lib/python3.6/site-packages/dask/threaded.py in get(dsk, result, cache, num_workers, pool, **kwargs)
82 get_id=_thread_get_id,
83 pack_exception=pack_exception,
---> 84 **kwargs
85 )
86
~/.local/lib/python3.6/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)
484 _execute_task(task, data) # Re-execute locally
485 else:
--> 486 raise_exception(exc, tb)
487 res, worker_id = loads(res_info)
488 state["cache"][key] = res
~/.local/lib/python3.6/site-packages/dask/local.py in reraise(exc, tb)
314 if exc.__traceback__ is not tb:
315 raise exc.with_traceback(tb)
--> 316 raise exc
317
318
~/.local/lib/python3.6/site-packages/dask/local.py in execute_task(key, task_info, dumps, loads, get_id, pack_exception)
220 try:
221 task, data = loads(task_info)
--> 222 result = _execute_task(task, data)
223 id = get_id()
224 result = dumps((result, id))
~/.local/lib/python3.6/site-packages/dask/core.py in
_execute_task(arg, cache, dsk)
119 # temporaries by their reference count and can execute certain
120 # operations in-place.
--> 121 return func(*(_execute_task(a, cache) for a in args))
122 elif not ishashable(arg):
123 return arg
~/.local/lib/python3.6/site-packages/pandas/core/series.py in
__init__(self, data, index, dtype, name, copy, fastpath)
312 if len(index) != len(data):
313 raise ValueError(
--> 314 f"Length of passed values is {len(data)}, "
315 f"index implies {len(index)}."
316 )
ValueError: Length of passed values is 3728270, index implies 2135992.