Input:
import statsmodels.api as sm
import pandas as pd
# reading data from the csv
data = pd.read_csv('/Users/justkiddings/Desktop/Python/TM/TM.csv')
# defining the variables
x = data['FSP'].tolist()
y = data['RSP'].tolist()
# adding the constant term
x = sm.add_constant(x)
# performing the regression
# and fitting the model
result = sm.OLS(y, x).fit()
# printing the summary table
print(result.summary())
Output:
runfile('/Users/justkiddings/Desktop/Python/Code/untitled28.py', wdir='/Users/justkiddings/Desktop/Python/Code')
Traceback (most recent call last):
File "/Users/justkiddings/opt/anaconda3/lib/python3.9/site-packages/spyder_kernels/py3compat.py", line 356, in compat_exec
exec(code, globals, locals)
File "/Users/justkiddings/Desktop/Python/Code/untitled28.py", line 24, in <module>
result = sm.OLS(y, x).fit()
File "/Users/justkiddings/opt/anaconda3/lib/python3.9/site-packages/statsmodels/regression/linear_model.py", line 890, in __init__
super(OLS, self).__init__(endog, exog, missing=missing,
File "/Users/justkiddings/opt/anaconda3/lib/python3.9/site-packages/statsmodels/regression/linear_model.py", line 717, in __init__
super(WLS, self).__init__(endog, exog, missing=missing,
File "/Users/justkiddings/opt/anaconda3/lib/python3.9/site-packages/statsmodels/regression/linear_model.py", line 191, in __init__
super(RegressionModel, self).__init__(endog, exog, **kwargs)
File "/Users/justkiddings/opt/anaconda3/lib/python3.9/site-packages/statsmodels/base/model.py", line 267, in __init__
super().__init__(endog, exog, **kwargs)
File "/Users/justkiddings/opt/anaconda3/lib/python3.9/site-packages/statsmodels/base/model.py", line 92, in __init__
self.data = self._handle_data(endog, exog, missing, hasconst,
File "/Users/justkiddings/opt/anaconda3/lib/python3.9/site-packages/statsmodels/base/model.py", line 132, in _handle_data
data = handle_data(endog, exog, missing, hasconst, **kwargs)
File "/Users/justkiddings/opt/anaconda3/lib/python3.9/site-packages/statsmodels/base/data.py", line 673, in handle_data
return klass(endog, exog=exog, missing=missing, hasconst=hasconst,
File "/Users/justkiddings/opt/anaconda3/lib/python3.9/site-packages/statsmodels/base/data.py", line 86, in __init__
self._handle_constant(hasconst)
File "/Users/justkiddings/opt/anaconda3/lib/python3.9/site-packages/statsmodels/base/data.py", line 132, in _handle_constant
raise MissingDataError('exog contains inf or nans')
MissingDataError: exog contains inf or nans
Some of the Data:
DATE,HOUR,STATION,CO,FSP,NO2,NOX,O3,RSP,SO2
1/1/2022,1,TUEN MUN,75,38,39,40,83,59,2
1/1/2022,2,TUEN MUN,72,35,29,30,90,61,2
1/1/2022,3,TUEN MUN,74,38,28,30,91,66,2
1/1/2022,4,TUEN MUN,76,39,31,32,79,61,2
1/1/2022,5,TUEN MUN,72,38,25,26,83,65,2
1/1/2022,6,TUEN MUN,74,37,24,25,86,60,2
I have removed the N.A. in my dataset and they have converted into blanks. (Eg. 3/1/2022,12,TUEN MUN,85,,53,70,59,,5) Why there is MissingDataError? How to fix it? Thanks.
Related
C:\Users\Misti\PycharmProjects\pythonProject\venv\Scripts\python.exe C:/Users/Misti/PycharmProjects/pythonProject/main.py
Traceback (most recent call last):
File "C:/Users/Misti/PycharmProjects/pythonProject/main.py", line 2, in <module>
df = pd.read_csv('milestone2.csv')
File "C:\Users\Misti\PycharmProjects\pythonProject\venv\lib\site-packages\pandas\util\_decorators.py", line 311, in wrapper
return func(*args, **kwargs)
File "C:\Users\Misti\PycharmProjects\pythonProject\venv\lib\site-packages\pandas\io\parsers\readers.py", line 586, in read_csv
return _read(filepath_or_buffer, kwds)
File "C:\Users\Misti\PycharmProjects\pythonProject\venv\lib\site-packages\pandas\io\parsers\readers.py", line 482, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "C:\Users\Misti\PycharmProjects\pythonProject\venv\lib\site-packages\pandas\io\parsers\readers.py", line 811, in __init__
self._engine = self._make_engine(self.engine)
File "C:\Users\Misti\PycharmProjects\pythonProject\venv\lib\site-packages\pandas\io\parsers\readers.py", line 1040, in _make_engine
return mapping[engine](self.f, **self.options) # type: ignore[call-arg]
File "C:\Users\Misti\PycharmProjects\pythonProject\venv\lib\site-packages\pandas\io\parsers\c_parser_wrapper.py", line 69, in __init__
self._reader = parsers.TextReader(self.handles.handle, **kwds)
File "pandas\_libs\parsers.pyx", line 549, in pandas._libs.parsers.TextReader.__cinit__
pandas.errors.EmptyDataError: No columns to parse from file
Process finished with exit code 1
I have an .npz file where I have stored a dictionary. The dictionary has some keys and the values are numpy arrays. I want to read the dictionary in my getitem() method of the dataloader. When I set the dataloader num_workers to 1, everything runs fine. But when I increase the num workers, it throws the following error when reading the data from that npz file:
Traceback (most recent call last):
File "scripts/train.py", line 235, in <module>
train(args)
File "scripts/train.py", line 186, in train
solver(args.epoch, args.verbose)
File "/local-scratch/codebase/cap/lib/solver.py", line 174, in __call__
self._feed(self.dataloader["train"], "train", epoch_id)
File "/local-scratch/codebase/cap/lib/solver.py", line 366, in _feed
for data_dict in dataloader:
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 819, in __next__
return self._process_data(data)
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 846, in _process_data
data.reraise()
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/site-packages/torch/_utils.py", line 369, in reraise
raise self.exc_type(msg)
zipfile.BadZipFile: Caught BadZipFile in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop
data = fetcher.fetch(index)
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/local-scratch/codebase/cap/lib/dataset.py", line 947, in __getitem__
other_bbox_feat = self.box_features['{}-{}_{}.{}'.format(scene_id, target_object_id, ann_id, object_id)]
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/site-packages/numpy/lib/npyio.py", line 255, in __getitem__
pickle_kwargs=self.pickle_kwargs)
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/site-packages/numpy/lib/format.py", line 763, in read_array
data = _read_bytes(fp, read_size, "array data")
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/site-packages/numpy/lib/format.py", line 892, in _read_bytes
r = fp.read(size - len(data))
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/zipfile.py", line 872, in read
data = self._read1(n)
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/zipfile.py", line 962, in _read1
self._update_crc(data)
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/zipfile.py", line 890, in _update_crc
raise BadZipFile("Bad CRC-32 for file %r" % self.name)
zipfile.BadZipFile: Bad CRC-32 for file 'scene0519_00-13_1.0.npy'
As far as I know, pytorch dataloader uses multiprocessing to for data loading. Perhaps the issue is with multiprocessing and .npz files. I really appreciate any help.
I have a pandas dataframe raw_data with two columns: 'T' and 'BP':
T BP
0 -0.500 115.790
1 -0.499 115.441
2 -0.498 115.441
3 -0.497 115.441
4 -0.496 115.790
... ... ...
647163 646.663 105.675
647164 646.664 105.327
647165 646.665 105.327
647166 646.666 105.327
647167 646.667 104.978
[647168 rows x 2 columns]
I want to apply the Hodges-Lehmann mean (it's a robust average) over a rolling window and create a new column. Here's the function:
def hodgesLehmannMean(x):
m = np.add.outer(x, x)
ind = np.tril_indices(len(x), 0)
return 0.5 * np.median(m[ind])
I therefore write:
raw_data[new_col] = raw_data['BP'].rolling(21, min_periods=1, center=True,
win_type=None, axis=0, closed=None).agg(hodgesLehmannMean)
but I get a string of error messages:
Traceback (most recent call last):
File "C:\Users\tkpme\miniconda3\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\tkpme\miniconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "c:\Users\tkpme\.vscode\extensions\ms-python.python-2020.8.101144\pythonFiles\lib\python\debugpy\__main__.py", line 45, in <module>
cli.main()
File "c:\Users\tkpme\.vscode\extensions\ms-python.python-2020.8.101144\pythonFiles\lib\python\debugpy/..\debugpy\server\cli.py", line 430, in main
run()
File "c:\Users\tkpme\.vscode\extensions\ms-python.python-2020.8.101144\pythonFiles\lib\python\debugpy/..\debugpy\server\cli.py", line 267, in run_file
runpy.run_path(options.target, run_name=compat.force_str("__main__"))
File "C:\Users\tkpme\miniconda3\lib\runpy.py", line 265, in run_path
return _run_module_code(code, init_globals, run_name,
File "C:\Users\tkpme\miniconda3\lib\runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "C:\Users\tkpme\miniconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "c:\Users\tkpme\OneDrive\Documents\Work\CMC\BP Satya and Suresh\Code\Naveen_peak_detect test.py", line 227, in <module>
main()
File "c:\Users\tkpme\OneDrive\Documents\Work\CMC\BP Satya and Suresh\Code\Naveen_peak_detect test.py", line 75, in main
raw_data[new_col] = raw_data['BP'].rolling(FILTER_WINDOW, min_periods=1, center=True, win_type=None,
File "C:\Users\tkpme\miniconda3\lib\site-packages\pandas\core\window\rolling.py", line 1961, in aggregate
return super().aggregate(func, *args, **kwargs)
File "C:\Users\tkpme\miniconda3\lib\site-packages\pandas\core\window\rolling.py", line 523, in aggregate
return self.apply(func, raw=False, args=args, kwargs=kwargs)
File "C:\Users\tkpme\miniconda3\lib\site-packages\pandas\core\window\rolling.py", line 1987, in apply
return super().apply(
File "C:\Users\tkpme\miniconda3\lib\site-packages\pandas\core\window\rolling.py", line 1300, in apply
return self._apply(
File "C:\Users\tkpme\miniconda3\lib\site-packages\pandas\core\window\rolling.py", line 507, in _apply
result = calc(values)
File "C:\Users\tkpme\miniconda3\lib\site-packages\pandas\core\window\rolling.py", line 495, in calc
return func(x, start, end, min_periods)
File "C:\Users\tkpme\miniconda3\lib\site-packages\pandas\core\window\rolling.py", line 1326, in apply_func
return window_func(values, begin, end, min_periods)
File "pandas\_libs\window\aggregations.pyx", line 1375, in pandas._libs.window.aggregations.roll_generic_fixed
File "c:\Users\tkpme\OneDrive\Documents\Work\CMC\BP Satya and Suresh\Code\Naveen_peak_detect test.py", line 222, in hodgesLehmannMean
m = np.add.outer(x, x)
File "C:\Users\tkpme\miniconda3\lib\site-packages\pandas\core\series.py", line 705, in __array_ufunc__
return construct_return(result)
File "C:\Users\tkpme\miniconda3\lib\site-packages\pandas\core\series.py", line 694, in construct_return
raise NotImplementedError
NotImplementedError
which appear to be driven by the line
m = np.add.outer(x, x)
and points to something not being implemented or numpy being missing. But I import numpy right at the beginning as follows:
import numpy as np
import pandas as pd
The function works perfectly well on its own if I feed it a list or a numpy array, so I'm not sure what the problem is. Interestingly, if I use the median instead of the Hodges-Lehmann Mean, it runs like a charm
raw_data[new_col] = raw_data['BP'].rolling(21, min_periods=1, center=True,
win_type=None, axis=0, closed=None).median()
What is the cause of my problem, and how do I fix it?
Sincerely
Thomas Philips
I've tried your code with a small dataframe and it worked well, so maybe there is something on your dataframe that must be cleaned or transformed.
Solved it. It turns out that
m = np.add.outer(x, x)
requires x to be array like. When I tested it using lists, numpy arrays, etc. it worked perfectly, just as it did for you. But the .rolling line generates a slice of a dataframe, which is not array like, and the function fails with a confusing error message. I modified the function to create a numpy array from the input and it now works as it should.
def hodgesLehmannMean(x):
x_array = np.array(x)
m = np.add.outer(x_array, x_array)
ind = np.tril_indices(len(x_array), 0)
return 0.5 * np.median(m[ind])
Thanks for looking at it!
I am trying to train my model and i have csv file and one gz file, which was generated earlier. I am getting this error as mentioned below and not sure what is wrong.
Traceback (most recent call last):
File "Model.py", line 87, in <module>
data = pd.concat([pd.read_csv(log)])
File "/usr/local/lib/python3.6/site-packages/pandas/io/parsers.py", line 678, in parser_f
return _read(filepath_or_buffer, kwds)
File "/usr/local/lib/python3.6/site-packages/pandas/io/parsers.py", line 440, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/usr/local/lib/python3.6/site-packages/pandas/io/parsers.py", line 787, in __init__
self._make_engine(self.engine)
File "/usr/local/lib/python3.6/site-packages/pandas/io/parsers.py", line 1014, in _make_engine
self._engine = CParserWrapper(self.f, **self.options)
File "/usr/local/lib/python3.6/site-packages/pandas/io/parsers.py", line 1708, in __init__
self._reader = parsers.TextReader(src, **kwds)
File "pandas/_libs/parsers.pyx", line 539, in pandas._libs.parsers.TextReader.__cinit__
File "pandas/_libs/parsers.pyx", line 767, in pandas._libs.parsers.TextReader._get_header
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x8b in position 1: invalid start byte
Mycode:
for foo in range(0,1):
# Read dataframe
#data = pd.concat([pd.read_csv(log.replace('0',str(idx),1)) for idx in range(5)])
log = path + 'train_features/log_.csv'
test_log = path + 'test_features/log_features.gz'
data = pd.concat([pd.read_csv(log)])
Try:
data = pd.read_csv(log, encoding = "utf-8")
Although I don't understand why you need the for loop or pd.concat
If you don't know your type of encoding, try: this:
import chardet
with open(log, 'rb') as f:
result = chardet.detect(f.read()) # or readline if the file is large
data = pd.read_csv(log, encoding=result['encoding'])
source
I am trying a simple parallel computation in Dask.
This is my code.
import time
import dask as dask
import dask.distributed as distributed
import dask.dataframe as dd
import dask.delayed as delayed
from dask.distributed import Client,progress
client = Client('localhost:8786')
df = dd.read_csv('file.csv')
ddf = df.groupby(['col1'])[['col2']].sum()
ddf = ddf.compute()
print ddf
It seems fine from the documentation but on running I am getting this :
Traceback (most recent call last):
File "dask_prg1.py", line 17, in <module>
ddf = ddf.compute()
File "/usr/local/lib/python2.7/site-packages/dask/base.py", line 156, in compute
(result,) = compute(self, traverse=False, **kwargs)
File "/usr/local/lib/python2.7/site-packages/dask/base.py", line 402, in compute
results = schedule(dsk, keys, **kwargs)
File "/usr/local/lib/python2.7/site-packages/distributed/client.py", line 2159, in get
direct=direct)
File "/usr/local/lib/python2.7/site-packages/distributed/client.py", line 1562, in gather
asynchronous=asynchronous)
File "/usr/local/lib/python2.7/site-packages/distributed/client.py", line 652, in sync
return sync(self.loop, func, *args, **kwargs)
File "/usr/local/lib/python2.7/site-packages/distributed/utils.py", line 275, in sync
six.reraise(*error[0])
File "/usr/local/lib/python2.7/site-packages/distributed/utils.py", line 260, in f
result[0] = yield make_coro()
File "/usr/local/lib/python2.7/site-packages/tornado/gen.py", line 1099, in run
value = future.result()
File "/usr/local/lib/python2.7/site-packages/tornado/concurrent.py", line 260, in result
raise_exc_info(self._exc_info)
File "/usr/local/lib/python2.7/site-packages/tornado/gen.py", line 1107, in run
yielded = self.gen.throw(*exc_info)
File "/usr/local/lib/python2.7/site-packages/distributed/client.py", line 1439, in _gather
traceback)
File "/usr/local/lib/python2.7/site-packages/dask/bytes/core.py", line 122, in read_block_from_file
with lazy_file as f:
File "/usr/local/lib/python2.7/site-packages/dask/bytes/core.py", line 166, in __enter__
f = SeekableFile(self.fs.open(self.path, mode=mode))
File "/usr/local/lib/python2.7/site-packages/dask/bytes/local.py", line 58, in open
return open(self._normalize_path(path), mode=mode)
IOError: [Errno 2] No such file or directory: 'file.csv'
I am not understanding what is wrong.Kindly help me with this .Thank you in advance .
You may wish to pass the absolute file path to read_csv. The reason is, that you are giving the work of opening and reading the file to a dask worker, and you might not have started that worked with the same working directory as your script/session.