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I have a year wise (1980-2020) precipitation data set in netCDF format. I am importing them in xarray to have 40 years of merged precipitation values:
import netCDF4
import numpy
import xarray as xr
import pandas as pd
prcp=xr.open_mfdataset('/home/hrsa/Sayantan/HAR_V2/prcp/HARv2_d10km_d_2d_prcp_*.nc',combine = 'nested', concat_dim="time")
prcp
which renders:
xarray.Dataset
Dimensions:
time: 14976west_east: 381south_north: 252
Coordinates:
time
(time)
datetime64[ns]
1980-01-01 ... 2020-12-31
west_east
(west_east)
float32
-1.675e+06 -1.665e+06 ... 2.125e+06
south_north
(south_north)
float32
-7.45e+05 -7.35e+05 ... 1.765e+06
lon
(south_north, west_east)
float32
dask.array<chunksize=(252, 381), meta=np.ndarray>
lat
(south_north, west_east)
float32
dask.array<chunksize=(252, 381), meta=np.ndarray>
Data variables:
prcp
(time, south_north, west_east)
float32
dask.array<chunksize=(366, 252, 381), meta=np.ndarray>
Attributes: (33)
This a large dataset, hence I am required to subset it according to an SRTM image whose extents (in EPSG:4326) is defined as
# Extents of the SRTM DEM covering Panchi_B and the SASE AWS/Base Camp
min_lon = 77.0
min_lat = 32.0
max_lon = 78.0
max_lat = 33.0
In order to subset according to above coordinates I have tried the following:
prcp = prcp.sel(lat = slice(min_lat,max_lat), lon = slice(min_lon,max_lon))
the Error output:
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
File ~/.pyenv/versions/3.9.7/envs/v3.9.7/lib/python3.9/site-packages/xarray/core/indexing.py:73, in group_indexers_by_index(data_obj, indexers, method, tolerance)
72 try:
---> 73 index = xindexes[key]
74 coord = data_obj.coords[key]
KeyError: 'lat'
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
Input In [25], in <cell line: 1>()
----> 1 prcp = prcp.sel(lat = slice(min_lat,max_lat), lon = slice(min_lon,max_lon))
File ~/.pyenv/versions/3.9.7/envs/v3.9.7/lib/python3.9/site-packages/xarray/core/dataset.py:2501, in Dataset.sel(self, indexers, method, tolerance, drop, **indexers_kwargs)
2440 """Returns a new dataset with each array indexed by tick labels
2441 along the specified dimension(s).
2442
(...)
2498 DataArray.sel
2499 """
2500 indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "sel")
-> 2501 pos_indexers, new_indexes = remap_label_indexers(
2502 self, indexers=indexers, method=method, tolerance=tolerance
2503 )
2504 # TODO: benbovy - flexible indexes: also use variables returned by Index.query
2505 # (temporary dirty fix).
2506 new_indexes = {k: v[0] for k, v in new_indexes.items()}
File ~/.pyenv/versions/3.9.7/envs/v3.9.7/lib/python3.9/site-packages/xarray/core/coordinates.py:421, in remap_label_indexers(obj, indexers, method, tolerance, **indexers_kwargs)
414 indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "remap_label_indexers")
416 v_indexers = {
417 k: v.variable.data if isinstance(v, DataArray) else v
418 for k, v in indexers.items()
419 }
--> 421 pos_indexers, new_indexes = indexing.remap_label_indexers(
422 obj, v_indexers, method=method, tolerance=tolerance
423 )
424 # attach indexer's coordinate to pos_indexers
425 for k, v in indexers.items():
File ~/.pyenv/versions/3.9.7/envs/v3.9.7/lib/python3.9/site-packages/xarray/core/indexing.py:110, in remap_label_indexers(data_obj, indexers, method, tolerance)
107 pos_indexers = {}
108 new_indexes = {}
--> 110 indexes, grouped_indexers = group_indexers_by_index(
111 data_obj, indexers, method, tolerance
112 )
114 forward_pos_indexers = grouped_indexers.pop(None, None)
115 if forward_pos_indexers is not None:
File ~/.pyenv/versions/3.9.7/envs/v3.9.7/lib/python3.9/site-packages/xarray/core/indexing.py:84, in group_indexers_by_index(data_obj, indexers, method, tolerance)
82 except KeyError:
83 if key in data_obj.coords:
---> 84 raise KeyError(f"no index found for coordinate {key}")
85 elif key not in data_obj.dims:
86 raise KeyError(f"{key} is not a valid dimension or coordinate")
KeyError: 'no index found for coordinate lat'
How can I resolve this issue? Any help will be appreciated, Thank you.
############# Edit (for #Robert Wilson) ##################
In order to find out the ranges, I did the following:
lon = prcp.lon.to_dataframe()
lon
lat = prcp.lat.to_dataframe()
lat
I'm uploading my dataset, and I'm copying my dataset, but an error is appearing.
import numpy as np
import pandas as pd
import mathplotlib.pyplot as plt
house_data=pd.read_csv("/home/houseprice.csv")
#we evaluate the price of a house for those cases where the information is missing, for each variable
def analyse_na_value(df, var):
df - df.copy()
# we indicate as a variable as 1 where the observation is missing
# we indicate as 0 where the observation has a real value
df[var] = np.where(df[var].isnull(), 1 , 0)
#print(df[var].isnull())
# we calculate the mean saleprice where the information is missing or present
df.groupby(var)['SalePrice'].median().plot.bar()
plt.title(var)
plt.show()
for var in vars_with_na:
analyse_na_value(house_data, var)
error,when I comment this code line, I don't get an error
df - df.copy()
TypeError Traceback (most recent call last)
~/anaconda3/lib/python3.8/site-packages/pandas/core/ops/array_ops.py in na_arithmetic_op(left, right, op, is_cmp)
142 try:
--> 143 result = expressions.evaluate(op, left, right)
144 except TypeError:
~/anaconda3/lib/python3.8/site-packages/pandas/core/computation/expressions.py in evaluate(op, a, b, use_numexpr)
232 if use_numexpr:
--> 233 return _evaluate(op, op_str, a, b) # type: ignore
234 return _evaluate_standard(op, op_str, a, b)
~/anaconda3/lib/python3.8/site-packages/pandas/core/computation/expressions.py in _evaluate_numexpr(op, op_str, a, b)
118 if result is None:
--> 119 result = _evaluate_standard(op, op_str, a, b)
120
~/anaconda3/lib/python3.8/site-packages/pandas/core/computation/expressions.py in _evaluate_standard(op, op_str, a, b)
67 with np.errstate(all="ignore"):
---> 68 return op(a, b)
69
TypeError: unsupported operand type(s) for -: 'str' and 'str'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-31-25d58bc46c86> in <module>
15
16 for var in vars_with_na:
---> 17 analyse_na_value(house_data, var)
<ipython-input-31-25d58bc46c86> in analyse_na_value(df, var)
1 #we evaluate the price of a house for those cases where the information is missing, for each variable
2 def analyse_na_value(df, var):
----> 3 df - df.copy()
4
5 # we indicate as a variable as 1 where the observation is missing
~/anaconda3/lib/python3.8/site-packages/pandas/core/ops/__init__.py in f(self, other, axis, level, fill_value)
649 if isinstance(other, ABCDataFrame):
650 # Another DataFrame
--> 651 new_data = self._combine_frame(other, na_op, fill_value)
652
653 elif isinstance(other, ABCSeries):
~/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py in _combine_frame(self, other, func, fill_value)
5864 return func(left, right)
5865
-> 5866 new_data = ops.dispatch_to_series(self, other, _arith_op)
5867 return new_data
5868
~/anaconda3/lib/python3.8/site-packages/pandas/core/ops/__init__.py in dispatch_to_series(left, right, func, axis)
273 # _frame_arith_method_with_reindex
274
--> 275 bm = left._mgr.operate_blockwise(right._mgr, array_op)
276 return type(left)(bm)
277
~/anaconda3/lib/python3.8/site-packages/pandas/core/internals/managers.py in operate_blockwise(self, other, array_op)
362 Apply array_op blockwise with another (aligned) BlockManager.
363 """
--> 364 return operate_blockwise(self, other, array_op)
365
366 def apply(self: T, f, align_keys=None, **kwargs) -> T:
~/anaconda3/lib/python3.8/site-packages/pandas/core/internals/ops.py in operate_blockwise(left, right, array_op)
36 lvals, rvals = _get_same_shape_values(blk, rblk, left_ea, right_ea)
37
---> 38 res_values = array_op(lvals, rvals)
39 if left_ea and not right_ea and hasattr(res_values, "reshape"):
40 res_values = res_values.reshape(1, -1)
~/anaconda3/lib/python3.8/site-packages/pandas/core/ops/array_ops.py in arithmetic_op(left, right, op)
188 else:
189 with np.errstate(all="ignore"):
--> 190 res_values = na_arithmetic_op(lvalues, rvalues, op)
191
192 return res_values
~/anaconda3/lib/python3.8/site-packages/pandas/core/ops/array_ops.py in na_arithmetic_op(left, right, op, is_cmp)
148 # will handle complex numbers incorrectly, see GH#32047
149 raise
--> 150 result = masked_arith_op(left, right, op)
151
152 if is_cmp and (is_scalar(result) or result is NotImplemented):
~/anaconda3/lib/python3.8/site-packages/pandas/core/ops/array_ops.py in masked_arith_op(x, y, op)
90 if mask.any():
91 with np.errstate(all="ignore"):
---> 92 result[mask] = op(xrav[mask], yrav[mask])
93
94 else:
TypeError: unsupported operand type(s) for -: 'str' and 'str'
1
As far to what I know the copy() function works with python3,
but in pandas,
and python3 does it work I don't know.
How can I get rid of this error without commenting that code line?
I think you are supposed to do df = df.copy(). I would recommend changing the variable though. Here is an official Pandas documentation on this function. What you are doing is subtracting the data frame from itself...
I want to One-hot-encoding several columns and used several solutions include simple one-hot-encoding, ColumnTransformer, make_column_transformer, Pipeline, and get_dummies but anytime I have got different errors.
x = dataset.iloc[:, :11].values
y = dataset.iloc[:, 11].values
""" data encoding """
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, OrdinalEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
# oe = OrdinalEncoder()
# x = oe.fit_transform(x)
non_cat = ["Make", "Model", "Vehicle", "Transmission", "Fuel"]
onehot_cat = ColumnTransformer([
("categorical", OrdinalEncoder(), non_cat),
("onehot_categorical", OneHotEncoder(), non_cat)],
remainder= "passthrough")
x = onehot_cat.fit_transform(x)
error:
[['ACURA' 'ILX' 'COMPACT' ... 6.7 8.5 33]
['ACURA' 'ILX' 'COMPACT' ... 7.7 9.6 29]
['ACURA' 'ILX HYBRID' 'COMPACT' ... 5.8 5.9 48]
...
['VOLVO' 'XC60 T6 AWD' 'SUV - SMALL' ... 8.6 10.3 27]
['VOLVO' 'XC90 T5 AWD' 'SUV - STANDARD' ... 8.3 9.9 29]
['VOLVO' 'XC90 T6 AWD' 'SUV - STANDARD' ... 8.7 10.7 26]]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~\Anaconda3\lib\site-packages\sklearn\utils\__init__.py in _get_column_indices(X, key)
424 try:
--> 425 all_columns = X.columns
426 except AttributeError:
AttributeError: 'numpy.ndarray' object has no attribute 'columns'
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-4-4008371c305f> in <module>
24 ("onehot_categorical", OneHotEncoder(), non_cat)],
25 remainder= "passthrough")
---> 26 x = onehot_cat.fit_transform(x)
27
28 print('OneHotEncode = ', x.shape)
~\Anaconda3\lib\site-packages\sklearn\compose\_column_transformer.py in fit_transform(self, X, y)
527 self._validate_transformers()
528 self._validate_column_callables(X)
--> 529 self._validate_remainder(X)
530
531 result = self._fit_transform(X, y, _fit_transform_one)
~\Anaconda3\lib\site-packages\sklearn\compose\_column_transformer.py in _validate_remainder(self, X)
325 cols = []
326 for columns in self._columns:
--> 327 cols.extend(_get_column_indices(X, columns))
328
329 remaining_idx = sorted(set(range(self._n_features)) - set(cols))
~\Anaconda3\lib\site-packages\sklearn\utils\__init__.py in _get_column_indices(X, key)
425 all_columns = X.columns
426 except AttributeError:
--> 427 raise ValueError("Specifying the columns using strings is only "
428 "supported for pandas DataFrames")
429 if isinstance(key, str):
ValueError: Specifying the columns using strings is only supported for pandas DataFrames
I got a similar error trying to make prediction using a model. It was expecting a dataframe but I was sending a numpy object instead. So I changed it from:
prediction = monitor_model.predict(s_df.to_numpy())
to:
prediction = monitor_model.predict(s_df)
I am running a function on every row of a Pandas dataset using .loc.
dm.loc[:50, 'address_components'] = dm.loc[:50, ['description', 'city']].apply(lambda row: get_address(row[0], row[1]), axis=1)
The function itself is below, although the error doesn't apply to the function I'm passing
def get_address(address, city):
geolocator = GoogleV3(api_key=api_key, domain='maps.googleapis.com', scheme=None, client_id=None,
secret_key=None,
user_agent=None)
geocode = RateLimiter(geolocator.geocode, min_delay_seconds=0.0, swallow_exceptions=True, return_value_on_exception=None)
# cleanup address
address = address.strip(' ').strip(',')
full_addr = '{}, {}'.format(address, city)
#print(full_addr)
data = geocode(full_addr, timeout=None, exactly_one=True)
if data:
#print(data.raw)
return data.raw
return None
and the response
TypeError Traceback (most recent call last)
<ipython-input-77-46aa2724c2f6> in <module>
1
2
----> 3 dm.loc[:50, 'address_components'] = dm.loc[:50, ['description', 'city']].apply(lambda row: get_address(row[0], row[1]), axis=1)
~/virt_env/virt2/lib/python3.6/site-packages/pandas/core/frame.py in apply(self, func, axis, broadcast, raw, reduce, result_type, args, **kwds)
6909 result_type=result_type,
6910 args=args,
-> 6911 kwds=kwds,
6912 )
6913 return op.get_result()
TypeError: frame_apply() got an unexpected keyword argument 'broadcast'
That error is new as the function did run before without issues, and started throwing an error message only after I updated Pandas to 1.0.4. However, it persists even after I downgraded pandas back to 0.25.1. Broadcast parameter also doesn’t make sense to me.
Turns out it really is a version issue. Downgraded back to pandas 0.25, restarted the kernel and it's flying again!
I can't be sure, but you seem to be assigning to the first 50 rows each time apply iterates, whilst iterating each row
dm.loc[:50, 'address_components'] =
should be
dm["address_components"] = dm.loc[:50, ['description', 'city']].apply(lambda row: get_address(row[0], row[1]), axis=1)
scratching my head on this issue. i dont know how to identify the positional indexers. am i even passing them?
attempting this for my first kaggle comp, can pass in the csv to a dataframe and make the needed edits. trying to create the ImageDataBunch so training a cnn can begin. This error pops up no matter which method is tried. Any advice would be appreciated.
data = ImageDataBunch.from_df(path, df, ds_tfms=tfms, size=24)
data.classes
Backtrace
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-25-5588812820e8> in <module>
----> 1 data = ImageDataBunch.from_df(path, df, ds_tfms=tfms, size=24)
2 data.classes
/opt/conda/lib/python3.7/site-packages/fastai/vision/data.py in from_df(cls, path, df, folder, label_delim, valid_pct, seed, fn_col, label_col, suffix, **kwargs)
117 src = (ImageList.from_df(df, path=path, folder=folder, suffix=suffix, cols=fn_col)
118 .split_by_rand_pct(valid_pct, seed)
--> 119 .label_from_df(label_delim=label_delim, cols=label_col))
120 return cls.create_from_ll(src, **kwargs)
121
/opt/conda/lib/python3.7/site-packages/fastai/data_block.py in _inner(*args, **kwargs)
477 assert isinstance(fv, Callable)
478 def _inner(*args, **kwargs):
--> 479 self.train = ft(*args, from_item_lists=True, **kwargs)
480 assert isinstance(self.train, LabelList)
481 kwargs['label_cls'] = self.train.y.__class__
/opt/conda/lib/python3.7/site-packages/fastai/data_block.py in label_from_df(self, cols, label_cls, **kwargs)
283 def label_from_df(self, cols:IntsOrStrs=1, label_cls:Callable=None, **kwargs):
284 "Label `self.items` from the values in `cols` in `self.inner_df`."
--> 285 labels = self.inner_df.iloc[:,df_names_to_idx(cols, self.inner_df)]
286 assert labels.isna().sum().sum() == 0, f"You have NaN values in column(s) {cols} of your dataframe, please fix it."
287 if is_listy(cols) and len(cols) > 1 and (label_cls is None or label_cls == MultiCategoryList):
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py in __getitem__(self, key)
1760 except (KeyError, IndexError, AttributeError):
1761 pass
-> 1762 return self._getitem_tuple(key)
1763 else:
1764 # we by definition only have the 0th axis
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py in _getitem_tuple(self, tup)
2065 def _getitem_tuple(self, tup: Tuple):
2066
-> 2067 self._has_valid_tuple(tup)
2068 try:
2069 return self._getitem_lowerdim(tup)
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py in _has_valid_tuple(self, key)
701 raise IndexingError("Too many indexers")
702 try:
--> 703 self._validate_key(k, i)
704 except ValueError:
705 raise ValueError(
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py in _validate_key(self, key, axis)
2007 # check that the key does not exceed the maximum size of the index
2008 if len(arr) and (arr.max() >= len_axis or arr.min() < -len_axis):
-> 2009 raise IndexError("positional indexers are out-of-bounds")
2010 else:
2011 raise ValueError(f"Can only index by location with a [{self._valid_types}]")
IndexError: positional indexers are out-of-bounds
I faced this error while creating a DataBunch when my dataframe/CSV did not have a class label explicitly defined.
I created a dummy column which stored 1's for all my rows in the dataframe and it seemed to work. Also please be sure to store your independent variable in the second column and the label(dummy variable in this case) in the first column.
I believe this error happens if there's just one column in the Pandas DataFrame.
Thanks.
Code:
df = pd.DataFrame(lines, columns=["dummy_value", "text"])
df.to_csv("./train.csv")
data_lm = TextLMDataBunch.from_csv(path, "train.csv", min_freq=1)
Note: This is my first attempt at answering a StackOverflow question. Hope it helped!
This error also appears when your dataset is not correctly split between test and validation.
In the case of dataframes, it assumes there is a column is_valid that indicates which rows are in validation set.
If all rows have True, then the training set is empty, so fastai cannot index into it to prepare the first example, thus raising this error.
Example:
data = pd.DataFrame({
'fname': [f'{x}.png' for x in range(10)],
'label': np.arange(10)%2,
'is_valid': True
})
blk = DataBlock((ImageBlock, CategoryBlock),
splitter=ColSplitter(),
get_x=ColReader('fname'),
get_y=ColReader('label'),
item_tfms=Resize(224, method=ResizeMethod.Squish),
)
blk.summary(data)
Results in the error.
Solution
The solution is to check that your data can be split correctly into train and valid sets. In the above example, it suffices to have one row that is not in validation set:
data.loc[0, 'is_valid'] = False
How to figure it out?
Work in a jupyter notebook. After the error, type %debug in a cell, and enter the post mortem debugging. Go to the frame of the setup function ( fastai/data/core.py(273) setup() ) by going up 5 frames.
This takes you to this line that is throwing the error.
You can then print(self.splits) and observe that the first one is empty.