I started learning Python 4 months ago and nearly everything went perfect until I got an excercise, where I have to do 3d plots.
For that I have to import mpl_toolkits.mplit3d, but I get this error:
ValueError Traceback (most recent call last)
<ipython-input-3-c39f93eee133> in <module>()
----> 1 get_ipython().magic('matplotlib notebook')
2 from pylab import *
3 from mpl_toolkits.mplot3d import Axes3D
4
5 Lp = 200
/usr/local/lib/python3.4/dist-packages/IPython/core/interactiveshell.py in magic(self, arg_s)
2334 magic_name, _, magic_arg_s = arg_s.partition(' ')
2335 magic_name = magic_name.lstrip(prefilter.ESC_MAGIC)
-> 2336 return self.run_line_magic(magic_name, magic_arg_s)
2337
2338 #-------------------------------------------------------------------------
/usr/local/lib/python3.4/dist-packages/IPython/core/interactiveshell.py in run_line_magic(self, magic_name, line)
2255 kwargs['local_ns'] = sys._getframe(stack_depth).f_locals
2256 with self.builtin_trap:
-> 2257 result = fn(*args,**kwargs)
2258 return result
2259
/usr/local/lib/python3.4/dist-packages/IPython/core/magics/pylab.py in matplotlib(self, line)
/usr/local/lib/python3.4/dist-packages/IPython/core/magic.py in <lambda>(f, *a, **k)
191 # but it's overkill for just that one bit of state.
192 def magic_deco(arg):
--> 193 call = lambda f, *a, **k: f(*a, **k)
194
195 if callable(arg):
/usr/local/lib/python3.4/dist-packages/IPython/core/magics/pylab.py in matplotlib(self, line)
98 print("Available matplotlib backends: %s" % backends_list)
99 else:
--> 100 gui, backend = self.shell.enable_matplotlib(args.gui)
101 self._show_matplotlib_backend(args.gui, backend)
102
/usr/local/lib/python3.4/dist-packages/IPython/core/interactiveshell.py in enable_matplotlib(self, gui)
3130 gui, backend = pt.find_gui_and_backend(self.pylab_gui_select)
3131
-> 3132 pt.activate_matplotlib(backend)
3133 pt.configure_inline_support(self, backend)
3134
/usr/local/lib/python3.4/dist-packages/IPython/core/pylabtools.py in activate_matplotlib(backend)
270 # the rcParam to update. This needs to be set *before* the module
271 # magic of switch_backend().
--> 272 matplotlib.rcParams['backend'] = backend
273
274 import matplotlib.pyplot
/usr/lib/python3/dist-packages/matplotlib/__init__.py in __setitem__(self, key, val)
808 warnings.warn(self.msg_depr_ignore % (key, alt))
809 return
--> 810 cval = self.validate[key](val)
811 dict.__setitem__(self, key, cval)
812 except KeyError:
/usr/lib/python3/dist-packages/matplotlib/rcsetup.py in validate_backend(s)
144 return s
145 else:
--> 146 return _validate_standard_backends(s)
147
148 validate_qt4 = ValidateInStrings('backend.qt4', ['PyQt4', 'PySide'])
/usr/lib/python3/dist-packages/matplotlib/rcsetup.py in __call__(self, s)
55 return self.valid[s]
56 raise ValueError('Unrecognized %s string "%s": valid strings are %s'
---> 57 % (self.key, s, list(self.valid.values())))
58
59
ValueError: Unrecognized backend string "nbagg": valid strings are ['GTK', 'template', 'svg', 'Qt4Agg', 'GTK3Cairo', 'emf', 'WX', 'GTK3Agg', 'CocoaAgg', 'WebAgg', 'TkAgg', 'agg', 'gdk', 'GTKAgg', 'cairo', 'ps', 'pdf', 'pgf', 'WXAgg', 'MacOSX', 'GTKCairo']
I really dont know what that means.
Obviously, I have this package, so what is the problem?
Related
Previously, this same code was running perfectly. However, I encountered this error recently "TypeError: string indices must be integers".
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import pandas_datareader.data as web
import datetime
start=datetime.datetime(2015,6,1)
end=datetime.datetime(2022,6,30)
sbin=web.DataReader('SBIN.BO','yahoo',start,end)
tatamotors=web.DataReader('TATAMOTORS.BO','yahoo',start,end)
reliance=web.DataReader('RELIANCE.BO','yahoo',start,end)
I have tried this code by considering other stock aslo. But same result obtained. After running the above code, the error occured as follows:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [3], in <cell line: 1>()
----> 1 sbin=web.DataReader('SBIN.BO','yahoo',start,end)
2 tatamotors=web.DataReader('TATAMOTORS.BO','yahoo',start,end)
3 reliance=web.DataReader('RELIANCE.BO','yahoo',start,end)
File C:\ProgramData\Anaconda3\lib\site-packages\pandas\util\_decorators.py:207, in deprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper(*args, **kwargs)
205 else:
206 kwargs[new_arg_name] = new_arg_value
--> 207 return func(*args, **kwargs)
File C:\ProgramData\Anaconda3\lib\site-packages\pandas_datareader\data.py:370, in DataReader(name, data_source, start, end, retry_count, pause, session, api_key)
367 raise NotImplementedError(msg)
369 if data_source == "yahoo":
--> 370 return YahooDailyReader(
371 symbols=name,
372 start=start,
373 end=end,
374 adjust_price=False,
375 chunksize=25,
376 retry_count=retry_count,
377 pause=pause,
378 session=session,
379 ).read()
381 elif data_source == "iex":
382 return IEXDailyReader(
383 symbols=name,
384 start=start,
(...)
390 session=session,
391 ).read()
File C:\ProgramData\Anaconda3\lib\site-packages\pandas_datareader\base.py:253, in _DailyBaseReader.read(self)
251 # If a single symbol, (e.g., 'GOOG')
252 if isinstance(self.symbols, (string_types, int)):
--> 253 df = self._read_one_data(self.url, params=self._get_params(self.symbols))
254 # Or multiple symbols, (e.g., ['GOOG', 'AAPL', 'MSFT'])
255 elif isinstance(self.symbols, DataFrame):
File C:\ProgramData\Anaconda3\lib\site-packages\pandas_datareader\yahoo\daily.py:153, in YahooDailyReader._read_one_data(self, url, params)
151 try:
152 j = json.loads(re.search(ptrn, resp.text, re.DOTALL).group(1))
--> 153 data = j["context"]["dispatcher"]["stores"]["HistoricalPriceStore"]
154 except KeyError:
155 msg = "No data fetched for symbol {} using {}"
TypeError: string indices must be integers.
Please help me in solving this issue.
There is a long-standing gh-issue that discusses your problem. As the corresponding PR hasn't been merged as of today, I would recommend to use the yfinance override instead:
import datetime
import pandas_datareader.data as web
import yfinance as yf
yf.pdr_override()
start=datetime.datetime(2015, 6, 1)
end=datetime.datetime(2022, 6, 30)
sbin = web.DataReader('SBIN.BO', start, end)
tatamotors = web.DataReader('TATAMOTORS.BO', start, end)
reliance = web.DataReader('RELIANCE.BO', start, end)
Output for SBIN:
Open High Low Close Adj Close Volume
Date
2015-06-01 00:00:00+05:30 279.000000 281.950012 277.600006 278.149994 265.453278 1331528
2015-06-02 00:00:00+05:30 278.500000 279.500000 265.500000 266.250000 254.096466 3382530
2015-06-03 00:00:00+05:30 267.149994 268.000000 255.100006 257.549988 245.793579 2706069
I am following this article on medium for this contest.
Everything seems to be fine up to the point where I am retrieving the dataset where I am getting a:
TypeError: '<' not supported between instances of 'L' and 'int'
My code is:
img_pipe = Pipeline([get_filenames, open_ms_tif])
mask_pipe = Pipeline([label_func, partial(open_tif, cls=TensorMask)])
db = DataBlock(blocks=(TransformBlock(img_pipe),
TransformBlock(mask_pipe)),
splitter=RandomSplitter(valid_pct=0.2, seed=42)
)
ds = db.datasets(source=train_files)
dl = db.dataloaders(source=train_files, bs=4)
train_files is a list of Paths. Here's the first 5.
[Path('nasa_rwanda_field_boundary_competition/nasa_rwanda_field_boundary_competition_source_train/nasa_rwanda_field_boundary_competition_source_train_09_2021_08/B01.tif'),
Path('nasa_rwanda_field_boundary_competition/nasa_rwanda_field_boundary_competition_source_train/nasa_rwanda_field_boundary_competition_source_train_39_2021_04/B01.tif'),
Path('nasa_rwanda_field_boundary_competition/nasa_rwanda_field_boundary_competition_source_train/nasa_rwanda_field_boundary_competition_source_train_12_2021_11/B01.tif'),
Path('nasa_rwanda_field_boundary_competition/nasa_rwanda_field_boundary_competition_source_train/nasa_rwanda_field_boundary_competition_source_train_06_2021_10/B01.tif'),
Path('nasa_rwanda_field_boundary_competition/nasa_rwanda_field_boundary_competition_source_train/nasa_rwanda_field_boundary_competition_source_train_08_2021_08/B01.tif')]
the full stack trace is:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [66], in <cell line: 10>()
2 mask_pipe = Pipeline([label_func, partial(open_tif, cls=TensorMask)])
4 db = DataBlock(blocks=(TransformBlock(img_pipe),
5 TransformBlock(mask_pipe)),
6 splitter=RandomSplitter(valid_pct=0.2, seed=42)
7 )
---> 10 ds = db.datasets(source=train_files)
11 dl = db.dataloaders(source=train_files, bs=4)
File /usr/local/lib/python3.9/dist-packages/fastai/data/block.py:147, in DataBlock.datasets(self, source, verbose)
145 splits = (self.splitter or RandomSplitter())(items)
146 pv(f"{len(splits)} datasets of sizes {','.join([str(len(s)) for s in splits])}", verbose)
--> 147 return Datasets(items, tfms=self._combine_type_tfms(), splits=splits, dl_type=self.dl_type, n_inp=self.n_inp, verbose=verbose)
File /usr/local/lib/python3.9/dist-packages/fastai/data/core.py:451, in Datasets.__init__(self, items, tfms, tls, n_inp, dl_type, **kwargs)
442 def __init__(self,
443 items:list=None, # List of items to create `Datasets`
444 tfms:list|Pipeline=None, # List of `Transform`(s) or `Pipeline` to apply
(...)
448 **kwargs
449 ):
450 super().__init__(dl_type=dl_type)
--> 451 self.tls = L(tls if tls else [TfmdLists(items, t, **kwargs) for t in L(ifnone(tfms,[None]))])
452 self.n_inp = ifnone(n_inp, max(1, len(self.tls)-1))
File /usr/local/lib/python3.9/dist-packages/fastai/data/core.py:451, in <listcomp>(.0)
442 def __init__(self,
443 items:list=None, # List of items to create `Datasets`
444 tfms:list|Pipeline=None, # List of `Transform`(s) or `Pipeline` to apply
(...)
448 **kwargs
449 ):
450 super().__init__(dl_type=dl_type)
--> 451 self.tls = L(tls if tls else [TfmdLists(items, t, **kwargs) for t in L(ifnone(tfms,[None]))])
452 self.n_inp = ifnone(n_inp, max(1, len(self.tls)-1))
File /usr/local/lib/python3.9/dist-packages/fastcore/foundation.py:98, in _L_Meta.__call__(cls, x, *args, **kwargs)
96 def __call__(cls, x=None, *args, **kwargs):
97 if not args and not kwargs and x is not None and isinstance(x,cls): return x
---> 98 return super().__call__(x, *args, **kwargs)
File /usr/local/lib/python3.9/dist-packages/fastai/data/core.py:361, in TfmdLists.__init__(self, items, tfms, use_list, do_setup, split_idx, train_setup, splits, types, verbose, dl_type)
359 if isinstance(tfms,TfmdLists): tfms = tfms.tfms
360 if isinstance(tfms,Pipeline): do_setup=False
--> 361 self.tfms = Pipeline(tfms, split_idx=split_idx)
362 store_attr('types,split_idx')
363 if do_setup:
File /usr/local/lib/python3.9/dist-packages/fastcore/transform.py:190, in Pipeline.__init__(self, funcs, split_idx)
188 else:
189 if isinstance(funcs, Transform): funcs = [funcs]
--> 190 self.fs = L(ifnone(funcs,[noop])).map(mk_transform).sorted(key='order')
191 for f in self.fs:
192 name = camel2snake(type(f).__name__)
File /usr/local/lib/python3.9/dist-packages/fastcore/foundation.py:136, in L.sorted(self, key, reverse)
--> 136 def sorted(self, key=None, reverse=False): return self._new(sorted_ex(self, key=key, reverse=reverse))
File /usr/local/lib/python3.9/dist-packages/fastcore/basics.py:619, in sorted_ex(iterable, key, reverse)
617 elif isinstance(key,int): k=itemgetter(key)
618 else: k=key
--> 619 return sorted(iterable, key=k, reverse=reverse)
TypeError: '<' not supported between instances of 'L' and 'int'
I'm not sure what thing is causing the issue. Let me know if you need more of the code.
I expected the data loader to create itself successfully.
I figured it out. It seems the TransformBlocks do not like accepting a Pipeline. I changed the
TransformBlock(img_pipe), TransformBlock(mask_pipe)
to
TransformBlock([get_filenames, open_ms_tif]), TransformBlock([label_func, partial(open_tif, cls=TensorMask)])
which removed the Pipeline wrapper.
I want to translate a dataframe column that's in French and Arabic:
0 Chef de projet
...
6 professeur
7 Chef de projet
8 مدير شركة
I tried:
from googletrans import Translator
translator = Translator()
df['new_professionactuelle']= df['new_professionactuelle'].apply(translator.translate)
But obtained
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-211-90b46ab0043a> in <module>
1 from googletrans import Translator
2 translator = Translator()
----> 3 df['new_professionactuelle']= df['new_professionactuelle'].apply(translator.translate)
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\series.py in apply(self, func, convert_dtype, args, **kwds)
3589 else:
3590 values = self.astype(object).values
-> 3591 mapped = lib.map_infer(values, f, convert=convert_dtype)
3592
3593 if len(mapped) and isinstance(mapped[0], Series):
pandas/_libs/lib.pyx in pandas._libs.lib.map_infer()
C:\ProgramData\Anaconda3\lib\site-packages\googletrans\client.py in translate(self, text, dest, src)
170
171 origin = text
--> 172 data = self._translate(text, dest, src)
173
174 # this code will be updated when the format is changed.
C:\ProgramData\Anaconda3\lib\site-packages\googletrans\client.py in _translate(self, text, dest, src)
73 text = text.decode('utf-8')
74
---> 75 token = self.token_acquirer.do(text)
76 params = utils.build_params(query=text, src=src, dest=dest,
77 token=token)
C:\ProgramData\Anaconda3\lib\site-packages\googletrans\gtoken.py in do(self, text)
199 def do(self, text):
200 self._update()
--> 201 tk = self.acquire(text)
202 return tk
C:\ProgramData\Anaconda3\lib\site-packages\googletrans\gtoken.py in acquire(self, text)
144 a = []
145 # Convert text to ints
--> 146 for i in text:
147 val = ord(i)
148 if val < 0x10000:
TypeError: 'NoneType' object is not iterable
I tried to get the rows that might be NoneType:
df['new_professionactuelle'][type(df['new_professionactuelle']) == "NoneType"]
But got:
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-215-f2597906f267> in <module>
----> 1 df['new_professionactuelle'][type(df['new_professionactuelle']) == "NoneType"]
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\series.py in __getitem__(self, key)
866 key = com.apply_if_callable(key, self)
867 try:
--> 868 result = self.index.get_value(self, key)
869
870 if not is_scalar(result):
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_value(self, series, key)
4373 try:
4374 return self._engine.get_value(s, k,
-> 4375 tz=getattr(series.dtype, 'tz', None))
4376 except KeyError as e1:
4377 if len(self) > 0 and (self.holds_integer() or self.is_boolean()):
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_value()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_value()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index_class_helper.pxi in pandas._libs.index.Int64Engine._check_type()
KeyError: False
You can try this code to convert all text into english text.
import googletrans
from googletrans import Translator
translator = Translator()
def toenglish(x):
print(x)
result = translator.translate(x, dest='en')
return result.text
df['new_professionactuelle'] = list(map(toenglish, df['text']))
I am loading data from netezza into a dataframe and then trying to write to dashdb. I am using ibmdpy to try to load the data into dashdb on bluemix. Ibmdpy requires a pandas dataframe so I convert the spark dataframe to pandas to load into dashdb.
all_disputes_df = sqlContext.read.format('jdbc').options(url='jdbc:netezza://pda1-wall.pok.ibm.com:5480/BACC_PRD_ISCNZ_GAPNZ', user=user, password=password, dbtable='METRICS.AR_EM_D2_02_AGG', driver='org.netezza.Driver').load()
from ibmdbpy import IdaDataBase
idadb = IdaDataBase(dsn='BLUDB', uid='dash107474', pwd='k5TY24AbzFjE')
print("current_schema is %s" % idadb.current_schema)
print("tables %s" % idadb.show_tables())
idadb.as_idadataframe(all_disputes_df.toPandas(), "all_disputes")
I am getting the following traceback.
ValueError Traceback (most recent call last)
<ipython-input-4-63dde713c67b> in <module>()
----> 1 idadb.as_idadataframe(all_disputes_df.toPandas(), "all_disputes")
/home/brente/spark/spark-1.6.1-bin-hadoop2.6/python/pyspark/sql/dataframe.pyc in toPandas(self)
1379 """
1380 import pandas as pd
-> 1381 return pd.DataFrame.from_records(self.collect(), columns=self.columns)
1382
1383 ##########################################################################################
/home/brente/spark/spark-1.6.1-bin-hadoop2.6/python/pyspark/sql/dataframe.pyc in collect(self)
279 with SCCallSiteSync(self._sc) as css:
280 port = self._jdf.collectToPython()
--> 281 return list(_load_from_socket(port, BatchedSerializer(PickleSerializer())))
282
283 #ignore_unicode_prefix
/home/brente/spark/spark-1.6.1-bin-hadoop2.6/python/pyspark/rdd.pyc in _load_from_socket(port, serializer)
140 try:
141 rf = sock.makefile("rb", 65536)
--> 142 for item in serializer.load_stream(rf):
143 yield item
144 finally:
/home/brente/spark/spark-1.6.1-bin-hadoop2.6/python/pyspark/serializers.pyc in load_stream(self, stream)
137 while True:
138 try:
--> 139 yield self._read_with_length(stream)
140 except EOFError:
141 return
/home/brente/spark/spark-1.6.1-bin-hadoop2.6/python/pyspark/serializers.pyc in _read_with_length(self, stream)
162 if len(obj) < length:
163 raise EOFError
--> 164 return self.loads(obj)
165
166 def dumps(self, obj):
/home/brente/spark/spark-1.6.1-bin-hadoop2.6/python/pyspark/serializers.pyc in loads(self, obj, encoding)
420 else:
421 def loads(self, obj, encoding=None):
--> 422 return pickle.loads(obj)
423
424
/home/brente/spark/spark-1.6.1-bin-hadoop2.6/python/pyspark/sql/types.pyc in <lambda>(*a)
1157 # This is used to unpickle a Row from JVM
1158 def _create_row_inbound_converter(dataType):
-> 1159 return lambda *a: dataType.fromInternal(a)
1160
1161
/home/brente/spark/spark-1.6.1-bin-hadoop2.6/python/pyspark/sql/types.pyc in fromInternal(self, obj)
563 return obj
564 if self._needSerializeAnyField:
--> 565 values = [f.fromInternal(v) for f, v in zip(self.fields, obj)]
566 else:
567 values = obj
/home/brente/spark/spark-1.6.1-bin-hadoop2.6/python/pyspark/sql/types.pyc in fromInternal(self, obj)
436
437 def fromInternal(self, obj):
--> 438 return self.dataType.fromInternal(obj)
439
440
/home/brente/spark/spark-1.6.1-bin-hadoop2.6/python/pyspark/sql/types.pyc in fromInternal(self, v)
174 def fromInternal(self, v):
175 if v is not None:
--> 176 return datetime.date.fromordinal(v + self.EPOCH_ORDINAL)
177
178
ValueError: ('ordinal must be >= 1', <function <lambda> at 0x7f97c0be76e0>, (u'788', u'10', u'00620000 ', u'0129101548 ', 1, u'000028628 ', 16520, Decimal('2124.76'), Decimal('2124.76'), 16525, 16525, u'000611099
Any ideas on what the problem is?
Reading your data from Netezza into dataframes fails. Everything beyond that is speculation from my side:
Could there be invalid data stored in Netezza, that throws off the deserialization into dataframes?
Maybe try some other queries, to make sure that there is no connectivity problem, no typo in the database name, things like that.
I want to call R's auto.arima function from Python. I think i have not yet fully understood this interface. Can someone help me here - to send a time series obj to R, call forecast related functions and get back the results?
This is what I have done so far:
from rpy2.robjects import r
from rpy2.robjects import pandas2ri
#create a python time series
count = range(1, 51)
df['count'] = count
df['date'] = pd.date_range('2016-01-01', '2016-02-19')
df.set_index('date', inlace = True)
df.sort_index(inplace = True)
pandas2ri.activate()
r_timeseries = pandas2ri.py2ri(df)
r('fit <- auto.arima(r_timeseries)')
I think I have to import some R packages (like forecast). Not sure how to go about doing that in Python, properly pass the python time series object to R etc.
In [63]: r_ts = pandas2ri.py2ri(df)
In [64]: r_ts
Out[64]:
<DataFrame - Python:0x1126a93f8 / R:0x7ff7bfa51bc8>
[IntVector]
X0: <class 'rpy2.robjects.vectors.IntVector'>
<IntVector - Python:0x1126a96c8 / R:0x7ff7be1af1c0>
[ 1, 2, 3, ..., 48, 49, 50]
And, when I attempt to call forecast
In [83]: x = r('forecast(r_ts)')
/Library/Python/2.7/site-packages/rpy2/robjects/functions.py:106: UserWarning: Error in forecast(r_ts) : object 'r_ts' not found
res = super(Function, self).__call__(*new_args, **new_kwargs)
---------------------------------------------------------------------------
RRuntimeError Traceback (most recent call last)
<ipython-input-83-0765ffc30741> in <module>()
----> 1 x = r('forecast(r_ts)')
/Library/Python/2.7/site-packages/rpy2/robjects/__init__.pyc in __call__(self, string)
319 def __call__(self, string):
320 p = _rparse(text=StrSexpVector((string,)))
--> 321 res = self.eval(p)
322 return conversion.ri2py(res)
323
/Library/Python/2.7/site-packages/rpy2/robjects/functions.pyc in __call__(self, *args, **kwargs)
176 v = kwargs.pop(k)
177 kwargs[r_k] = v
--> 178 return super(SignatureTranslatedFunction, self).__call__(*args, **kwargs)
179
180 pattern_link = re.compile(r'\\link\{(.+?)\}')
/Library/Python/2.7/site-packages/rpy2/robjects/functions.pyc in __call__(self, *args, **kwargs)
104 for k, v in kwargs.items():
105 new_kwargs[k] = conversion.py2ri(v)
--> 106 res = super(Function, self).__call__(*new_args, **new_kwargs)
107 res = conversion.ri2ro(res)
108 return res
RRuntimeError: Error in forecast(r_ts) : object 'r_ts' not found
I tried the following as well:
In [99]: f = r('forecast.auto.arima(r_ts)')
---------------------------------------------------------------------------
RRuntimeError Traceback (most recent call last)
<ipython-input-99-1c4610d2740d> in <module>()
----> 1 f = r('forecast.auto.arima(r_ts)')
/Library/Python/2.7/site-packages/rpy2/robjects/__init__.pyc in __call__(self, string)
319 def __call__(self, string):
320 p = _rparse(text=StrSexpVector((string,)))
--> 321 res = self.eval(p)
322 return conversion.ri2py(res)
323
/Library/Python/2.7/site-packages/rpy2/robjects/functions.pyc in __call__(self, *args, **kwargs)
176 v = kwargs.pop(k)
177 kwargs[r_k] = v
--> 178 return super(SignatureTranslatedFunction, self).__call__(*args, **kwargs)
179
180 pattern_link = re.compile(r'\\link\{(.+?)\}')
/Library/Python/2.7/site-packages/rpy2/robjects/functions.pyc in __call__(self, *args, **kwargs)
104 for k, v in kwargs.items():
105 new_kwargs[k] = conversion.py2ri(v)
--> 106 res = super(Function, self).__call__(*new_args, **new_kwargs)
107 res = conversion.ri2ro(res)
108 return res
RRuntimeError: Error in eval(expr, envir, enclos) :
could not find function "forecast.auto.arima"
you could try what I do
import rpy2.robjects as ro
from rpy2.robjects import pandas2ri
pandas2ri.activate()
ro.r('library(forecast)')
rdf = pandas2ri.py2ri(df)
ro.globalenv['r_timeseries'] = rdf
pred = ro.r('as.data.frame(forecast(auto.arima(r_timeseries),h=5))')
this way, you can handle pred as a data frame like this
Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
51 51 51 51 51 51
52 52 52 52 52 52
53 53 53 53 53 53
54 54 54 54 54 54
55 55 55 55 55 55
In the first attempt you are telling R to use a variable r_ts that it does not now much about (the name r_ts is defined in your Python namespace), and in the second attempt you are added to this a function name R does not know anything about. Both error message are precisely reporting this as a problem.
Your first attempt could be rewritten as:
x = r('forecast')(r_ts)