I am getting the following error:
--------------------------------------------------------------------------- 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 -: 'datetime.time' and
'builtin_function_or_method'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call
last) in
----> 1 NVAX['snap_ts'].dt.time - datetime.strptime('14:30:00', '%H:%M:%S').time
~/anaconda3/lib/python3.8/site-packages/pandas/core/ops/common.py in
new_method(self, other)
63 other = item_from_zerodim(other)
64
---> 65 return method(self, other)
66
67 return new_method
~/anaconda3/lib/python3.8/site-packages/pandas/core/ops/init.py in
wrapper(left, right)
341 lvalues = extract_array(left, extract_numpy=True)
342 rvalues = extract_array(right, extract_numpy=True)
--> 343 result = arithmetic_op(lvalues, rvalues, op)
344
345 return left._construct_result(result, name=res_name)
~/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)
94 else:
95 if not is_scalar(y):
---> 96 raise TypeError(
97 f"Cannot broadcast np.ndarray with operand of type { type(y) }"
98 )
TypeError: Cannot broadcast np.ndarray with operand of type <class
'builtin_function_or_method'>
when performing this:
df['snap_ts'].dt.time - datetime.strptime('14:30:00', '%H:%M:%S')
df['snap_ts'].dt.time is equivalent to this:
0 14:30:10
1 14:30:20
2 14:30:30
3 14:30:40
4 14:30:50
...
157763 19:59:20
157764 19:59:30
157765 19:59:40
157766 19:59:50
157767 20:00:00
Name: snap_ts, Length: 157768, dtype: object
and it's a pandas.core.series.Series
What am I doing wrong?
Are you looking for that?
df['snap_ts'].sub(pd.Timedelta('14:30:00')).dt.time
Related
Hi I am trying to divide expenditure by area. My expenditure variable is Jan 2021 and my area is 'Common area (sq ft)'. Dividing the two gives me "TypeError: unsupported operand type(s) for /: 'str' and 'float' and trying to make Jan 2021 float I get a different error, see below.
Thanks in advance!
df_joint['Common area (sq ft)'].value_counts()
df_joint['Common area (sq ft)'].value_counts()
117765.0 1
13749.0 1
45805.0 1
4858.0 1
2235.0 1
..
7201.0 1
14326.0 1
4486.0 1
17368.0 1
8565.0 1
Name: Common area (sq ft), Length: 90, dtype: int64
1
df_joint['Jan 2021'].value_counts()
0.0 31
0.0 29
1,440,457.88 1
348,173.86 1
318,957.28 1
122,538.05 1
143,639.35 1
18,111.89 1
1,010,853.25 1
45,184.16 1
19,711.75 1
27,091.04 1
114,748.1 1
192,943.1 1
19,901.71 1
9,558.08 1
4,738.39 1
23,193.68 1
26,425.39 1
836,492.73 1
17,332.27 1
11,268.59 1
15,125.6 1
17,475.25 1
15,265.12 1
37,796.19 1
28,830.43 1
160,338.86 1
18,454.16 1
24,130.81 1
30,031.68 1
2,948.92 1
Name: Jan 2021, dtype: int64
df_joint['Jan 2021']= float(df_joint['Jan 2021'])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [299], in <cell line: 1>()
----> 1 df_joint['Jan 2021']= float(df_joint['Jan 2021'])
File ~\Anaconda3\lib\site-packages\pandas\core\series.py:191, in _coerce_method.<locals>.wrapper(self)
189 if len(self) == 1:
190 return converter(self.iloc[0])
--> 191 raise TypeError(f"cannot convert the series to {converter}")
TypeError: cannot convert the series to <class 'float'>
df_joint['Result'] = df_joint['Jan 2021']/df_joint['Common area (sq ft)']
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
File ~\Anaconda3\lib\site-packages\pandas\core\ops\array_ops.py:163, in _na_arithmetic_op(left, right, op, is_cmp)
162 try:
--> 163 result = func(left, right)
164 except TypeError:
File ~\Anaconda3\lib\site-packages\pandas\core\computation\expressions.py:239, in evaluate(op, a, b, use_numexpr)
237 if use_numexpr:
238 # error: "None" not callable
--> 239 return _evaluate(op, op_str, a, b) # type: ignore[misc]
240 return _evaluate_standard(op, op_str, a, b)
File ~\Anaconda3\lib\site-packages\pandas\core\computation\expressions.py:128, in _evaluate_numexpr(op, op_str, a, b)
127 if result is None:
--> 128 result = _evaluate_standard(op, op_str, a, b)
130 return result
File ~\Anaconda3\lib\site-packages\pandas\core\computation\expressions.py:69, in _evaluate_standard(op, op_str, a, b)
68 _store_test_result(False)
---> 69 return op(a, b)
TypeError: unsupported operand type(s) for /: 'str' and 'float'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
Input In [293], in <cell line: 1>()
----> 1 df_joint['Result'] = df_joint['Jan 2021']/df_joint['Common area (sq ft)']
File ~\Anaconda3\lib\site-packages\pandas\core\ops\common.py:70, in _unpack_zerodim_and_defer.<locals>.new_method(self, other)
66 return NotImplemented
68 other = item_from_zerodim(other)
---> 70 return method(self, other)
File ~\Anaconda3\lib\site-packages\pandas\core\arraylike.py:124, in OpsMixin.__truediv__(self, other)
122 #unpack_zerodim_and_defer("__truediv__")
123 def __truediv__(self, other):
--> 124 return self._arith_method(other, operator.truediv)
File ~\Anaconda3\lib\site-packages\pandas\core\series.py:5639, in Series._arith_method(self, other, op)
5637 def _arith_method(self, other, op):
5638 self, other = ops.align_method_SERIES(self, other)
-> 5639 return base.IndexOpsMixin._arith_method(self, other, op)
File ~\Anaconda3\lib\site-packages\pandas\core\base.py:1295, in IndexOpsMixin._arith_method(self, other, op)
1292 rvalues = ensure_wrapped_if_datetimelike(rvalues)
1294 with np.errstate(all="ignore"):
-> 1295 result = ops.arithmetic_op(lvalues, rvalues, op)
1297 return self._construct_result(result, name=res_name)
File ~\Anaconda3\lib\site-packages\pandas\core\ops\array_ops.py:222, in arithmetic_op(left, right, op)
217 else:
218 # TODO we should handle EAs consistently and move this check before the if/else
219 # (https://github.com/pandas-dev/pandas/issues/41165)
220 _bool_arith_check(op, left, right)
--> 222 res_values = _na_arithmetic_op(left, right, op)
224 return res_values
File ~\Anaconda3\lib\site-packages\pandas\core\ops\array_ops.py:170, in _na_arithmetic_op(left, right, op, is_cmp)
164 except TypeError:
165 if not is_cmp and (is_object_dtype(left.dtype) or is_object_dtype(right)):
166 # For object dtype, fallback to a masked operation (only operating
167 # on the non-missing values)
168 # Don't do this for comparisons, as that will handle complex numbers
169 # incorrectly, see GH#32047
--> 170 result = _masked_arith_op(left, right, op)
171 else:
172 raise
File ~\Anaconda3\lib\site-packages\pandas\core\ops\array_ops.py:108, in _masked_arith_op(x, y, op)
106 # See GH#5284, GH#5035, GH#19448 for historical reference
107 if mask.any():
--> 108 result[mask] = op(xrav[mask], yrav[mask])
110 else:
111 if not is_scalar(y):
TypeError: unsupported operand type(s) for /: 'str' and 'float'
I tried changing the type but that doesn't seem to work. I see both variables as numeric and can't get them to divide, note removed the one 0 I had for area.
I am trying to calculate the log returns on two time series. I have imported numpy as np and wrote the following code:
# Calculate the Log Returns
stocks_data_log = np.log(stocks_data / stocks_data.shift(1))
stocks_data_log.head()
Unfortunately I am getting the following (never ending) error message.
# Calculate the Log Returns
stocks_data_log = np.log(stocks_data / stocks_data.shift(1))
stocks_data_log.head()
1
# Calculate the Log Returns
2
3
stocks_data_log = np.log(stocks_data / stocks_data.shift(1))
4
stocks_data_log.head()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~\anaconda3\lib\site-packages\pandas\core\ops\array_ops.py in na_arithmetic_op(left, right, op, str_rep)
148 try:
--> 149 result = expressions.evaluate(op, str_rep, left, right)
150 except TypeError:
~\anaconda3\lib\site-packages\pandas\core\computation\expressions.py in evaluate(op, op_str, a, b, use_numexpr)
207 if use_numexpr:
--> 208 return _evaluate(op, op_str, a, b)
209 return _evaluate_standard(op, op_str, a, b)
~\anaconda3\lib\site-packages\pandas\core\computation\expressions.py in _evaluate_numexpr(op, op_str, a, b)
120 if result is None:
--> 121 result = _evaluate_standard(op, op_str, a, b)
122
~\anaconda3\lib\site-packages\pandas\core\computation\expressions.py in _evaluate_standard(op, op_str, a, b)
69 with np.errstate(all="ignore"):
---> 70 return op(a, b)
71
TypeError: unsupported operand type(s) for /: 'str' and 'float'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-62-5c8fcb5cdb27> in <module>
1 # Calculate the Log Returns
2
----> 3 stocks_data_log = np.log(stocks_data / stocks_data.shift(1))
4 stocks_data_log.head()
~\anaconda3\lib\site-packages\pandas\core\ops\__init__.py in f(self, other, axis, level, fill_value)
701
702 left, right = self.align(other, join="outer", level=level, copy=False)
--> 703 new_data = left._combine_frame(right, pass_op, fill_value)
704 return left._construct_result(new_data)
705
~\anaconda3\lib\site-packages\pandas\core\frame.py in _combine_frame(self, other, func, fill_value, level)
5298 if ops.should_series_dispatch(self, other, func):
5299 # iterate over columns
-> 5300 new_data = ops.dispatch_to_series(self, other, _arith_op)
5301 else:
5302 with np.errstate(all="ignore"):
~\anaconda3\lib\site-packages\pandas\core\ops\__init__.py in dispatch_to_series(left, right, func, str_rep, axis)
414 raise NotImplementedError(right)
415
--> 416 new_data = expressions.evaluate(column_op, str_rep, left, right)
417 return new_data
418
~\anaconda3\lib\site-packages\pandas\core\computation\expressions.py in evaluate(op, op_str, a, b, use_numexpr)
206 use_numexpr = use_numexpr and _bool_arith_check(op_str, a, b)
207 if use_numexpr:
--> 208 return _evaluate(op, op_str, a, b)
209 return _evaluate_standard(op, op_str, a, b)
210
~\anaconda3\lib\site-packages\pandas\core\computation\expressions.py in _evaluate_numexpr(op, op_str, a, b)
119
120 if result is None:
--> 121 result = _evaluate_standard(op, op_str, a, b)
122
123 return result
~\anaconda3\lib\site-packages\pandas\core\computation\expressions.py in _evaluate_standard(op, op_str, a, b)
68 _store_test_result(False)
69 with np.errstate(all="ignore"):
---> 70 return op(a, b)
71
72
~\anaconda3\lib\site-packages\pandas\core\ops\__init__.py in column_op(a, b)
383
384 def column_op(a, b):
--> 385 return {i: func(a.iloc[:, i], b.iloc[:, i]) for i in range(len(a.columns))}
386
387 elif isinstance(right, ABCSeries) and axis == "columns":
~\anaconda3\lib\site-packages\pandas\core\ops\__init__.py in <dictcomp>(.0)
383
384 def column_op(a, b):
--> 385 return {i: func(a.iloc[:, i], b.iloc[:, i]) for i in range(len(a.columns))}
386
387 elif isinstance(right, ABCSeries) and axis == "columns":
~\anaconda3\lib\site-packages\pandas\core\ops\common.py in new_method(self, other)
62 other = item_from_zerodim(other)
63
---> 64 return method(self, other)
65
66 return new_method
~\anaconda3\lib\site-packages\pandas\core\ops\__init__.py in wrapper(left, right)
498 lvalues = extract_array(left, extract_numpy=True)
499 rvalues = extract_array(right, extract_numpy=True)
--> 500 result = arithmetic_op(lvalues, rvalues, op, str_rep)
501
502 return _construct_result(left, result, index=left.index, name=res_name)
~\anaconda3\lib\site-packages\pandas\core\ops\array_ops.py in arithmetic_op(left, right, op, str_rep)
195 else:
196 with np.errstate(all="ignore"):
--> 197 res_values = na_arithmetic_op(lvalues, rvalues, op, str_rep)
198
199 return res_values
~\anaconda3\lib\site-packages\pandas\core\ops\array_ops.py in na_arithmetic_op(left, right, op, str_rep)
149 result = expressions.evaluate(op, str_rep, left, right)
150 except TypeError:
--> 151 result = masked_arith_op(left, right, op)
152
153 return missing.dispatch_fill_zeros(op, left, right, result)
~\anaconda3\lib\site-packages\pandas\core\ops\array_ops.py in masked_arith_op(x, y, op)
92 if mask.any():
93 with np.errstate(all="ignore"):
---> 94 result[mask] = op(xrav[mask], yrav[mask])
95
96 else:
TypeError: unsupported operand type(s) for /: 'str' and 'str'
Does anyone know what I am doing wrong?
Here the whole script
import quandl
import pandas as pd
import numpy as np
import statsmodels.formula.api as sm
from scipy import stats
import matplotlib.pyplot as plt
import matplotlib
# Load ABB Data
ABB = pd.read_csv('C:/Users/Startklar/AppData/Roaming/Microsoft/Windows/Start Menu/Programs/Anaconda3 (64-bit)/ABB.csv',sep=";",names=['Datum','ABBN_Preis'])
Roche = pd.read_csv('C:/Users/Startklar/AppData/Roaming/Microsoft/Windows/Start Menu/Programs/Anaconda3 (64-bit)/Roche.csv',sep=";", names=['ROG_Datum','ROG_Preis'])
Roche.head(10)
# Concatenate the different dataframes
stocks_data = pd.concat([ABB,Roche], axis=1)
stocks_data.head()
stocks_data['Datum'] = pd.to_datetime(stocks_data['Datum'])
stocks_data.head()
# Kick out the Dates
stocks_data = stocks_data.loc[:,['Datum','ABBN_Preis','ROG_Preis']]
stocks_data.head()
# Compute Log Returns
stocks_data_log = np.log(stocks_data / stocks_data.shift(1))
stocks_data_log.head()
According to the logs, it seems you are trying to perform / operation on a string or float column. It is not clear what is stocks_data from the post.
This question already has an answer here:
Pandas filtering based on OR AND
(1 answer)
Closed 2 years ago.
I am trying to code a condition that the date for a trade being entered is the same as the trade date. My code is below. Similar to creating a filter.
Tradedate = ba[ba['TIMESTAMP'] <= '10.15'] & ba[ba['TRADE_ENTRY'] == 'TRADE_DATE']
I get the following error below. Any advice on how to only display the trades if trade date = trade entry date. Both dates are in datetime64[ns] format.
For example:
Trade Date Trade Entry Date
2019-06-17 2019-06-17
2019-06-18 2019-06-19
The only trade I want to be displayed is the first date since the trade date and trade entry dare for the second one is different.
Thanks
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-31-5cdaf27939a8> in <module>
1 df['TRADE_ENTRY'] = pd.to_datetime(df['TRADE_ENTRY']).dt.date
2 df['TRADE_DATE'] = pd.to_datetime(df['TRADE_DATE']).dt.date
----> 3 Tradedate = ba[ba['TIMESTAMP'] <= '10.15'] & ba[ba['TRADE_ENTRY'] == 'TRADE_DATE']
4
5 #Trade date = trade entry date as filter
~\Anaconda3\lib\site-packages\pandas\core\ops\__init__.py in f(self, other, axis, level, fill_value)
701
702 left, right = self.align(other, join="outer", level=level, copy=False)
--> 703 new_data = left._combine_frame(right, pass_op, fill_value)
704 return left._construct_result(new_data)
705
~\Anaconda3\lib\site-packages\pandas\core\frame.py in _combine_frame(self, other, func, fill_value, level)
5298 if ops.should_series_dispatch(self, other, func):
5299 # iterate over columns
-> 5300 new_data = ops.dispatch_to_series(self, other, _arith_op)
5301 else:
5302 with np.errstate(all="ignore"):
~\Anaconda3\lib\site-packages\pandas\core\ops\__init__.py in dispatch_to_series(left, right, func, str_rep, axis)
414 raise NotImplementedError(right)
415
--> 416 new_data = expressions.evaluate(column_op, str_rep, left, right)
417 return new_data
418
~\Anaconda3\lib\site-packages\pandas\core\computation\expressions.py in evaluate(op, op_str, a, b, use_numexpr)
206 use_numexpr = use_numexpr and _bool_arith_check(op_str, a, b)
207 if use_numexpr:
--> 208 return _evaluate(op, op_str, a, b)
209 return _evaluate_standard(op, op_str, a, b)
210
~\Anaconda3\lib\site-packages\pandas\core\computation\expressions.py in _evaluate_numexpr(op, op_str, a, b)
119
120 if result is None:
--> 121 result = _evaluate_standard(op, op_str, a, b)
122
123 return result
~\Anaconda3\lib\site-packages\pandas\core\computation\expressions.py in _evaluate_standard(op, op_str, a, b)
68 _store_test_result(False)
69 with np.errstate(all="ignore"):
---> 70 return op(a, b)
71
72
~\Anaconda3\lib\site-packages\pandas\core\ops\__init__.py in column_op(a, b)
383
384 def column_op(a, b):
--> 385 return {i: func(a.iloc[:, i], b.iloc[:, i]) for i in range(len(a.columns))}
386
387 elif isinstance(right, ABCSeries) and axis == "columns":
~\Anaconda3\lib\site-packages\pandas\core\ops\__init__.py in <dictcomp>(.0)
383
384 def column_op(a, b):
--> 385 return {i: func(a.iloc[:, i], b.iloc[:, i]) for i in range(len(a.columns))}
386
387 elif isinstance(right, ABCSeries) and axis == "columns":
~\Anaconda3\lib\site-packages\pandas\core\ops\common.py in new_method(self, other)
62 other = item_from_zerodim(other)
63
---> 64 return method(self, other)
65
66 return new_method
~\Anaconda3\lib\site-packages\pandas\core\ops\__init__.py in wrapper(self, other)
547 rvalues = extract_array(other, extract_numpy=True)
548
--> 549 res_values = logical_op(lvalues, rvalues, op)
550 return _construct_result(self, res_values, index=self.index, name=res_name)
551
~\Anaconda3\lib\site-packages\pandas\core\ops\array_ops.py in logical_op(left, right, op)
349
350 if should_extension_dispatch(lvalues, rvalues):
--> 351 res_values = dispatch_to_extension_op(op, lvalues, rvalues)
352
353 else:
~\Anaconda3\lib\site-packages\pandas\core\ops\dispatch.py in dispatch_to_extension_op(op, left, right)
123 # The op calls will raise TypeError if the op is not defined
124 # on the ExtensionArray
--> 125 res_values = op(left, right)
126 return res_values
TypeError: unsupported operand type(s) for &: 'DatetimeArray' and 'DatetimeArray'
Use this :
Tradedate = ba[(ba['TIMESTAMP'] <= '10.15') & (ba['TRADE_ENTRY'] == 'TRADE_DATE')]
I will admit that I am constantly stumped when I arrive at the common error ValueError: shapes *value* and *value* not aligned and with some recent code I ran with the Statsmodels library I'm stumped again.
I run smModel = smf.ols(formula='leads ~ sessions', data=trainingRegressionData).fit() with no issues, but at print(smModel.summary()) I'm hit with the following error:
ValueError: shapes (181,61) and (181,61) not aligned: 61 (dim 1) != 181 (dim 0)
Now trainingRegressionData is a <class 'pandas.core.frame.DataFrame'> and the Shape is (181, 2), so I'm not sure how the summary spit out 61 columns, but even with that, the shapes are the same so why would the error say not aligned?
Any help with my issue above and an explanation of debugging shapes error would be appreciated.
Full Error:
ValueError Traceback (most recent call last)
<ipython-input-14-6777963ed99f> in <module>()
----> 1 print(smModel.summary())
~/.pyenv/versions/3.6.5/lib/python3.6/site-packages/statsmodels/regression/linear_model.py in summary(self, yname, xname, title, alpha)
2374 top_left.append(('Covariance Type:', [self.cov_type]))
2375
-> 2376 top_right = [('R-squared:', ["%#8.3f" % self.rsquared]),
2377 ('Adj. R-squared:', ["%#8.3f" % self.rsquared_adj]),
2378 ('F-statistic:', ["%#8.4g" % self.fvalue]),
~/.pyenv/versions/3.6.5/lib/python3.6/site-packages/statsmodels/tools/decorators.py in __get__(self, obj, type)
95 if _cachedval is None:
96 # Call the "fget" function
---> 97 _cachedval = self.fget(obj)
98 # Set the attribute in obj
99 # print("Setting %s in cache to %s" % (name, _cachedval))
~/.pyenv/versions/3.6.5/lib/python3.6/site-packages/statsmodels/regression/linear_model.py in rsquared(self)
1541 def rsquared(self):
1542 if self.k_constant:
-> 1543 return 1 - self.ssr/self.centered_tss
1544 else:
1545 return 1 - self.ssr/self.uncentered_tss
~/.pyenv/versions/3.6.5/lib/python3.6/site-packages/statsmodels/tools/decorators.py in __get__(self, obj, type)
95 if _cachedval is None:
96 # Call the "fget" function
---> 97 _cachedval = self.fget(obj)
98 # Set the attribute in obj
99 # print("Setting %s in cache to %s" % (name, _cachedval))
~/.pyenv/versions/3.6.5/lib/python3.6/site-packages/statsmodels/regression/linear_model.py in ssr(self)
1513 def ssr(self):
1514 wresid = self.wresid
-> 1515 return np.dot(wresid, wresid)
1516
1517 #cache_readonly
ValueError: shapes (181,61) and (181,61) not aligned: 61 (dim 1) != 181 (dim 0)
Head and Tail of trainingRegressionData:
[181 rows x 2 columns]>
sessions leads
366 197 33
367 408 71
368 404 59
369 412 60
...
544 357 58
545 285 48
546 275 38
[181 rows x 2 columns]
I'm getting a floating point error on a simple time series in pandas. I'm trying to do shift operations... but this also happens with the window functions like rolling_mean.
EDIT: For some more info... I tried to actually build this from source yesterday prior to the error. I'm not sure if the error would've occurred prior the build attempt, as I'd never messed around w/ these functions.
EDIT2: I thought I'd fixed this, but when I run this inside python it works, but when it's in ipython I get the error.
EDIT3: Numpy 1.7.0, iPython 0.13, pandas 0.7.3
In [35]: ts = Series(np.arange(12), index=DateRange('1/1/2000', periods=12, freq='T'))
In [36]: ts.shift(0)
Out[36]:
2000-01-03 0
2000-01-04 1
2000-01-05 2
2000-01-06 3
2000-01-07 4
2000-01-10 5
2000-01-11 6
2000-01-12 7
2000-01-13 8
2000-01-14 9
2000-01-17 10
2000-01-18 11
In [37]: ts.shift(1)
Out[37]: ---------------------------------------------------------------------------
FloatingPointError Traceback (most recent call last)
/Users/trenthauck/Repository/work/SQS/analysis/campaign/tv2/data/<ipython-input-37-2b7cec97d440> in <module>()
----> 1 ts.shift(1)
/Library/Python/2.7/site-packages/ipython-0.13.dev-py2.7.egg/IPython/core/displayhook.pyc in __call__(self, result)
236 self.start_displayhook()
237 self.write_output_prompt()
--> 238 format_dict = self.compute_format_data(result)
239 self.write_format_data(format_dict)
240 self.update_user_ns(result)
/Library/Python/2.7/site-packages/ipython-0.13.dev-py2.7.egg/IPython/core/displayhook.pyc in compute_format_data(self, result)
148 MIME type representation of the object.
149 """
--> 150 return self.shell.display_formatter.format(result)
151
152 def write_format_data(self, format_dict):
/Library/Python/2.7/site-packages/ipython-0.13.dev-py2.7.egg/IPython/core/formatters.pyc in format(self, obj, include, exclude)
124 continue
125 try:
--> 126 data = formatter(obj)
127 except:
128 # FIXME: log the exception
/Library/Python/2.7/site-packages/ipython-0.13.dev-py2.7.egg/IPython/core/formatters.pyc in __call__(self, obj)
445 type_pprinters=self.type_printers,
446 deferred_pprinters=self.deferred_printers)
--> 447 printer.pretty(obj)
448 printer.flush()
449 return stream.getvalue()
/Library/Python/2.7/site-packages/ipython-0.13.dev-py2.7.egg/IPython/lib/pretty.pyc in pretty(self, obj)
353 if callable(obj_class._repr_pretty_):
354 return obj_class._repr_pretty_(obj, self, cycle)
--> 355 return _default_pprint(obj, self, cycle)
356 finally:
357 self.end_group()
/Library/Python/2.7/site-packages/ipython-0.13.dev-py2.7.egg/IPython/lib/pretty.pyc in _default_pprint(obj, p, cycle)
473 if getattr(klass, '__repr__', None) not in _baseclass_reprs:
474 # A user-provided repr.
--> 475 p.text(repr(obj))
476 return
477 p.begin_group(1, '<')
/Library/Python/2.7/site-packages/pandas/core/series.pyc in __repr__(self)
696 result = self._get_repr(print_header=True,
697 length=len(self) > 50,
--> 698 name=True)
699 else:
700 result = '%s' % ndarray.__repr__(self)
/Library/Python/2.7/site-packages/pandas/core/series.pyc in _get_repr(self, name, print_header, length, na_rep, float_format)
756 length=length, na_rep=na_rep,
757 float_format=float_format)
--> 758 return formatter.to_string()
759
760 def __str__(self):
/Library/Python/2.7/site-packages/pandas/core/format.pyc in to_string(self)
99
100 fmt_index, have_header = self._get_formatted_index()
--> 101 fmt_values = self._get_formatted_values()
102
103 maxlen = max(len(x) for x in fmt_index)
/Library/Python/2.7/site-packages/pandas/core/format.pyc in _get_formatted_values(self)
90 return format_array(self.series.values, None,
91 float_format=self.float_format,
---> 92 na_rep=self.na_rep)
93
94 def to_string(self):
/Library/Python/2.7/site-packages/pandas/core/format.pyc in format_array(values, formatter, float_format, na_rep, digits, space, justify)
431 justify=justify)
432
--> 433 return fmt_obj.get_result()
434
435
/Library/Python/2.7/site-packages/pandas/core/format.pyc in get_result(self)
528
529 # this is pretty arbitrary for now
--> 530 has_large_values = (np.abs(self.values) > 1e8).any()
531
532 if too_long and has_large_values:
FloatingPointError: invalid value encountered in absolute
In [38]: ts.shift(-1)
Out[38]: ---------------------------------------------------------------------------
FloatingPointError Traceback (most recent call last)
/Users/myusername/Repository/work/SQS/analysis/campaign/tv2/data/<ipython-input-38-314ec815a7c5> in <module>()
----> 1 ts.shift(-1)
/Library/Python/2.7/site-packages/ipython-0.13.dev-py2.7.egg/IPython/core/displayhook.pyc in __call__(self, result)
236 self.start_displayhook()
237 self.write_output_prompt()
--> 238 format_dict = self.compute_format_data(result)
239 self.write_format_data(format_dict)
240 self.update_user_ns(result)
/Library/Python/2.7/site-packages/ipython-0.13.dev-py2.7.egg/IPython/core/displayhook.pyc in compute_format_data(self, result)
148 MIME type representation of the object.
149 """
--> 150 return self.shell.display_formatter.format(result)
151
152 def write_format_data(self, format_dict):
/Library/Python/2.7/site-packages/ipython-0.13.dev-py2.7.egg/IPython/core/formatters.pyc in format(self, obj, include, exclude)
124 continue
125 try:
--> 126 data = formatter(obj)
127 except:
128 # FIXME: log the exception
/Library/Python/2.7/site-packages/ipython-0.13.dev-py2.7.egg/IPython/core/formatters.pyc in __call__(self, obj)
445 type_pprinters=self.type_printers,
446 deferred_pprinters=self.deferred_printers)
--> 447 printer.pretty(obj)
448 printer.flush()
449 return stream.getvalue()
/Library/Python/2.7/site-packages/ipython-0.13.dev-py2.7.egg/IPython/lib/pretty.pyc in pretty(self, obj)
353 if callable(obj_class._repr_pretty_):
354 return obj_class._repr_pretty_(obj, self, cycle)
--> 355 return _default_pprint(obj, self, cycle)
356 finally:
357 self.end_group()
/Library/Python/2.7/site-packages/ipython-0.13.dev-py2.7.egg/IPython/lib/pretty.pyc in _default_pprint(obj, p, cycle)
473 if getattr(klass, '__repr__', None) not in _baseclass_reprs:
474 # A user-provided repr.
--> 475 p.text(repr(obj))
476 return
477 p.begin_group(1, '<')
/Library/Python/2.7/site-packages/pandas/core/series.pyc in __repr__(self)
696 result = self._get_repr(print_header=True,
697 length=len(self) > 50,
--> 698 name=True)
699 else:
700 result = '%s' % ndarray.__repr__(self)
/Library/Python/2.7/site-packages/pandas/core/series.pyc in _get_repr(self, name, print_header, length, na_rep, float_format)
756 length=length, na_rep=na_rep,
757 float_format=float_format)
--> 758 return formatter.to_string()
759
760 def __str__(self):
/Library/Python/2.7/site-packages/pandas/core/format.pyc in to_string(self)
99
100 fmt_index, have_header = self._get_formatted_index()
--> 101 fmt_values = self._get_formatted_values()
102
103 maxlen = max(len(x) for x in fmt_index)
/Library/Python/2.7/site-packages/pandas/core/format.pyc in _get_formatted_values(self)
90 return format_array(self.series.values, None,
91 float_format=self.float_format,
---> 92 na_rep=self.na_rep)
93
94 def to_string(self):
/Library/Python/2.7/site-packages/pandas/core/format.pyc in format_array(values, formatter, float_format, na_rep, digits, space, justify)
431 justify=justify)
432
--> 433 return fmt_obj.get_result()
434
435
/Library/Python/2.7/site-packages/pandas/core/format.pyc in get_result(self)
528
529 # this is pretty arbitrary for now
--> 530 has_large_values = (np.abs(self.values) > 1e8).any()
531
532 if too_long and has_large_values:
FloatingPointError: invalid value encountered in absolute
I would add this as a comment, but I don't have the privilege to do that yet :)
It works for me in python and iPython 0.12; iPython 0.13 is still in development (see http://ipython.org/ ), and, since the errors you're getting seem to involve formatting in the iPython 0.13 egg, I suspect that might be the cause. Try with iPython 0.12 instead-- if it works, file a bug report with iPython and then probably stick with 0.12 until 0.13 is (more) stable.