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Can't divide two columns that both show as int64 types

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.

Seaborn the hue attribute causing error in plots

The hue feature is not working when I am using pairplot.
Here is my data frame:
Here is the code that doesn't work:
sns.pairplot(activities, hue="Day")
If I remove the hue option it works. Also if I change the hue to a numerical column (such as Distance) it works, but it is not working with the Day column for some reason. Here's the error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/tmp/ipykernel_121/1783531066.py in <module>
----> 1 sns.pairplot(activities, hue="Day")
/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py in inner_f(*args, **kwargs)
44 )
45 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46 return f(**kwargs)
47 return inner_f
48
/opt/conda/lib/python3.7/site-packages/seaborn/axisgrid.py in pairplot(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, height, aspect, corner, dropna, plot_kws, diag_kws, grid_kws, size)
2020 elif diag_kind == "kde":
2021 diag_kws.setdefault("fill", True)
-> 2022 grid.map_diag(kdeplot, **diag_kws)
2023
2024 # Maybe plot on the off-diagonals
/opt/conda/lib/python3.7/site-packages/seaborn/axisgrid.py in map_diag(self, func, **kwargs)
1400 plot_kwargs.setdefault("hue_order", self._hue_order)
1401 plot_kwargs.setdefault("palette", self._orig_palette)
-> 1402 func(x=vector, **plot_kwargs)
1403 self._clean_axis(ax)
1404
/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py in inner_f(*args, **kwargs)
44 )
45 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46 return f(**kwargs)
47 return inner_f
48
/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py in kdeplot(x, y, shade, vertical, kernel, bw, gridsize, cut, clip, legend, cumulative, shade_lowest, cbar, cbar_ax, cbar_kws, ax, weights, hue, palette, hue_order, hue_norm, multiple, common_norm, common_grid, levels, thresh, bw_method, bw_adjust, log_scale, color, fill, data, data2, **kwargs)
1733 legend=legend,
1734 estimate_kws=estimate_kws,
-> 1735 **plot_kws,
1736 )
1737
/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py in plot_univariate_density(self, multiple, common_norm, common_grid, fill, legend, estimate_kws, **plot_kws)
914 common_grid,
915 estimate_kws,
--> 916 log_scale,
917 )
918
/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py in _compute_univariate_density(self, data_variable, common_norm, common_grid, estimate_kws, log_scale)
314
315 # Estimate the density of observations at this level
--> 316 density, support = estimator(observations, weights=weights)
317
318 if log_scale:
/opt/conda/lib/python3.7/site-packages/seaborn/_statistics.py in __call__(self, x1, x2, weights)
185 """Fit and evaluate on univariate or bivariate data."""
186 if x2 is None:
--> 187 return self._eval_univariate(x1, weights)
188 else:
189 return self._eval_bivariate(x1, x2, weights)
/opt/conda/lib/python3.7/site-packages/seaborn/_statistics.py in _eval_univariate(self, x, weights)
144 support = self.support
145 if support is None:
--> 146 support = self.define_support(x, cache=False)
147
148 kde = self._fit(x, weights)
/opt/conda/lib/python3.7/site-packages/seaborn/_statistics.py in define_support(self, x1, x2, weights, cache)
117 """Create the evaluation grid for a given data set."""
118 if x2 is None:
--> 119 support = self._define_support_univariate(x1, weights)
120 else:
121 support = self._define_support_bivariate(x1, x2, weights)
/opt/conda/lib/python3.7/site-packages/seaborn/_statistics.py in _define_support_univariate(self, x, weights)
89 def _define_support_univariate(self, x, weights):
90 """Create a 1D grid of evaluation points."""
---> 91 kde = self._fit(x, weights)
92 bw = np.sqrt(kde.covariance.squeeze())
93 grid = self._define_support_grid(
/opt/conda/lib/python3.7/site-packages/seaborn/_statistics.py in _fit(self, fit_data, weights)
135 fit_kws["weights"] = weights
136
--> 137 kde = stats.gaussian_kde(fit_data, **fit_kws)
138 kde.set_bandwidth(kde.factor * self.bw_adjust)
139
/opt/conda/lib/python3.7/site-packages/scipy/stats/kde.py in __init__(self, dataset, bw_method, weights)
204 self._neff = 1/sum(self._weights**2)
205
--> 206 self.set_bandwidth(bw_method=bw_method)
207
208 def evaluate(self, points):
/opt/conda/lib/python3.7/site-packages/scipy/stats/kde.py in set_bandwidth(self, bw_method)
552 raise ValueError(msg)
553
--> 554 self._compute_covariance()
555
556 def _compute_covariance(self):
/opt/conda/lib/python3.7/site-packages/scipy/stats/kde.py in _compute_covariance(self)
564 bias=False,
565 aweights=self.weights))
--> 566 self._data_inv_cov = linalg.inv(self._data_covariance)
567
568 self.covariance = self._data_covariance * self.factor**2
/opt/conda/lib/python3.7/site-packages/scipy/linalg/basic.py in inv(a, overwrite_a, check_finite)
937
938 """
--> 939 a1 = _asarray_validated(a, check_finite=check_finite)
940 if len(a1.shape) != 2 or a1.shape[0] != a1.shape[1]:
941 raise ValueError('expected square matrix')
/opt/conda/lib/python3.7/site-packages/scipy/_lib/_util.py in _asarray_validated(a, check_finite, sparse_ok, objects_ok, mask_ok, as_inexact)
294 if not objects_ok:
295 if a.dtype is np.dtype('O'):
--> 296 raise ValueError('object arrays are not supported')
297 if as_inexact:
298 if not np.issubdtype(a.dtype, np.inexact):
ValueError: object arrays are not supported
Any ideas why hue isn't working?

You can see the error:
ValueError: object arrays are not supported
Means the variable needs to be numerical.

Python Pandas time difference between string and series

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

Date Manipulation in Python [duplicate]

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')]

Pandas FloatingPoint Error

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.