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https://api.github.com/repos/pandas-dev/pandas/issues/7801 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7801/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7801/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7801/events | https://github.com/pandas-dev/pandas/pull/7801 | 38,229,614 | MDExOlB1bGxSZXF1ZXN0MTg2MTkxMjI= | 7,801 | BUG: timeseries subplots may display unnecessary minor ticklabels | {
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} | 7 | 2014-07-19T08:57:16Z | 2014-07-23T11:08:36Z | 2014-07-22T11:38:59Z | MEMBER | null | Related to #7457. The fix was incomplete because it only hides major ticklabels, not minor ticklabels. This causes incorrect result in time-series plot.
(And another problem is `rot` default is not applied to minor ticks. I'll check this separatelly)
```
df = pd.DataFrame(np.random.randn(10, 4), index=pd.date_range(start='2014-07-01', freq='M', periods=10))
df.plot(subplots=True)
```
### Current result:
Minor ticklabels of top 3 axes are not hidden.
### Result after fix:
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} | 1 | 2014-07-19T09:10:42Z | 2014-07-19T15:24:03Z | 2014-07-19T13:39:59Z | MEMBER | null | Closes #7746, Closes #7793.
Sorry, caused by #7533. The `ValueError` is being raised when `PeriodIndex` or `DatetimeIndex` with tz 's unique values are less than `MultiIndex` length.
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} | 3 | 2014-07-19T10:50:15Z | 2014-07-19T15:21:58Z | 2014-07-19T14:11:35Z | MEMBER | null | Closes #7772, Closes #7760.
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| null | 3 | 2014-07-19T23:23:40Z | 2014-07-20T01:45:03Z | 2014-07-20T01:45:03Z | NONE | null | Pandas appears to still be lacking a clean method for dealing with filling a pre-allocated DataFrame (if I'm missing it please direct me). Consider the case when a known amount of rows will be generated over time, but during that period the data needs to be used as a DataFrame in some computation or access. By constructing the DataFrame using an np.empty array of the total size and reporting only those rows of the array that are filled, operations on the DataFrame would correctly occur and be flexible as the buffer was filled. An outline of how this might be done follows -- this doesn't work yet but should give you the idea. I'm still not familiar with the full pandas API, so I thought it would be best to report this as an enhancement and see what people think.
``` python
class BufferDataFrame(pd.DataFrame):
""" Constructs a pandas DataFrame that grows to a specified size to act as a
buffer that can be efficiently filled
"""
def __init__(self, count, columns, dtype, **kwargs):
data = np.empty((count,len(columns)), dtype=dtype)
super(BufferDataFrame, self).__init__(data=data, columns=columns, **kwargs)
self._pointer = 0
def __len__(self):
return self._pointer
def push(self, values):
""" Pushes an array of values onto the DataFrame and increments the pointer
"""
self.loc[self._pointer] = values
self._pointer += 1
```
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} | 2 | 2014-07-20T06:16:29Z | 2016-11-25T10:05:39Z | 2016-11-25T10:05:30Z | NONE | null | The page http://pandas.pydata.org/pandas-docs/dev/generated/pandas.Series.html says:
> index : array-like or Index (1d)
> Values must be unique and hashable, same length as data.
A little bit earlier the same page says:
> Labels need not be unique but must be any hashable type.
Other pages also mention non-unique index support (e.g. http://pandas.pydata.org/pandas-docs/dev/dsintro.html) It looks like `index` description should read:
> index : array-like or Index (1d)
> Values must be hashable, same length as data. Non-unique index values are allowed.
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} | 18 | 2014-07-20T14:35:02Z | 2014-08-12T02:00:00Z | 2014-08-11T15:30:26Z | MEMBER | null | Because `hist` and `boxplot` are separated from normal `plot`, there are some inconsistencies with these functions. Looks better to include them to `MPLPlot` framework.
Maybe `scatter` and `hist` can be deprecated in 0.15 if `MPLPlot` can offer better `GroupBy` plot (plan to do in separate PR).
### Example
This allows to use `kind='hist` in `DataFrame.plot` and `Series.plot`. (No changes for `DataFrame.hist`)
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame(np.random.randn(1000, 5))
df.plot(kind='hist', subplots=True)
```
```
df.plot(kind='hist', stacked=True)
```
### Remaining Items
- [x] Add a release note in API section detailing this change/enhancement
- [x] Modify doc
- [x] Add tests (both histogram and kde)
- [x] Add support for `rot` and `fontsize` (depending on `orientation` kw) (rely on #7844)
- [x] Add tests for xticklabels and yticklabels
- [x] Add tests for colors
- [x] Handling nan
- [x] Check `stacked=True` can be supported (`DataFrame.hist` doesn't support it though..).
- [x] Add tests for stacking
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} | 1 | 2014-07-20T14:52:20Z | 2014-07-23T11:08:16Z | 2014-07-21T11:43:55Z | MEMBER | null | Closes #6572. It seems to be fixed by #7465 as a side effect.
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} | 1 | 2014-07-20T23:10:28Z | 2015-09-03T14:04:18Z | 2015-09-03T14:04:18Z | MEMBER | null | `date_range` can accept freq with mult != 1 and output `DatetimeIndex`, otherwise `period_range` raises `ValueError`.
```
pd.date_range('2014-01-01 09:00', '2014-01-01 20:00', freq='4H')
# <class 'pandas.tseries.index.DatetimeIndex'>
# [2014-01-01 09:00:00, ..., 2014-01-01 21:00:00]
# Length: 4, Freq: 4H, Timezone: None
pd.date_range('2014-01-01 20:00', '2014-01-01 09:00', freq='-1H')
# <class 'pandas.tseries.index.DatetimeIndex'>
# [2014-01-01 20:00:00, ..., 2014-01-01 09:00:00]
# Length: 12, Freq: -1H, Timezone: None
pd.period_range('2014-01-01 09:00', '2014-01-01 20:00', freq='4H')
# ValueError: Only mult == 1 supported
```
Is it reasonable to accept `period_range` and `PeriodIndex.__new__` to this kind of freq? What I want to do is creating skipped or reversed ordered `PeriodIndex` easily.
(Considering internal representation of `PeriodIndex`, it looks better to result have normal freq, not to add numberized-freq like `4H`).
#### Expected
```
pd.period_range('2014-01-01 09:00', '2014-01-01 20:00', freq='4H')
# <class 'pandas.tseries.period.PeriodIndex'>
# [2014-01-01 09:00:00, ..., 2014-01-01 21:00:00]
# Length: 4, Freq: H
pd.period_range('2014-01-01 20:00', '2014-01-01 09:00', freq='-1H')
# <class 'pandas.tseries.period.PeriodIndex'>
# [2014-01-01 20:00:00, ..., 2014-01-01 09:00:00]
# Length: 12, Freq: H
```
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} | 20 | 2014-07-20T23:13:15Z | 2014-08-05T14:49:29Z | 2014-08-05T14:49:29Z | MEMBER | null | There looks no easy way to remove tz from tz-aware `DatetimeIndex` holding the timestamp represented. What required is inverse operation of `tz_localize`.
```
idx = pd.date_range('2014-01-01 09:00', '2014-01-01 20:00', freq='H', tz='Asia/Tokyo')
idx
# <class 'pandas.tseries.index.DatetimeIndex'>
# [2014-01-01 09:00:00+09:00, ..., 2014-01-01 20:00:00+09:00]
# Length: 12, Freq: H, Timezone: Asia/Tokyo
# What I want to do is:
idx.tz_reset()
# <class 'pandas.tseries.index.DatetimeIndex'>
# [2014-01-01 09:00:00, ..., 2014-01-01 20:00:00]
# Length: 12, Freq: H, Timezone: None
# This raises TypeError
idx.tz_localize(None)
# TypeError: Already tz-aware, use tz_convert to convert.
# This change the timestamp to UTC
idx.tz_convert(None)
# <class 'pandas.tseries.index.DatetimeIndex'>
# [2014-01-01 00:00:00, ..., 2014-01-01 11:00:00]
# Length: 12, Freq: H, Timezone: None
```
In my case, there are some globally distributed data sources which holds local timezones. And want to merge them and analyze based on local subjective times (business hours, etc).
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} | 10 | 2014-07-21T12:28:28Z | 2018-07-06T21:41:21Z | 2018-07-06T21:41:13Z | CONTRIBUTOR | null | xref #7788
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} | 0 | 2014-07-21T15:22:57Z | 2014-09-19T15:40:58Z | 2014-09-19T15:40:58Z | CONTRIBUTOR | null | xref #7651
```
In [19]: s = pd.Series([1,2,3], index=list('abc'))
In [20]: s.at['a']
Out[20]: 1
In [23]: s.at[0]
Out[23]: 1
```
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} | 9 | 2014-07-21T23:41:26Z | 2014-09-10T06:26:28Z | 2014-09-09T07:55:57Z | CONTRIBUTOR | null | Demonstration code, running on windows (so case insensitive) with pandas 0.14.0
``` python
engine=create_engine('mysql://{username}:{password}@{host}/{database}?charset=utf8'.format(**db))
metadata=MetaData(bind=engine)
table=Table('foo', metadata, Column('id', Integer, primary_key=True), Column('language', Unicode(255)), Column('animal', Unicode(255)))
data=DataFrame.from_dict({1: {'language': 'python', 'animal': 'snake'}, 2: {'language': 'perl', 'animal': 'camel'}}, orient='index')
data.to_sql('Foo', engine, if_exists='replace')
data.to_sql('Foo', engine, if_exists='replace')
```
Fails with:
```
OperationalError Traceback (most recent call last)
<ipython-input-48-9d53c43e59f6> in <module>()
1 data.to_sql('Foo', engine, if_exists='replace')
----> 2 data.to_sql('Foo', engine, if_exists='replace')
C:\Anaconda\lib\site-packages\pandas\core\generic.pyc in to_sql(self, name, con, flavor, if_exists, index, index_label)
948 sql.to_sql(
949 self, name, con, flavor=flavor, if_exists=if_exists, index=index,
--> 950 index_label=index_label)
951
952 def to_pickle(self, path):
C:\Anaconda\lib\site-packages\pandas\io\sql.pyc in to_sql(frame, name, con, flavor, if_exists, index, index_label)
438
439 pandas_sql.to_sql(frame, name, if_exists=if_exists, index=index,
--> 440 index_label=index_label)
441
442
C:\Anaconda\lib\site-packages\pandas\io\sql.pyc in to_sql(self, frame, name, if_exists, index, index_label)
812 table = PandasSQLTable(
813 name, self, frame=frame, index=index, if_exists=if_exists,
--> 814 index_label=index_label)
815 table.insert()
816
C:\Anaconda\lib\site-packages\pandas\io\sql.pyc in __init__(self, name, pandas_sql_engine, frame, index, if_exists, prefix, index_label)
530 else:
531 self.table = self._create_table_statement()
--> 532 self.create()
533 else:
534 # no data provided, read-only mode
C:\Anaconda\lib\site-packages\pandas\io\sql.pyc in create(self)
546
547 def create(self):
--> 548 self.table.create()
549
550 def insert_statement(self):
C:\Anaconda\lib\site-packages\sqlalchemy\sql\schema.pyc in create(self, bind, checkfirst)
646 bind._run_visitor(ddl.SchemaGenerator,
647 self,
--> 648 checkfirst=checkfirst)
649
650 def drop(self, bind=None, checkfirst=False):
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _run_visitor(self, visitorcallable, element, connection, **kwargs)
1545 connection=None, **kwargs):
1546 with self._optional_conn_ctx_manager(connection) as conn:
-> 1547 conn._run_visitor(visitorcallable, element, **kwargs)
1548
1549 class _trans_ctx(object):
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _run_visitor(self, visitorcallable, element, **kwargs)
1192 def _run_visitor(self, visitorcallable, element, **kwargs):
1193 visitorcallable(self.dialect, self,
-> 1194 **kwargs).traverse_single(element)
1195
1196
C:\Anaconda\lib\site-packages\sqlalchemy\sql\visitors.pyc in traverse_single(self, obj, **kw)
117 meth = getattr(v, "visit_%s" % obj.__visit_name__, None)
118 if meth:
--> 119 return meth(obj, **kw)
120
121 def iterate(self, obj):
C:\Anaconda\lib\site-packages\sqlalchemy\sql\ddl.pyc in visit_table(self, table, create_ok)
726 self.traverse_single(column.default)
727
--> 728 self.connection.execute(CreateTable(table))
729
730 if hasattr(table, 'indexes'):
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in execute(self, object, *multiparams, **params)
718 type(object))
719 else:
--> 720 return meth(self, multiparams, params)
721
722 def _execute_function(self, func, multiparams, params):
C:\Anaconda\lib\site-packages\sqlalchemy\sql\ddl.pyc in _execute_on_connection(self, connection, multiparams, params)
65
66 def _execute_on_connection(self, connection, multiparams, params):
---> 67 return connection._execute_ddl(self, multiparams, params)
68
69 def execute(self, bind=None, target=None):
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _execute_ddl(self, ddl, multiparams, params)
772 compiled,
773 None,
--> 774 compiled
775 )
776 if self._has_events or self.engine._has_events:
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _execute_context(self, dialect, constructor, statement, parameters, *args)
945 parameters,
946 cursor,
--> 947 context)
948
949 if self._has_events or self.engine._has_events:
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _handle_dbapi_exception(self, e, statement, parameters, cursor, context)
1106 self.dialect.dbapi.Error,
1107 connection_invalidated=self._is_disconnect),
-> 1108 exc_info
1109 )
1110
C:\Anaconda\lib\site-packages\sqlalchemy\util\compat.pyc in raise_from_cause(exception, exc_info)
183 exc_info = sys.exc_info()
184 exc_type, exc_value, exc_tb = exc_info
--> 185 reraise(type(exception), exception, tb=exc_tb)
186
187 if py3k:
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _execute_context(self, dialect, constructor, statement, parameters, *args)
938 statement,
939 parameters,
--> 940 context)
941 except Exception as e:
942 self._handle_dbapi_exception(
C:\Anaconda\lib\site-packages\sqlalchemy\engine\default.pyc in do_execute(self, cursor, statement, parameters, context)
433
434 def do_execute(self, cursor, statement, parameters, context=None):
--> 435 cursor.execute(statement, parameters)
436
437 def do_execute_no_params(self, cursor, statement, context=None):
C:\Anaconda\lib\site-packages\MySQLdb\cursors.pyc in execute(self, query, args)
203 del tb
204 self.messages.append((exc, value))
--> 205 self.errorhandler(self, exc, value)
206 self._executed = query
207 if not self._defer_warnings: self._warning_check()
C:\Anaconda\lib\site-packages\MySQLdb\connections.pyc in defaulterrorhandler(***failed resolving arguments***)
34 del cursor
35 del connection
---> 36 raise errorclass, errorvalue
37
38 re_numeric_part = re.compile(r"^(\d+)")
OperationalError: (OperationalError) (1050, "Table 'foo' already exists") '\nCREATE TABLE `Foo` (\n\t`index` INTEGER, \n\tanimal TEXT, \n\tlanguage TEXT\n)\n\n' ()
```
I haven't poked around the code to see if this is a SQLAlchemy issue or a pandas issue, but I'd hope it shouldn't be too difficult to fix
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} | 14 | 2014-07-22T02:59:39Z | 2014-07-23T13:40:44Z | 2014-07-23T13:40:44Z | NONE | null | If I use SataReader to read a Stata dataset saved in Stata 13 using the `save` command, I can get the data but not the variable labels.
If, however, I use the `saveold` command in Stata 13, I am able to get the variable labels in Python3 using `StataReader.variable_labels()`.
Can anyone suggest how to accommodate Stata 13? Thanks,
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| null | 18 | 2014-07-22T09:35:00Z | 2020-10-29T05:49:15Z | 2014-07-22T11:31:56Z | NONE | null | I did:
``` python
63 In : df = P.DataFrame(index=P.period_range('2000-1-1 10:20', '2005-1-1 12:00'))
64 In : df.index[0]
64 Out: Period('2000-01-01', 'D')
65 In : df.index[0].hour
65 Out: 0
```
it should have been 10, right?
why does period_range() loses time information?
I was recommended to use periods and period_range() because of pandas Timestamp limitation (nanoseconds time units)
now I'm stuck, because I need datetimes outside Timestamp range
ps.
would it be too drastic to change time units from nanoseconds to microseconds in Timestamp? so that we would be happier? :P
see also #7307
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} | 10 | 2014-07-22T17:24:49Z | 2014-08-20T15:32:51Z | 2014-07-23T13:40:44Z | CONTRIBUTOR | null | Stata's implementation does not match the online dta file format description.
The solution used here is to directly compute the offset rather than reading
it from the dta file. If Stata fixes their implementation, the original code
can be restored.
closes #7816
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| null | 4 | 2014-07-22T19:13:10Z | 2014-07-22T19:52:57Z | 2014-07-22T19:52:57Z | CONTRIBUTOR | null | The below fails with "ValueError: Length of values does not match length of index". However, without the timezone it works fine. It's because of pandas/core/frame.py _maybe_casted_values index.tz is not None clause. Something similar was done in #7533 but the added examples don't test this case. cc @sinhrks
```
import pandas as pd
import numpy as np
dts = pd.date_range('1/1/2012', '1/31/2012', freq='D', tz='US/Eastern')
columns = [1, 2, 3]
data = np.random.randn(31, 3)
frame = pd.DataFrame(data, columns=columns, index=dts)
frame = frame.stack()
frame.reset_index()
```
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} | 21 | 2014-07-23T15:10:08Z | 2019-12-31T15:04:45Z | 2019-12-31T15:04:45Z | CONTRIBUTOR | null | **When this is merged, make the below change in Categorical as well!**
Having `np.nan` in an `Index` is not returning the position of NaN but `-1`:
```
In[5]: from pandas.core.index import _ensure_index
In[6]: import numpy as np
In[7]: idx = _ensure_index([1,2,3,4, np.nan])
In[8]: np.nan in idx
Out[8]: True
In[9]: idx.get_indexer([np.nan])
Out[9]: array([-1])
```
I'm not sure if that's a bug or intended. What happens is that this (new) test for Categoricals fails:
```
# if nan in levels, the proper code should be set!
cat = pd.Categorical([1,2,3, np.nan], levels=[1,2,3])
cat.levels = [1,2,3, np.nan]
cat[1] = np.nan
exp = np.array([0,3,2,-1])
self.assert_numpy_array_equal(cat.codes, exp)
Traceback (most recent call last):
File "C:\data\external\pandas\pandas\tests\test_categorical.py", line 555, in test_set_item_nan
self.assert_numpy_array_equal(cat.codes, exp)
File "C:\data\external\pandas\pandas\util\testing.py", line 99, in assert_numpy_array_equal
raise AssertionError('{0} is not equal to {1}.'.format(np_array, assert_equal))
AssertionError: [ 0 -1 2 -1] is not equal to [ 0 3 2 -1].
```
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} | 2 | 2014-07-23T15:15:57Z | 2014-07-28T20:25:49Z | 2014-07-28T20:25:49Z | CONTRIBUTOR | null | http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0150-cat
references to the `api` section for some reason are not showing up correctly (while other, similar references work ok).
@jorisvandenbossche
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} | 1 | 2014-07-23T15:21:19Z | 2014-07-23T17:01:23Z | 2014-07-23T17:01:23Z | CONTRIBUTOR | null | I think this could be done w/o breaking anything else.
xref: #3950
```
In [16]: ts = pd.date_range('1/1/2011', periods=5, freq='10s', tz = 'US/Eastern')
In [17]: ts
Out[17]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-01-01 00:00:00-05:00, ..., 2011-01-01 00:00:40-05:00]
Length: 5, Freq: 10S, Timezone: US/Eastern
In [18]: pd.DataFrame( {'a' : ts } )
```
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} | 0 | 2014-07-23T15:45:55Z | 2014-07-23T17:01:23Z | 2014-07-23T17:01:23Z | CONTRIBUTOR | null | closes #7822
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} | 4 | 2014-07-23T16:02:41Z | 2018-07-06T20:21:04Z | 2017-07-11T09:53:51Z | CONTRIBUTOR | null | This seems to not conform to the `DataFrame.groupby/Series.groupby`
should be straightforward to fix this I think, just simply removing it in favor of `core/generic/NDFrame.groupby`, but would need to fix tests, maybe. The `mapper` argument is the `by` argument.
http://stackoverflow.com/questions/24914748/why-does-panel-groupby-not-accept-level-parameter-in-pandas-0-14/24915441#24915441
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} | 19 | 2014-07-23T18:59:44Z | 2018-06-18T22:34:30Z | 2018-06-18T22:34:30Z | CONTRIBUTOR | null | ``` python
In [1]: import pandas as pd
In [2]: t = pd.Timestamp('2013-11-3', tz='America/Chicago')
In [3]: t
Out[3]: Timestamp('2013-11-03 00:00:00-0500', tz='America/Chicago')
In [4]: t.replace(hour=3)
Out[4]: Timestamp('2013-11-03 03:00:00-0500', tz='America/Chicago')
# This time doesn't exist.
In [5]: pd.Timestamp('2013-11-3 03:00:00', tz='America/Chicago')
Out[5]: Timestamp('2013-11-03 03:00:00-0600', tz='America/Chicago')
```
In trying to replicate the definition of `Timestamp.replace` in [tslib.pyx](https://github.com/pydata/pandas/blob/320f9d4c44d60cc4ae5e08ab4a26dc49d3fd71c1/pandas/tslib.pyx#L421), I encountered this further issue:
``` python
In [6]: from datetime import datetime
In [7]: datetime.replace(t, hour=3)
Segmentation fault: 11
```
Trying another tack, I found this:
``` python
In [1]: from datetime import datetime
In [2]: import pytz
In [3]: t = pytz.timezone('America/Chicago').localize(datetime(2013, 11, 3))
In [4]: t.strftime('%Y-%m-%d %H:%M:%S %Z%z')
Out[4]: '2013-11-03 00:00:00 CDT-0500'
In [5]: t.replace(hour=3).strftime('%Y-%m-%d %H:%M:%S %Z%z')
Out[5]: '2013-11-03 03:00:00 CDT-0500'
# This time still never happened
```
In summary: `datetime` is horribly broken, and the current workaround doesn't actually get all the way around. I know breaking backward compatibility is painful, and throwing the stdlib out the window is even more so, but this situation reminds me of nothing so forcefully as Microsoft Excel's 1900/1904 issue.
Version information:
``` python
In [1]: from pandas.util.print_versions import show_versions
In [2]: show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Darwin
OS-release: 13.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.1
nose: 1.3.0
Cython: None
numpy: 1.8.1
scipy: 0.12.1
statsmodels: None
IPython: 1.1.0
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.4
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.3.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 15 | 2014-07-23T21:34:03Z | 2014-08-22T08:50:47Z | 2014-08-22T08:50:47Z | CONTRIBUTOR | null | Not sure if this is a pandas bug or an upstream one, but here's an example of the bug (pandas-0.14.1, mariadb 10, sqlalchemy-0.9.4)
``` python
engine=create_engine('mysql://{username}:{password}@{host}/{database}?charset=utf8'.format(**db))
pandas.io.sql.read_sql('SELECT onlinetransactions.id FROM onlinetransactions LIMIT 1', engine) #Does what you'd expect
pandas.io.sql.read_sql('SELECT onlinetransactions.id as firstid FROM onlinetransactions LIMIT 1', engine) #Fails
```
The error you get back is:
``` python
C:\Anaconda\lib\site-packages\pandas\io\sql.pyc in read_sql(sql, con, index_col, coerce_float, params, parse_dates, columns)
421 coerce_float=coerce_float, parse_dates=parse_dates)
422
--> 423 if pandas_sql.has_table(sql):
424 pandas_sql.meta.reflect(only=[sql])
425 return pandas_sql.read_table(
C:\Anaconda\lib\site-packages\pandas\io\sql.pyc in has_table(self, name)
847
848 def has_table(self, name):
--> 849 return self.engine.has_table(name)
850
851 def get_table(self, table_name):
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in has_table(self, table_name, schema)
1757
1758 """
-> 1759 return self.run_callable(self.dialect.has_table, table_name, schema)
1760
1761 def raw_connection(self):
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in run_callable(self, callable_, *args, **kwargs)
1661 """
1662 with self.contextual_connect() as conn:
-> 1663 return conn.run_callable(callable_, *args, **kwargs)
1664
1665 def execute(self, statement, *multiparams, **params):
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in run_callable(self, callable_, *args, **kwargs)
1188
1189 """
-> 1190 return callable_(self, *args, **kwargs)
1191
1192 def _run_visitor(self, visitorcallable, element, **kwargs):
C:\Anaconda\lib\site-packages\sqlalchemy\dialects\mysql\base.pyc in has_table(self, connection, table_name, schema)
2274 try:
2275 try:
-> 2276 rs = connection.execute(st)
2277 have = rs.fetchone() is not None
2278 rs.close()
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in execute(self, object, *multiparams, **params)
710 """
711 if isinstance(object, util.string_types[0]):
--> 712 return self._execute_text(object, multiparams, params)
713 try:
714 meth = object._execute_on_connection
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _execute_text(self, statement, multiparams, params)
859 statement,
860 parameters,
--> 861 statement, parameters
862 )
863 if self._has_events or self.engine._has_events:
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _execute_context(self, dialect, constructor, statement, parameters, *args)
945 parameters,
946 cursor,
--> 947 context)
948
949 if self._has_events or self.engine._has_events:
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _handle_dbapi_exception(self, e, statement, parameters, cursor, context)
1106 self.dialect.dbapi.Error,
1107 connection_invalidated=self._is_disconnect),
-> 1108 exc_info
1109 )
1110
C:\Anaconda\lib\site-packages\sqlalchemy\util\compat.pyc in raise_from_cause(exception, exc_info)
183 exc_info = sys.exc_info()
184 exc_type, exc_value, exc_tb = exc_info
--> 185 reraise(type(exception), exception, tb=exc_tb)
186
187 if py3k:
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _execute_context(self, dialect, constructor, statement, parameters, *args)
938 statement,
939 parameters,
--> 940 context)
941 except Exception as e:
942 self._handle_dbapi_exception(
C:\Anaconda\lib\site-packages\sqlalchemy\engine\default.pyc in do_execute(self, cursor, statement, parameters, context)
433
434 def do_execute(self, cursor, statement, parameters, context=None):
--> 435 cursor.execute(statement, parameters)
436
437 def do_execute_no_params(self, cursor, statement, context=None):
C:\Anaconda\lib\site-packages\MySQLdb\cursors.pyc in execute(self, query, args)
203 del tb
204 self.messages.append((exc, value))
--> 205 self.errorhandler(self, exc, value)
206 self._executed = query
207 if not self._defer_warnings: self._warning_check()
C:\Anaconda\lib\site-packages\MySQLdb\connections.pyc in defaulterrorhandler(***failed resolving arguments***)
34 del cursor
35 del connection
---> 36 raise errorclass, errorvalue
37
38 re_numeric_part = re.compile(r"^(\d+)")
ProgrammingError: (ProgrammingError) (1103, "Incorrect table name 'SELECT onlinetransactions.id as firstid FROM onlinetransactions LIMIT 1'") 'DESCRIBE `SELECT onlinetransactions.id as firstid FROM onlinetransactions LIMIT 1`' ()
```
So it never gets as far as running the actual query, because it's tried to run a `DESCRIBE` query with ``` quotes which fails. i.e.
```
MariaDB [transactions]> DESCRIBE `SELECT onlinetransactions.id as firstid FROM onlinetransactions LIMIT 1`;
ERROR 1103 (42000): Incorrect table name 'SELECT onlinetransactions.id as firstid FROM onlinetransactions LIMIT 1'
```
But
```
MariaDB [transactions]> DESCRIBE SELECT onlinetransactions.id as firstid FROM onlinetransactions LIMIT 1;
+------+-------------+--------------------+-------+---------------+--------------------------------+---------+------+----------+-------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+------+-------------+--------------------+-------+---------------+--------------------------------+---------+------+----------+-------------+
| 1 | SIMPLE | onlinetransactions | index | NULL | ix_OnlineTransactions_Rpt_Year | 5 | NULL | 11485535 | Using index |
+------+-------------+--------------------+-------+---------------+--------------------------------+---------+------+----------+-------------+
1 row in set (0.00 sec)
```
Ok. So looking at the stacktrace, I reckon this _is_ a pandas bug as it seems to be calling `has_table` on my sql query, which doesn't seem to make any sense?
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} | 7 | 2014-07-24T01:02:01Z | 2021-04-11T04:57:47Z | null | MEMBER | null | ``` python
import pandas as pd
index = pd.to_datetime(['2000-01-02', '2000-01-01'])
index.get_loc('1900-01-01')
```
```
array([], dtype=int64)
```
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} | 5 | 2014-07-24T04:53:48Z | 2015-06-07T22:24:15Z | 2015-06-07T22:24:15Z | CONTRIBUTOR | null | http://pandas.pydata.org/pandas-docs/dev/api.html
Not here.
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| null | 1 | 2014-07-24T09:24:40Z | 2014-07-24T11:39:52Z | 2014-07-24T11:39:42Z | NONE | null | I am using pandas version 0.14.0 and try to parse a csv file which variable number of tokens (fields) using read_csv(). While parsing, I got the below error.
File "parser.pyx", line 727, in pandas.parser.TextReader.read (pandas\parser.c:7146)
File "parser.pyx", line 749, in pandas.parser.TextReader._read_low_memory (pandas\parser.c:7370)
File "parser.pyx", line 802, in pandas.parser.TextReader._read_rows (pandas\parser.c:7979)
File "parser.pyx", line 789, in pandas.parser.TextReader._tokenize_rows (pandas\parser.c:7853)
File "parser.pyx", line 1697, in pandas.parser.raise_parser_error (pandas\parser.c:19604)
CParserError: Error tokenizing data. C error: Expected 4 fields in line 2, saw 5
The file contains the below rows.
1,2,3,4
3,4,5,6,7
3,4,5
Kindly help to provide the solution to read this kind of files.
Thanks and regards
KB Devaraj
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} | 8 | 2014-07-24T10:40:23Z | 2018-08-01T09:19:44Z | 2018-08-01T09:19:44Z | CONTRIBUTOR | null | Test case:
``` python
def test_datetimeindex__eq__():
"""Equality comparisons should never raise an exception"""
pd.DatetimeIndex(['01-Jan-2015']) == ()
```
``` python
In [115]: test_datetimeindex__eq__()
Traceback (most recent call last):
File "<ipython-input-115-9b7967ca9de2>", line 1, in <module>
test_datetimeindex__eq__()
File "<ipython-input-114-7ae5783187b8>", line 3, in test_datetimeindex__eq__
pd.DatetimeIndex(['01-Jan-2015']) == ()
File "C:\dev\bin\Anaconda\lib\site-packages\pandas\tseries\index.py", line 90, in wrapper
other = _ensure_datetime64(other)
File "C:\dev\bin\Anaconda\lib\site-packages\pandas\tseries\index.py", line 112, in _ensure_datetime64
raise TypeError('%s type object %s' % (type(other), str(other)))
TypeError: <type 'tuple'> type object ()
```
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| null | 1 | 2014-07-24T13:33:18Z | 2014-07-24T17:28:19Z | 2014-07-24T17:28:15Z | CONTRIBUTOR | null | This readme file had some problems with it. I cleaned it up a bit.
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} | 55 | 2014-07-24T13:47:39Z | 2015-09-03T14:14:34Z | 2015-09-03T14:04:18Z | MEMBER | null | Closes #7811.
- [x] Change `Period.freq` and `PeriodIndex.freq` to store offsets.
- [x] Add `freqstr` to `Period`, `PeriodIndex` can use `DatetimeIndexOpsMixin`'s logic
- [x] Logic and tests for pickles taken in prev versions
- [x] Perform shift/arithmetic considering freq's mult.
- [x] Test all offsets has accessible `n` proeprties (#10350)
- [x] Explicit tests for `asfreq` and `to_timestamp` using freq with mult.
- [x] Ops with different base freq must be prohibited. Change freq comparison to base comparison.
- [x] Fix partial string slicing bug (some tests are commented out because of this)
- [x] Fix order bug (#10295, removing temp logic for `PeriodIndex`)
- ~~Decide a policy for legacy freq aliases, like "WK"~~ (handled in #10878)
- [x] Update doc.
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} | 8 | 2014-07-24T15:53:19Z | 2014-09-10T17:46:09Z | 2014-08-11T12:17:58Z | CONTRIBUTOR | null | xref #7825
``` python
In [1]: import pandas as pd
In [2]: pd.Timestamp('2013-11-01 00:00:00-0500', tz='America/Chicago')
Out[2]: Timestamp('2013-10-31 23:09:00-0551', tz='America/Chicago')
```
This bug is inherited from `datetime`, which is using local mean time (the first entry for `America/Chicago` in tzinfo).
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} | 0 | 2014-07-24T19:23:15Z | 2014-09-10T17:46:09Z | 2014-08-05T17:14:01Z | CONTRIBUTOR | null | xref #7825
xref (addtl test cases) #7901
I believe `Out[2]` is correct and `Out[3]` is a bug, and that others will agree with me.
I believe `Out[4]` is a bug, `Out[7]` is correct and `Out[10]` is a bug, but that I will have to do some work to convince others to agree with me.
``` python
In [1]: import pandas as pd
In [2]: list(pd.date_range('2013-11-1', tz='America/Chicago', periods=7, freq='D'))
Out[2]:
[Timestamp('2013-11-01 00:00:00-0500', tz='America/Chicago', offset='D'),
Timestamp('2013-11-02 00:00:00-0500', tz='America/Chicago', offset='D'),
Timestamp('2013-11-03 00:00:00-0500', tz='America/Chicago', offset='D'),
Timestamp('2013-11-04 00:00:00-0600', tz='America/Chicago', offset='D'),
Timestamp('2013-11-05 00:00:00-0600', tz='America/Chicago', offset='D'),
Timestamp('2013-11-06 00:00:00-0600', tz='America/Chicago', offset='D'),
Timestamp('2013-11-07 00:00:00-0600', tz='America/Chicago', offset='D')]
In [3]: list(pd.date_range(pd.Timestamp('2013-11-1', tz='America/Chicago'), periods=7, freq='D'))
Out[3]:
[Timestamp('2013-11-01 00:00:00-0500', tz='America/Chicago', offset='D'),
Timestamp('2013-11-02 00:00:00-0500', tz='America/Chicago', offset='D'),
Timestamp('2013-11-03 00:00:00-0500', tz='America/Chicago', offset='D'),
Timestamp('2013-11-03 23:00:00-0600', tz='America/Chicago', offset='D'),
Timestamp('2013-11-04 23:00:00-0600', tz='America/Chicago', offset='D'),
Timestamp('2013-11-05 23:00:00-0600', tz='America/Chicago', offset='D'),
Timestamp('2013-11-06 23:00:00-0600', tz='America/Chicago', offset='D')]
In [4]: list(pd.date_range('2013-11-1', tz='America/Chicago', periods=7, freq='24H'))
Out[4]:
[Timestamp('2013-11-01 00:00:00-0500', tz='America/Chicago', offset='24H'),
Timestamp('2013-11-02 00:00:00-0500', tz='America/Chicago', offset='24H'),
Timestamp('2013-11-03 00:00:00-0500', tz='America/Chicago', offset='24H'),
Timestamp('2013-11-04 00:00:00-0600', tz='America/Chicago', offset='24H'),
Timestamp('2013-11-05 00:00:00-0600', tz='America/Chicago', offset='24H'),
Timestamp('2013-11-06 00:00:00-0600', tz='America/Chicago', offset='24H'),
Timestamp('2013-11-07 00:00:00-0600', tz='America/Chicago', offset='24H')]
In [5]: out = [pd.Timestamp('2013-11-1', tz='America/Chicago')]
In [6]: for i in range(6):
...: out.append(out[-1] + pd.offsets.Hour(24))
...:
In [7]: out
Out[7]:
[Timestamp('2013-11-01 00:00:00-0500', tz='America/Chicago'),
Timestamp('2013-11-02 00:00:00-0500', tz='America/Chicago'),
Timestamp('2013-11-03 00:00:00-0500', tz='America/Chicago'),
Timestamp('2013-11-03 23:00:00-0600', tz='America/Chicago'),
Timestamp('2013-11-04 23:00:00-0600', tz='America/Chicago'),
Timestamp('2013-11-05 23:00:00-0600', tz='America/Chicago'),
Timestamp('2013-11-06 23:00:00-0600', tz='America/Chicago')]
In [8]: out = [pd.Timestamp('2013-11-1', tz='America/Chicago')]
In [9]: for i in range(6):
...: out.append(out[-1] + pd.offsets.Day(1))
...:
In [10]: out
Out[10]:
[Timestamp('2013-11-01 00:00:00-0500', tz='America/Chicago'),
Timestamp('2013-11-02 00:00:00-0500', tz='America/Chicago'),
Timestamp('2013-11-03 00:00:00-0500', tz='America/Chicago'),
Timestamp('2013-11-03 23:00:00-0600', tz='America/Chicago'),
Timestamp('2013-11-04 23:00:00-0600', tz='America/Chicago'),
Timestamp('2013-11-05 23:00:00-0600', tz='America/Chicago'),
Timestamp('2013-11-06 23:00:00-0600', tz='America/Chicago')]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/7836 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7836/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7836/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7836/events | https://github.com/pandas-dev/pandas/pull/7836 | 38,665,287 | MDExOlB1bGxSZXF1ZXN0MTg4NTgyNTI= | 7,836 | TST/BUG: html tests not skipping properly if lxml is not installed | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7837 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7837/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7837/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7837/events | https://github.com/pandas-dev/pandas/issues/7837 | 38,688,250 | MDU6SXNzdWUzODY4ODI1MA== | 7,837 | pivot_table / groupby with miss value in Index | {
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| null | 5 | 2014-07-25T00:38:23Z | 2015-10-20T22:29:53Z | 2015-10-20T22:29:53Z | NONE | null | Hello,
When I try to use multi-index which has some miss value, to build a pivot table, I find pandas take a strange behavior.
Pandas will autofill nan value with the value just above this index value, and this may give a wrong result , which make the value of second last index equal to its value plus nan value.
萧玮 0
ＮＨＫソフトウェア 0
Name: 5, Length: 27137, dtype: int64
（監督） - 楠葉宏三 1
ＮＨＫソフトウェア 8827
Name: 5, Length: 27137, dtype: int64
As you can see above, I use director to group movie data , and some movie has no director data.
The first one is I just use director to groupby data , and it will auto ignore nan index, so the value ＮＨＫソフトウェア is low.
The second is I use director and other value to do multi-index pivot_table and then I groupby the value with director to see the data about director. This last data (ＮＨＫソフトウェア) has a very high value. Pandas auto fill the nan index with the index value just above it , in this situation is ＮＨＫソフトウェア .
Is it a issue or I need do some work before do pivot_table with nan index?
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} | 14 | 2014-07-25T02:56:31Z | 2018-07-06T22:02:57Z | 2018-07-06T21:24:41Z | NONE | null | There are several errors associated with this. One of them is that the HDFStore fails to load some tables inside the store file, but fails silently. Another error is a loud HDF5 error that gives a full traceback. Both should be visible in the following code.
```
import pandas as pd
import multiprocessing as mp
def f(x):
tt = pd.HDFStore('storage.h5', mode = 'r')
print tt
p = mp.Pool()
p.map(f, range(50))
```
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} | 1 | 2014-07-25T10:22:30Z | 2014-07-25T23:30:04Z | 2014-07-25T23:30:04Z | CONTRIBUTOR | null | Code Snippet
``` python
df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})
df['grade'] = pd.Categorical(df['raw_grade'])
for t in df.itertuples(index=False):
print(t)
```
Error
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-45-182b29164b1a> in <module>()
1 df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})
2 df['grade'] = pd.Categorical(df['raw_grade'])
----> 3 for t in df.itertuples(index=False):
4 print(t)
/home/has2k1/scm/pandas/pandas/core/frame.pyc in itertuples(self, index)
549 # use integer indexing because of possible duplicate column names
550 arrays.extend(self.iloc[:, k] for k in range(len(self.columns)))
--> 551 return zip(*arrays)
552
553 if compat.PY3: # pragma: no cover
TypeError: izip argument #3 must support iteration
```
This is on master at commit 24b309f.
Edit:
Version string - pandas: 0.14.1-78-g24b309f
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} | 8 | 2014-07-25T11:12:29Z | 2014-10-06T19:56:38Z | 2014-10-06T19:56:38Z | NONE | null | Hello,
it will be nice if `to_dict` method could provide same `orient` parameter as `to_json`.
For example when `outtype='split'` we get same results as `outtype='series'`.
I also noticed that `df.to_dict(outtype='split1234')` is understood as `df.to_dict(outtype='series')` which is quite strange but `df.to_dict(outtype='a1234')` raises `ValueError: outtype a1234 not understood` which is a correct behavior
Kind regards
Femto
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} | 7 | 2014-07-25T15:44:41Z | 2017-06-10T22:00:39Z | 2017-06-10T22:00:39Z | NONE | null | With pandas 0.14.1, the following code produces an empty string:
```
import pandas
idx = pandas.MultiIndex(levels=[['a'],[0,1,2,3,4,4]],
labels=[[0,0,0,0,0],[0,1,2,3,4]],
names=[0,1])
print idx.tostring()
```
Is this behavior correct? `Index.tostring()` appears to produce a non-empty string for a non-empty index.
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} | 0 | 2014-07-25T22:41:05Z | 2014-07-25T23:30:04Z | 2014-07-25T23:30:04Z | CONTRIBUTOR | null | closes #7839
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} | 2 | 2014-07-25T23:26:27Z | 2014-07-27T01:11:02Z | 2014-07-26T14:44:41Z | MEMBER | null | Closes #7540.
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} | 2 | 2014-07-25T23:37:18Z | 2014-08-30T21:40:48Z | 2014-07-28T16:00:10Z | MEMBER | null | In some plots, `rot` and `fontsize` arguments are not applied properly.
- timeseries line / area plot: `rot` is not applied to minor ticklabels, and `fontsize` is completely ignored. (Fixed to apply to xticklabels)
- kde plot: `rot` and `fontsize` are completely ignored. (Fixed to apply to xticklabels)
- scatter and hexbin plots: `rot` and `fontsize` are completely ignored. (Under confirmation)
```
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(10, 4), index=pd.date_range(start='2014-07-01', freq='M', periods=10))
df.plot(rot=80, fontsize=15)
```
### Current Result
### After Fix
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} | 5 | 2014-07-26T04:08:04Z | 2014-08-07T20:47:32Z | 2014-08-07T20:47:32Z | CONTRIBUTOR | null | Example below for tz_localize() (Same error for tz_convert())
```
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.14.1'
In [3]: cohort_date = pd.date_range('20140701', periods=5, freq='D')
In [4]: event_date = pd.date_range('20140701', periods=5, freq='D')
In [5]: multi_index = pd.MultiIndex.from_product([cohort_date, event_date], names=['cohort_date', 'event_date'])
In [6]: df = pd.DataFrame(index=multi_index)
In [7]: df
Out[7]:
Empty DataFrame
Columns: []
Index: [(2014-07-01 00:00:00, 2014-07-01 00:00:00), (2014-07-01 00:00:00, 2014-07-02 00:00:00), (2014-07-01 00:00:00, 2014-07-03 00:00:00), (2014-07-01 00:00:00, 2014-07-04 00:00:00), (2014-07-01 00:00:00, 2014-07-05 00:00:00), (2014-07-02 00:00:00, 2014-07-01 00:00:00), (2014-07-02 00:00:00, 2014-07-02 00:00:00), (2014-07-02 00:00:00, 2014-07-03 00:00:00), (2014-07-02 00:00:00, 2014-07-04 00:00:00), (2014-07-02 00:00:00, 2014-07-05 00:00:00), (2014-07-03 00:00:00, 2014-07-01 00:00:00), (2014-07-03 00:00:00, 2014-07-02 00:00:00), (2014-07-03 00:00:00, 2014-07-03 00:00:00), (2014-07-03 00:00:00, 2014-07-04 00:00:00), (2014-07-03 00:00:00, 2014-07-05 00:00:00), (2014-07-04 00:00:00, 2014-07-01 00:00:00), (2014-07-04 00:00:00, 2014-07-02 00:00:00), (2014-07-04 00:00:00, 2014-07-03 00:00:00), (2014-07-04 00:00:00, 2014-07-04 00:00:00), (2014-07-04 00:00:00, 2014-07-05 00:00:00), (2014-07-05 00:00:00, 2014-07-01 00:00:00), (2014-07-05 00:00:00, 2014-07-02 00:00:00), (2014-07-05 00:00:00, 2014-07-03 00:00:00), (2014-07-05 00:00:00, 2014-07-04 00:00:00), (2014-07-05 00:00:00, 2014-07-05 00:00:00)]
In [8]: df.tz_localize('UTC')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-8-fa0ae4582d5d> in <module>()
----> 1 df.tz_localize('UTC')
/Users/mtrbean/.virtualenvs/stats/lib/python2.7/site-packages/pandas/core/generic.pyc in tz_localize(self, tz, axis, copy, infer_dst)
3492 ax_name = self._get_axis_name(axis)
3493 raise TypeError('%s is not a valid DatetimeIndex or PeriodIndex' %
-> 3494 ax_name)
3495 else:
3496 ax = DatetimeIndex([],tz=tz)
TypeError: index is not a valid DatetimeIndex or PeriodIndex
```
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| null | 6 | 2014-07-26T08:23:53Z | 2014-07-28T18:07:17Z | 2014-07-28T12:50:03Z | CONTRIBUTOR | null | I ran into this nasty error when building / testing OSX wheels.
```
======================================================================
ERROR: test_timedelta (pandas.io.tests.test_json.test_pandas.TestPandasContainer)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/buildslave/osx-10.6-clean/scipy-stack-2_7_6-wheel-requires/build/venv/lib/python2.7/site-packages/pandas/io/tests/test_json/test_pandas.py", line 611, in test_timedelta
assert_series_equal(s, pd.read_json(s.to_json(),typ='series').apply(converter))
File "/Users/buildslave/osx-10.6-clean/scipy-stack-2_7_6-wheel-requires/build/venv/lib/python2.7/site-packages/pandas/core/series.py", line 1998, in apply
mapped = lib.map_infer(values, f, convert=convert_dtype)
File "inference.pyx", line 1016, in pandas.lib.map_infer (pandas/lib.c:53184)
File "/Users/buildslave/osx-10.6-clean/scipy-stack-2_7_6-wheel-requires/build/venv/lib/python2.7/site-packages/pandas/io/tests/test_json/test_pandas.py", line 607, in <lambda>
converter = lambda x: pd.to_timedelta(x,unit='ms')
File "/Users/buildslave/osx-10.6-clean/scipy-stack-2_7_6-wheel-requires/build/venv/lib/python2.7/site-packages/pandas/tseries/timedeltas.py", line 69, in to_timedelta
return _coerce_scalar_to_timedelta_type(arg, unit=unit)
File "/Users/buildslave/osx-10.6-clean/scipy-stack-2_7_6-wheel-requires/build/venv/lib/python2.7/site-packages/pandas/tseries/timedeltas.py", line 123, in _coerce_scalar_to_timedelta_type
return tslib.convert_to_timedelta(r,unit)
File "tslib.pyx", line 1361, in pandas.tslib.convert_to_timedelta (pandas/tslib.c:22899)
File "tslib.pyx", line 1427, in pandas.tslib.convert_to_timedelta64 (pandas/tslib.c:23733)
ValueError: Invalid type for timedelta scalar: <class 'pandas.tslib.Timestamp'>
```
(see http://nipy.bic.berkeley.edu/builders/scipy-stack-2.7.6-wheel-requires/builds/88/steps/shell_10/logs/stdio)
After some tiring debugging, it turns out that the error is caused by an incorrect output of `pandas.json.dumps` if pandas is compiled against numpy 1.6. This script:
```
from datetime import timedelta
from pandas import Series
import pandas.json
s = Series([timedelta(23), timedelta(seconds=5)])
print pandas.json.dumps(s,
orient='index',
double_precision=10,
ensure_ascii=True,
date_unit='ms',
iso_dates=False,
default_handler=None)
```
gives the (I think) correct output below when pandas is compiled against numpy 1.7.1:
```
{"0":1987200000,"1":5000}
```
If pandas is compiled against numpy 1.6.1 (current recorded dependency) then the same script gives:
```
{"0":1987200000000000,"1":5000000000}
```
This in turn leads to the error at the top of this issue because the correct JSON passed into pandas.read_json generates a series of numpy.int64 whereas the incorrect JSON generates numpy.datetime64 elements:
```
In [10]: pd.read_json('{"0":1987200000,"1":5000}', typ='series')
Out[10]:
0 1987200000
1 5000
dtype: int64
In [12]: pd.read_json('{"0":1987200000000000,"1":5000000000}', typ='series')
Out[12]:
0 2032-12-21 00:00:00
1 1970-01-01 01:23:20
dtype: datetime64[ns]
```
Should the numpy dependency be updated to 1.7.1? I was afraid when I looked at the `dumps` C code but maybe there is some way of making it more robust to numpy version.
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} | 1 | 2014-07-26T10:03:14Z | 2014-07-29T22:44:36Z | 2014-07-29T22:44:36Z | CONTRIBUTOR | null | code
``` python
df = pd.DataFrame({"id":[6,5,4,3,2,1], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})
df["grade"] = pd.Categorical(df["raw_grade"])
df['grade'].cat.reorder_levels(['b', 'e', 'a'])
# sorts 'grade' according to the order of the levels
df.sort(columns=['grade'])
```
correct output
```
id raw_grade grade
4 5 b b
3 4 b b
0 1 e e
2 6 a a
1 3 a a
5 2 a a
```
---
code
``` python
# sorts 'grade' lexically
df.sort(columns=['grade', 'id'])
```
wrong output
```
id raw_grade grade
4 2 a a
3 3 a a
0 6 a a
2 4 b b
1 5 b b
5 1 e e
```
When there is more than one element in the columns list, the Categoricals columns are sorted lexically.
`pandas: 0.14.1-78-g24b309f`
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} | 22 | 2014-07-26T10:18:17Z | 2014-07-31T02:22:40Z | 2014-07-29T09:58:55Z | CONTRIBUTOR | null | I believe installing pandas via Anaconda should definitely be the recommended installation method for most users.
It is cross platform, doesn't require a compiler, and will install all requirements for the user also (including NumPy).
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} | 14 | 2014-07-26T13:53:03Z | 2014-07-31T21:27:35Z | 2014-07-29T22:44:36Z | CONTRIBUTOR | null | CLN: refactor _lexsort_indexer to use Categoricals
closes #7848
closes #7864
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} | 9 | 2014-07-26T14:47:20Z | 2014-07-28T13:24:39Z | 2014-07-28T13:24:39Z | MEMBER | null | Also, `infer_types` now has no effect _for real_ :)
closes #7762
closes #7032
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} | 8 | 2014-07-26T14:52:42Z | 2014-08-07T22:13:11Z | 2014-08-05T14:49:29Z | MEMBER | null | Closes #7812. Allow `tz_localize(None)` for tz-aware `Timestamp` and `DatetimeIndex` to reset tz.
CC @rockg, @nehalecky
```
t = pd.Timestamp('2014-01-01 09:00', tz='Asia/Tokyo')
# tz_localize resets tz holding current tz representation
t.tz_localize(None)
#2014-01-01 09:00:00
# tz_convert reset tz using UTC representation (no changes by this PR)
t.tz_convert(None)
#2014-01-01 00:00:00
idx = pd.date_range(start='2011-01-01 09:00', freq='H', periods=10, tz='Asia/Tokyo')
idx.tz_localize(None)
# <class 'pandas.tseries.index.DatetimeIndex'
# [2011-01-01 09:00:00, ..., 2011-01-01 18:00:00]
# Length: 10, Freq: H, Timezone: None
# (no changes by this PR)
idx.tz_convert(None)
# <class 'pandas.tseries.index.DatetimeIndex'>
# [2011-01-01 00:00:00, ..., 2011-01-01 09:00:00]
# Length: 10, Freq: H, Timezone: None
```
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} | 20 | 2014-07-27T00:05:31Z | 2014-09-04T00:24:19Z | 2014-08-21T22:10:45Z | CONTRIBUTOR | null | closes #5391
```
>>> ### left join with multiple matches - single index case
>>> left = DataFrame([
... ['X', 'Y', 'C', 'a'],
... ['W', 'Y', 'C', 'e'],
... ['V', 'Q', 'A', 'h'],
... ['V', 'R', 'D', 'i'],
... ['X', 'Y', 'D', 'b'],
... ['X', 'Y', 'A', 'c'],
... ['W', 'Q', 'B', 'f'],
... ['W', 'R', 'C', 'g'],
... ['V', 'Y', 'C', 'j'],
... ['X', 'Y', 'B', 'd']],
... columns=['cola', 'colb', 'colc', 'tag'],
... index=[3, 2, 0, 1, 7, 6, 4, 5, 9, 8])
>>>
... right = DataFrame([
... ['W', 'R', 'C', 0],
... ['W', 'Q', 'B', 3],
... ['W', 'Q', 'B', 8],
... ['X', 'Y', 'A', 1],
... ['X', 'Y', 'A', 4],
... ['X', 'Y', 'B', 5],
... ['X', 'Y', 'C', 6],
... ['X', 'Y', 'C', 9],
... ['X', 'Q', 'C', -6],
... ['X', 'R', 'C', -9],
... ['V', 'Y', 'C', 7],
... ['V', 'R', 'D', 2],
... ['V', 'R', 'D', -1],
... ['V', 'Q', 'A', -3]],
... columns=['col1', 'col2', 'col3', 'val'])
>>>
... right.set_index(['col1', 'col2', 'col3'], inplace=True)
>>> result = left.join(right, on=['cola', 'colb', 'colc'], how='left')
>>>
... expected = DataFrame([
... ['X', 'Y', 'C', 'a', 6],
... ['X', 'Y', 'C', 'a', 9],
... ['W', 'Y', 'C', 'e', nan],
... ['V', 'Q', 'A', 'h', -3],
... ['V', 'R', 'D', 'i', 2],
... ['V', 'R', 'D', 'i', -1],
... ['X', 'Y', 'D', 'b', nan],
... ['X', 'Y', 'A', 'c', 1],
... ['X', 'Y', 'A', 'c', 4],
... ['W', 'Q', 'B', 'f', 3],
... ['W', 'Q', 'B', 'f', 8],
... ['W', 'R', 'C', 'g', 0],
... ['V', 'Y', 'C', 'j', 7],
... ['X', 'Y', 'B', 'd', 5]],
... columns=['cola', 'colb', 'colc', 'tag', 'val'],
... index=[3, 3, 2, 0, 1, 1, 7, 6, 6, 4, 4, 5, 9, 8])
>>>
... tm.assert_frame_equal(result, expected)
>>> print(left, right, result, sep='\n')
cola colb colc tag
3 X Y C a
2 W Y C e
0 V Q A h
1 V R D i
7 X Y D b
6 X Y A c
4 W Q B f
5 W R C g
9 V Y C j
8 X Y B d
val
col1 col2 col3
W R C 0
Q B 3
B 8
X Y A 1
A 4
B 5
C 6
C 9
Q C -6
R C -9
V Y C 7
R D 2
D -1
Q A -3
cola colb colc tag val
3 X Y C a 6
3 X Y C a 9
2 W Y C e NaN
0 V Q A h -3
1 V R D i 2
1 V R D i -1
7 X Y D b NaN
6 X Y A c 1
6 X Y A c 4
4 W Q B f 3
4 W Q B f 8
5 W R C g 0
9 V Y C j 7
8 X Y B d 5
>>> ### left join with multiple matches - multi index case
>>> left = DataFrame([
... ['c', 0],
... ['b', 1],
... ['a', 2],
... ['b', 3]],
... columns=['tag', 'val'],
... index=[2, 0, 1, 3])
>>>
... right = DataFrame([
... ['a', 'v'],
... ['c', 'w'],
... ['c', 'x'],
... ['d', 'y'],
... ['a', 'z'],
... ['c', 'r'],
... ['e', 'q'],
... ['c', 's']],
... columns=['tag', 'char'])
>>>
... right.set_index('tag', inplace=True)
>>> result = left.join(right, on='tag', how='left')
>>>
... expected = DataFrame([
... ['c', 0, 'w'],
... ['c', 0, 'x'],
... ['c', 0, 'r'],
... ['c', 0, 's'],
... ['b', 1, nan],
... ['a', 2, 'v'],
... ['a', 2, 'z'],
... ['b', 3, nan]],
... columns=['tag', 'val', 'char'],
... index=[2, 2, 2, 2, 0, 1, 1, 3])
>>>
... tm.assert_frame_equal(result, expected)
>>> print(left, right, result, sep='\n')
tag val
2 c 0
0 b 1
1 a 2
3 b 3
char
tag
a v
c w
c x
d y
a z
c r
e q
c s
tag val char
2 c 0 w
2 c 0 x
2 c 0 r
2 c 0 s
0 b 1 NaN
1 a 2 v
1 a 2 z
3 b 3 NaN
```
This closes on: https://github.com/pydata/pandas/issues/5391
By providing all the matches when doing left join on index, both in the case of single index and multi-index. It also preserves the index order of the calling (left) DataFrame (as it used to), though when there are multiple matches the indices repeat and the index loses integrity.
The added test cases should be self-explanatory.
Thank you,
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} | 1 | 2014-07-27T13:22:43Z | 2014-08-04T13:18:06Z | 2014-07-28T14:57:03Z | MEMBER | null | Fixed:
- Some modules are imported different aliases
- Merge duplicated functions (`maybe_get_tz` and `ensure_int64`)
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} | 10 | 2014-07-27T16:23:32Z | 2015-04-08T15:03:14Z | 2015-04-08T15:03:14Z | MEMBER | null | - [ ] clear the FIXME in Categorical in #7820 (comment)
closes #7820
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} | 5 | 2014-07-27T22:09:31Z | 2015-04-28T12:00:23Z | 2015-04-28T12:00:23Z | NONE | null | Hi All,
Might be pilot error (or the incorrect expectations), but I was expecting the max_colwidth option to work on groupby terminal output. Replication here:
```
$ python
> import pandas as pd
> df = pd.DataFrame([{'a':'foo','b':'bar','c':'uncomfortably long line with lots of stuff','d':1}, \
{'a':'foo','b':'bar','c':'stuff', 'd':1}])
> df
a b c d
0 foo bar uncomfortably long line with lots of stuff 1
1 foo bar stuff 1
> pd.set_option('max_colwidth', 20)
> df
a b c d
0 foo bar uncomfortably lo... 1
1 foo bar stuff 1
> group_df = df.groupby(['a','b','c']).sum()
> group_df
d
a b c
foo bar stuff 1
uncomfortably long line with lots of stuff 1
>>>
```
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Darwin
OS-release: 13.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.1
nose: None
Cython: 0.20.2
numpy: 1.8.1
scipy: None
statsmodels: None
IPython: 2.1.0
sphinx: 1.2.2
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.4
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
>>>
```
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} | 5 | 2014-07-28T14:38:00Z | 2014-08-02T12:53:36Z | 2014-08-02T12:53:36Z | MEMBER | null | ```
df = DataFrame(index=MultiIndex.from_product([[2011, 2012], [1,2,3]],
names=['year', 'month']))
df = df.reset_index()
str_year = df.year.astype('str')
str_month = df.month.astype('str')
str_both = str_year.str.cat(str_month, sep=' ')
```
... so far, everything is fine. Now filter the index and retry:
```
df = df[df.month > 1]
str_year = df.year.astype('str')
str_month = df.month.astype('str')
str_both = str_year.str.cat(str_month, sep=' ')
```
... you will get a KeyError (tested against git, commit 90fa87e7a51141ac318382b979f60a8c043b8505 ):
```
KeyError Traceback (most recent call last)
<ipython-input-12-9d3f1fbb70fc> in <module>()
11 str_year = df.year.astype('str')
12 str_month = df.month.astype('str')
---> 13 str_both = str_year.str.cat(str_month, sep=' ')
/home/pietro/nobackup/repo/pandas/pandas/core/strings.py in cat(self, others, sep, na_rep)
933 @copy(str_cat)
934 def cat(self, others=None, sep=None, na_rep=None):
--> 935 result = str_cat(self.series, others=others, sep=sep, na_rep=na_rep)
936 return self._wrap_result(result)
937
/home/pietro/nobackup/repo/pandas/pandas/core/strings.py in str_cat(arr, others, sep, na_rep)
41
42 if others is not None:
---> 43 arrays = _get_array_list(arr, others)
44
45 n = _length_check(arrays)
/home/pietro/nobackup/repo/pandas/pandas/core/strings.py in _get_array_list(arr, others)
13
14 def _get_array_list(arr, others):
---> 15 if len(others) and isinstance(others[0], (list, np.ndarray)):
16 arrays = [arr] + list(others)
17 else:
/home/pietro/nobackup/repo/pandas/pandas/core/series.py in __getitem__(self, key)
491 def __getitem__(self, key):
492 try:
--> 493 result = self.index.get_value(self, key)
494
495 if not np.isscalar(result):
/home/pietro/nobackup/repo/pandas/pandas/core/index.py in get_value(self, series, key)
1194
1195 try:
-> 1196 return self._engine.get_value(s, k)
1197 except KeyError as e1:
1198 if len(self) > 0 and self.inferred_type in ['integer','boolean']:
/home/pietro/nobackup/repo/pandas/pandas/index.so in pandas.index.IndexEngine.get_value (pandas/index.c:2991)()
/home/pietro/nobackup/repo/pandas/pandas/index.so in pandas.index.IndexEngine.get_value (pandas/index.c:2806)()
/home/pietro/nobackup/repo/pandas/pandas/index.so in pandas.index.IndexEngine.get_loc (pandas/index.c:3532)()
/home/pietro/nobackup/repo/pandas/pandas/hashtable.so in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:7033)()
/home/pietro/nobackup/repo/pandas/pandas/hashtable.so in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:6974)()
KeyError: 0
```
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} | 23 | 2014-07-28T14:38:16Z | 2014-12-02T16:29:03Z | 2014-08-01T13:30:09Z | NONE | null | I'm writing some simple data, including a string of len()=5 using to_stata.
In Stata, I then have trouble with it due to its length. codebook shows that my variable as been encoded as a string of length 244:
of type "string (str244), but longest is str5"
If tthe longest string is length 5, so should be the output variable type in Stata.
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} | 3 | 2014-07-28T15:14:58Z | 2014-08-02T13:04:33Z | 2014-08-02T07:23:53Z | MEMBER | null | Closes #7857
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} | 17 | 2014-07-28T19:23:34Z | 2014-11-02T22:41:28Z | 2014-11-02T22:41:28Z | NONE | null | conceptually not hard (and you can look at a the slices to figure this out, e.g. if start >= end or start>last_endpoint, you can just do a reversed is_monotonic), to avoid a perf hit I think, then just reverse the searching operations for slices would need to do `is_monotonic_decreasing` here : https://github.com/pydata/pandas/blob/master/pandas/core/index.py#L1764
Hello,
I am working with spectral data, which for various spectral units such as
wavenumber, is often presented with decreasing spectral values along the
index. For example:
http://www.chemguide.co.uk/analysis/ir/irpropanone.GIF
In my dataframe, the index is stored in descending order (eg 500, 499,
498... 2, 1); however, when I try to slice using .ix[]; it becomes
impossible, giving me a long key error.
Likewise, df.plot() is sorting the xvalues from low to high, so I need to
reverse the plot axis after the fact. Not really a big deal, but wondered
if there's a better workaround.
Any suggestions?
**Note: This behavior works fine for int64 index:**
```
#Create dataframe and reverse index
x = DataFrame(np.random.randn(50,50))
x.index = x.index[::-1]
#Slice 30-10
x.ix[30:10, ::]
```
But fails for float index
```
x = DataFrame(np.random.randn(50,50),
index=np.linspace(0,50))
x.index = x.index[::-1]
x.ix[30.0:10.0, ::]
```
With error:
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-68-1af3b9a79d3d> in <module>()
11 x.index = x.index[::-1]
12
---> 13 x.ix[30.0:10.0, ::]
14
/home/glue/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/indexing.pyc in __getitem__(self, key)
67 pass
68
---> 69 return self._getitem_tuple(key)
70 else:
71 return self._getitem_axis(key, axis=0)
/home/glue/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_tuple(self, tup)
673 continue
674
--> 675 retval = getattr(retval, self.name)._getitem_axis(key, axis=i)
676
677 return retval
/home/glue/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_axis(self, key, axis, validate_iterable)
859 labels = self.obj._get_axis(axis)
860 if isinstance(key, slice):
--> 861 return self._get_slice_axis(key, axis=axis)
862 elif _is_list_like(key) and not (isinstance(key, tuple) and
863 isinstance(labels, MultiIndex)):
/home/glue/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/indexing.pyc in _get_slice_axis(self, slice_obj, axis)
1106 if not _need_slice(slice_obj):
1107 return obj
-> 1108 indexer = self._convert_slice_indexer(slice_obj, axis)
1109
1110 if isinstance(indexer, slice):
/home/glue/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/indexing.pyc in _convert_slice_indexer(self, key, axis)
161 # if we are accessing via lowered dim, use the last dim
162 ax = self.obj._get_axis(min(axis, self.ndim - 1))
--> 163 return ax._convert_slice_indexer(key, typ=self.name)
164
165 def _has_valid_setitem_indexer(self, indexer):
/home/glue/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/index.pyc in _convert_slice_indexer(self, key, typ)
2027
2028 # translate to locations
-> 2029 return self.slice_indexer(key.start, key.stop, key.step)
2030
2031 def get_value(self, series, key):
/home/glue/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/index.pyc in slice_indexer(self, start, end, step)
1704 This function assumes that the data is sorted, so use at your own peril
1705 """
-> 1706 start_slice, end_slice = self.slice_locs(start, end)
1707
1708 # return a slice
/home/glue/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/index.pyc in slice_locs(self, start, end)
1777
1778 start_slice = _get_slice(0, offset=0, search_side='left',
-> 1779 slice_property='start', search_value=start)
1780 end_slice = _get_slice(len(self), offset=1, search_side='right',
1781 slice_property='stop', search_value=end)
/home/glue/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/index.pyc in _get_slice(starting_value, offset, search_side, slice_property, search_value)
1746
1747 try:
-> 1748 slc = self.get_loc(search_value)
1749
1750 if not is_unique:
/home/glue/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/index.pyc in get_loc(self, key)
2091 except (TypeError, NotImplementedError):
2092 pass
-> 2093 return super(Float64Index, self).get_loc(key)
2094
2095 @property
/home/glue/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/index.pyc in get_loc(self, key)
1179 loc : int if unique index, possibly slice or mask if not
1180 """
-> 1181 return self._engine.get_loc(_values_from_object(key))
1182
1183 def get_value(self, series, key):
/home/glue/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/index.so in pandas.index.IndexEngine.get_loc (pandas/index.c:3354)()
/home/glue/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/index.so in pandas.index.IndexEngine.get_loc (pandas/index.c:3234)()
/home/glue/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/hashtable.so in pandas.hashtable.Float64HashTable.get_item (pandas/hashtable.c:9018)()
/home/glue/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/hashtable.so in pandas.hashtable.Float64HashTable.get_item (pandas/hashtable.c:8962)()
KeyError: 30.0
```
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} | 13 | 2014-07-29T00:48:01Z | 2018-03-07T09:50:25Z | 2018-03-07T09:50:25Z | CONTRIBUTOR | null | As per the title, `Series.view(…)` fails when the `dtype` is `"object"`:
```
In [1]: import pandas as pd
In [2]: s = pd.Series([u'a', u'b', u'c'])
In [3]: s.view(pd.Series)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-abcb4acb36d2> in <module>()
----> 1 s.view(pd.Series)
/Users/wolever/code/sandbox/env/sandbox/lib/python2.7/site-packages/pandas/core/series.pyc in view(self, dtype)
363
364 def view(self, dtype=None):
--> 365 return self._constructor(self.values.view(dtype),
366 index=self.index).__finalize__(self)
367
TypeError: Cannot change data-type for object array.
In [4]: pd.__version__
Out[4]: '0.14.1'
```
Compare with the behavior of `0.12`:
```
In [1]: import pandas as pd
In [2]: s = pd.Series([u'a', u'b', u'c'])
In [3]: s.view(pd.Series)
Out[3]:
0 a
1 b
2 c
dtype: object
In [4]: pd.__version__
Out[4]: '0.12.0'
```
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} | 17 | 2014-07-29T06:19:15Z | 2014-08-20T15:32:49Z | 2014-08-01T13:30:09Z | CONTRIBUTOR | null | Strings were incorrectly written using 244 character irrespective of the actual
length of the underlying due to changes in pandas where the underlying NumPy
datatype of strings is always np.object_, and never np.string_. Closes #7858
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| null | 4 | 2014-07-29T06:45:43Z | 2014-08-05T16:21:47Z | 2014-08-05T16:21:47Z | NONE | null | Hi,
I'm exporting a data frame from pandas and importing it in a C++ program using rapidjson. I believe I have tested all available orientations and I am wondering if it was possible to obtain the following:
``` python
c1 c2
r1 1 2
r2 3 4
```
converted to
``` python
{ c1: [1, 3], c2: [2,4], index: [r1, r2]}
```
Is my idea inherently flawed somehow? I feel it would be very intuitive to parse this from C++.
The other question is regarding the precision of the double values. I am only getting fixed-point values in the text output.
Can I get 1.234e-5 instead of 0.00001234 somehow?
If no, can you point out the process for possible contributions in this direction?
Best regards,
Matthias Kauer
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} | 0 | 2014-07-29T09:53:55Z | 2014-07-29T22:44:36Z | 2014-07-29T22:44:36Z | CONTRIBUTOR | null | Look at `dfx`
``` python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})
...: df["grade"] = pd.Categorical(df["raw_grade"])
...: df['grade'].cat.reorder_levels(['e', 'a', 'b'])
...:
In [3]: df1 = df[0:3]
...: df2 = df[3:]
...:
In [4]: df['grade'].cat.levels
Out[4]: Index([u'e', u'a', u'b'], dtype='object')
In [5]: df1['grade'].cat.levels
Out[5]: Index([u'e', u'a', u'b'], dtype='object')
In [6]: df2['grade'].cat.levels
Out[6]: Index([u'e', u'a', u'b'], dtype='object')
In [7]: dfx = pd.concat([df1, df2])
In [8]: dfx['grade'].cat.levels
Out[8]: Index([u'a', u'b', u'e'], dtype='object')
```
version: `pandas: 0.14.1-78-g24b309f`
This is still the case after either of PR #7768 and PR #7850.
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} | 2 | 2014-07-29T09:59:48Z | 2018-07-06T22:03:12Z | 2018-07-06T21:22:13Z | CONTRIBUTOR | null | xref #7849
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} | 0 | 2014-07-29T11:39:45Z | 2014-07-31T15:49:45Z | 2014-07-30T23:48:47Z | CONTRIBUTOR | null | I think the following _could_ work and not raise `KeyError` because of a missing indexer. This would follow the API of loc which will be effectively a reindex as long as you have at least 1 found value.
```
s = pd.Series(np.arange(9),index=pd.MultiIndex.from_product([['A','B','C'],['foo','bar','baz']],names=['one','two'])).sortlevel()
s.loc[['A','D']]
idx = pd.IndexSlice
s.loc[idx[:,['foo','bah']]]
```
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} | 9 | 2014-07-29T11:55:44Z | 2014-07-31T11:25:56Z | 2014-07-30T23:48:47Z | CONTRIBUTOR | null | closes #7866
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} | 2 | 2014-07-29T12:59:59Z | 2015-03-08T16:57:52Z | 2015-03-08T16:57:49Z | CONTRIBUTOR | null | I'm interested if there are recommended best practices for extending DataFrames.
I work on a project where we have an API for querying a database that returns DataFrames. I basically want a way to associate extra metadata with each column in the DataFrame. At a minimum this would allow me to run commands like `df.some_column.canonical_url` and get a link back to a web service.
There are some other things that I would like to do like giving columns aliases for better legends when plotted.
I know I could return a wrapped DataFrame object from the api, but the DataFrame would lose its wrapper once operations were performed.
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} | 12 | 2014-07-29T15:54:38Z | 2014-09-13T22:59:30Z | 2014-09-13T22:59:30Z | CONTRIBUTOR | null | ``` python
In [1]: import pandas as pd
In [2]: pd.Series(dtype='m8[ns]').sum()
Out[2]: 0
In [3]: pd.Series(dtype='m8[ns]').mean()
Out[3]:
0 NaT
dtype: timedelta64[ns]
In [4]: pd.Series(dtype='m8[ns]').median()
Out[4]: nan
In [5]: pd.Series(dtype=float).sum()
Out[5]: 0
In [6]: pd.Series([0], dtype=float).sum()
Out[6]: 0.0
```
It looks like `_wrap_results` is doing double duty, both converting an output to the right time dtype, _and_ wrapping that result in a series (I don't know why it does this), and in some cases results aren't getting wrapped. But the wrapper ignores other dtypes, which doesn't make sense to me; aside from the unfortunate fact that numpy provides no `_NA_integer` equivalent, I as a user expect the dtype of a result to depend only on the dtype of the inputs, not on their shapes or values, so it would make sense to have a more general wrapper somewhere.
I'm not sure how I'd implement such a wrapper, because ideally it would be able to handle situations like that the standard deviation of a `Series` of `datetime64`s is a `timedelta64` or such. Maybe that's a bit ambitious, though, since it's inching towards a general notion of symbolic units; in such a case the user can unwrap, operate and wrap manually.
It looks like there are several points in pandas/core/nanops.py where a result is returned without wrapping, and I could submit a narrow PR for just this issue but I want to wait until I have time to really go over `nanops.py` and get all of these.
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} | 13 | 2014-07-29T16:14:26Z | 2016-10-12T23:05:03Z | 2014-07-30T23:15:10Z | NONE | null | I brought this up on the mailing list. @cpcloud modified my example into very concise sample showing expected and resulting output:
```
In [20]: df = pd.DataFrame([
['best', 'a', 'x'],
['worst', 'b', 'y'],
['best', 'c', 'x'],
['best','d', 'y'],
['worst','d', 'y'],
['worst','d', 'y'],
['best','d', 'z'],
], columns=['a', 'b', 'c'])
In [21]: pd.concat(v[v.a == 'best'] for _, v in df.groupby('c'))
Out[21]:
a b c
0 best a x
2 best c x
3 best d y # <--- missing from the next statement
6 best d z
In [22]: df.groupby('c').filter(lambda g: g.a == 'best')
Out[22]:
a b c
0 best a x
2 best c x
6 best d z
```
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} | 3 | 2014-07-29T17:43:20Z | 2014-07-30T23:15:11Z | 2014-07-30T23:15:10Z | MEMBER | null | closes #7870
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} | 4 | 2014-07-29T19:36:57Z | 2015-04-14T14:21:22Z | 2015-04-14T14:19:43Z | CONTRIBUTOR | null | Currently some NotImplementedErrors don't have a description (most do!) .
```
raise NotImplemented
# should be
raise NotImplemented("short description of thing which isn't implemented")
```
I think this would be pretty useful, and makes it less confusing for users, and at least they would be pointed in the right direction to request said feature (and know which bit of specifically is NotImplemented)...
You can find them all with grep (probably more usefully the same regex in your text editor):
```
grep "NotImplementedError$" pandas/**/*.py -r -n
```
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} | 12 | 2014-07-29T19:42:25Z | 2014-11-18T11:18:52Z | 2014-11-18T11:18:52Z | NONE | null | The asof functionality int the at_time function gives a NotImplemented Error
`df1 = df.at_time(time(15, 0), asof=True)`
asof is very useful. If implemented it would allow us to use the at_time functionality and work with data that might not be complete, or don't have homogeneous time stamps. It would enable us to get the "closest" observation to the desired time for subsetting.
Thanks
Hernan
`NotImplementedError Traceback (most recent call last)
<ipython-input-28-3c10b7e963c9> in <module>()
----> 1 df2 = df1.at_time(time(15, 0), asof=True)
C:\Users\Hernan\AppData\Local\Enthought\Canopy\User\lib\site-packages\pandas\core\generic.pyc in at_time(self, time, asof)
2767 """
2768 try:
-> 2769 indexer = self.index.indexer_at_time(time, asof=asof)
2770 return self.take(indexer, convert=False)
2771 except AttributeError:
C:\Users\Hernan\AppData\Local\Enthought\Canopy\User\lib\site-packages\pandas\tseries\index.pyc in indexer_at_time(self, time, asof)
1689
1690 if asof:
-> 1691 raise NotImplementedError
1692
1693 if isinstance(time, compat.string_types):
NotImplementedError:`
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} | 3 | 2014-07-29T21:07:09Z | 2014-07-30T22:38:48Z | 2014-07-30T22:13:57Z | CONTRIBUTOR | null | Closes #7792
New `level` argument:
```
set_names(self, names, level=None, inplace=False)
set_levels(self, levels, level=None, inplace=False, verify_integrity=True)
set_labels(self, labels, level=None, inplace=False, verify_integrity=True)
```
e.g.
set_names('foo',level=1)
set_names(['foo','bar'],level=[1,2])
set_levels(['a','b','c'],level=1)
set_levels([['a','b','c'],[1,2,3]],level=[1,2])
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| null | 1 | 2014-07-30T08:56:03Z | 2014-07-30T11:06:51Z | 2014-07-30T11:06:51Z | NONE | null | ``` python
import pandas as pd
import numpy as np
from datetime import datetime
import pytz
print "This is working:"
data = {23:np.nan, 24:-1949, 25:-1848, 26: np.nan}
date_without_timezone = datetime.now()
df = pd.DataFrame(data, index=[date_without_timezone,])
print df
print
print df.index
print
print df.stack()
print
print df.stack().reset_index() #Good (expected) result
print
print "--------------------"
print
print "This is NOT working:"
date_with_timezone = datetime.now(pytz.utc)
df_with_timezone = pd.DataFrame(data, index=[date_with_timezone,])
print df_with_timezone
print
print df_with_timezone.index
print
print df_with_timezone.stack()
print
print df_with_timezone.stack().reset_index() # EXCEPTION: ValueError: Length of values does not match length of index
```
## INSTALLED VERSIONS
commit: None
python: 2.7.5.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: PL
pandas: 0.14.1
nose: None
Cython: None
numpy: 1.8.1
scipy: None
statsmodels: None
IPython: 2.1.0
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.1
pytz: 2014.2
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.3.1
openpyxl: 2.0.3
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: 2.5.3 (dt dec pq3 ext)
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| null | 4 | 2014-07-30T10:38:02Z | 2014-07-30T11:26:42Z | 2014-07-30T11:20:59Z | NONE | null | I have a DataFrame of stock close prices with a DatetimeIndex , `close`.
```
BARC:LON HSBA:LON LLOY:LON STAN:LON
timestamp
2014-07-03 08:02:00 217.551 601.938 76.2100 1223.36
2014-07-03 08:04:00 217.200 602.100 75.8520 1220.00
2014-07-03 08:06:00 217.500 602.700 75.8700 1221.50
2014-07-03 08:08:00 217.750 602.400 75.9777 1222.50
2014-07-03 08:10:00 218.450 602.675 75.9440 1221.50
```
I want to plot these in separate graphs for each day, so I do:
```
for day,data in close.groupby(close.index.date):
data.plot(title=day)
show()
```
But this throws
```
C:\Users\xxx\local\Enthought\Canopy\User\lib\site-packages\pandas\tseries\plotting.py in <module>()
19 TimeSeries_DateFormatter)
20
---> 21 from pandas.tools.plotting import _get_all_lines, _get_xlim
22
23 #----------------------------------------------------------------------
ImportError: cannot import name _get_all_lines
```
I can't find _get_all_lines anywhere. I recall doing this on my linux machine and it worked fine. This is running on Windows, pandas 0.14.1.
Also this works if I call matplotlib directly using plot(data), so the code doesn't seem to be at fault.
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} | 14 | 2014-07-30T12:24:35Z | 2015-07-03T17:44:56Z | 2015-07-03T17:44:56Z | CONTRIBUTOR | null | Feature request for supporting also Connection objects instead of only Engines.
Also reported in #8533, #10104
---
My use case - I often add a week's worth of data (stored in a pandas dataframe) to an existing table. Before I add the data, I want to be able to delete any existing data for this week from the table, but I want the whole thing to be wrapped in a transaction so I'm not left in a mess halfway through.
Here's what I therefore want to be able to do
``` python
# runs a transaction
with engine.begin() as connection:
r1 = connection.execute(table.delete().where(table.c[WEEK]==week))
data.to_sql(table.name, connection, if_exists='append')
```
i.e. my change is that I want to be able to pass a SQLAlchemy `Connection` object into `DataFrame.to_sql` instead of an `Engine`. (The underlying `Engine` of the `Connection` object can be accessed as `.engine`, so we should still be able to pull metadata etc)
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} | 3 | 2014-07-30T12:30:58Z | 2014-08-11T12:17:58Z | 2014-08-11T12:17:58Z | NONE | null | Hi,
I'm on pandas 0.14.1. Timestamp seems to lose nanoseconds when converting to string and back. Wonder if this is bug.
``` python
In [18]: pd.Timestamp('20010101 00:00:00')+pd.tseries.offsets.Nano()
Out[18]: Timestamp('2001-01-01 00:00:00.000000001')
In [19]: pd.Timestamp('2001-01-01 00:00:00.000000001')
Out[19]: Timestamp('2001-01-01 00:00:00')
```
Thanks,
Mark
## INSTALLED VERSIONS
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Linux
OS-release: 3.11.0-26-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.14.1
nose: 1.3.0
Cython: 0.19.2
numpy: 1.8.1
scipy: 0.13.2
statsmodels: 0.5.0
IPython: 0.13.2
sphinx: None
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.2
bottleneck: 0.8.0
tables: 3.0.0
numexpr: 2.3
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.8
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
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} | 17 | 2014-07-30T13:54:09Z | 2018-07-06T20:21:04Z | 2017-07-11T09:58:47Z | NONE | null | As already described in issue #3690, panel.to_frame discards all minor entries with nan in the data, which can be very confusing. I believe there should be a warning, first time data is dropped or the opposite should be the default behavior.
The warning could be treated similar to a ZeroDivisionWarning in numpy only on the first occurrence.
See below for an example:
``` python
df1 = pd.DataFrame(np.random.randn(2, 3), columns=['A', 'B', 'C'],
index=['foo', 'bar'])
df2 = pd.DataFrame(np.random.randn(2, 3), columns=['A', 'B', 'C'],
index=['foo', 'bar'])
df2.loc['foo', 'B'] = np.nan
mydict = {'df1': df1, 'df2': df2}
pd.Panel(mydict).to_frame()
```
Output:
| major | minor | df1 | df2 |
| --- | --- | --- | --- |
| foo | A | 1.9097545931480682 | -0.6710202447941566 |
| foo | C | 1.3335254610685865 | 1.53372538551507 |
| bar | A | 0.3145550744497975 | -1.7221352144306152 |
| bar | B | -0.15681197178861878 | -1.2308510354641322 |
| bar | C | -0.09598971674309852 | -0.1268630728124487 |
Using filter_observations=False, nan won't be dropped:
``` python
pd.Panel(mydict).to_frame(filter_observations=False)
```
Output:
| major | minor | df1 | df2 |
| --- | --- | --- | --- |
| foo | A | 1.9097545931480682 | -0.6710202447941566 |
| foo | B | 2.092552358833253 | |
| foo | C | 1.333525461068586 | 1.53372538551507 |
| bar | A | 0.3145550744497975 | -1.7221352144306152 |
| bar | B | -0.15681197178861878 | -1.2308510354641322 |
| bar | C | -0.09598971674309852 | -0.1268630728124487 |
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} | 3 | 2014-07-30T16:24:32Z | 2014-08-03T01:41:58Z | 2014-08-03T01:41:58Z | CONTRIBUTOR | null | When taking the intersection of two `DatetimeIndex`s that have timezone information and don't overlap, I get an`IndexError`. I don't have any problems if they don't have any timezone information or they do overlap.
``` python
import pandas as pd
a = pd.date_range('2014-07-07 12', freq='3min', periods=20,)
b = pd.date_range('2014-07-07 13', freq='3min', periods=20,)
print a.intersection(b)
c = a.tz_localize('MST')
d = b.tz_localize('MST')
print c.intersection(d)
```
``` python
<class 'pandas.tseries.index.DatetimeIndex'>
Length: 0, Freq: None, Timezone: None
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-17-b073ce8aa1f2> in <module>()
7 c = a.tz_localize('MST')
8 d = b.tz_localize('MST')
----> 9 print c.intersection(d)
/home/supas/forecasting_python/python_env_051214/lib/python2.7/site-packages/pandas/tseries/index.pyc in intersection(self, other)
1131 if isinstance(result, DatetimeIndex):
1132 if result.freq is None:
-> 1133 result.offset = to_offset(result.inferred_freq)
1134 return result
1135
/home/supas/forecasting_python/python_env_051214/lib/python2.7/site-packages/pandas/lib.so in pandas.lib.cache_readonly.__get__ (pandas/lib.c:36380)()
/home/supas/forecasting_python/python_env_051214/lib/python2.7/site-packages/pandas/tseries/index.pyc in inferred_freq(self)
1415 def inferred_freq(self):
1416 try:
-> 1417 return infer_freq(self)
1418 except ValueError:
1419 return None
/home/supas/forecasting_python/python_env_051214/lib/python2.7/site-packages/pandas/tseries/frequencies.pyc in infer_freq(index, warn)
657
658 index = pd.DatetimeIndex(index)
--> 659 inferer = _FrequencyInferer(index, warn=warn)
660 return inferer.get_freq()
661
/home/supas/forecasting_python/python_env_051214/lib/python2.7/site-packages/pandas/tseries/frequencies.pyc in __init__(self, index, warn)
697
698 if index.tz is not None:
--> 699 self.values = _tz_convert_with_transitions(self.values,'UTC',index.tz)
700
701 self.warn = warn
/home/supas/forecasting_python/python_env_051214/lib/python2.7/site-packages/pandas/tseries/frequencies.pyc in _tz_convert_with_transitions(values, to_tz, from_tz)
685 return tslib.tz_convert(values,to_tz,from_tz)
686
--> 687 return np.vectorize(f)(values)
688
689 class _FrequencyInferer(object):
/home/supas/forecasting_python/python_env_051214/lib/python2.7/site-packages/numpy/lib/function_base.pyc in __call__(self, *args, **kwargs)
1571 vargs.extend([kwargs[_n] for _n in names])
1572
-> 1573 return self._vectorize_call(func=func, args=vargs)
1574
1575 def _get_ufunc_and_otypes(self, func, args):
/home/supas/forecasting_python/python_env_051214/lib/python2.7/site-packages/numpy/lib/function_base.pyc in _vectorize_call(self, func, args)
1631 _res = func()
1632 else:
-> 1633 ufunc, otypes = self._get_ufunc_and_otypes(func=func, args=args)
1634
1635 # Convert args to object arrays first
/home/supas/forecasting_python/python_env_051214/lib/python2.7/site-packages/numpy/lib/function_base.pyc in _get_ufunc_and_otypes(self, func, args)
1594 # Assumes that ufunc first evaluates the 0th elements in the input
1595 # arrays (the input values are not checked to ensure this)
-> 1596 inputs = [asarray(_a).flat[0] for _a in args]
1597 outputs = func(*inputs)
1598
IndexError: index 0 is out of bounds for axis 0 with size 0
```
My system info is:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.17.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.1
nose: 1.3.3
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.0.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.2
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: 1.8.6
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.3.5
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
```
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} | 1 | 2014-07-30T16:50:51Z | 2014-07-30T17:56:01Z | 2014-07-30T17:56:01Z | CONTRIBUTOR | null | ```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
groupby_frame_apply_overhead | 9.1400 | 70.3743 | 0.1299 |
groupby_frame_apply | 43.5640 | 186.6427 | 0.2334 |
groupby_apply_dict_return | 39.6016 | 80.1926 | 0.4938 |
```
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} | 4 | 2014-07-30T18:52:32Z | 2021-02-07T19:00:26Z | null | NONE | null | The [Comparison with R](http://pandas.pydata.org/pandas-docs/stable/comparison_with_r.html) docs have a great section on how to go from R's `aggregate` function to Pandas's `groupby`, but as [this StackOverflow](http://stackoverflow.com/questions/25043927/dcast-replicate-in-python) question shows, there are similar but not identical problems going from R's `dcast` to Panda's `groupby` and `aggregate`.
I'm not sure exactly what the documentation should say here. (By the way, I'm also by no means sure that my answer to that question was the best one, so feel free to chime in there too…), just that maybe it should say _something_.
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} | 29 | 2014-07-30T20:41:51Z | 2019-12-31T15:04:44Z | 2019-12-31T15:04:44Z | CONTRIBUTOR | null | The original thread is [here](http://stackoverflow.com/questions/25046252/filling-in-with-the-last-entry-of-the-group?noredirect=1#comment38958154_25046252).
``` python
> df
C D E
A B
bar one -1.350006 0.260339 2
three -0.236451 -0.056614 0
flux six -0.515571 -0.155078 1
three -0.365032 1.055669 2
foo five 1.145811 1.514191 0
one -0.108427 -0.643262 2
two 0.286897 0.405798 2
two -1.452286 1.149264 2
```
The goal is to get a dataframe with the same index with each entry having the last value in the groups associated with `A`, i.e.
```
> df
C D E
A B
bar one -0.236451 -0.056614 2
three -0.236451 -0.056614 2
flux six -0.365032 1.055669 2
three -0.365032 1.055669 2
foo five -1.452286 1.149264 2
one -1.452286 1.149264 2
two -1.452286 1.149264 2
two -1.452286 1.149264 2
```
However, none of the following work:
- `df.groupby(level='A').apply(lambda x: x[-1])`
- `df.groupby(level='A').transform(pd.Series.last)`
- `df.groupby(level='A').transform(lambda x: x.iloc[-1])`
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} | 3 | 2014-07-31T02:13:35Z | 2014-08-20T01:51:33Z | 2014-08-02T13:46:41Z | CONTRIBUTOR | null | In the examples below with `min_periods=2`, `expanding_mean/std` start to give values once there are two values, but `ewma/std` only once there are three values. Is this intentional? It looks like a bug to me.
```
In [1]: from pandas import Series, expanding_mean, expanding_std, ewma, ewmstd, show_versions
In [2]: s = Series(range(4))
In [3]: expanding_mean(s, min_periods=2)
Out[3]:
0 NaN
1 0.5
2 1.0
3 1.5
dtype: float64
In [4]: expanding_std(s, min_periods=2)
Out[4]:
0 NaN
1 0.707107
2 1.000000
3 1.290994
dtype: float64
In [5]: ewma(s, min_periods=2, halflife=3.)
Out[5]:
0 NaN
1 NaN
2 1.152678
3 1.784530
dtype: float64
In [6]: ewmstd(s, min_periods=2, halflife=3.)
Out[6]:
0 NaN
1 NaN
2 0.856493
3 1.162100
dtype: float64
In [7]: show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.14.1
nose: 1.3.3
Cython: 0.20.2
numpy: 1.9.0b1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.1.0
sphinx: 1.2.2
patsy: 0.3.0
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.4
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
```
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} | 0 | 2014-07-31T02:50:57Z | 2014-09-07T14:30:53Z | 2014-09-07T14:30:53Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/25050015/resampling-over-dates-in-both-levels-of-a-multiindex-pandas-dataframe/25050074#25050074
```
from datetime import date
d0 = date.today() - timedelta(days=14)
dates = pd.date_range(d0, date.today())
date_index = pd.MultiIndex.from_product([dates, dates], names=['cohort_date', 'event_date'])
df = pd.DataFrame(np.random.randint(0, 100, 225), index=date_index)
```
Works
```
df.reset_index().groupby([pd.Grouper(key='cohort_date',freq='W'),pd.Grouper(key='event_date',freq='W')]).sum()
```
This should be equivalent, but raises
```
df.groupby([pd.Grouper(level='cohort_date',freq='W'),pd.Grouper(level='event_date',freq='W')]).sum()
```
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} | 17 | 2014-07-31T11:32:38Z | 2014-08-01T00:54:35Z | 2014-07-31T17:03:41Z | CONTRIBUTOR | null | related #4088
related #7867
I think this should raise as this is not clear how this should work (e.g. should you get all the other levels set to `nan`?)
or see my comment below, maybe just document?
```
In [11]: s = pd.Series(np.arange(9),index=pd.MultiIndex.from_product([['A','B','C'],['foo','bar','baz']],names=['one','two'])).sortlevel()
In [12]: s
Out[12]:
one two
A bar 1
baz 2
foo 0
B bar 4
baz 5
foo 3
C bar 7
baz 8
foo 6
dtype: int64
In [13]: s.reindex(['A','B','D'],level=0)
Out[13]:
one two
A bar 1
baz 2
foo 0
B bar 4
baz 5
foo 3
dtype: int64
```
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} | 8 | 2014-07-31T11:36:43Z | 2021-04-11T04:59:23Z | 2021-04-11T04:59:23Z | CONTRIBUTOR | null | from #7867
```
In [1]: s = pd.Series([1,2,3])
In [2]: s
Out[2]:
0 1
1 2
2 3
dtype: int64
# this is de-facto: s.reindex([[1,2,3]])
In [3]: s.loc[[1,2,3]]
Out[3]:
1 2
2 3
3 NaN
dtype: float64
```
should this work?
```
In [4]: s.loc[[1,2,3]] = 4
KeyError: '[3] not in index'
```
(Doing this with only included labels works)
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} | 0 | 2014-07-31T12:58:58Z | 2014-09-14T01:09:42Z | 2014-09-14T01:09:42Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/25059140/pandas-multiindex-not-working-with-read-csv-and-datetime-objects/25059472#25059472
```
In [3]: date1 = datetime.date.today()
In [4]: date2 = datetime.date.today().replace(month=1)
In [5]: date_cols=['date1', 'date2']
In [6]: index = pd.MultiIndex.from_product([[date1],[date2]])
```
The first level of this multi-index is a `Index` of object dtyped `datetimes`, rather than a `DatetimeIndex`.
might be a bug in the creation process
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} | 30 | 2014-07-31T16:42:43Z | 2017-12-13T22:22:23Z | 2017-12-13T22:22:19Z | NONE | null | ```
In [1]: import pandas as pd
In [2]: pd.Series().product()
Out[2]: nan
```
I excepted:
```
Out[2]: 1
```
http://en.wikipedia.org/wiki/Empty_product
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} | 4 | 2014-07-31T17:03:42Z | 2014-08-04T20:26:24Z | 2014-08-04T20:26:24Z | CONTRIBUTOR | null | xref #7886
It would be nice to be able to write:
``` python
s[s.index.isin(['a', 'b', 'c'], level='foo')]
```
instead of
``` python
s[s.index.get_level_values('foo').isin(['a', 'b', 'c'])]
```
The former variant is not only shorter but is also potentially more performant: one only has to look up levels in given container and then operate on labels only avoiding instantiation of sub-index completely.
Default value should be `None`, plain `Index` objects should only accept `None`, `0`, and, `Index.name` for the sake of consistency (the last one is especially arguable).
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} | 39 | 2014-07-31T17:15:09Z | 2014-08-07T12:10:59Z | 2014-08-07T11:34:54Z | CONTRIBUTOR | null | make `Index` now subclass `PandasObject/IndexOpsMixin` rather than `ndarray`
should allow much easier new Index classes (e.g. #7640)
This doesn't change the public API at all, and provides compat
closes #5080
back compat for pickles is now way simpler
ToDo:
- docs
- [x] release note
- [x] warnings in io.rst about this change
- [x] fixes `.repeat` on MultiIndex (broken in master)
- [x] minor API compat issue with comparisons of `DatetimeIndex` with `NaT` vs ndarrays
- [x] merge with searchsorted issues/PR ( #6712, #7447, #6469)
- [x] tests fixed (FIXMES), just a few left
- [x] perf
- [x] json completely broken ATM
- [x] #7796 FIXME (PeriodIndex not supported in HDF), not really a big deal
- [x] #7439 since `Index` now doesn't have implicit ops (aside from `__sub__/__add__`, these need to be added in (e.g. `__mul__,__div__,__truediv__`).
- [x] bool(Index/MultiIndex) : https://github.com/pydata/pandas/issues/7897 (will address this later)
closes #5155 (perf fix for Period creation), slight increase on the plotting
because of the the plottling routines holding array of Periods (rather than PeriodIndex).
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
period_setitem | 16.2210 | 122.9340 | 0.1319 |
timeseries_iter_periodindex | 1197.7839 | 6906.1464 | 0.1734 |
timeseries_iter_periodindex_preexit | 12.4850 | 69.8563 | 0.1787 |
timeseries_period_downsample_mean | 11.2850 | 11.1457 | 1.0125 |
plot_timeseries_period | 107.6056 | 86.5277 | 1.2436 |
```
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} | 10 | 2014-07-31T20:01:01Z | 2014-11-02T21:35:14Z | 2014-08-04T20:26:24Z | CONTRIBUTOR | null | closes #7890
This PR adds `level` kwarg for `Index` objects as discussed (briefly in #7890):
``` python
Index.isin(iterable, level=None)
```
### Summary
- for non-`MultiIndex` classes:
- no functional changes in the actual membership test
- valid `level` values are `None`, `0`, `-1` and `self.name`
- if `level` doesn't match the name, it's a `KeyError` (was `AssertionError` before)
- for `MultiIndex` classes:
- if `level=None`, elements of `self.values` (tuples) are used
- otherwise elements of corresponding level are used
- valid `level` values are the same as in `MultiIndex.get_level_values` method, `-self.nlevels..(self.nlevels - 1)` plus all unique index names
### TODO
- [x] add to documentation
- [x] optimize MultiIndex case
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} | 7 | 2014-07-31T21:23:52Z | 2021-05-10T21:45:31Z | 2021-05-10T21:45:31Z | CONTRIBUTOR | null | xref #13421 with a `MultiIndex` as the columns
Hi,
I encountered an edge case in DataFrame initialization with something like the following:
``` python
In [1]: from pandas import *
In [2]: s = Series(1, name='foo')
In [3]: df = DataFrame(s, columns=['bar'])
In [4]: df
Empty DataFrame
Columns: [bar]
Index: []
```
This happens in both 0.14.1 and 0.13.1, but this isn't really a bug as the docs exclude `Series` as a valid type for `data=`. That being said, this casting appears to work whenever `.name` is `None` or when `.name` equals what's passed to `columns=`, so failure in this particular case is rather surprising.
The mechanism appears to be:
- `DataFrame.__init__` upgrades `.name` to the column name, if it is not `None`
- Then, the data columns are sliced with the list passed to `columns=`, resulting in an empty data set when the two differ.
- This seems to only occur when a `Series` is directly passed as `data=`. I can't get this to occur with `[Series, ...]` or a dict of `Series`.
The options I see are (in order of my personal preference):
- do the implicit rename (only occurs with single `Series`, so no ambiguity)
- just don't allow a `Series` being passed as `data=`.
- throw an exception due to the ambiguity
I don't see just documenting this behavior as being viable, as this edge case effectively leads to data loss.
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| null | 31 | 2014-07-31T21:52:23Z | 2014-08-02T13:47:56Z | 2014-08-02T13:47:56Z | NONE | null | I have a large dataset (4 million rows, 50 columns) stored via pandas store.append. When I use either store.select or read_hdf with a query for 2 columns being greater than a certain value (i.e. "(a > 10) & (b > 1)" I get 15,000 or so rows returned.
When I read in the entire table, as say df, and do df[(df.a > 10) & (df.b > 1)] I get 30,000 rows. I narrowed down the problem - when I read in the entire table and do df.query("(a > 10) & (b > 1)") it's the same 15,000 rows, but when I set the engine to python ---> df.query("(a > 10) & (b > 1)", engine = 'python') I get the 30,000 rows.
I suspect it has something to do with the eval/numexpr method of querying in the HDF and Query methods.
The types are float64's in columns a and b, and even if I query with a float (ie. 1. instead of 1) the problem still persists.
I would appreciate any feedback or if others have the same problem we need to fix this.
Regards,
Neil
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} | 6 | 2014-08-01T00:51:23Z | 2021-04-11T05:01:19Z | null | CONTRIBUTOR | null | xref #10347
I'm having trouble understand how exactly reindex is supposed to work in the case of MultiIndex. What I would like is the exact behavior in the Index case where values can be filled or padded according to some level of the MultiIndex. I can't understand what reindex is doing in the MultiIndex case. The below is an example where I would like to interpolate using the prior value a month frame to an hourly frame.
```
import pandas as pd
d1 = pd.date_range('1/1/2012', '3/1/2012', freq='MS')
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, index=d1)
dfu = df.unstack()
dfu
Out[12]:
A 2012-01-01 1
2012-02-01 2
2012-03-01 3
B 2012-01-01 4
2012-02-01 5
2012-03-01 6
dtype: int64
d2 = pd.date_range('1/1/2012', '3/31/2012', freq='D')
dfu.reindex(d2, level=1) # What is this supposed to do?
Out[17]:
A 2012-01-01 1
2012-02-01 2
2012-03-01 3
B 2012-01-01 4
2012-02-01 5
2012-03-01 6
dtype: int64
df.reindex(d2, method='pad').unstack() #Ideally something like this, but without the trickery
Out[19]:
A 2012-01-01 1
2012-01-02 1
2012-01-03 1
2012-01-04 1
2012-01-05 1
2012-01-06 1
2012-01-07 1
2012-01-08 1
2012-01-09 1
2012-01-10 1
2012-01-11 1
2012-01-12 1
2012-01-13 1
2012-01-14 1
2012-01-15 1
...
B 2012-03-17 6
2012-03-18 6
2012-03-19 6
2012-03-20 6
2012-03-21 6
2012-03-22 6
2012-03-23 6
2012-03-24 6
2012-03-25 6
2012-03-26 6
2012-03-27 6
2012-03-28 6
2012-03-29 6
2012-03-30 6
2012-03-31 6
Length: 182, dtype: int64
```
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} | 10 | 2014-08-01T16:37:34Z | 2014-09-10T00:12:18Z | 2014-08-01T18:30:44Z | CONTRIBUTOR | null | The previous code in https://github.com/pydata/pandas/pull/7603 wasn't quite right for the case that both `adjust=False` and `ignore_na=False`. This PR fixes the handling of that case. Note that `ignore_na=False` behavior is new in v0.15.0, so this bug and its fix don't affect any prior behavior.
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} | 6 | 2014-08-01T17:19:09Z | 2014-08-07T14:27:08Z | 2014-08-07T14:27:08Z | NONE | null | This means that patterns such as:
``` python
if my_frame.index:
# do something
```
with `my_frame` having a `MultiIndex` will never enter the `if` block. One can check that `bool(my_frame.index)` returns False. A single index (`pandas.Index`) will (correctly IMO) raise a `ValueError` warning that the truth value of an array is ambiguous. However, a `MultiIndex` fails silently.
This is such an awesome source of silent bugs that I'm surprised this hasn't come up before. Is there any reason why we want `bool` to return `False` every time for a `MultiIndex`?
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} | 6 | 2014-08-01T19:00:22Z | 2014-09-10T00:12:07Z | 2014-08-02T13:46:41Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/7884.
This addresses only the `ewm*()` functions' off-by-one interpretation of `min_periods`.
It doesn't address the inconsistency between the `ewm*()` functions and the `rolling_*()` functions in the meaning of `min_periods`.
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} | 19 | 2014-08-01T19:10:43Z | 2015-04-14T14:21:28Z | 2015-04-14T14:21:12Z | NONE | null | closes #7872
I have updated the NonImplementedError messages #7872 in all places applicable.
It includes changes to statements of type
- raise NotImplementedError
- raise NotImplementedError()
Comment on the changes.
The one place I have doubt and not sure whether to add one or not would be https://github.com/pydata/pandas/blob/master/.coveragerc#L17
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} | 15 | 2014-08-01T22:29:59Z | 2014-09-15T06:23:09Z | 2014-09-10T00:02:26Z | CONTRIBUTOR | null | I noticed (in https://github.com/pydata/pandas/issues/7884) that `ewmvar`, `ewmstd`, `ewmvol`, `ewmcov`, `rolling_var`, and `rolling_std` return `0.0` for a single value (assuming `min_periods=0`); whereas `Series.std`, `Series.var`, `ewmcorr`, `expanding_cov`, `expanding_corr`, `rolling_cov`, and `rolling_corr` all return `NaN` for a single value. `expanding_std` and `expanding_var` produce `Value Error: min_periods (2) must be <= window (1)`.
I think all of these should all return `NaN` for a single value. At any rate, I would expect greater consistency one way or the other.
Mildly related, when calculating the correlation of a constant series with itself, `Series.corr()`, `expanding_corr`, and `rolling_corr` return `NaN`, while `ewmcorr` sometimes returns `NaN`, sometimes `1` and sometimes `-1`, due to numerical accuracy issues. Actually, as shown in a separate comment below, `rolling_corr` also produces inconsistent results for a constant subseries following different prior values.
Inconsistencies in calculating second moments of a single point:
```
Python 3.4.1 (v3.4.1:c0e311e010fc, May 18 2014, 10:45:13) [MSC v.1600 64 bit (AMD64)]
Type "copyright", "credits" or "license" for more information.
IPython 2.1.0 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]: from pandas import Series, ewmvar, ewmstd, ewmvol, ewmcov, rolling_var, rolling_std, ewmcorr, expanding_cov, expanding_corr,
expanding_std, expanding_var, rolling_cov, rolling_corr
In [2]: s = Series([1.])
In [3]: ewmvar(s, halflife=2., min_periods=0)
Out[3]:
0 0
dtype: float64
In [4]: ewmstd(s, halflife=2., min_periods=0)
Out[4]:
0 0
dtype: float64
In [5]: ewmvol(s, halflife=2., min_periods=0)
Out[5]:
0 0
dtype: float64
In [6]: ewmcov(s, s, halflife=2., min_periods=0)
Out[6]:
0 0
dtype: float64
In [7]: rolling_var(s, window=2, min_periods=0)
Out[7]:
0 0
dtype: float64
In [8]: rolling_std(s, window=2, min_periods=0)
Out[8]:
0 0
dtype: float64
In [9]: s.std()
Out[9]: nan
In [10]: s.var()
Out[10]: nan
In [11]: ewmcorr(s, s, halflife=2., min_periods=0)
Out[11]:
0 NaN
dtype: float64
In [12]: expanding_cov(s, s, min_periods=0)
Out[12]:
0 NaN
dtype: float64
In [13]: expanding_corr(s, s, min_periods=0)
Out[13]:
0 NaN
dtype: float64
In [16]: rolling_cov(s, s, window=3, min_periods=0)
Out[16]:
0 NaN
dtype: float64
In [17]: rolling_corr(s, s, window=3, min_periods=0)
Out[17]:
0 NaN
dtype: float64
In [14]: expanding_std(s, min_periods=0)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-14-7320ee579e6a> in <module>()
----> 1 expanding_std(s, min_periods=0)
C:\Python34\lib\site-packages\pandas\stats\moments.py in f(arg, min_periods, freq, center, **kwargs)
825 return func(arg, window, minp, **kwds)
826 return _rolling_moment(arg, window, call_cython, min_periods, freq=freq,
--> 827 center=center, **kwargs)
828
829 return f
C:\Python34\lib\site-packages\pandas\stats\moments.py in _rolling_moment(arg, window, func, minp, axis, freq, center, how, args, kwa
rgs, **kwds)
345 result = np.apply_along_axis(calc, axis, values)
346 else:
--> 347 result = calc(values)
348
349 rs = return_hook(result)
C:\Python34\lib\site-packages\pandas\stats\moments.py in <lambda>(x)
339 arg = _conv_timerule(arg, freq, how)
340 calc = lambda x: func(x, window, minp=minp, args=args, kwargs=kwargs,
--> 341 **kwds)
342 return_hook, values = _process_data_structure(arg)
343 # actually calculate the moment. Faster way to do this?
C:\Python34\lib\site-packages\pandas\stats\moments.py in call_cython(arg, window, minp, args, kwargs, **kwds)
823 def call_cython(arg, window, minp, args=(), kwargs={}, **kwds):
824 minp = check_minp(minp, window)
--> 825 return func(arg, window, minp, **kwds)
826 return _rolling_moment(arg, window, call_cython, min_periods, freq=freq,
827 center=center, **kwargs)
C:\Python34\lib\site-packages\pandas\stats\moments.py in <lambda>(*a, **kw)
604 how='median')
605
--> 606 _ts_std = lambda *a, **kw: _zsqrt(algos.roll_var(*a, **kw))
607 rolling_std = _rolling_func(_ts_std, 'Unbiased moving standard deviation.',
608 check_minp=_require_min_periods(1))
C:\Python34\lib\site-packages\pandas\algos.pyd in pandas.algos.roll_var (pandas\algos.c:28990)()
C:\Python34\lib\site-packages\pandas\algos.pyd in pandas.algos._check_minp (pandas\algos.c:16394)()
ValueError: min_periods (2) must be <= window (1)
In [15]: expanding_var(s, min_periods=0)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-15-28e2018aee68> in <module>()
----> 1 expanding_var(s, min_periods=0)
C:\Python34\lib\site-packages\pandas\stats\moments.py in f(arg, min_periods, freq, center, **kwargs)
825 return func(arg, window, minp, **kwds)
826 return _rolling_moment(arg, window, call_cython, min_periods, freq=freq,
--> 827 center=center, **kwargs)
828
829 return f
C:\Python34\lib\site-packages\pandas\stats\moments.py in _rolling_moment(arg, window, func, minp, axis, freq, center, how, args, kwa
rgs, **kwds)
345 result = np.apply_along_axis(calc, axis, values)
346 else:
--> 347 result = calc(values)
348
349 rs = return_hook(result)
C:\Python34\lib\site-packages\pandas\stats\moments.py in <lambda>(x)
339 arg = _conv_timerule(arg, freq, how)
340 calc = lambda x: func(x, window, minp=minp, args=args, kwargs=kwargs,
--> 341 **kwds)
342 return_hook, values = _process_data_structure(arg)
343 # actually calculate the moment. Faster way to do this?
C:\Python34\lib\site-packages\pandas\stats\moments.py in call_cython(arg, window, minp, args, kwargs, **kwds)
823 def call_cython(arg, window, minp, args=(), kwargs={}, **kwds):
824 minp = check_minp(minp, window)
--> 825 return func(arg, window, minp, **kwds)
826 return _rolling_moment(arg, window, call_cython, min_periods, freq=freq,
827 center=center, **kwargs)
C:\Python34\lib\site-packages\pandas\algos.pyd in pandas.algos.roll_var (pandas\algos.c:28990)()
C:\Python34\lib\site-packages\pandas\algos.pyd in pandas.algos._check_minp (pandas\algos.c:16394)()
ValueError: min_periods (2) must be <= window (1)
In [20]: show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.14.1
nose: 1.3.3
Cython: 0.20.2
numpy: 1.9.0b1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.1.0
sphinx: 1.2.2
patsy: 0.3.0
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.4
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
```
Instability in `ewmcorr` of a constant series with itself:
```
In [1]: from pandas import Series, ewmcorr, expanding_corr, rolling_corr
In [2]: s = Series([5., 5., 5.])
In [3]: s.corr(s)
Out[3]: nan
In [4]: expanding_corr(s, s)
Out[4]:
0 NaN
1 NaN
2 NaN
dtype: float64
In [5]: rolling_corr(s, s, window=3)
Out[5]:
0 NaN
1 NaN
2 NaN
dtype: float64
In [6]: ewmcorr(s, s, halflife=3.)
Out[6]:
0 -1
1 -1
2 -1
dtype: float64
In [9]: ewmcorr(Series([3., 3., 3.]), halflife=3.)
Out[9]:
0 NaN
1 1
2 1
dtype: float64
```
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