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| null | 4 | 2014-11-13T11:59:35Z | 2014-11-13T15:07:16Z | 2014-11-13T12:50:49Z | NONE | null | I recently updated Pandas and found this strange behaviour which broke some of my existing code.
I was using a column of Datetime.date objects as a the second level in a two-level MulitIndex.
However, when setting the index with the latest version, the Datetime.date objects are converted to Timestamp objects with 00:00:00 as the time component:
> > > pd.**version**
> > > '0.15.1'
> > > df
> > > 0 ID date
> > > 0 0.486567 10 2014-11-12
> > > 1 0.214374 20 2014-11-13
> > > df.date[0]
> > > datetime.date(2014, 11, 12)
> > > df.set_index(['ID', 'date']).index[0](10, Timestamp%28'2014-11-12 00:00:00'%29)
This doesn't happen with version 0.14 or older.
There is a hack to get around it, setting the dates to a single level index, adding the other level and then swapping:
> > > df.set_index('date').set_index('ID', append=True).index.swaplevel(0, 1)[0](10, datetime.date%282014, 11, 12%29)
This seems strange and I wondered was it intentional.
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| null | 8 | 2014-11-13T16:41:53Z | 2020-04-10T02:36:37Z | null | CONTRIBUTOR | null | `DateParseError` is raised when Series with DateTimeIndex is plotted using bar-plot on axis that already contains another plot:
```
import pandas as pd
df = pd.Series([4,2,1,3,5], index=pd.to_datetime(['2004','2005','2006','2007','2008']))
df.plot()
df.plot(kind='bar')
```
Both of plots work independently, but being called one after another they result in `DateParseError: day is out of range for month`
If called in different order:
```
df.plot(kind='bar')
df.plot()
```
exception is not raised, but x-axis scale is incorrect
pandas version: 0.15.1
numpy verson: 1.9.1
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| null | 13 | 2014-11-13T17:32:54Z | 2014-11-26T13:47:28Z | 2014-11-20T15:20:53Z | CONTRIBUTOR | null | ``` python
x = pd.Index([1, 2, 3])
y = pd.Index(['a', 'b', 'c'])
x.union(y)
```
Sorry no stack trace (on a remote server). Raises with an AttributeError and a message about no attribute is_monotonic_increasing. This is on 0.15.1
I think I saw a thing in the release notes about adding that property in 0.15.1 and I can confirm that the example worked in 0.15.0.
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} | 5 | 2014-11-13T17:49:29Z | 2021-08-23T15:19:57Z | null | CONTRIBUTOR | null | Running 0.15.1
```
DataFrame.update(other, join='left', overwrite=True, filter_func=None, raise_conflict=False)
Modify DataFrame in place using non-NA values from passed DataFrame. Aligns on indices
Parameters :
other : DataFrame, or object coercible into a DataFrame
join : {‘left’, ‘right’, ‘outer’, ‘inner’}, default ‘left’
```
But..
```
C:\Anaconda\lib\site-packages\pandas\core\frame.pyc in update(self, other, join, overwrite, filter_func, raise_conflict)
3292 # TODO: Support other joins
3293 if join != 'left': # pragma: no cover
-> 3294 raise NotImplementedError("Only left join is supported")
3295
3296 if not isinstance(other, DataFrame):
NotImplementedError: Only left join is supported
```
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| null | 4 | 2014-11-13T20:37:20Z | 2014-11-14T15:36:40Z | 2014-11-14T15:36:26Z | NONE | null | Fix for #3505
This PR allows to directly write filters to ga.py `read_ga` function.
Currently only **OR** filters are supported. with this PR we can support directly writing the filters as a string, allowing for example **AND** filtering.
**Example here**
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| null | 1 | 2014-11-13T22:06:21Z | 2014-11-13T22:29:18Z | 2014-11-13T22:28:46Z | CONTRIBUTOR | null | Consider the following:
```
> df['lifespan'].head()
0 -7 days +13:01:00
1 -26 days +00:01:00
2 -18 days +00:01:00
3 -7 days +13:01:00
4 -42 days +00:01:00
Name: flight_time_lifespan, dtype: timedelta64[ns]
```
```
> df['lifespan'].values[:5]
array([ -557940000000000, -2246340000000000, -1555140000000000,
-557940000000000, -3628740000000000], dtype='timedelta64[ns]')
```
```
> df['lifespan'][0].days
-7
```
But this does not work:
```
> df['lifespan'].apply(lambda x: x.days)
AttributeError: 'numpy.timedelta64' object has no attribute 'days'
```
Nor does this:
```
> df['flight_time_lifespan'].days
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-29-b649e97cca35> in <module>()
----> 1 df['flight_time_lifespan'].days
/Users/josh/anaconda3/envs/py34/lib/python3.4/site-packages/pandas/core/generic.py in __getattr__(self, name)
1934 return self[name]
1935 raise AttributeError("'%s' object has no attribute '%s'" %
-> 1936 (type(self).__name__, name))
1937
1938 def __setattr__(self, name, value):
AttributeError: 'Series' object has no attribute 'days'
```
Nor does this:
```
> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.2.final.0
python-bits: 64
OS: Darwin
OS-release: 14.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.15.1
nose: 1.3.4
Cython: 0.21
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.3.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.1
pytz: 2014.9
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.2
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: 2.5.4 (dt dec pq3 ext)
```
The only thing that worked was:
```
df['days'] = [x.days for x in df['lifespan']]
```
I have tried restarting things several times without luck.
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} | 10 | 2014-11-14T00:18:43Z | 2014-11-17T15:25:57Z | 2014-11-17T15:24:10Z | CONTRIBUTOR | null | closes #8584
Stacking should now work as expected for the cases described in the bug report (#8584). So we can pass a list of both ints and strs to `DataFrame.stack`, provided the ints are level names. Mixed ints and strs when the ints aren't level names raises `ValueError`, but that's unchanged.
I've added a new hidden method `_swaplevel_assume_numbers` since the main cause of the bug was
ambiguity between ints as names and ints as level numbers, and this allows us to make sure the
swap is done correctly once we reach that point.
Another alternative would have been to add an `as_numbers=True` flag to the existing `swaplevel` method. We could keep existing behaviour intact by having the default be `as_numbers=False`. If that
seems like the better option now that you've seen the PR, let me know and it should be pretty easy
to switch.
``` python
import pandas as pd
from pandas import DataFrame, MultiIndex
from numpy.random import randn
columns = MultiIndex.from_tuples([('A', 'cat', 'long'), ('B', 'cat', 'long'), ('A', 'dog', 'short'), ('B', 'dog', 'short')],
names=['exp', 'animal', 'hair_length'])
df = DataFrame(randn(4, 4), columns=columns)
df.columns.names = ['exp', 'animal', 1]
print(df.stack(level=['animal', 1]))
# exp A B
# animal 1
#0 cat long 0.758850 0.327957
# dog short 0.429752 0.236901
#1 cat long 0.572633 1.057861
# dog short 0.802539 1.321191
#2 cat long 0.283352 0.088000
# dog short -0.814458 -1.688529
#3 cat long 2.009912 -1.738292
# dog short 1.272569 -0.133434
df.columns.names = ['exp', 'animal', 0]
print(df.stack(level=['animal', 0]))
# exp A B
# animal 0
#0 cat long 0.758850 0.327957
# dog short 0.429752 0.236901
#1 cat long 0.572633 1.057861
# dog short 0.802539 1.321191
#2 cat long 0.283352 0.088000
# dog short -0.814458 -1.688529
#3 cat long 2.009912 -1.738292
# dog short 1.272569 -0.133434
```
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} | 1 | 2014-11-14T01:16:21Z | 2014-11-14T12:45:52Z | 2014-11-14T12:45:36Z | CONTRIBUTOR | null | Not a huge issue, just skipping an incompatible test when testing with matplotlib 1.2.
closes #8648.
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| null | 4 | 2014-11-14T01:36:36Z | 2014-11-15T09:55:31Z | 2014-11-15T09:55:31Z | CONTRIBUTOR | null | Added a get_group example in the Iterating through groups section of grouby.html to demonstrate selecting
single group from a DataFrameGrouBy object
Fixed typo in README
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} | 5 | 2014-11-14T03:42:40Z | 2014-11-16T14:09:39Z | 2014-11-16T14:09:36Z | CONTRIBUTOR | null | Fixes #8558. As far as I can tell the two plot types where this applies are line and bar plots, if there any others they should also be easy to fix. Bar and line plots no longer include the index name:
``` python
import pandas as pd
from pandas import Series
import matplotlib.pyplot as plt
s = Series([1, 2, 3], index=['a', 'b', 'c'])
s.index.name = 'The Index'
ax = s.plot(use_index=False)
plt.show()
```

``` python
ax2 = s.plot(kind='bar', use_index=False)
plt.show()
```
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} | 17 | 2014-11-14T04:23:32Z | 2014-11-26T02:30:02Z | 2014-11-26T02:30:02Z | MEMBER | null | I encountered this today when rerunning a script that worked prior to the 0.15 arrival of Timedelta:
```
In [2]: pd.to_timedelta(10, unit='D') * pd.Series(range(10))
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-a5e3bc3be480> in <module>()
----> 1 pd.to_timedelta(10, unit='D') * pd.Series(range(10))
/Users/shoyer/dev/pandas/pandas/tslib.so in pandas.tslib.Timedelta.__mul__ (pandas/tslib.c:31355)()
TypeError: cannot multiply a Timedelta with <class 'pandas.core.series.Series'>
```
@jreback I think there might be another issue for this already?
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} | 20 | 2014-11-14T07:56:34Z | 2015-03-11T01:35:33Z | 2015-03-11T01:35:33Z | MEMBER | null | From http://stackoverflow.com/questions/26924904/check-if-dataframe-column-is-categorical/26925340#26925340
What is the preferred way to check for categorical dtype?
I now answered:
```
In [42]: isinstance(df.cat_column.dtype, pd.core.common.CategoricalDtype)
Out[42]: True
In [43]: pd.core.common.is_categorical_dtype(df.cat_column)
Out[43]: True
```
But:
- this seems somewhat buried in pandas. Should there be a more top-level function to do this?
- we should add the preferred way to the categorical docs.
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} | 3 | 2014-11-14T09:33:27Z | 2015-03-06T02:47:09Z | 2015-03-06T02:47:09Z | MEMBER | null | From http://stackoverflow.com/questions/26918045/python-pandas-typeerror-cannot-compare-type-timestamp-with-type-float
The docstring of `Index.asof` says:
```
In [73]: pd.Index.asof?
Type: instancemethod
String form: <unbound method Index.asof>
File: c:\anaconda\lib\site-packages\pandas\core\index.py
Definition: pd.Index.asof(self, label)
Docstring:
For a sorted index, return the most recent label up to and including
the passed label. Return NaN if not found
```
while `asof` only works for a time series.
- Clarify the docs that `asof` only works for `DatetimeIndex` (and PeriodIndex?)
- Why is this actually restricted to timestamps? This functions seems also possibly useful to me for other index types? Something like
```
In [74]: idx = pd.Index([1,2,3,8,9])
In [75]: idx.asof(5) # gives now TypeError
3
```
Instead of having to do:
```
In [78]: idx[idx.searchsorted(5) - 1]
Out[78]: 3
```
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| null | 25 | 2014-11-14T13:07:03Z | 2014-11-18T13:23:22Z | 2014-11-18T13:23:22Z | CONTRIBUTOR | null | {
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| null | 2 | 2014-11-14T15:08:10Z | 2014-11-14T15:13:42Z | 2014-11-14T15:13:21Z | NONE | null | When shifting a DateTimeIndex with a StaticTzInfo (namely EST in my case), I'm seeing odd behavior in which it first seems to be shifting -5 (the offset for EST), and then shifting relative to that:
``` python
import pandas as pd
import pytz
import datetime
dt = datetime.datetime(2014, 11, 14, 0)
dt_est = pytz.timezone('EST').localize(dt)
s = pd.Series(data=[1], index=[dt_est])
s.shift(0, freq='h') # 2014-11-14 00:00:00-05:00 (seems okay)
s.shift(-1, freq='h') # 2014-11-13 18:00:00-05:00 (expected 2014-11-13 23:00:00)
s.shift(1, freq='h') # 2014-11-13 20:00:00-05:00 (expected 2014-11-14 01:00:00)
s.shift(-1, freq='s') # 2014-11-13 18:59:59-05:00 (same with other freq)
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-39-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.0
nose: 1.3.4
Cython: 0.21.1
numpy: 1.9.0
scipy: 0.14.0
statsmodels: None
IPython: 2.3.0
sphinx: None
patsy: 0.3.0
dateutil: 2.2
pytz: 2014.4
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.0
openpyxl: 1.8.6
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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| null | 1 | 2014-11-14T15:59:40Z | 2014-11-14T16:02:16Z | 2014-11-14T16:02:07Z | NONE | null | ```
import pandas as pd
import numpy as np
s = pd.Series(np.random.randn(100))
pd.cut(s, 10, retbins=True)
```
This throws
```
ValueError: Wrong number of items passed 2, placement implies 100
```
Am I doing something obviously wrong?
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} | 1 | 2014-11-14T19:36:58Z | 2021-04-11T05:33:22Z | null | CONTRIBUTOR | null | from #8626 discussion
cc @fkaufer
allow `pd.factorize` to take a Dataframe and process with tuples of columns.
further allow `DataFrame.factorize` with a `subset` argument which just calls `pd.factorize`
impl is below (e.g. simple fast-zipping then factorizing after dense conversion)
just needs some tests
```
In [41]: df = pd.DataFrame({'A':['a1','a1','a2','a2','a1'], 'B':['b1','b2','b1','b2','b1']})
In [42]: df
Out[42]:
A B
0 a1 b1
1 a1 b2
2 a2 b1
3 a2 b2
4 a1 b1
In [43]: cols_as_tuples = pd.lib.fast_zip([df[col].get_values() for col in df.columns])
In [44]: cols_as_tuples
Out[44]: array([('a1', 'b1'), ('a1', 'b2'), ('a2', 'b1'), ('a2', 'b2'), ('a1', 'b1')], dtype=object)
In [47]: pd.factorize(cols_as_tuples)
Out[47]:
(array([0, 1, 2, 3, 0]),
array([('a1', 'b1'), ('a1', 'b2'), ('a2', 'b1'), ('a2', 'b2')], dtype=object))
In [48]: pd.Categorical(cols_as_tuples)
Out[48]:
[(a1, b1), (a1, b2), (a2, b1), (a2, b2), (a1, b1)]
Categories (4, object): [(a1, b1) < (a1, b2) < (a2, b1) < (a2, b2)]
In [59]: pd.Categorical(df.to_records(index=False))
Out[59]:
[(a1, b1), (a1, b2), (a2, b1), (a2, b2), (a1, b1)]
Categories (4, object): [(a1, b1) < (a1, b2) < (a2, b1) < (a2, b2)]
```
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} | 14 | 2014-11-14T23:00:23Z | 2020-05-16T03:37:49Z | null | NONE | null | With Oracle Database Express Edition 11g Release 2 in use as a SQL DB I have tried to reproduce **Writing DataFrames** example from http://pandas.pydata.org/pandas-docs/stable/io.html#io-sql
``` python
import pandas as pd
from cStringIO import StringIO
from sqlalchemy import create_engine
csv_sample = '''id,Date,Col_1,Col_2,Col_3
26,2012-10-18,X,25.7,True
42,2012-10-19,Y,-12.4,False
63,2012-10-20,Z,5.73,True'''
b = StringIO(csv_sample)
data = pd.read_csv(b, index_col=0)
engine_oracle = create_engine('oracle://admin:whatever@localhost:1521/xe')
data.to_sql('data', engine_oracle)
```
The code produces a warning:
> pandas/io/sql.py:1140: UserWarning: The provided table name 'data' is not found exactly as such in the database after writing the table, possibly due to case sensitivity issues. Consider using lower case table names.
> "using lower case table names.".format(name), UserWarning)
Nonetheless, the table is written into a DB, except the table has been already created, in which case the write is refused.
Trying in addition to execute:
``` python
df = pd.read_sql_table('data', engine_oracle)
```
brings an error:
> pandas/io/sql.pyc in read_sql_table(table_name, con, schema, index_col, coerce_float, parse_dates, columns, chunksize)
> 333 meta.reflect(only=[table_name])
> 334 except sqlalchemy.exc.InvalidRequestError:
> --> 335 raise ValueError("Table %s not found" % table_name)
> 336
> 337 pandas_sql = SQLDatabase(con, meta=meta)
>
> ValueError: Table data not found
On the other hand:
``` python
print pd.io.sql.has_table('data', engine_oracle)
df = pd.read_sql_query('select * from data', engine_oracle)
```
prints **True** and imports DataFrame regularly. It seems like sqlalchemy is a culprit, but
``` python
from sqlalchemy import MetaData, Table
from sqlalchemy.sql import select
metadata = MetaData()
sa_data = Table('data', metadata, autoload=True, autoload_with=engine_oracle)
s = select([sa_data])
conn = engine_oracle.connect()
s = select([sa_data])
result = conn.execute(s)
for row in result:
print row
```
passes with no issues producing the following output:
> (26, '2012-10-18', 'X', 25.7, 1)
> (42, '2012-10-19', 'Y', -12.4, 0)
> (63, '2012-10-20', 'Z', 5.73, 1)
Also, I have tried the code with sqlite and postgresql and it shows no issues.
Additional info about the pandas and it's dependencies:
> ## INSTALLED VERSIONS
>
> commit: None
> python: 2.7.8.final.0
> python-bits: 64
> OS: Linux
> OS-release: 3.13.0-39-generic
> machine: x86_64
> processor: x86_64
> byteorder: little
> LC_ALL: None
> LANG: en_US.UTF-8
>
> pandas: 0.15.1
> nose: 1.3.3
> Cython: 0.20.1
> numpy: 1.9.1
> scipy: 0.14.0
> statsmodels: 0.5.0
> IPython: 2.3.0
> sphinx: 1.2.3
> patsy: 0.2.1
> dateutil: 1.5
> pytz: 2014.9
> bottleneck: None
> tables: 3.1.1
> numexpr: 2.3.1
> matplotlib: 1.3.1
> openpyxl: 1.8.5
> xlrd: 0.9.3
> xlwt: 0.7.5
> xlsxwriter: 0.5.5
> lxml: 3.3.5
> bs4: 4.3.1
> html5lib: 0.999
> httplib2: None
> apiclient: None
> rpy2: None
> sqlalchemy: 0.9.8
> pymysql: None
> psycopg2: 2.4.6 (dt dec pq3 ext)
cx_oracle 5.1.2 is installed through _conda_
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| null | 2 | 2014-11-15T00:36:52Z | 2014-11-15T20:16:37Z | 2014-11-15T20:16:37Z | NONE | null | I found scatter_matrix super useful to discover correlations between variables. However, it doesn't support distinguishing different categories of points. Check check [this](http://trellischarts.com/images/Scatter-Plot-Matrix-Window.png) out.
I've modified scatter_matrix a a bit so it supports this functionality. See [this](http://nbviewer.ipython.org/github/tikazyq/stuff/blob/master/scatter_matrix_test.ipynb) or below images to check the effect.
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} | 19 | 2014-11-15T01:33:18Z | 2015-04-11T18:35:21Z | 2015-04-11T18:35:12Z | CONTRIBUTOR | null | Right now the following raises an exception:
```
from pandas import DataFrame, Series
from numpy import nan
nan_colname = DataFrame(Series(1.0,index=[0]),columns=[nan])
nan_colname_sparse = nan_colname.to_sparse()
```
This is because sparse dataframes use a dictionary to store information about columns, with the column label as the key. `nan`'s do not equal themselves and create problems as dictionary keys. This avoids the issue by uses a dataframe to store this information.
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| null | 5 | 2014-11-15T03:13:47Z | 2015-04-13T13:24:45Z | 2015-04-13T13:24:45Z | CONTRIBUTOR | null | For dataframes with a large number of unique values, `get_dummies` can use enormous amounts of memory. This provides a `sparse` flag to `get_dummies` which returns a much more memory-efficient structure.
For example:
```
import pandas as pd
df1 = pd.get_dummies(pd.DataFrame({'a':range(1000)}, dtype='category').a, sparse=False)
print 'dense:', df1.memory_usage().sum()
df2 = pd.get_dummies(pd.DataFrame({'a':range(1000)}, dtype='category').a, sparse=True)
print 'sparse:', df2.memory_usage().sum()
pd.util.testing.assert_frame_equal(df1, df2)
```
returns
```
dense: 8000000
sparse: 8000
```
Performance could probably be improved a lot.
Fails `pandas/tests/test_reshape.py:TestGetDummiesSparse.test_include_na` due to #8822 .
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} | 12 | 2014-11-15T09:58:09Z | 2015-03-06T00:06:18Z | 2015-03-06T00:06:18Z | MEMBER | null | See http://pandas-docs.github.io/pandas-docs-travis/remote_data.html#yahoo-finance-options and http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0151-api
@dstephens99
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} | 1 | 2014-11-15T17:26:14Z | 2016-12-05T14:39:14Z | 2016-12-05T14:39:14Z | CONTRIBUTOR | null | Hello,
Pandas Series provide `to_csv`method but they doesn't provide `to_excel` method.
Adding `to_excel` will avoid user to convert a `pandas.core.series.Series` to a `pandas.core.frame.DataFrame` in order to output Excel file
DataFrames provide
```
df.to_clipboard df.to_hdf df.to_period df.to_string
df.to_csv df.to_html df.to_pickle df.to_timestamp
df.to_dense df.to_json df.to_records df.to_wide
df.to_dict df.to_latex df.to_sparse
df.to_excel df.to_msgpack df.to_sql
df.to_gbq df.to_panel df.to_stata
```
Series provide
```
ts.to_clipboard ts.to_frame ts.to_period ts.to_string
ts.to_csv ts.to_hdf ts.to_pickle ts.to_timestamp
ts.to_dense ts.to_json ts.to_sparse ts.tolist
ts.to_dict ts.to_msgpack ts.to_sql
```
Kind regards
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} | 2 | 2014-11-15T17:33:31Z | 2018-12-13T01:47:43Z | 2018-12-13T01:47:43Z | CONTRIBUTOR | null | Hello,
IPython autocomplete (tab) shows
```
In [1]: ts.to
ts.to_clipboard ts.to_frame ts.to_period ts.to_string
ts.to_csv ts.to_hdf ts.to_pickle ts.to_timestamp
ts.to_dense ts.to_json ts.to_sparse ts.tolist
ts.to_dict ts.to_msgpack ts.to_sql
```
maybe `tolist` should be named `to_list`
and `tolist` should be marked as deprecated
because it seems that other methods follow
an implicit naming convention (`to_...`) .
Kind regards
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} | 3 | 2014-11-15T22:51:12Z | 2014-12-05T09:07:50Z | 2014-11-17T08:31:13Z | CONTRIBUTOR | null | Closes #8697 .
sqlalchemy `CreateTable` needs to be called slightly differently to create dialect-specific SQL. See http://stackoverflow.com/questions/2128717/sqlalchemy-printing-raw-sql-from-create .
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} | 15 | 2014-11-16T05:01:57Z | 2015-12-09T15:18:25Z | 2015-12-09T15:18:25Z | CONTRIBUTOR | null | Fix for #8664. The simplest way to fix this involved adding an `assignment_allowed=True` arg to the internal `eval()` function. All existing behaviour should be preserved. If adding the arg isn't okay I'm not quite sure how else to do it, as it's only once we reach the internal eval function that the expression is actually parsed and we know that it includes the assignment.
It also seems like a pretty bad side effect if query can silently overwrite values (obviously only if you accidentally include an assignment), so hopefully that's a good argument in favour of having the
`assignment_allowed` arg.
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} | 13 | 2014-11-16T08:27:31Z | 2021-04-11T05:34:01Z | null | CONTRIBUTOR | null | Hello,
Pandas Series provide `to_json` method but they doesn't provide `to_html` method.
Adding `to_html` will avoid user to convert a `pandas.core.series.Series` to a `pandas.core.frame.DataFrame` in order to output HTML file using `s.to_frame().to_html(...)` but directly use `s.to_html(...)`
see also https://github.com/pydata/pandas/issues/8825
Kind regards
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} | 1 | 2014-11-16T13:40:44Z | 2014-11-17T00:34:05Z | 2014-11-17T00:34:05Z | CONTRIBUTOR | null | closes #8602
easy enough, that import should not have been there. we don't import from other pyx files unless its explicity exported (which is in very few cases).
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| null | 1 | 2014-11-16T14:26:57Z | 2014-11-16T14:29:50Z | 2014-11-16T14:29:36Z | NONE | null | I put the pandas upgrade from 0.13 to 0.15,when I use cx_freeze to generate the executable file，I get the error “AttributeError: 'module' object has no attribute '**pyx_capi**‘ ”，but no such error in version 0.13，why？The following are the contents of the file：
import os,pandas as pd
print('hello world！！！')
os.system('pause')
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} | 6 | 2014-11-16T14:29:01Z | 2014-11-27T17:48:15Z | 2014-11-27T17:48:12Z | NONE | null | closes #8771
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} | 1 | 2014-11-16T14:34:48Z | 2014-11-17T12:54:59Z | 2014-11-17T12:54:59Z | CONTRIBUTOR | null | ```
In [1]: data = """1.0 1
...: 2.0 2
...: 3.0 3"""
In [2]: read_csv(StringIO(data),sep='\s+')
Out[2]:
1.0 1
0 2 2
1 3 3
In [3]: read_csv(StringIO(data),sep='\s+',header=None)
Out[3]:
0 1
0 1 1
1 2 2
2 3 3
In [4]: read_csv(StringIO(data),sep='\s+',header=None,names=['a','b'])
Out[4]:
a b
0 1 1
1 2 2
2 3 3
In [6]: read_csv(StringIO(data),sep='\s+',header=None,names=['a','b'],dtype={'a' : int})
'NoneType' object has no attribute 'dtype'
```
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```
In [5]: data = """1.0,a
...: nan,b
...: 3.0,c
...: """
In [8]: read_csv(StringIO(data),sep=',',names=['A','B'])
Out[8]:
A B
0 1 a
1 NaN b
2 3 c
In [9]: read_csv(StringIO(data),sep=',',names=['A','B'],dtype={'A' : int})
ValueError: cannot safely convert passed user dtype of <i8 for float64 dtyped data in column 0
```
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} | 10 | 2014-11-16T21:31:19Z | 2014-11-18T20:12:59Z | 2014-11-17T11:05:18Z | CONTRIBUTOR | null | added the GA doc to the correct file: "remote_data.rst"
closes #3508
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} | 17 | 2014-11-17T04:09:58Z | 2014-11-19T17:15:00Z | 2014-11-19T11:20:03Z | CONTRIBUTOR | null | Ensure that category codes have the same order as the underlying Stata data.
xref #8816
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} | 2 | 2014-11-17T04:13:57Z | 2014-11-17T10:56:49Z | 2014-11-17T10:56:44Z | CONTRIBUTOR | null | - `utcfromtimestamp`,`timestamp`, and `combine` now supported
Closes #5351.
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| null | 1 | 2014-11-17T10:09:04Z | 2014-11-17T10:16:10Z | 2014-11-17T10:16:01Z | NONE | null | This problem does not happen with all DataFrames, but only with the one I'm using.
The original DataFrame looks something like this:
``` python
>>> df.head()
```
| | date | text1 | number1 | text2 | text3 | text4 |
| --- | --- | --- | --- | --- | --- | --- |
| 0 | 2014-10-02 | foo | NaN | NaN | NaN | NaN |
| 1 | 2014-10-02 | foo2 | NaN | NaN | Lannister | NaN |
| 2 | 2014-10-02 | foo3 | NaN | NaN | NaN | Stark |
| 3 | 2014-10-02 | foo4 | 1992 | Y | NaN | Baratheon |
| 4 | 2014-10-02 | foo4 | NaN | NaN | NaN | NaN |
After running the following code:
``` python
date_centric = df.copy()
date_centric['week'] = date_centric['date'].apply(lambda x: x.isocalendar()[1])
date_centric = df.set_index('date').sort()
date_centric.head()
```
This is the result:
| | text1 | number1 | text2 | text3 | text4 |
| --- | --- | --- | --- | --- | --- |
| date | | | | | |
| 2014-10-02 | foo | NaN | NaN | NaN | NaN |
| 2014-10-02 | foo2 | NaN | NaN | Lannister | NaN |
| 2014-10-02 | foo3 | NaN | NaN | NaN | Stark |
| 2014-10-02 | foo4 | 1992 | Y | NaN | Baratheon |
| 2014-10-02 | foo4 | NaN | NaN | NaN | NaN |
Please note that the `week` column that was added does not appear in the `date_centric` DataFrame. If the `date_centric = df.copy()` statement is removed, then both `df` and `date_centric` will contain the `week` column.
Finally, some information that might help pinpoint the issue:
``` python
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 26207 entries, 0 to 26206
Data columns (total 6 columns):
date 26207 non-null datetime64[ns]
imsi 26207 non-null object
birth_date 16142 non-null float64
gender 16511 non-null object
postal_sector_business 1863 non-null object
postal_sector_consumer 16558 non-null object
dtypes: datetime64[ns](1), float64(1), object(4)
memory usage: 1.4+ MB
>>> pd.show_versions
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.15.1
nose: 1.3.4
Cython: 0.21
numpy: 1.9.0
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.2.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.2
pytz: 2014.9
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.0
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
```
I have also been able to repro this with pandas 0.14.1
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} | 2 | 2014-11-17T15:09:52Z | 2018-11-19T01:21:33Z | 2018-11-19T01:21:33Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/26973393/between-time-not-working-on-a-pandas-panel/26974754#26974754)
xref #2141 | {
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} | 1 | 2014-11-17T15:15:44Z | 2014-11-18T10:15:19Z | 2014-11-18T10:15:13Z | MEMBER | null | Closes #5620
Also added a docstring.
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} | 14 | 2014-11-17T16:40:39Z | 2015-05-09T16:07:09Z | 2015-05-09T16:07:09Z | CONTRIBUTOR | null | - single byte UTF8 separators will always work if utf8 encoding is specified (i.e. seps in range(256))
- single byte delimiters with encodings will always work in Python 3, but will not work in Python 2 if they are multibyte when coerced to UTF8
- multi byte delimiters with encodings will not work on reads with C engine and will not work with writes in Python 2. (but I believe they'll work in Python 3 if they're considered to be length 1).
Closes #6035.
This was slightly complicated validation-wise, but I think it's in a good place
with tests and such. I'll squash together the commits and make sure I've
covered the relevant cases in tests.
I also noticed some weirdness with passing `delimiter` as a keyword argument that
might need to be addressed in the future.
CC @maxgrenderjones, @Midnighter if you want to take a look.
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| null | 19 | 2014-11-17T17:14:19Z | 2015-02-09T17:34:06Z | 2015-02-09T17:34:06Z | CONTRIBUTOR | null | ME_breakpoints.zip is not read because it is a different format.
In this zip, there is only one file "ME_Breakpoints.txt". How should this be handed?
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| null | 1 | 2014-11-17T20:48:47Z | 2014-11-18T10:14:55Z | 2014-11-18T10:14:22Z | CONTRIBUTOR | null | Resolves #8754.
Turned out to be dead simple too :)
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} | 12 | 2014-11-17T22:34:21Z | 2014-12-02T11:14:42Z | 2014-12-02T11:14:42Z | CONTRIBUTOR | null | xref #8850
In the example below, `[11]` is incorrectly reflecting columns in `dfa` that should not be visible to `dfa1`. Note that this is not a problem when the columns are not a multi-index (`[5]` and `[6]`), or when `dropna=True` (the default; `[10]` and `[12]`).
```
Python 3.4.2 (v3.4.2:ab2c023a9432, Oct 6 2014, 22:16:31) [MSC v.1600 64 bit (AMD64)]
Type "copyright", "credits" or "license" for more information.
IPython 2.3.1 -- 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]: import pandas as pd
In [2]: import numpy as np
In [3]: df = pd.DataFrame(np.zeros((2,5)), columns=list('ABCDE'))
In [4]: df1 = df[df.columns[:2]]
In [5]: df1.stack()
Out[5]:
0 A 0
B 0
1 A 0
B 0
dtype: float64
In [6]: df1.stack(dropna=False)
Out[6]:
0 A 0
B 0
1 A 0
B 0
dtype: float64
In [7]: dfa = pd.DataFrame(np.zeros((2,5)),
columns=pd.MultiIndex.from_tuples([(1,'A'), (1,'B'), (1,'C'), (1,'D'), (1,'E')],
names=['num', 'let']))
In [8]: dfa1 = dfa[dfa.columns[:2]]
In [10]: dfa1.stack()
Out[10]:
num 1
let
0 A 0
B 0
1 A 0
B 0
In [11]: dfa1.stack(dropna=False)
Out[11]:
num 1
let
0 A 0
B 0
C NaN
D NaN
E NaN
1 A 0
B 0
C NaN
D NaN
E NaN
In [12]: dfa1.stack(dropna=True)
Out[12]:
num 1
let
0 A 0
B 0
1 A 0
B 0
In [13]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.2.final.0
python-bits: 64
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.15.1
nose: 1.3.4
Cython: 0.21.1
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.6.0
IPython: 2.3.1
sphinx: None
patsy: 0.3.0
dateutil: 2.2
pytz: 2014.9
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.6.3
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
```
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} | 9 | 2014-11-18T04:41:29Z | 2015-02-23T18:30:41Z | 2015-02-23T18:30:41Z | MEMBER | null | xref #3004
xref #841
xref #7873
xref #7223
xref #8815
Building on @immerrr's excellent refactor in #8753, I would like to propose adding a `get_nearest` method to pandas.Index that does nearest neighbor lookups. The idea is that nearest neighbor lookups are usually the desirable/sane thing to do when using inexact indexes. Eventually, we might want to add an alternative "wrapper index" (like IntervalIndex), e.g., `NearestNeighborIndex` which switches the default behavior; this would be an intermediate step in that direction.
The implementation would be a simple wrapper that calls `Index.get_slice_bound` twice, once to the left and once to the right. Ideally, this would this would even work for array-like arguments, though perhaps there should be separate `get_nearest_loc` and `get_nearest_indexer` methods.
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} | 2 | 2014-11-18T08:56:24Z | 2014-11-18T11:56:11Z | 2014-11-18T11:56:11Z | NONE | null | I just noticed that after upgrading to numpy 1.9.0, when I'm trying to split dataframe with pandas 0.15.1 with the code:
```
split_dfs = np.array_split(big_df,8)
```
I get the error:
```
Traceback (most recent call last):
File "./test.py", line 127, in <module>
split_dfs = np.array_split(big_df,8)
File "/usr/lib/python2.7/site-packages/numpy/lib/shape_base.py", line 426, in array_split
if sub_arys[-1].size == 0 and sub_arys[-1].ndim != 1:
File "/usr/lib/python2.7/site-packages/pandas-0.15.1-py2.7-linux-x86_64.egg/pandas /core/generic.py", line 1936, in __getattr__
(type(self).__name__, name))
AttributeError: 'DataFrame' object has no attribute 'size'
```
with pandas 0.15.1 and numpy 1.8.1 it works fine.
I'm using pandas 0.15.1 on arch linux and python2.7
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} | 0 | 2014-11-18T11:09:54Z | 2014-11-18T11:56:11Z | 2014-11-18T11:56:11Z | CONTRIBUTOR | null | closes #8846
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} | 2 | 2014-11-18T12:26:04Z | 2016-02-27T16:03:10Z | 2016-02-27T16:02:50Z | CONTRIBUTOR | null | methods
- to_timestamp
- asfreq
properties
- start_time/end_time
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| null | 4 | 2014-11-18T14:14:21Z | 2021-04-11T05:35:58Z | null | NONE | null | The behaviour below occurs in version `'0.15.1'`.
When a series has a duplicate index, the method `reindex` will raise an exception, unless the index passed to `reindex` is identical to the series' index.
I propose that when a series has a duplicate index, the method `reindex` should always raise an exception, because when a series with a duplicate index is to be conformed to a new index, the intended behaviour is _always_ ambiguous.
This issue applies to the methods `reindex_like` and `reindex_axis` too.
Examples of current behaviour:
(a)
``` python
>>> pd.Series([1, 2, 3], index=['a', 'b', 'b']).reindex(['a', 'b'])
ValueError: cannot reindex from a duplicate axis
```
(b)
``` python
>>> pd.Series([1, 2, 3], index=['a', 'b', 'b']).reindex(['a', 'b', 'b'])
a 1
b 2
b 3
dtype: int64
```
The exception message in (a) implies that (b) should raise; but it doesn't.
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} | 1 | 2014-11-18T14:48:27Z | 2014-11-20T22:55:08Z | 2014-11-20T22:55:08Z | NONE | null | In the example below, `[13]` gives a different result than `[11]`, even though you would think that `[12]` has no effect on data.
```
Python 2.7.8 (default, Oct 19 2014, 16:02:00)
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]: import pandas as pd
In [2]: import numpy as np
In [3]: d = {'COLA': {0: 0, 1: 0, 2: 0},
...: 'COLB': {0: 'a', 1: 'a', 2: 'a'},
...: 'COLC': {0: 0, 1: 0, 2: 1}}
In [4]: df = pd.DataFrame(d)
In [5]: df
Out[5]:
COLA COLB COLC
0 0 a 0
1 0 a 0
2 0 a 1
In [6]: df = df.groupby(['COLA','COLB'])['COLC']\
...: .agg({'Zeros': lambda x: 0,
...: 'Averages': lambda x: 100.*x.mean(),
...: 'Weird_stuff': np.size})\
...: .unstack()
In [7]: df
Out[7]:
Averages Weird_stuff Zeros
COLB a a a
COLA
0 33.333333 3 0
In [8]: df.columns
Out[8]:
MultiIndex(levels=[[u'Averages', u'Weird_stuff', u'Zeros'], [u'a']],
labels=[[0, 1, 2], [0, 0, 0]],
names=[None, u'COLB'])
In [9]: df.index
Out[9]: Int64Index([0], dtype='int64')
In [10]: df['Weird_stuff'] = df['Weird_stuff'].apply(lambda x: 1000000., axis=1)
/usr/local/lib/python2.7/site-packages/numpy/lib/function_base.py:3612: FutureWarning: in the future negative indices will not be ignored by `numpy.delete`.
"`numpy.delete`.", FutureWarning)
In [11]: df
Out[11]:
Averages Weird_stuff Zeros
COLB a a a
COLA
0 33.333333 1000000 0
In [12]: df['Zeros']
Out[12]:
COLB a
COLA
0 0
In [13]: df # Look the value below ('Weird_stuf', 'a') here and above.
Out[13]:
Averages Weird_stuff Zeros
COLB a a a
COLA
0 33.333333 3 0
In [14]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Darwin
OS-release: 14.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: pl_PL.UTF-8
pandas: 0.15.1
nose: 1.3.0
Cython: 0.20.2
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.1.0
sphinx: 1.2.2
patsy: 0.2.1
dateutil: 2.2
pytz: 2014.7
bottleneck: 0.8.0
tables: 3.1.0
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.3.2
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9
apiclient: 1.2
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
```
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} | 3 | 2014-11-18T21:08:25Z | 2018-07-06T21:15:40Z | 2018-07-06T21:15:39Z | CONTRIBUTOR | null | `df.stack(level=[0,1], dropna=False)` appears to be equivalent to `df.stack(level=0, dropna=False).stack(level=0, dropna=False)`. While this makes a certain amount of sense, it results in additional rows that in many cases are probably not expected/desired. In particular, `df.stack(level=[0,1], dropna=False)` is not equivalent to `df.stack(level=[0,1], dropna=True)` even when `df` contains no missing values, which seems counterintuitive.
I think that when stacking multiple levels, one may want them stacked in one go, rather than sequentially -- so that when there are no missing values, `df.stack(level=[0,1], dropna=False)` would produce the same result as `df.stack(level=[0,1], dropna=True)`.
Here is an example of current behavior. Since `df` has no missing values, I would want `[6]` to produce the same results as `[5]`, not `[7]`.
```
Python 3.4.2 (v3.4.2:ab2c023a9432, Oct 6 2014, 22:16:31) [MSC v.1600 64 bit (AMD64)]
Type "copyright", "credits" or "license" for more information.
IPython 2.3.1 -- 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]: import numpy as np
In [2]: import pandas as pd
In [3]: df = pd.DataFrame(np.zeros((2,3)), columns=pd.MultiIndex.from_tuples([('A','x'), ('A','y'), ('B','z')], names=['Upper', 'Lower']))
In [4]: df
Out[4]:
Upper A B
Lower x y z
0 0 0 0
1 0 0 0
In [5]: df.stack(level=[0,1], dropna=True)
Out[5]:
Upper Lower
0 A x 0
y 0
B z 0
1 A x 0
y 0
B z 0
dtype: float64
In [6]: df.stack(level=[0,1], dropna=False)
Out[6]:
Upper Lower
0 A x 0
y 0
z NaN
B x NaN
y NaN
z 0
1 A x 0
y 0
z NaN
B x NaN
y NaN
z 0
dtype: float64
In [7]: df.stack(level=0, dropna=False).stack(level=0, dropna=False)
Out[7]:
Upper Lower
0 A x 0
y 0
z NaN
B x NaN
y NaN
z 0
1 A x 0
y 0
z NaN
B x NaN
y NaN
z 0
dtype: float64
In [13]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.2.final.0
python-bits: 64
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.15.1
nose: 1.3.4
Cython: 0.21.1
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.6.0
IPython: 2.3.1
sphinx: None
patsy: 0.3.0
dateutil: 2.2
pytz: 2014.9
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.6.3
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
```
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} | 16 | 2014-11-18T22:59:04Z | 2014-12-03T22:33:53Z | 2014-12-03T22:33:53Z | CONTRIBUTOR | null | ``` python
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
c = np.array([1, 0])
df.plot(kind='scatter', x='A', y='B', c=c, cmap='spring')
```
fails with a TypeError.
we check `elif c in self.data.columns` which tries to hash the array.
(I know I can just add the array as a column to the DataFrame)
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} | 3 | 2014-11-19T03:07:16Z | 2014-11-21T01:00:59Z | 2014-11-20T22:55:08Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/8850
on master:
```
>>> cols = MultiIndex.from_tuples([('1st', 'a'), ('2nd', 'b'), ('3rd', 'c')])
>>> df = DataFrame([[1.0, 2, 3], [4.0, 5, 6]], columns=cols)
>>> df['2nd'] = df['2nd'] * 2.0 # type change in block manager
/usr/lib/python3.4/site-packages/numpy/lib/function_base.py:3612: FutureWarning: in the future negative indices will not be ignored by `numpy.delete`.
"`numpy.delete`.", FutureWarning)
>>> df.values
...
File "/usr/lib/python3.4/site-packages/pandas-0.15.1_72_gf504885-py3.4-linux-x86_64.egg/pandas/core/internals.py", line 2392, in _verify_integrity
tot_items))
AssertionError: Number of manager items must equal union of block items
# manager items: 3, # tot_items: 4
>>> df.blocks
...
File "/usr/lib/python3.4/site-packages/pandas-0.15.1_72_gf504885-py3.4-linux-x86_64.egg/pandas/core/internals.py", line 2392, in _verify_integrity
tot_items))
AssertionError: Number of manager items must equal union of block items
# manager items: 3, # tot_items: 4
```
`._data` is also broken:
```
>>> df._data
BlockManager
Items:
1st a
2nd b
3rd c
Axis 1: Int64Index([0, 1], dtype='int64')
FloatBlock: slice(0, 1, 1), 1 x 2, dtype: float64
IntBlock: slice(1, 3, 1), 2 x 2, dtype: int64
FloatBlock: slice(1, 2, 1), 1 x 2, dtype: float64
```
integer block is bigger than what it should be and overlaps with one of the float blocks.
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} | 2 | 2014-11-19T06:02:11Z | 2015-05-09T16:06:55Z | 2015-05-09T16:06:55Z | CONTRIBUTOR | null | This is another tweak to `_setitem_with_indexer` that allows it better to comprehend higher dimensional frames of mixed-dtype. With this tweak setting by indexer to a Series now works. Unfortunately setting to a DataFrame is still broken. I plan to open an issue for that.
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Fixes `DataFrame.stack(..., dropna=False)` when the columns consist of a "partial" `MultiIndex`, i.e. one in which the `labels` don't reference all the `levels`.
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} | 8 | 2014-11-19T11:25:19Z | 2021-05-10T21:45:31Z | 2021-05-10T21:45:31Z | CONTRIBUTOR | null | Here's how it looks like:
``` python
In [11]: s = pd.Series(np.arange(10), pd.MultiIndex.from_product(([0, 1], list('abcde'))))
In [12]: s
Out[12]:
0 a 0
0 b 1
0 c 2
0 d 3
0 e 4
1 a 5
1 b 6
1 c 7
1 d 8
1 e 9
dtype: int64
In [13]: s.iloc[::4] = 100; s
Out[13]:
0 a 100
0 b 100
0 c 100
0 d 100
0 e 100
1 a 5
1 b 6
1 c 7
1 d 8
1 e 9
dtype: int64
In [14]: s.loc[::4] = 200; s
Out[14]:
0 a 200
0 b 200
0 c 200
0 d 200
0 e 200
1 a 5
1 b 6
1 c 7
1 d 8
1 e 9
dtype: int64
In [15]: s.ix[::4] = 300; s
Out[15]:
0 a 300
0 b 300
0 c 300
0 d 300
0 e 300
1 a 5
1 b 6
1 c 7
1 d 8
1 e 9
dtype: int64
```
Regular setitem works fine:
``` python
In [16]: s[::4] = 10000; s
Out[16]:
0 a 10000
0 b 300
0 c 300
0 d 300
0 e 10000
1 a 5
1 b 6
1 c 7
1 d 10000
1 e 9
dtype: int64
```
I have some ideas about iloc refactoring that will fix that for iloc, but loc/ix will still be affected.
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} | 1 | 2014-11-19T17:22:00Z | 2016-07-04T11:03:20Z | 2016-07-04T11:03:14Z | NONE | null | I am interested in seeing if there is a method, or a method could be built to only read in the header column of a text or excel file. The reason I am proposing this is that I generally have to read in files from sources that use different header names for the same underlying data. Internally, I keep a list of column names, and its respective data type. This might look something like:
DATA_TYPES = {
'name':str,
'ISIN':str,
'SEDOL':str,
'CUSIP':str,
'price': np.float64,
'volume': np.int64,
}
I then create a column mapper to map internal column names to header column names, for instance:
col_map = {
'name':'ISSUER_NAME',
'CUSIP':'ISSUER_CUSIP'
}
in order to create a dictionary to set as the dtype arguement in read_csv to be:
dtype = {
'ISSUER_NAME':str,
'ISSUER_CUSIP':str
}
This avoids pd.read_csv to accidently convert cusip's to integers for instance. However, sometimes, the column name(s) will be consistent between the file and the internal name, so I would want to read in the file column names, and based on the given names, remap any necessary columns, and then get each columns dtype. For example, say the file had a column name='ticker', which is the same as the internal column name='ticker', so I want my dtype dictionary to be:
dtype = {
'ISSUER_NAME':str,
'ISSUER_CUSIP':str
'ticker':str
}
headers = pd.read_csv(...,header_only=True)
headers_intern = remap_list_function(headers,col_map)
dtype_intern = filter_dict_key_function(headers_intern,DATA_TYPES)
dtype = remap_dict_function(col_map,dtypes_intern)
df = pd.read_csv(....,dtype=dtype,header_only=False)
df.rename(columns=flip_dict_function(col_map),inplace=True)
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} | 3 | 2014-11-19T21:26:03Z | 2016-12-06T21:34:41Z | 2016-12-06T19:10:40Z | NONE | null | xref #8731, soln might be the same
Currently when we do a `crosstab`, the distinct values in each column is reported in the lexical order. But crosstabs are usually useful when we have categorical data (that may have an inherent ordering).
``` python
import pandas as pd
d = {'MAKE' : pd.Series(['Honda', 'Acura', 'Tesla', 'Honda', 'Honda', 'Acura']),
'MODEL' : pd.Series(['Sedan', 'Sedan', 'Electric', 'Pickup', 'Sedan', 'Sedan'])}
data = pd.DataFrame(d)
pd.crosstab(data['MAKE'],data['MODEL'])
data['MODEL'] = data['MODEL'].astype('category')
data['MODEL'] = data['MODEL'].cat.set_categories(['Sedan','Electric','Pickup'])
pd.crosstab(data['MAKE'],data['MODEL'])
```
Both the cross-tab statements above result in the same output as below - essentially the code I believe is performing a lexical sort on the contents of the `Series` being passed.
```
Output:
MODEL Electric Pickup Sedan
MAKE
Acura 0 0 2
Honda 0 1 2
Tesla 1 0 0
```
Would it be possible for `crosstab` to maintain the ordering of the categorical variable if column.cat.ordered on the passed column is True? Thanks!
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} | 6 | 2014-11-19T22:31:24Z | 2016-07-04T11:04:22Z | 2016-07-04T11:04:15Z | NONE | null | Hi!
Can someone please help me understand how `adjust = True` in the function `ewma()` works mathematically?
I understand the idea of simple exponential smoothing and it's basic formula when `adjust = FALSE`,
then alpha is constant, as in the equation:
When `adjusted = True` then I assume that, alpha does not stay constant and it is adjusted "automatically".But what is the **mathematical representaiton** for such case? I've looked into Pandas documentation
http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-moment-functions
and there it says that when it is **adjusted**, then the function uses this weights:
It is still not clear to me how this adjustment is working.
The only simple exponential method using an adaptive/adjusting alpha that I know of, is from Rob Hyndman's book: "Forecasting Methods and Application" .There I found a variation of the simple exponential smoothing (p.155-157), where alpha is adjusted automatically, called "adaptive-respose single exponential smoothing" (ARRSES). Unfortunately I got different results from each, so I guess this is not the one.
Does anybody know how to explain this specific case mathematically? or knows a good reference?
Thanks!
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| null | 20 | 2014-11-20T09:11:02Z | 2019-12-05T15:08:25Z | 2014-11-20T13:49:46Z | NONE | null | While running "nosetests" after installing python anaconda, git repositories from command prompt, I get this error on my windows PC
How to resolve this??
thanks
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} | 1 | 2014-11-20T12:28:00Z | 2015-06-18T16:15:21Z | 2015-06-18T16:15:21Z | CONTRIBUTOR | null | string Timedelta parsing is done via regexes now
would have a large perf boost to in-line parse.
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| null | 6 | 2014-11-20T18:49:27Z | 2014-11-21T15:58:31Z | 2014-11-21T15:58:31Z | NONE | null | idx0 =
timestamp sequence attribute
2014-11-12 17:02:23.635000-06:00 0 price
count
quantity
idx1 =
timestamp sequence attribute
2014-11-12 17:02:24.060000-06:00 1 price
count
quantity
idx0 + idx1 =
timestamp sequence attribute
2014-11-12 23:02:23.635000 0 count
price
quantity
2014-11-12 23:02:24.060000 1 count
price
quantity
Any way to solve this?
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} | 1 | 2014-11-20T19:26:54Z | 2014-12-04T02:48:19Z | 2014-12-04T02:48:19Z | MEMBER | null | Currently it returns a `datetime.timedelta` object:
```
>>> pd.Timestamp('2000-01-01') - pd.Timestamp('1999-01-01')
datetime.timedelta(365)
```
Somewhat related: #8813
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} | 1 | 2014-11-20T23:41:52Z | 2014-12-05T02:34:01Z | 2014-12-05T02:34:01Z | CONTRIBUTOR | null | ```
In [15]: s = Series(np.arange(8),index=pd.MultiIndex.from_product([list('ab'),range(2),pd.date_range('20130101',periods=2)],names=['one','two','three']))
In [16]: s
Out[16]:
one two three
a 0 2013-01-01 0
2013-01-02 1
1 2013-01-01 2
2013-01-02 3
b 0 2013-01-01 4
2013-01-02 5
1 2013-01-01 6
2013-01-02 7
dtype: int64
# datetimelikes work
In [17]: s.groupby(pd.Grouper(level='three',freq='M')).sum()
Out[17]:
three
2013-01-31 28
Freq: M, dtype: int64
# just specifying a level breaks
In [18]: s.groupby(pd.Grouper(level='one')).sum()
ValueError: No axis named None for object type <class 'pandas.core.series.Series'>
```
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} | 1 | 2014-11-21T02:24:20Z | 2014-11-21T14:05:16Z | 2014-11-21T14:05:11Z | CONTRIBUTOR | null | nose.SkipTest used but nose not imported.
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} | 1 | 2014-11-21T09:29:30Z | 2015-02-18T11:27:50Z | 2015-02-18T11:27:50Z | NONE | null | Hello everyone,
I stumbled upon the following behavior of groubpy with categorical, which seems at least inconsistent with the way groupby usually operates.
When grouping on a string type column with `sort=False`, the order of the groups is the order in which the keys first appear in the column.
However, when grouping with a categorical column, the groups seem to be always ordered by the categorical, even when `sort=False`.
``` python
import pandas as pd
d = {'foo': [10, 8, 5, 6, 4, 1, 7], 'bar': [10, 20, 30, 40, 50, 60, 70],
'baz': ['d', 'c', 'e', 'a', 'a', 'd', 'c']}
df = pd.DataFrame(d)
cat = pd.cut(df['foo'], np.linspace(0, 10, 5))
df['range'] = cat
groups = df.groupby('range', sort=True)
# Expected behaviour
result = groups.agg('mean')
# Why are the categorical still sorted in this case ?
groups2 = df.groupby('range', sort=False)
result2 = groups2.agg('mean')
# I would expect an output like this one: keep the order in which the groups
# are first encountered
groups3 = df.groupby('baz', sort=False)
result3 = groups3.agg('mean')
```
``` python
result
```
| | bar | foo |
| --- | --- | --- |
| range | | |
| (0, 2.5] | 60 | 1.0 |
| (2.5, 5] | 40 | 4.0 |
| (5, 7.5] | 55 | 6.5 |
| (7.5, 10] | 15 | CC |
``` python
result2
```
| | bar | foo |
| --- | --- | --- |
| range | | |
| (0, 2.5] | 60 | 1.0 |
| (2.5, 5] | 40 | 4.0 |
| (5, 7.5] | 55 | 6.5 |
| (7.5, 10] | 15 | CC |
``` python
result3
```
| | bar | foo |
| --- | --- | --- |
| baz | | |
| d | 35 | 5.5 |
| c | 45 | 7.5 |
| e | 30 | 5.0 |
| a | 45 | 9.0 |
``` python
pd.__version__
Out[110]: '0.15.1'
```
Setting `as_index=False` does not change the presented bahavior.
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} | 3 | 2014-11-21T10:14:22Z | 2018-05-01T15:09:11Z | 2018-05-01T15:09:11Z | NONE | null | Hello,
The following example definitely seems like a bug. The grouper is not dropped from the index of the resulting DataFrame, even when `as_index = False`.
Actually, even the aggregation step completely fails, so there may be more to it, as shown in this example.
``` python
import pandas as pd
d = {'foo': [10, 8, 4, 8, 4, 1, 1], 'bar': [10, 20, 30, 40, 50, 60, 70],
'baz': ['d', 'c', 'e', 'a', 'a', 'd', 'c']}
df = pd.DataFrame(d)
cat = pd.cut(df['foo'], np.linspace(0, 10, 3))
df['range'] = cat
groups = df.groupby(['range', 'baz'], as_index=False)
result = groups.agg('mean')
```
``` python
result
```
| | | range | baz | bar | foo |
| --- | --- | --- | --- | --- | --- |
| range | baz | | | | |
| (0, 5] | a | NaN | NaN | NaN | NaN |
| | c | NaN | NaN | NaN | NaN |
| | d | NaN | NaN | NaN | NaN |
| | e | NaN | NaN | NaN | NaN |
| (5, 10] | a | NaN | NaN | NaN | NaN |
| | c | NaN | NaN | NaN | NaN |
| | d | NaN | NaN | NaN | NaN |
| | e | NaN | NaN | NaN | NaN |
Compare to the expected result:
``` python
groups2 = df.groupby(['range', 'baz'], as_index=True)
expected = groups2.agg('mean').reset_index()
```
``` python
expected
```
| | range | baz | bar | foo |
| --- | --- | --- | --- | --- |
| 0 | (0, 5] | a | 50 | 4 |
| 1 | (0, 5] | c | 70 | 1 |
| 2 | (0, 5] | d | 60 | 1 |
| 3 | (0, 5] | e | 30 | 4 |
| 4 | (5, 10] | a | 40 | 8 |
| 5 | (5, 10] | c | 20 | 8 |
| 6 | (5, 10] | d | 10 | 10 |
| 7 | (5, 10] | e | NaN | |
``` python
pd.__version__
Out[181]: '0.15.1'
```
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} | 4 | 2014-11-21T11:15:57Z | 2020-01-03T23:23:43Z | 2020-01-03T23:23:43Z | NONE | null | - [ ] Series groupby excluding NaN groups with Categorical (DataFrame DOES include)
- [ ] sorting via a returned Interval-like-Index (string based)
Hello,
When grouping a DataFrame over more than one column including a categorical, the empty groups are kept in the aggregation result. A test for this behaviour was introduced in #8138.
However, when performing aggregation on only one column of the DataFrame, the empty groups are dropped. This seems inconsistent to me and I guess that it's an edge case that wasn't thought of at the time.
``` python
d = {'foo': [10, 8, 4, 1], 'bar': [10, 20, 30, 40],
'baz': ['d', 'c', 'd', 'c']}
df = pd.DataFrame(d)
cat = pd.cut(df['foo'], np.linspace(0, 20, 5))
df['range'] = cat
groups = df.groupby(['range', 'baz'], as_index=True, sort=True)
# Expected result, fixed as part of #8138
fixed = groups.agg('mean')
# Inconsistent behaviour with series
inconsistent = groups['foo'].agg('mean')
# Expected result
expected = fixed['foo']
```
``` python
fixed
```
| | | bar | foo |
| --- | --- | --- | --- |
| range | baz | | |
| (0, 5] | c | 1 | 40 |
| | d | 4 | 30 |
| (10, 15] | c | NaN | NaN |
| | d | NaN | NaN |
| (15, 20] | c | NaN | NaN |
| | d | NaN | NaN |
| (5, 10] | c | 8 | 20 |
| | d | 10 | 10 |
``` python
inconsistent
```
| range | baz | |
| --- | --- | --- |
| (0, 5] | c | 1 |
| | d | 4 |
| (5, 10] | c | 8 |
| | d | 10 |
``` python
expected
```
| | | |
| --- | --- | --- |
| range | baz | |
| (0, 5] | c | 1 |
| | d | 4 |
| (10, 15] | c | NaN |
| | d | NaN |
| (15, 20] | c | NaN |
| | d | NaN |
| (5, 10] | c | 8 |
| | d | 10 |
Note the strange ordering of the categorical index. I would expect `sorted = True` to sort by categorical level and not by lexical order?
Also note that using `as_index=False` fails due to #8869
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| null | 10 | 2014-11-21T14:23:26Z | 2019-10-06T00:47:41Z | 2019-10-06T00:47:41Z | NONE | null | I'm working with OpenStreetMap data queried from a postgresql database. When I extract one row and try to access the name column, the resulting value is inconsistent based on the access method:
the row:
```
osm_id 1895277228
name None
admin_level -1
leisure None
building None
brand Total
operator None
amenity fuel
shop None
sport None
aeroway None
office None
public_transport None
railway None
tourism None
```
Then using the dot-attribute way (poi.name) returns value 0, while (poi["name"]) returns the original None value. This behavior is not happening with other columns.
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| null | 2 | 2014-11-21T16:19:59Z | 2014-11-21T17:33:14Z | 2014-11-21T17:33:14Z | NONE | null | I am building a GUI application that uses pandas for processing data. I am now trying to deploy this application using cx_freeze.
Initially I encountered the issue as described in https://github.com/pydata/pandas/issues/8602 and fixed by https://github.com/pydata/pandas/pull/8830.
I was using pandas as installed through pip. I used latest from GitHub and did a
```
python setup.py build --compiler=mingw
python setup.py
```
and I now get the following error when running the application:
```
Traceback (most recent call last):
File "C:\Users\James\Documents\GitHub\CSVviewer\application.py", line 16, in <
module>
from csv_datamanager import CSV_DataManager
File "C:\Users\James\Documents\GitHub\CSVviewer\csv_datamanager.py", line 9, i
n <module>
import pandas as pd
File "C:\Python34\lib\site-packages\pandas-0.15.1.dev-py3.4-win32.egg\pandas\_
_init__.py", line 53, in <module>
from pandas.io.api import *
File "C:\Python34\lib\site-packages\pandas-0.15.1.dev-py3.4-win32.egg\pandas\i
o\api.py", line 14, in <module>
from pandas.io.packers import read_msgpack, to_msgpack
File "C:\Python34\lib\site-packages\pandas-0.15.1.dev-py3.4-win32.egg\pandas\i
o\packers.py", line 63, in <module>
from pandas.msgpack import Unpacker as _Unpacker, Packer as _Packer
ImportError: DLL load failed: The specified module could not be found.
```
This error occurs when running the application with python (i.e. "normally") or with the built cx_freeze application.
I'm a bit lost for a fix, application/DLL stuff isn't my strong point.
Python version 3.4.2
Windows 7 32-bit
I believe I have the correct Visual Studio redistributable installed (2010).
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} | 6 | 2014-11-21T17:08:15Z | 2019-10-09T06:00:28Z | 2019-10-09T05:49:25Z | NONE | null | If you have a series of binary strings containing numbers in a DataFrame, if you try to convert it using `convert_objects`, the first will show up as `NaN`.
```
In [3]: df = pd.DataFrame([b'1',b'1'])
In [4]: df.convert_objects(convert_numeric=True)
Out[4]:
0
0 NaN
1 1
```
Python 3, obviously; happens on Windows and Linux.
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} | 8 | 2014-11-21T19:49:36Z | 2015-06-12T21:50:02Z | 2015-06-12T21:50:02Z | CONTRIBUTOR | null | I'm not sure if this is supported or not -- it isn't in the doc string for HDFStore, but it seems to be allowed by the HDFStore (nothing is raised).
Unfortunately so far I can only get it to show the bad behavior on a proprietary dataset, which is storing a `pd.Panel` which contains items of of mixed types.
Some of the float values are be changed from small (|x|<1.0) to very large (3.xe+308).
Is the compressor just passed through to pytables? If so, this might be a pytables issue.
```
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.23.3.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.1-24-g54e237b
nose: 1.3.4
Cython: 0.21.1
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.7.0.dev-c8e980d
IPython: 2.3.1
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 1.5
pytz: 2014.9
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
```
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} | 9 | 2014-11-22T02:16:30Z | 2021-04-11T05:39:14Z | null | MEMBER | null | - [ ] Add `__inverse__` to Index.
- [x] Change duplicated to return np.array (PR #9112)
Related to #7979
Because `Index` is no more the subclass of `np.array`, `Index.duplicated` returns `Index` with `dtype=object`. And `Index` don't accepts logical not.
```
idx = pd.Index([1, 2, 1, 3])
idx.duplicated()
# Index([False, False, True, False], dtype='object')
~idx.duplicated()
# TypeError: bad operand type for unary ~: 'Index'
```
As a result, it is impossible to drop data which have duplicated index using expression like `df[~df.index.duplicated()]`. This expression was worked at the timing of #7979.
Does `Index.duplicated` should return `np.array` with `dtype=bool`? Or `Index` should accept logical not?
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} | 20 | 2014-11-22T04:28:48Z | 2015-02-15T01:33:27Z | 2015-02-12T14:22:59Z | MEMBER | null | Closes #8234. Sorry to take a long.
I understand there are 2 warnings:
1. Warning in /Users/sin/Documents/Git/pandas/doc/source/visualization.rst at block ending on line 1275
This is raised from `mpl` as plotting empty axes for demonstration purpose. Thus simply suppressed.
2. Warning in /Users/sin/Documents/Git/pandas/doc/source/visualization.rst at block ending on line 1312
This is a warning displayed when passing multiple axes. Added explanations.
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} | 3 | 2014-11-22T11:41:02Z | 2016-07-04T11:05:42Z | 2016-07-04T11:05:37Z | NONE | null | nils@linux-5fmr:~/git/pandas/doc> python make.py html
Traceback (most recent call last):
File "/home/nils/local/bin/sphinx-build", line 5, in <module>
from pkg_resources import load_entry_point
File "/usr/lib/python2.7/site-packages/pkg_resources.py", line 2829, in <module>
working_set.require(**requires**)
File "/usr/lib/python2.7/site-packages/pkg_resources.py", line 706, in require
needed = self.resolve(parse_requirements(requirements))
File "/usr/lib/python2.7/site-packages/pkg_resources.py", line 604, in resolve
raise DistributionNotFound(req)
pkg_resources.DistributionNotFound: Sphinx==1.3b2dev-20141122
Building HTML failed.
nils@linux-5fmr:~/git/pandas/doc> sphinx-build --version
Sphinx (sphinx-build) 1.3b2
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} | 0 | 2014-11-22T16:45:33Z | 2021-04-11T05:40:34Z | null | CONTRIBUTOR | null | http://stackoverflow.com/questions/27074469/memory-leak-in-python-pandas-reshuffling-index
```
In [1]: df = DataFrame(np.random.randn(10000))
In [2]: def f(df):
...: for dfi in np.array_split(df,100):
...: shuffle = dfi.reindex(np.random.permutation(dfi.index))
...: one = shuffle.iloc[:50]
...: two = shuffle.iloc[50:]
```
so the `np.random.permutation` seems to be shuffling the index in a weird way
this doesn't leak with `dfi.index.values`
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} | 8 | 2014-11-22T22:48:30Z | 2017-09-24T19:31:39Z | 2017-09-24T19:31:39Z | NONE | null | I have been doing some experiments with Numba and groupby operations such as max() and sum().
Using Numba might make them faster, though my analysis in incomplete as my functions are returning results as ndarrays, rather than Pandas Series (I don't know how large the overhead of creating the Series result is, and can't work out how to create it efficiently).
Here is my code, which shows that if you just return the results as an ndarray, you can get a 5x speed improvement:
```
import numpy as np
import os
import pandas as pd
from numba import autojit
N = 1e7
m = 1e6
p = 1e4
x = np.random.randint( 0, m, N )
y = np.random.randint( 0, p, N )
z = np.random.randn(N)
df = pd.DataFrame( {'x':x, 'y':y, 'z':z} )
key = [ 'x', 'y' ]
grps = df.groupby( key )
print( df.head() )
@autojit
def gb_max( grps, fld ):
arr = grps.obj[ fld ].values
comp_ids, _, ngroups = grps.grouper.group_info
result = np.ndarray( ngroups, dtype=arr.dtype )
result[:] = np.finfo( np.float ).min
for i in range( len( comp_ids ) ):
cid = comp_ids[i]
ai = arr[i]
if arr[i]>result[cid]:
result[ cid ] = arr[i]
return result
pd_max = grps[ 'z' ].max()
nb_max = gb_max( grps, 'z' )
print( pd_max.values )
print( nb_max )
assert np.all( pd_max.values==nb_max ), 'Results are different'
print( 'Timing gb_max' )
get_ipython().magic("timeit s=gb_max( grps, 'y' )")
print( 'Timing pd_max' )
get_ipython().magic("timeit grps['y'].max()")
@autojit
def gb_sum( grps, fld ):
arr = grps.obj[ fld ].values
comp_ids, _, ngroups = grps.grouper.group_info
result = np.ndarray( ngroups, dtype=arr.dtype )
for i in range( len( comp_ids ) ):
result[ comp_ids[i] ] += arr[i]
return result
pd_sum = grps ['y'].sum()
nb_sum = gb_sum( grps, 'y' )
assert np.all( pd_sum.values==nb_sum ), 'Results are different'
print( 'Timing gb_sum' )
get_ipython().magic("timeit gb_sum( grps, 'y' )")
print( 'pandas sum' )
get_ipython().magic("timeit grps ['y'].sum()")
mi = pd.MultiIndex.from_arrays( grps.groups.keys() )
nb_sum2 = pd.DataFrame( data=nb_sum, index=mi )
print( nb_sum2 )
```
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} | 4 | 2014-11-23T15:43:06Z | 2015-06-30T10:55:04Z | 2015-06-30T10:55:04Z | CONTRIBUTOR | null | Pandas could release the GIL in parts of the Cython code. This would enable the parallel use of multiple cores with threading.
From off-line conversation with @jreback @cpcloud
http://lbolla.info/blog/2013/12/23/python-threads-cython-gil
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} | 5 | 2014-11-23T22:20:35Z | 2015-03-05T12:46:51Z | 2015-03-04T20:42:39Z | CONTRIBUTOR | null | related issue: https://github.com/pydata/pandas/issues/8597.
among changes:
[tests/test_generic.py](https://github.com/pydata/pandas/blob/0a2ea0ac39a74771d54b91522728f6092b0ef897/pandas/tests/test_generic.py#L603) `downcast` is misspelled `donwcast`, but no error is raised, because of how `**kwargs` is used.
[core/categorical.py:sort](https://github.com/pydata/pandas/blob/0a2ea0ac39a74771d54b91522728f6092b0ef897/pandas/core/categorical.py#L945) `na_position` is not passed to `self.order`.
[core/categorical.py:fillna](https://github.com/pydata/pandas/blob/0a2ea0ac39a74771d54b91522728f6092b0ef897/pandas/core/categorical.py#L981) `Categorical.fillna` does not support `inplace` as opposed to say `Series.fillna` or `DataFrame.fillna`, but `**kwargs` being there causes below code to just silently ignore passed argument:
```
>>> ca = pd.Categorical([0, 0, 2, 2, 3, np.nan])
>>> ca.fillna(0, inplace=True)
[0, 0, 2, 2, 3, 0]
Categories (3, int64): [0 < 2 < 3]
>>> ca
[0, 0, 2, 2, 3, NaN]
Categories (3, int64): [0 < 2 < 3]
```
[This test](https://github.com/pydata/pandas/blob/0a2ea0ac39a74771d54b91522728f6092b0ef897/pandas/tests/test_groupby.py#L4418) is basically testing that you can specify _any_ keyword argument and it is just silently ignored. I cannot think of any case that this would be a good idea, or has any use case.
multiple instances where `*args` or `*kwargs` are not passed to inner function or have any use at all, as can be seen in the PR diff.
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} | 8 | 2014-11-24T05:08:33Z | 2014-11-26T10:45:31Z | 2014-11-26T02:30:02Z | MEMBER | null | Fixes #8813
Fixes #5963
Fixes #5436
If the other argument has a dtype attribute, I assume that it is ndarray-like
and convert the `Timedelta` into a `np.timedelta64` object. Alternatively, we
could just return `NotImplemented` and let the other type handle it, but this
has the bonus of making `Timedelta` compatible with ndarrays.
I also added a `Timedelta.to_timedelta64()` method to the public API. I
couldn't find a listing for `Timedelta` in the API docs -- we should probably
add that, right?
Next up would be a similar treatment for `Timestamp`.
CC @immerrr
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} | 15 | 2014-11-24T10:47:40Z | 2015-05-14T15:47:47Z | 2015-05-09T20:11:46Z | CONTRIBUTOR | null | Originally discussed in #8244. Currently this only uses the column from the dataframe if the `s` argument is a string, supporting int column names is ambiguous and liable to break existing code, but if people can think of a good way to do this I'll try to add it.
Example output:
``` python
import pandas as pd
import numpy as np
import random
df = pd.DataFrame({
'x': np.linspace(0, 50, 6),
'y': np.linspace(0, 20, 6),
'cat_column': random.sample('abcdabcdabcd', 6)
})
df['cat_column'] = pd.Categorical(df['cat_column'])
# Numeric column: linear scaling of point sizes
df.plot(kind='scatter', x='x', y='y', s='x')
```
``` python
# Categorical columns: fixed set of sizes
df.plot(kind='scatter', x='x', y='y', s='cat_column')
```
The minimum and maximum point sizes we scale have a default range of `(50, 1000)`, and
can be adjusted with the `size_range` arg:
``` python
# Smaller range of sizes than default
df.plot(kind='scatter', x='x', y='y', s='x',
size_range=(200, 800))
```
Picking good defaults for the range of sizes is pretty hard as these sizes are absolute- matplotlib
sets the size in terms of points. The ability to tweak up and down with the `size_range` arg is
probably going to be needed, but open to suggestions on good defaults.
.
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} | 6 | 2014-11-24T16:40:16Z | 2021-04-11T05:40:57Z | null | MEMBER | null | Here I expected a TimedeltaIndex with 11 elements from 0 hours through 11 hours:
```
>>> pd.timedelta_range(0, 10, freq='H')
<class 'pandas.tseries.tdi.TimedeltaIndex'>
['0 days']
Length: 1, Freq: <Hour>
```
It appears that `freq` is taken to refer only to the step size, not the start/stop, and the unit defaults to `ns` like Timedelta itself? I think we should either:
1. add a separate `unit` argument -- but this is a subtle distinction
2. use `freq` to also imply the `unit`
3. raise on integer input (possibly in addition to 1?)
Similarly strange and somewhat inconsistent with the above example:
```
>>> pd.timedelta_range(0, 10, periods=100)
ValueError: Must specify two of start, end, or periods
```
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} | 4 | 2014-11-24T18:28:40Z | 2020-05-16T20:14:08Z | null | NONE | null | UPDATE:
```
In [157]: problem_date = old.dob.loc[4231354]
In [158]: problem_date
Out[158]: datetime.date(2939, 6, 2)
In [159]: test_series = pd.Series([problem_date])
In [160]: pd.to_datetime(test_series)
Out[160]:
0 2939-06-02
dtype: object
```
It seems this is the source of the problem. I think there may be other dates in my dataset that are breaking the `to_datetime` method
UPDATE 2:
It seems that maybe it's that the date is later than 2900 that's causing the problem?
```
In [194]: pd.to_datetime(old.dob[8230866])
Out[194]: datetime.date(2955, 8, 22)
In [195]: another_bad_date = old.dob.loc[8230866]
In [196]: pd.to_datetime(pd.Series([another_bad_date]))
Out[196]:
0 2955-08-22
dtype: object
```
#
original issue:
The column in question came from a read_sql query, and the column has datetimes. It consists solely of pandas datetime objects and NoneType objects. I have iterated over the Series to be sure. The column has 11 million rows.
I've tried casting with to_datetime (and the dtype remains object--shouldn't the dtype change after that call?), to no avail.
Here's some stuff I get from poking around after sticking an `import pdb; pdb.set_trace()` into line 3329 of pytables.py (after `except (NotImplementedError, ValueError, TypeError) as e:`):
```
(Pdb) b
(Pdb) i
3
(Pdb) blocks[3]
ObjectBlock: [1, 2, 3, 4, 9, 12, 13, 14], 8 x 8255524, dtype: object
(Pdb) blk_items[3]
Index([u'dob', u'City', u'Region', u'Zip', u'lang', u'UnsubscribedDate', u'BadAddressDate', u'ISP'], dtype='object')
(Pdb) existing_col
(Pdb) col
name->values_block_3,cname->values_block_3,dtype->None,shape->None
(Pdb) b
(Pdb) type(b)
<class 'pandas.core.internals.ObjectBlock'>
(Pdb) block_items
*** NameError: name 'block_items' is not defined
(Pdb) b_items
Index([u'dob', u'City', u'Region', u'Zip', u'lang', u'UnsubscribedDate', u'BadAddressDate', u'ISP'], dtype='object')
(Pdb) existing_col
(Pdb) e
TypeError('Cannot serialize the column [dob] because\nits data contents are [mixed] object dtype',)
(Pdb) type(col)
<class 'pandas.io.pytables.DataCol'>
(Pdb) lib
<module 'pandas.lib' from '/home/mmccrea/anaconda/lib/python2.7/site-packages/pandas/lib.so'>
```
My debugging kinds of hits a wall here, because it seems `infer_dtype` seems to be throwing the error, which is in lib.so, which is a compiled binary and I'm not sure how to look into that to figure out what's going on. I would love a suggestion about how to deal with that in the future, in addition to some answers about what's going on in this case.
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} | 4 | 2014-11-24T19:17:29Z | 2015-01-08T01:59:16Z | 2015-01-08T01:59:16Z | CONTRIBUTOR | null | The `StringMethod` `slice_replace` currently raises `NotImplementedError`. Couldn't find an issue for it, so opening this. PR with implementation in the works.
Could use thoughts on how to handle slices that return an empty string on some subset of strings - should the replacement string appear or not? This could happen when the input string is empty, the stop index is less than or equal to the start index (separate cases?), or both start and stop are greater than the length of the string.
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| null | 7 | 2014-11-24T19:34:04Z | 2014-11-25T15:03:46Z | 2014-11-25T15:03:46Z | NONE | null | ```
dt = datetime.datetime(2014,11,24,12,0,0)
dt = dt.replace(tzinfo=td.CHICAGO_TZ)
mi = pd.MultiIndex.from_product([[dt],[1],[2]])
print mi
print
print mi.get_level_values(0)[0]
print
```
When I use get_level_values function and try to filter dataframe based on time index(which is the first level), it always loses the tzinfo. Surely I can add tzinfo back after calling the function, but is there a better way of dealing with this?
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} | 7 | 2014-11-24T21:39:18Z | 2014-12-04T02:47:31Z | 2014-12-04T02:47:31Z | NONE | null | **Summary:**
The trigger is the `tzoffset` timezone. This bug can be reproduced as follows:
```
In [86]: index = pd.date_range("2012-01-01", periods=3, freq='H', tz=dateutil.tz.tzoffset(None, -28800))
In [87]: index
Out[87]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-01-01 00:00:00-08:00, ..., 2012-01-01 02:00:00-08:00]
Length: 3, Freq: H, Timezone: tzoffset(None, -28800)
In [88]: index[0]
Out[88]: Timestamp('2012-01-01 00:00:00-0800', tz='tzoffset(None, -28800)', offset='H')
In [90]: list(iter(index))[0]
Out[90]: Timestamp('2011-12-31 16:00:00-0800', tz='tzoffset(None, -28800)', offset='H')
In [91]: list(iter(index))[0] == index[0]
Out[91]: False
```
In 0.14 this last comparison gives True.
This appears in iterating over the index (`for time in index: ...`) or with using `DataFrame.iterrows()` (#8951).
---
**Original report**:
I see there are a number of issues related to datetime and timeindex here in issues, and I suspect that mine has a lot in common with them. The cure for one of them will probably solve all of them. So here it goes.
My code was using Pandas 0.13.1 without an issue. I recently upgraded to 0.15.1
This is where my code acts unexpectedly:
```
time_points = df.index[df['candidate'] == 1]
for time in time_points:
[...]
```
The index is in US/Pacific timezone. When the for loop returns `time`, it is still in US/Pacific timezone but with an added 8h to the time. So, while the actual time is `2014-11-23 23:25:02.916000-08:00`, `time` is set to `2014-11-23 15:49:12.972000-08:00`.
I have made sure that the type of index is `pandas.Timestamp` and I can't find an elegant workaround that would ensure running it in both versions of Pandas.
Any thoughts on this?
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} | 1 | 2014-11-25T00:06:54Z | 2019-12-18T03:04:45Z | null | CONTRIBUTOR | null | In many engineering and measurement applications, the time is counted from 01-24 to indicate the end of a measurement or integration period.
This issue is to propose that pandas IO functions support importing such timestamps from CSV, Excel etc. out of the box and furthermore, Series can have a flag indicating the reference interval for integration or aggregation fo values (e.g. averaging).
See also:
page 9, clause 5.3 of ISO 8601
http://www.probabilityof.com/iso/8601.pdf
"Time is the time of the recording in the format of hh:mm. Midnight should be referenced as
24 h in the previous day, not 0 h in the next day;
or:
IEC 61724, clause 6.1b, page6
https://law.resource.org/pub/in/bis/S05/is.iec.61724.1998.pdf
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| null | 2 | 2014-11-25T16:34:46Z | 2019-10-09T05:49:56Z | 2019-10-09T05:49:56Z | NONE | null | ``` python
import pandas
df = pandas.DataFrame({u'превед':[1,2,3]})
df
```
I get:
```
UnicodeDecodeError Traceback (most recent call last)
<ipython-input-1-114199e6a3fa> in <module>()
3 print pandas.__version__
4 df = pandas.DataFrame({u'превед':[1,2,3]})
----> 5 df
/home/gutman/trash/ipython/IPython/core/displayhook.pyc in __call__(self, result)
242 self.update_user_ns(result)
243 self.log_output(format_dict)
--> 244 self.finish_displayhook()
245
246 def flush(self):
/home/gutman/trash/ipython/IPython/kernel/zmq/displayhook.pyc in finish_displayhook(self)
66 sys.stdout.flush()
67 sys.stderr.flush()
---> 68 self.session.send(self.pub_socket, self.msg, ident=self.topic)
69 self.msg = None
70
/home/gutman/trash/ipython/IPython/kernel/zmq/session.pyc in send(self, stream, msg_or_type, content, parent, ident, buffers, track, header, metadata)
647 if self.adapt_version:
648 msg = adapt(msg, self.adapt_version)
--> 649 to_send = self.serialize(msg, ident)
650 to_send.extend(buffers)
651 longest = max([ len(s) for s in to_send ])
/home/gutman/trash/ipython/IPython/kernel/zmq/session.pyc in serialize(self, msg, ident)
551 content = self.none
552 elif isinstance(content, dict):
--> 553 content = self.pack(content)
554 elif isinstance(content, bytes):
555 # content is already packed, as in a relayed message
/home/gutman/trash/ipython/IPython/kernel/zmq/session.pyc in <lambda>(obj)
83 # disallow nan, because it's not actually valid JSON
84 json_packer = lambda obj: jsonapi.dumps(obj, default=date_default,
---> 85 ensure_ascii=False, allow_nan=False,
86 )
87 json_unpacker = lambda s: jsonapi.loads(s)
/usr/local/lib/python2.7/dist-packages/zmq/utils/jsonapi.pyc in dumps(o, **kwargs)
38 kwargs['separators'] = (',', ':')
39
---> 40 s = jsonmod.dumps(o, **kwargs)
41
42 if isinstance(s, unicode):
/usr/lib/python2.7/json/__init__.pyc in dumps(obj, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, encoding, default, **kw)
236 check_circular=check_circular, allow_nan=allow_nan, indent=indent,
237 separators=separators, encoding=encoding, default=default,
--> 238 **kw).encode(obj)
239
240
/usr/lib/python2.7/json/encoder.pyc in encode(self, o)
202 if not isinstance(chunks, (list, tuple)):
203 chunks = list(chunks)
--> 204 return ''.join(chunks)
205
206 def iterencode(self, o, _one_shot=False):
UnicodeDecodeError: 'ascii' codec can't decode byte 0xd0 in position 4: ordinal not in range(128)
```
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| null | 4 | 2014-11-25T17:31:50Z | 2014-11-26T00:44:18Z | 2014-11-26T00:44:18Z | NONE | null | Let 'b' be a large dataframe with MulitiIndex.
I pull the 4th block based on the 1st level of the index, and I got the block fine.
> > > a = b.ix[b.index.levels[0][4]]
Then I printed the index of the block, which also seemed fine, code and result as follows:
> > > print a.index
sequence attribute
4 count
price
quantity
and when I pull values, they seemed fine too:
> > > a.index.values
array([(4L, 'count'), (4L, 'price'), (4L, 'quantity')], dtype=object)
but when I looked at it in a different way, things didn't make much sense:
> > > a.index
MultiIndex(levels=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...], [u'count', u'price', u'quantity']],
labels=[[4, 4, 4], [0, 1, 2]],
names=[u'sequence', u'attribute'])
and when I pull the first level from the index of the block, I got:
> > > a.index.levels[0]
Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...], dtype='int64')
Which is not the
Int64Index([4], dtype='int64')
I anticipated.
Of course I can
> > > pd.MultiIndex.from_tuples(a.index.values)
and create the thing I am expecting, but this is a pain in the ass. Any solutions? Thanks!
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| null | 5 | 2014-11-26T00:23:40Z | 2015-03-25T23:51:50Z | 2015-03-25T23:20:41Z | NONE | null | ```
ENH
I've made a number of alterations to this module to improve the
usability of it.
1. A feature has been added to the 'filters' keyword, in that
compound filters can now be created with AND and OR combinations
as in the API, while still allowing the user to create
the filters via lists of strings without the 'ga:' prefix. In
particulare, compound filters can be formed as:
filters = ['filter_00', 'and', 'filter_01', 'or', 'filter_02']
where no defuault behaviour has been created, thus ensuring that
the compound item is formed the way the user intends it to be.
The 'and' and 'or' separators are case-insensitive.
2. The 'sort' keyword is now passed to the query, and works as
it should with the API. That is, the current 'sort' keyword only
acts on the retrieved data when the parser is constructing the
local DataFrame object. This behaviour is now changed, in that
the 'sort' keyword is included in the query, as well as acting
as previous on the parser. Reverse-ordering as in the API is
maintained by simply including negaitve sign in front of the
metric / dimension that is being sorted on.
This is a sublte but important difference, in that the user can
now include something like:
sort = '-pageviews'
to obtain the top N-many results in a query, which is currently
not possible without obtaining all results and sorting them
locally.
Defualt behaviour has been altered, in that the defualt sort
is now None (sent to query) while the local dataframe is still
sorted on it's index.
3. I didn't like the combination of positional and named keyword
arguments in the methods, so I have altered the bahaviour of
the module to use only named keyword aguments. Thus, the user
is now able to form a query as a single dictionary item and pass
it to, say, the read_ga() method.
```
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} | 2 | 2014-11-26T07:52:28Z | 2015-03-06T00:05:24Z | null | NONE | null | Here is the code:
```
import pandas as pd
import numpy as np
import io
df = pd.DataFrame(np.random.rand(6, 3), columns=list("ABC"))
df.index = list("abcdef")
df.index.name = "index"
df2 = pd.read_html(df.to_html(), index_col=0)
print df2[0]
```
Here is the output:
```
A B C index Unnamed: 5 Unnamed: 6 Unnamed: 7
a 0.759803 0.307306 0.348905 NaN NaN NaN NaN
b 0.003259 0.400688 0.859137 NaN NaN NaN NaN
c 0.824059 0.817198 0.308370 NaN NaN NaN NaN
d 0.067031 0.517689 0.354966 NaN NaN NaN NaN
e 0.043291 0.388853 0.342978 NaN NaN NaN NaN
f 0.064113 0.707188 0.971005 NaN NaN NaN NaN
```
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} | 4 | 2014-11-26T14:45:41Z | 2017-02-25T16:08:07Z | 2015-05-09T16:06:08Z | NONE | null | closes #6360
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| null | 2 | 2014-11-26T19:46:57Z | 2014-11-27T18:18:00Z | 2014-11-26T20:45:54Z | CONTRIBUTOR | null | Hi,
I suggest to add the number of missing values to
pandas.DataFrame.describe()
regards
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| null | 3 | 2014-11-26T19:54:45Z | 2014-11-27T18:12:58Z | 2014-11-26T20:44:22Z | CONTRIBUTOR | null | Hi,
I suggest a method is.na() for pandas.DataFrame that gives back an boolean array.
If a value in the DataFrame is NaN the corresponding element is 1 otherwise 0.
That would make it easy to determine the number of missing elements in a DataFrame.
regards
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| null | 3 | 2014-11-26T20:19:50Z | 2014-11-28T09:41:50Z | 2014-11-26T20:48:08Z | CONTRIBUTOR | null | Hi,
I realized that having a pandas.DataFame object DF with columns 'a','b','c'
DF['a'] is not a DataFrame but a pandas.core.series.Series
Is there a reason for that? I think, it would make the dealing with DataFrames more convenient when a DataFrame would be returned. With header and indexes.
regards
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