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https://api.github.com/repos/pandas-dev/pandas/issues/8101 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8101/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8101/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8101/events | https://github.com/pandas-dev/pandas/issues/8101 | 40,970,024 | MDU6SXNzdWU0MDk3MDAyNA== | 8,101 | Series.isbetween(min,max) method | {
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| null | 2 | 2014-08-23T07:52:33Z | 2014-08-28T04:13:09Z | 2014-08-28T04:13:09Z | NONE | null | There is currently a `Series.isin(list)` for checking containment by descrete values. I suggest that a similar method called `isbetween(min,max)` be introduced that checks for if the series value is between `min` and `max`. The method should return exactly the same result as `(Series >= min) & (Series < max)` but is likely faster as each series element is only visited once. An optional flag can be used for choosing whether `max` should be considered absolutely less than or less than equal to. The method should support the overloading of the comparison operators, so that it e.g. possible to use this for both a floating series, and a time series.
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} | 3 | 2014-08-23T11:07:57Z | 2014-09-09T08:17:37Z | 2014-09-09T08:17:36Z | MEMBER | null | xref #6701, #6416
WIP for now: intitial benchmarks for sql functions
- Where should the benches be placed? Because there is `io_bench.py`, `packers.py` and `parsers_vb.py` which all contain io related benchmarks. Or should I create a new file?
- The first tests are a generic mixed-type frame writing/reading, both with sqlalchemy (sqlite) and sqlite fallback
- I am going to add seperate benches for different types (float, string, datetime)
- other important things to test?
First run with `./test_perf.sh -b master -t HEAD -r sql` gives:
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
sql_write_fallback | 218.5094 | 219.5977 | 0.9950 |
sql_write_sqlalchemy | 347.1383 | 347.9416 | 0.9977 |
sql_read_query_fallback | 28.5973 | 28.6067 | 0.9997 |
sql_read_table_sqlalchemy | 39.0046 | 38.9366 | 1.0017 |
sql_read_query_fallback | 35.9294 | 35.4296 | 1.0141 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [3bcc9c2] : BENCH: add benchmarks for SQL
Base [86ecb99] : BUG: fix iat and at for Float64Index
```
You can see that the sqlalchemy version is somewhat slower than the fallback version.
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} | 0 | 2014-08-23T12:22:07Z | 2014-08-30T01:19:10Z | 2014-08-29T18:58:35Z | MEMBER | null | `pivot_table` raises `KeyError` when args passed to `index` and `columns` don't have `name` attribute. The fix looks especially useful when pivotting by `dt` accessor.
```
df = pd.DataFrame({'dt1': [datetime.datetime(2011, 1, 1), datetime.datetime(2011, 2, 1),
datetime.datetime(2011, 3, 1), datetime.datetime(2011, 4, 1),
datetime.datetime(2011, 5, 1), datetime.datetime(2012, 1, 1),
datetime.datetime(2012, 2, 1), datetime.datetime(2012, 3, 1),
datetime.datetime(2012, 4, 1)],
'col1': 'A A A B B B C C C'.split(),
'val1': [1, 2, 3, 4, 5, 6, 7, 8, 9]})
pd.pivot_table(df, index=df['dt1'].dt.month, columns=df['dt1'].dt.year, values='val1'))
# KeyError: 'Level None not found'
```
### After fix
```
pd.pivot_table(df, index=df['dt1'].dt.month, columns=df['dt1'].dt.year, values='val1'))
# 2011 2012
#1 1 6
#2 2 7
#3 3 8
#4 4 9
#5 5 NaN
```
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} | 5 | 2014-08-23T23:37:38Z | 2014-12-01T00:57:26Z | 2014-12-01T00:57:09Z | CONTRIBUTOR | null | Make assert_numpy_array_equal available without subclassing TestCase; see #8023
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| null | 2 | 2014-08-24T10:16:33Z | 2014-08-24T10:48:59Z | 2014-08-24T10:28:40Z | NONE | null | pandas 0.14.0
pandas.io.sql.read_sql(sql,dbconn) does not work if the string sql does not have 'select' in it. If I'm reading a sproc, sql will start with 'exec' and no 'select'.
There is a test on line 377 in pandas\io\sql.py
if 'select' in sql.lower()
I modified it to
if 'select' in sql.lower() or 'exec' in sql.lower()
then it works.
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} | 11 | 2014-08-24T20:43:49Z | 2015-03-06T16:29:14Z | 2015-03-06T16:29:14Z | CONTRIBUTOR | null | Adds division import to cases where something is already imported from **future** . #5445
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} | 12 | 2014-08-24T23:10:42Z | 2014-09-29T13:23:30Z | 2014-09-29T13:23:30Z | CONTRIBUTOR | null | Without setuptools pandas fails to import at https://github.com/pydata/pandas/blob/master/pandas/io/gbq.py#L9
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} | 15 | 2014-08-25T02:48:37Z | 2015-03-06T02:51:12Z | 2015-03-05T23:43:41Z | CONTRIBUTOR | null | currently
```
>>> df = pd.DataFrame([[1, 2], [3, 5], [7, 11], [9, 23]],
... index=[2, np.nan, 1, 5], columns=['joe', 'jim'])
>>> df
joe jim
2 1 2
NaN 3 5
1 7 11
5 9 23
>>> df.reindex([2, np.nan, 1])
joe jim
2 1 2
NaN NaN NaN
1 7 11
```
with this patch:
```
>>> df.reindex([2, np.nan, 1])
joe jim
2 1 2
NaN 3 5
1 7 11
>>> df.reindex([np.nan, 5, 5, np.nan, 1, 2, np.nan])
joe jim
NaN 3 5
5 9 23
5 9 23
NaN 3 5
1 7 11
2 1 2
NaN 3 5
```
Also, `Float64HashTable` cannot lookup `nan` values:
```
>>> from pandas.hashtable import Float64HashTable
>>> xs = np.array([2.718, 3.14, np.nan, -7, 5, 2, 3])
>>> m = Float64HashTable()
>>> m.map_locations(xs)
>>> m.lookup(xs)
array([ 0, 1, -1, 3, 4, 5, 6])
```
this is fixed to:
```
>>> m.map_locations(xs)
>>> m.lookup(xs)
array([0, 1, 2, 3, 4, 5, 6])
>>> np.all(m.lookup(xs) == np.arange(len(xs)))
True
```
same way that python built-in lists work:
```
>>> [2.718, 3.14, np.nan, -7, 5, 2, 3].index(np.nan)
2
```
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} | 4 | 2014-08-25T13:44:05Z | 2014-08-29T20:43:41Z | 2014-08-29T20:43:30Z | CONTRIBUTOR | null | See https://travis-ci.org/pydata/pandas/jobs/33461059
```
The command "echo "script"" exited with 0.
0.18s$ ci/script.sh
inside ci/script.sh
nosetests --exe -w /tmp -A not slow and not disabled pandas --with-xunit --xunit-file=/tmp/nosetests.xml
Traceback (most recent call last):
File "/home/travis/virtualenv/python3.4.1/lib/python3.4/site-packages/pkg_resources.py", line 449, in _build_master
ws.require(__requires__)
File "/home/travis/virtualenv/python3.4.1/lib/python3.4/site-packages/pkg_resources.py", line 745, in require
needed = self.resolve(parse_requirements(requirements))
File "/home/travis/virtualenv/python3.4.1/lib/python3.4/site-packages/pkg_resources.py", line 644, in resolve
raise VersionConflict(dist, req)
pkg_resources.VersionConflict: (nose 1.3.3 (/home/travis/virtualenv/python3.4.1/lib/python3.4/site-packages), Requirement.parse('nose==1.3.4'))
```
Trying to reproduce locally right now. Hopefully we just need to bump the nose version.
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https://api.github.com/repos/pandas-dev/pandas/issues/8110 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8110/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8110/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8110/events | https://github.com/pandas-dev/pandas/pull/8110 | 41,065,850 | MDExOlB1bGxSZXF1ZXN0MjAyNDg3MTg= | 8,110 | ER: clarify import error message of BeautifulSoup (read_html) | {
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| null | 1 | 2014-08-25T14:23:08Z | 2014-08-25T15:51:59Z | 2014-08-25T15:51:59Z | MEMBER | null | You still have to install it with pip/conda as BeautifulSoup4 and not bs4, so I think this is a bit more clear.
@cpcloud
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https://api.github.com/repos/pandas-dev/pandas/issues/8111 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8111/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8111/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8111/events | https://github.com/pandas-dev/pandas/pull/8111 | 41,068,211 | MDExOlB1bGxSZXF1ZXN0MjAyNTAwNTA= | 8,111 | DOC: fix formatting in dependencies in install.rst | {
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| null | 0 | 2014-08-25T14:45:52Z | 2014-08-25T14:57:19Z | 2014-08-25T14:57:15Z | MEMBER | null | http://pandas.pydata.org/pandas-docs/stable/install.html#recommended-dependencies: "Needed for gbq" was not formatted correctly in its own sublist. At once removed the unnecessary indentation of the lists in install.rst
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} | 0 | 2014-08-25T15:41:52Z | 2014-10-30T11:19:43Z | 2014-10-28T00:04:25Z | CONTRIBUTOR | null | ```
>>> df = pd.DataFrame(np.random.randint(1, 9, (2, 3)), columns=['jim', 'joe', 'jolie'])
>>> df
jim joe jolie
0 4 7 8
1 7 8 3
>>> ts = df['joe'] * 0
>>> ts.name
'joe'
>>> gr = df.groupby(ts)
>>> gr.nth(0) # this invokes _set_selection_from_grouper internally
jim jolie
joe
7 4 8
>>> gr.apply(sum) # joe column is gone
jim jolie
joe
0 11 11
```
whereas:
```
>>> df.groupby(ts).apply(sum)
jim joe jolie
joe
0 11 15 11
```
What happens is that [this line](https://github.com/pydata/pandas/blob/35a95274bcc90e1a44a881aac2e0183663272fc2/pandas/core/groupby.py#L474) removes the column from selection if it has the same name as the grouper.
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} | 27 | 2014-08-25T19:55:47Z | 2020-01-01T16:26:02Z | 2020-01-01T16:26:02Z | CONTRIBUTOR | null | This works fine:
``` python
In [13]: df = pd.DataFrame(np.random.randn(300), columns=['a'])
In [14]: df['dtime'] = pd.DatetimeIndex(start='2014-01-01', freq='h', periods=300).time
In [15]: df.plot(x='dtime', y='a')
Out[15]: <matplotlib.axes._subplots.AxesSubplot at 0x118fd17f0>
```
This raises a `KeyError`
``` python
In [17]: df.plot(x='dtime', y='a', kind='scatter')
```
We call `df._get_numeric_data()` which excludes datetimes.
May happen for other kinds too.
I'll fix this; just have to decide how much refactoring.
Matplotlib is ok with `datetime.time` values, it chokes on `datetime` values.
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} | 5 | 2014-08-25T20:45:24Z | 2014-09-12T13:47:03Z | 2014-09-12T13:47:03Z | CONTRIBUTOR | null | Had this minor bug working with pandas from the command line
```
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 891 entries, 0 to 890
Data columns (total 12 columns):
PassengerId 891 non-null int64
# ... etc
Embarked 889 non-null object
dtypes: float64(2), int64(5), object(5)>>>
```
(The interpreter prompt is not on a new line at the end.)
This most likely went unnoticed because pandas users tend to work in the ipython interpreter.
I had to change 2 tests, because they were not expecting the extra newline (using split('\n') and counting strings to determine if output was valid) and I don't know if that is a big issue or not.
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} | 9 | 2014-08-26T14:05:11Z | 2014-08-28T17:01:12Z | 2014-08-28T17:01:12Z | NONE | null | I expect the following to produce a DataFrame with 3 rows, 5 columns and no NaNs. Instead it produces 5 rows with the comment lines filled with NaNs. Pandas version 0.14.1.
``` python
In [2]: !cat test.txt
# comment
0 1 2 3 4
5 6 7 8 9
# comment
10 11 12 13 15
In [3]: df = pd.read_table("test.txt", skipinitialspace=True,
...: names=["A", "B", "C", "D", "E"], delim_whitespace=True, comment="#")
In [4]: df
Out[4]:
A B C D E
0 NaN NaN NaN NaN NaN
1 0 1 2 3 4
2 5 6 7 8 9
3 NaN NaN NaN NaN NaN
4 10 11 12 13 15
```
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} | 0 | 2014-08-26T15:50:21Z | 2014-08-29T16:19:48Z | 2014-08-29T16:19:48Z | NONE | null | Pandas objects (`Series`, `DataFrame`, etc...) can broadcast operations with python scalars. For example:
```
import pandas as pd
pd.DataFrame([1,2,3])*2
```
But when performing the same operation with a [numpy scalar array](http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html):
```
import pandas as pd
import numpy as np
pd.DataFrame([1,2,3])*np.array(2)
```
pandas returns an error:
```
ValueError: Incompatible argument shape: ()
```
I can't find an explicit mention of scalar broadcasting in Pandas docs but this seems a natural case to be supported.
_See also the original [SO question](http://stackoverflow.com/questions/25496538/pandas-broadcast-with-numpy-scalars)._
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| null | 3 | 2014-08-26T17:02:46Z | 2014-08-27T00:14:42Z | 2014-08-27T00:14:42Z | NONE | null | This code works fine to produce a single row of 3 subplots
``` python
g = np.random.choice([1,2,3], 10)
s = np.random.normal(size=10)
s2 =np.random.normal(size=10)
df = pd.DataFrame([g, s, s2]).T
df.columns = ['key', 's1', 's2']
gb = df.groupby('key')
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(12, 4))
i = 0
for key, df2 in gb:
df2.plot(ax=axes[i], x='s1', y='s2', title=key)
i = i + 1
```
But if I try to add a second row (nrows=2), it blows up with an Attribute Error
AttributeError: 'numpy.ndarray' object has no attribute 'get_figure'
``` python
fig, axes = plt.subplots(nrows=2, ncols=3)
fig.tight_layout() # Or equivalently, "plt.tight_layout()"
i = 1
for key, df2 in gb:
df2.plot(ax=axes[i])
i = i + 1
```
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| null | 3 | 2014-08-26T19:20:26Z | 2014-08-26T21:05:45Z | 2014-08-26T21:02:17Z | NONE | null | I've run into a number of bugs that were introduced by columns with the same name. For example, if I try to select a duplicate column from a dataframe, I get a dataframe back, which leads to confusing bugs:
df = pd.DataFrame({'name': ('jimmy', 'hortense', 'knut'), 'age': (10, 13, 20)})
df = df[['name','name','age']]
df['name']
Gives a dataframe, not a series.
Any reason for not automatically renaming duplicated columns with a .1, .2... .n or throwing an error?
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} | 5 | 2014-08-27T02:10:20Z | 2014-08-30T01:52:44Z | 2014-08-29T16:19:48Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/8116
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| null | 5 | 2014-08-27T11:21:46Z | 2014-08-29T10:23:50Z | 2014-08-28T13:11:35Z | CONTRIBUTOR | null | ... but R using the foreign package can.
Is some support for those old stata files planned ?
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| null | 5 | 2014-08-27T12:59:55Z | 2014-08-28T02:17:47Z | 2014-08-28T01:58:41Z | CONTRIBUTOR | null | Here is a simple test case that exposes the problem:
```
df = pd.DataFrame(pd.Series([(1,), (1,2), (1,), (1, 2)]), columns = ['ids'])
gb = df.groupby('ids')
for i in gb.size().index :
print i
gb.get_group(i)
```
The issues is that in _get_index of GroupBy, these lines assume that if there is a tuple in the index, then the index is a multi-index, which in the above test case isn't true. Maybe there is some other way to detect that values are from a multi-index, or should pandas explicitly not support tuples in this situation (in an index of a groupby)
```
pandas/core/groupby.py:
sample = next(iter(self.indices))
if isinstance(sample, tuple):
if not isinstance(name, tuple):
raise ValueError("must supply a tuple to get_group with multiple grouping keys")
if not len(name) == len(sample):
raise ValueError("must supply a a same-length tuple to get_group with multiple grouping keys")
```
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} | 4 | 2014-08-27T15:01:32Z | 2014-08-28T17:02:07Z | 2014-08-28T17:01:12Z | CONTRIBUTOR | null | This extends the functionality in #7582 to whitespace-delimited and custom line terminated parsing in the C reader (fixes #8115).
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| null | 0 | 2014-08-27T15:11:22Z | 2014-08-28T01:58:41Z | 2014-08-28T01:58:41Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/8121
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| null | 2 | 2014-08-27T17:43:30Z | 2014-08-28T11:34:05Z | 2014-08-28T11:34:05Z | CONTRIBUTOR | null | The following code:
```
import pandas as pd
from datetime import time
x = pd.DataFrame({'name': ['A', 'B', 'C', 'D'],
'time': [time(14, 12), time(12, 14), time(2, 45), time(5, 55)],
'category': ['X', 'X', 'Y', 'Y']})
y = x.set_index(['category', 'name']).transpose()
y.loc['time', ('X', 'A')]
```
Gives the following traceback:
```
Traceback (most recent call last):
File "evil_loc.py", line 9, in <module>
y.loc['time', ('X', 'A')]
File "/users/is/whughes/pyenvs/73b3a6c24834a212/lib/python2.7/site-packages/pandas/core/indexing.py", line 1142, in __getitem__
return self._getitem_tuple(key)
File "/users/is/whughes/pyenvs/73b3a6c24834a212/lib/python2.7/site-packages/pandas/core/indexing.py", line 655, in _getitem_tuple
return self._getitem_lowerdim(tup)
File "/users/is/whughes/pyenvs/73b3a6c24834a212/lib/python2.7/site-packages/pandas/core/indexing.py", line 763, in _getitem_lowerdim
return self._getitem_nested_tuple(tup)
File "/users/is/whughes/pyenvs/73b3a6c24834a212/lib/python2.7/site-packages/pandas/core/indexing.py", line 844, in _getitem_nested_tuple
if obj.ndim < current_ndim:
AttributeError: 'datetime.time' object has no attribute 'ndim'
```
This may only occur with numpy 1.7, I haven't managed to reproduce on 1.8 yet.
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} | 7 | 2014-08-27T17:57:51Z | 2014-09-11T18:19:11Z | 2014-09-11T18:19:06Z | NONE | null | Trying to make it easier to access the list of encodings from the documentation. Adding this was suggested on this [SO question](http://stackoverflow.com/questions/25530142/list-of-pandas-read-csv-encoding-list/25530279#25530279).
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https://api.github.com/repos/pandas-dev/pandas/issues/8126 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8126/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8126/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8126/events | https://github.com/pandas-dev/pandas/pull/8126 | 41,337,124 | MDExOlB1bGxSZXF1ZXN0MjA0MDYzMTI= | 8,126 | BUG: Fix bad axis limits when boxplotting with 'by' (#7528) | {
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} | 8 | 2014-08-27T22:19:58Z | 2014-09-14T07:24:58Z | 2014-08-30T14:10:48Z | CONTRIBUTOR | null | closes #5517
The original bug involved the y-limits for boxplots being wrong when using `'by': turns out it has to do with the dummy axis that gets created when the plots don't fill the axis layout, e.g. when we're plotting 3 columns in a 2 by 2 layout.
The fix for this basically involves not setting the `sharey`/`sharex` argument for blank/dummy axes.
I've tried to test this in a couple of different ways, but some feedback on better ways to test would be good. In particular, I'm not sure if it's okay to check the `_sharey` attribute of the axes, given that it's private.
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| null | 3 | 2014-08-28T00:30:53Z | 2015-10-21T17:17:47Z | 2015-10-20T22:39:56Z | CONTRIBUTOR | null | The following code works when `sz = 1000000`, but produces IndexError when `sz=10000000`. Seems similar to #5727.
```
df = pd.DataFrame({'A':np.random.randint(0, sz, sz),
'B':np.random.randint(0, 2, sz),
'C':np.random.randn(sz)})
df.set_index(['A', 'B'])\
.groupby(level=[0, 1])['C'].sum()\
.groupby(level=[0]).idxmax()
```
Exception:
```
Traceback (most recent call last):
File "<stdin>", line 3, in <module>
File "/home/amcpherson/scratch/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py", line 482, in __getattr__
if attr in self.obj:
File "/home/amcpherson/scratch/anaconda/lib/python2.7/site-packages/pandas/core/generic.py", line 682, in __contains__
return key in self._info_axis
File "/home/amcpherson/scratch/anaconda/lib/python2.7/site-packages/pandas/core/index.py", line 2908, in __contains__
self.get_loc(key)
File "/home/amcpherson/scratch/anaconda/lib/python2.7/site-packages/pandas/core/index.py", line 3411, in get_loc
return self._get_level_indexer(key, level=0)
File "/home/amcpherson/scratch/anaconda/lib/python2.7/site-packages/pandas/core/index.py", line 3585, in _get_level_indexer
loc = level_index.get_loc(key)
File "/home/amcpherson/scratch/anaconda/lib/python2.7/site-packages/pandas/core/index.py", line 1181, in get_loc
return self._engine.get_loc(_values_from_object(key))
File "index.pyx", line 129, in pandas.index.IndexEngine.get_loc (pandas/index.c:3354)
File "index.pyx", line 138, in pandas.index.IndexEngine.get_loc (pandas/index.c:3107)
File "util.pxd", line 40, in util.get_value_at (pandas/index.c:13413)
IndexError: index out of bounds
```
Version information:
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.18-194.el5
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.1
nose: 1.3.0
Cython: 0.20.1
numpy: 1.8.2
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 1.1.0
sphinx: 1.2.1
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.4
bottleneck: None
tables: 3.1.0
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: 1.8.2
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: 0.5.2
lxml: 3.3.1
bs4: 4.3.1
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.2
pymysql: None
psycopg2: None
```
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} | 8 | 2014-08-28T02:47:09Z | 2014-08-30T02:31:57Z | 2014-08-29T19:16:53Z | CONTRIBUTOR | null | Improves multi-key `groupby` speed; On master:
```
In [3]: pd.__version__
Out[3]: '0.14.1-276-g995f91c'
In [4]: np.random.seed(2718281)
In [5]: n = 20000
In [6]: df = pd.DataFrame(np.random.randint(1, n, (n, 3)),
...: columns=['jim', 'joe', 'jolie'])
In [7]: %timeit df.groupby(['jim', 'joe'])['jolie'].transform('max')
1 loops, best of 3: 1.09 s per loop
In [8]: df['joe'] = df['jim']
In [9]: %timeit df.groupby(['jim', 'joe'])['jolie'].transform('max')
1 loops, best of 3: 1.02 s per loop
```
note that it is not responsive to the reduction in number of groups. With this patch:
```
In [9]: %timeit df.groupby(['jim', 'joe'])['jolie'].transform('max')
10 loops, best of 3: 122 ms per loop
In [10]: df['joe'] = df['jim']
In [11]: %timeit df.groupby(['jim', 'joe'])['jolie'].transform('max')
10 loops, best of 3: 82.3 ms per loop
```
benching:
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
groupby_transform_multi_key2 | 48.2820 | 810.2150 | 0.0596 |
groupby_transform_multi_key4 | 137.5050 | 1986.8030 | 0.0692 |
groupby_transform_multi_key1 | 70.1749 | 841.8140 | 0.0834 |
groupby_transform_multi_key3 | 713.8393 | 2613.6247 | 0.2731 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [11cc057] : Merge branch 'groupby-speed-up' of https://github.com/behzadnouri/pandas into behzadnouri-groupby-speed-up
Base [3bb0803] : Merge pull request #8103 from sinhrks/pivot_dt
BUG: pivot_table raises KeyError with nameless index and columns
```
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} | 3 | 2014-08-28T08:25:11Z | 2014-09-17T15:30:14Z | 2014-09-17T15:30:14Z | CONTRIBUTOR | null | I think I hit another 32bits issue. This happens in Win7 32 bits:
```
Python 2.7.6 (default, Nov 10 2013, 19:24:18) [MSC v.1500 32 bit (Intel)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> index = pd.date_range('2000-01-01', periods=5, freq='D')
>>> s1 = pd.Series(range(5), index=index)
>>> p = s1.iloc[1]
>>> s1.shift(periods=p)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "D:\Python27\lib\site-packages\pandas\core\generic.py", line 3290, in shift
new_data = self._data.shift(periods=periods, axis=block_axis)
File "D:\Python27\lib\site-packages\pandas\core\internals.py", line 2228, in shift
return self.apply('shift', **kwargs)
File "D:\Python27\lib\site-packages\pandas\core\internals.py", line 2192, in apply
applied = getattr(b, f)(**kwargs)
File "D:\Python27\lib\site-packages\pandas\core\internals.py", line 791, in shift
new_values = np.roll(new_values, periods, axis=axis)
File "D:\Python27\lib\site-packages\numpy\core\numeric.py", line 1294, in roll
res = a.take(indexes, axis)
TypeError: Cannot cast array data from dtype('int64') to dtype('int32') according to the rule 'safe'
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: x86
processor: x86 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.14.1
nose: 1.3.3
Cython: None
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.2.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.3
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.0
openpyxl: None
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.3.5
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8130 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8130/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8130/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8130/events | https://github.com/pandas-dev/pandas/issues/8130 | 41,380,136 | MDU6SXNzdWU0MTM4MDEzNg== | 8,130 | Compilation of master fails following datetime parsing changes. | {
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| null | 3 | 2014-08-28T11:21:57Z | 2014-09-09T04:15:04Z | 2014-08-28T11:23:55Z | CONTRIBUTOR | null | As of commit 67323069fb5e8f1de3000d7606d26172a2e18a9f, I'm no longer able to compile pandas.
```
$ python setup.py install
running install
running bdist_egg
running egg_info
writing requirements to pandas.egg-info/requires.txt
writing pandas.egg-info/PKG-INFO
writing top-level names to pandas.egg-info/top_level.txt
writing dependency_links to pandas.egg-info/dependency_links.txt
reading manifest file 'pandas.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no files found matching 'README.rst'
warning: no previously-included files matching '*.pyd' found anywhere in distribution
warning: no previously-included files matching '*~' found anywhere in distribution
warning: no previously-included files matching '#*' found anywhere in distribution
warning: no previously-included files matching '.DS_Store' found anywhere in distribution
writing manifest file 'pandas.egg-info/SOURCES.txt'
installing library code to build/bdist.linux-x86_64/egg
running install_lib
running build_py
copying pandas/version.py -> build/lib.linux-x86_64-2.7/pandas
running build_ext
building 'pandas.index' extension
gcc -pthread -fno-strict-aliasing -O2 -g -m64 -mtune=generic -D_GNU_SOURCE -fPIC -fwrapv -I/usr/kerberos/include -DNDEBUG -O2 -g -m64 -mtune=generic -D_GNU_SOURCE -fPIC -fwrapv -fPIC -Ipandas/src/klib -Ipandas/src -I/users/is/whughes/pyenvs/pandas2/lib/python2.7/site-packages/numpy-1.8.2-py2.7-linux-x86_64.egg/numpy/core/include -I/opt/ahl/releases/python/2.7.3/include/python2.7 -c pandas/index.c -o build/temp.linux-x86_64-2.7/pandas/index.o
In file included from /users/is/whughes/pyenvs/pandas2/lib/python2.7/site-packages/numpy-1.8.2-py2.7-linux-x86_64.egg/numpy/core/include/numpy/ndarraytypes.h:1761:0,
from /users/is/whughes/pyenvs/pandas2/lib/python2.7/site-packages/numpy-1.8.2-py2.7-linux-x86_64.egg/numpy/core/include/numpy/ndarrayobject.h:17,
from /users/is/whughes/pyenvs/pandas2/lib/python2.7/site-packages/numpy-1.8.2-py2.7-linux-x86_64.egg/numpy/core/include/numpy/arrayobject.h:4,
from pandas/index.c:346:
/users/is/whughes/pyenvs/pandas2/lib/python2.7/site-packages/numpy-1.8.2-py2.7-linux-x86_64.egg/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
pandas/index.c: In function '__pyx_f_8datetime__cstring_to_dts':
pandas/index.c:11472:3: warning: passing argument 6 of 'parse_iso_8601_datetime' from incompatible pointer type [enabled by default]
In file included from pandas/index.c:357:0:
pandas/src/datetime/np_datetime_strings.h:46:1: note: expected 'int *' but argument is of type 'npy_bool *'
pandas/index.c:11472:3: warning: passing argument 7 of 'parse_iso_8601_datetime' from incompatible pointer type [enabled by default]
In file included from pandas/index.c:357:0:
pandas/src/datetime/np_datetime_strings.h:46:1: note: expected 'int *' but argument is of type 'enum PANDAS_DATETIMEUNIT *'
pandas/index.c:11472:3: warning: passing argument 8 of 'parse_iso_8601_datetime' from incompatible pointer type [enabled by default]
In file included from pandas/index.c:357:0:
pandas/src/datetime/np_datetime_strings.h:46:1: note: expected 'enum PANDAS_DATETIMEUNIT *' but argument is of type 'npy_bool *'
pandas/index.c:11472:3: error: too few arguments to function 'parse_iso_8601_datetime'
In file included from pandas/index.c:357:0:
pandas/src/datetime/np_datetime_strings.h:46:1: note: declared here
error: command 'gcc' failed with exit status 1
```
I've tested with GCC 4.1 and 4.7, and numpy 1.7 and 1.8. The traceback shown is GCC 4.7 with numpy 1.8.
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} | 4 | 2014-08-28T12:47:37Z | 2014-09-17T14:48:25Z | 2014-09-17T14:48:25Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/25517342/adding-a-sparse-series-to-a-dense-dataframe-in-pandas)
thought this was well tested.....
```
df = pd.DataFrame({'c_1':['a', 'b', 'c'], 'n_1': [1., 2., 3.]})
df['new_column'] = pd.Series([0, 0, 1]).to_sparse(fill_value=0)
# AssertionError: Shape of new values must be compatible with manager shape
```
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} | 0 | 2014-08-28T12:52:15Z | 2014-08-28T15:04:07Z | 2014-08-28T15:04:07Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/25530234/in-the-weeds-on-pandas-multi-indexing-using-slicers)
```
df = pd.DataFrame({'A': ['A0'] * 5 + ['A1']*5 + ['A2']*5,
'B': ['B0','B0','B1','B1','B2'] * 3,
'DATE': ["2013-06-11",
"2013-07-02",
"2013-07-09",
"2013-07-30",
"2013-08-06",
"2013-06-11",
"2013-07-02",
"2013-07-09",
"2013-07-30",
"2013-08-06",
"2013-09-03",
"2013-10-01",
"2013-07-09",
"2013-08-06",
"2013-09-03"],
'VALUES': [22, 35, 14, 9, 4, 40, 18, 4, 2, 5, 1, 2, 3,4, 2]})
df.DATE = df['DATE'].apply(lambda x: pd.to_datetime(x))
df1 = df.set_index(['A', 'B', 'DATE'])
df1 = df1.sortlevel()
df2 = df.set_index('DATE')
# A1 - Works - Get all values under "A0" and "A1"
df1.loc[(slice('A1')),:]
# A2 - Works - Get all values from the start to "A2"
df1.loc[(slice('A2')),:]
# A3 - Works - Get all values under "B1" or "B2"
df1.loc[(slice(None),slice('B1','B2')),:]
# A4 - Works - Get all values between 2013-07-02 and 2013-07-09
df1.loc[(slice(None),slice(None),slice('20130702','20130709')),:]
##############################################
# These do not work and I'm wondering why... #
##############################################
# B1 - Does not work - Get all values in B0 that are also under A0, A1 and A2
df1.loc[(slice('A2'),slice('B0')),:]
# B2 - Does not work - Get all values in B0, B1 and B2 (similar to what #2 is doing for the As)
df1.loc[(slice(None),slice('B2')),:]
# B3 - Does not work - Get all values from B1 to B2 and up to 2013-08-06
df1.loc[(slice(None),slice('B1','B2'),slice('2013-08-06')),:]
# B4 - Does not work - Same as A4 but the start of the date slice is not a key.
# Would have thought the behavior would be similar to something like df2['20130701':]
# In other words, date indexing allowed starting on non-key points
df1.loc[(slice(None),slice(None),slice('20130701','20130709')),:]
```
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} | 4 | 2014-08-28T13:29:02Z | 2014-09-01T01:27:13Z | 2014-09-01T01:27:13Z | CONTRIBUTOR | null | `get_dummies` currently just expects a Series.
``` python
In [17]: data
Out[17]:
PassengerId Survived Pclass \
0 1 0 3
1 2 1 1
Name Sex Age SibSp \
0 Braund, Mr. Owen Harris male 22 1
1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1
Parch Ticket Fare Cabin Embarked
0 0 A/5 21171 7.2500 NaN S
1 0 PC 17599 71.2833 C85 C
```
If it took DataFrames we could change the required call from
```
features = pd.concat([data.get(['Fare', 'Age']),
pd.get_dummies(data.Sex, prefix='Sex'),
pd.get_dummies(data.Pclass, prefix='Pclass'),
pd.get_dummies(data.Embarked, prefix='Embarked')],
axis=1)
```
to
```
features = pd.get_dummies(data)
```
We'll infer that things with `object` dtype need to be encoded as 0's and 1's, but also take arguments to explicitly encode a column, or not.
The column names in the output will automatically include the original column name as a prefix, which can be overridden by the `prefix` kwarg by passing a list or dictionary.
Same thing with prefix separators.
On NaN handling, I think we'll have one `{prefix}_NaN` output column per original column when `dummy_na` is True.
I've got some tests written already.
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} | 3 | 2014-08-28T14:21:03Z | 2014-08-28T15:04:07Z | 2014-08-28T15:04:07Z | CONTRIBUTOR | null | closes #8132
```
# A1 - Works - Get all values under "A0" and "A1"
df1.loc[(slice('A1')),:]
VALUES
A B DATE
A0 B0 2013-06-11 22
2013-07-02 35
B1 2013-07-09 14
2013-07-30 9
B2 2013-08-06 4
A1 B0 2013-06-11 40
2013-07-02 18
B1 2013-07-09 4
2013-07-30 2
B2 2013-08-06 5
```
```
# A2 - Works - Get all values from the start to "A2"
df1.loc[(slice('A2')),:]
VALUES
A B DATE
A0 B0 2013-06-11 22
2013-07-02 35
B1 2013-07-09 14
2013-07-30 9
B2 2013-08-06 4
A1 B0 2013-06-11 40
2013-07-02 18
B1 2013-07-09 4
2013-07-30 2
B2 2013-08-06 5
A2 B0 2013-09-03 1
2013-10-01 2
B1 2013-07-09 3
2013-08-06 4
B2 2013-09-03 2
```
```
# A3 - Works - Get all values under "B1" or "B2"
df1.loc[(slice(None),slice('B1','B2')),:]
VALUES
A B DATE
A0 B1 2013-07-09 14
2013-07-30 9
B2 2013-08-06 4
A1 B1 2013-07-09 4
2013-07-30 2
B2 2013-08-06 5
A2 B1 2013-07-09 3
2013-08-06 4
B2 2013-09-03 2
```
```
# A4 - Works - Get all values between 2013-07-02 and 2013-07-09
df1.loc[(slice(None),slice(None),slice('20130702','20130709')),:]
VALUES
A B DATE
A0 B0 2013-07-02 35
B1 2013-07-09 14
A1 B0 2013-07-02 18
B1 2013-07-09 4
A2 B1 2013-07-09 3
```
```
# B1 - Get all values in B0 that are also under A0, A1 and A2
df1.loc[(slice('A2'),slice('B0')),:]
VALUES
A B DATE
A0 B0 2013-06-11 22
2013-07-02 35
A1 B0 2013-06-11 40
2013-07-02 18
A2 B0 2013-09-03 1
2013-10-01 2
```
```
# B2 - Get all values in B0, B1 and B2 (similar to what #2 is doing for the As)
df1.loc[(slice(None),slice('B2')),:]
VALUES
A B DATE
A0 B0 2013-06-11 22
2013-07-02 35
B1 2013-07-09 14
2013-07-30 9
B2 2013-08-06 4
A1 B0 2013-06-11 40
2013-07-02 18
B1 2013-07-09 4
2013-07-30 2
B2 2013-08-06 5
A2 B0 2013-09-03 1
2013-10-01 2
B1 2013-07-09 3
2013-08-06 4
B2 2013-09-03 2
```
```
# B3 - Get all values from B1 to B2 and up to 2013-08-06
df1.loc[(slice(None),slice('B1','B2'),slice('2013-08-06')),:]
VALUES
A B DATE
A0 B1 2013-07-09 14
2013-07-30 9
B2 2013-08-06 4
A1 B1 2013-07-09 4
2013-07-30 2
B2 2013-08-06 5
A2 B1 2013-07-09 3
2013-08-06 4
```
```
# B4 - Same as A4 but the start of the date slice is not a key.
df1.loc[(slice(None),slice(None),slice('20130701','20130709')),:]
VALUES
A B DATE
A0 B0 2013-07-02 35
B1 2013-07-09 14
A1 B0 2013-07-02 18
B1 2013-07-09 4
A2 B1 2013-07-09 3
```
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} | 0 | 2014-08-28T15:59:28Z | 2014-08-30T12:17:50Z | 2014-08-30T12:17:50Z | CONTRIBUTOR | null | http://pandas-docs.github.io/pandas-docs-travis/install.html#optional-dependencies
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} | 2 | 2014-08-28T21:43:22Z | 2016-11-11T10:37:00Z | 2016-11-11T10:37:00Z | CONTRIBUTOR | null | I was a little surprised by this
```
In [11]: pd.Series([])
Out[11]: Series([], dtype: float64)
In [12]: pd.Series([None]) # should be NaN / float ?
Out[12]:
0 None
dtype: object
In [13]: pd.Series([None, 1])
Out[13]:
0 NaN
1 1
dtype: float64
```
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| null | 9 | 2014-08-28T22:01:16Z | 2014-09-03T16:03:58Z | 2014-09-03T13:49:13Z | NONE | null | I'm pretty new to pandas and made what is obviously a rookie mistake. I was attempting to add two columns together to generate a third column. What I meant to write was this:
``` python
df['Col3'] = df.Col1 + df.Col2
```
what I actually wrote was this:
``` python
df['Col3'] = df[df.Col1] + df[df.Col2]
```
This is clearly my fault and incorrect, but I assume that this shouldn't crash my computer and require a hard shutdown. I did it twice before I realized that this was the issue, both times resulted in a full crash.
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https://api.github.com/repos/pandas-dev/pandas/issues/8138 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8138/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8138/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8138/events | https://github.com/pandas-dev/pandas/issues/8138 | 41,489,016 | MDU6SXNzdWU0MTQ4OTAxNg== | 8,138 | BUG: inconsistent behaviour of Groupby with empty bins when grouping by single/multiple keys | {
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} | 6 | 2014-08-29T10:38:19Z | 2014-11-21T10:50:37Z | 2014-11-21T10:50:37Z | NONE | null | Hello everyone,
I just stumbled upon an inconsistent behaviour of the groupby function which is causing me a lot of trouble. When grouping on bins, I expect the empty bins to be kept as NA values, for dimension consistency when one wants to aggregate and compare data.
This is effectively the case when grouping on a single key, but the empty bins are dropped as soon as one adds a second key to the groupby function.
``` python
import pandas as pd
import numpy as np
d = {'Col 1': [3, 3, 4, 5], 'Col 2': [1, 2, 3, 4], 'Col 3': [10, 100, 200, 34]}
test = pd.DataFrame(d)
values = pd.cut(test['Col 1'], [1, 2, 3, 6])
# Grouping on a single column
groups_single_key = test.groupby(values)
# Grouping on two columns
groups_double_key = test.groupby([values,'Col 2'])
# The empty group is kept as NA, which is the behaviour I was expecting
groups_single_key.describe()
# The empty groups are dropped
groups_double_key.describe()
# This is not just an artifact of the describe() method: the empty group really
# does exist and is taken into account when performing aggregation
print(groups_single_key.agg('mean'))
print(groups_double_key.agg('mean'))
```
``` python
pd.show_versions()
```
pandas: 0.14.1
nose: 1.3.1
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 2.2.0
sphinx: 1.2.2
patsy: 0.3.0
scikits.timeseries: None
dateutil: 2.2
pytz: 2013.9
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.0
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.5.7
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
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} | 2 | 2014-08-29T12:57:52Z | 2017-07-08T02:30:53Z | 2017-07-08T02:30:49Z | NONE | null | It would be nice if the join method had `lprefix` and `rprefix` parameters in analogy to `lsuffix` and `rsuffix`.
P.S. No pull request, sorry.
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} | 15 | 2014-08-29T14:18:52Z | 2014-09-01T01:27:14Z | 2014-09-01T01:27:13Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/8133
Thoughts? I encode every column with `object` dtype. You can control the ones that get encoded with `categorical_variables`. The encoded columns are `concat`ed to the columns that don't need encoding.
`prefix` and `prefix_sep` can be passed as a string, list, or dict mapping column names to strs.
``` python
In [1]: from pandas import *
In [2]: df = DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'],
'C': [1, 2, 3]})
In [3]: df
Out[3]:
A B C
0 a b 1
1 b a 2
2 a c 3
In [4]: get_dummies(df)
Out[4]:
C A_a A_b B_a B_b B_c
0 1 1 0 0 1 0
1 2 0 1 1 0 0
2 3 1 0 0 0 1
In [5]: get_dummies(df, prefix=['from_A', 'from_B'])
Out[5]:
C from_A_a from_A_b from_B_a from_B_b from_B_c
0 1 1 0 0 1 0
1 2 0 1 1 0 0
2 3 1 0 0 0 1
In [6]: get_dummies(df, prefix={"A": 'from_A', "B": 'from_B'})
Out[6]:
C from_A_a from_A_b from_B_a from_B_b from_B_c
0 1 1 0 0 1 0
1 2 0 1 1 0 0
2 3 1 0 0 0 1
In [7]: get_dummies(df, prefix_sep='.')
Out[7]:
C A.a A.b B.a B.b B.c
0 1 1 0 0 1 0
1 2 0 1 1 0 0
2 3 1 0 0 0 1
In [8]: get_dummies(df, prefix_sep='.', categorical_variables=['A'])
Out[8]:
B C A.a A.b
0 b 1 1 0
1 a 2 0 1
2 c 3 1 0
```
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} | 2 | 2014-08-29T17:21:38Z | 2021-04-11T05:07:59Z | null | CONTRIBUTOR | null | Currently `get_dummies` has both `prefix` and `prefix_sep` parameters. This is inconsistent with the rest of pandas prefix / suffix arguments that have you included the separator in the pre/suffix.
- `read_csv`
- `join`
probably others.
We should add a warning to `get_dummies` to have users change `prefix_sep` to `''` and include the separators in `prefix`.
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} | 4 | 2014-08-29T17:49:31Z | 2017-06-08T10:48:23Z | 2017-06-08T10:48:23Z | NONE | null | add currently behaves like this:
def **add**(self, other):
if isinstance(other, Index):
return self.union(other)
else:
return Index(np.array(self) + other)
but subtraction falls straight through to diff.
so if index_object is an Int64Index, the following behavior takes place:
index_object + 1 => add one to all indices
index_object - 1 => error in diff
index_object + -1 => subtract one from all indices
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} | 1 | 2014-08-29T19:17:29Z | 2014-08-29T20:44:25Z | 2014-08-29T20:44:25Z | CONTRIBUTOR | null | Should this work?
``` python
In [70]: cat = pd.Categorical(['a', 'b', 'c'])
In [71]: obj = pd.Series(['a', 'b', 'c'])
In [72]: num = pd.Series([1, 2, 3])
In [73]: df = pd.concat([pd.Series(cat), obj, num], axis=1, keys=['cat', 'obj', 'num'])
In [74]: df
Out[74]:
cat obj num
0 a a 1
1 b b 2
2 c c 3
In [75]: df.dtypes
Out[75]:
cat category
obj object
num int64
dtype: object
In [76]: df.dtypes == 'category'
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-76-48fb3d0c49cf> in <module>()
----> 1 df.dtypes == 'category'
/Users/tom/Envs/py3/lib/python3.4/site-packages/pandas-0.14.1_291_g7a8a030-py3.4-macosx-10.9-x86_64.egg/pandas/core/ops.py in wrapper(self, other)
579
580 # scalars
--> 581 res = na_op(values, other)
582 if np.isscalar(res):
583 raise TypeError('Could not compare %s type with Series'
/Users/tom/Envs/py3/lib/python3.4/site-packages/pandas-0.14.1_291_g7a8a030-py3.4-macosx-10.9-x86_64.egg/pandas/core/ops.py in na_op(x, y)
526 msg = "Cannot compare a Categorical for op {op} with type {typ}. If you want to \n" \
527 "compare values, use 'series <op> np.asarray(cat)'."
--> 528 raise TypeError(msg.format(op=op,typ=type(y)))
529 if x.dtype == np.object_:
530 if isinstance(y, list):
TypeError: Cannot compare a Categorical for op <built-in function eq> with type <class 'str'>. If you want to
compare values, use 'series <op> np.asarray(cat)'.
```
For comparison
``` python
In [77]: df.dtypes == 'object'
Out[77]:
cat False
obj True
num False
dtype: bool
In [78]: df.dtypes == 'int64'
Out[78]:
cat False
obj False
num True
dtype: bool
```
This doesn't work either: `In [87]: df.dtypes == df.cat.dtype` (raise the same TypeError)
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} | 6 | 2014-08-29T19:18:30Z | 2018-01-09T12:53:43Z | 2018-01-09T12:53:43Z | CONTRIBUTOR | null | all of the groupby vbenches need a `np.random.seed(1234)` to make them consistent from run-to-run (where they don't have them now).
Possibly should do this on other benches as well.
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} | 0 | 2014-08-29T19:49:11Z | 2014-08-29T20:44:25Z | 2014-08-29T20:44:25Z | CONTRIBUTOR | null | closes #8143
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} | 5 | 2014-08-30T00:00:19Z | 2014-08-30T22:05:22Z | 2014-08-30T22:05:22Z | NONE | null | I'm coming from version 0.13 to 0.14 and testing out querying a database with the new SQLAlchemy back-end. I'm seeing some exceptionally slow behavior. Consider:
``` python
from sqlalchemy import create_engine
import pandas as pd
query = "select count(*) from transactions where started > '2014-08-29'"
engine = create_engine('mysql+mysqlconnector://myuser:mypass@db_host/db')
df = pd.read_sql_query(query, engine)
```
The query above executes in less than a second with pandas 0.13. It now takes more than 30 seconds. I've monitored the MySQL server and the query is not in the process list during this extra time. So something in the middle is getting stuck.
If I change that to:
``` python
from sqlalchemy import create_engine
import pandas as pd
query = "select count(*) from transactions where started > '2014-08-29'"
engine = create_engine('mysql+mysqlconnector://myuser:mypass@db_host/db')
connection = engine.raw_connection()
df = pd.read_sql_query(query, connection)
```
The query only takes 2-3 seconds. I arrived at this speed-up through lots of trial and error. Why is this faster?
Here's my current setup (it's the Anaconda Python 3.4 distribution, but I get the same thing with their Python 2.7 distribution. It also doesn't seem to matter if I'm using MySQLdb our mysql-connector):
## INSTALLED VERSIONS
commit: None
python: 3.4.1.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: 0.14.0
nose: 1.3.3
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.14.0
statsmodels: None
IPython: 2.1.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.1
pytz: 2014.3
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.1
html5lib: None
bq: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
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} | 6 | 2014-08-30T02:31:57Z | 2014-09-06T16:55:50Z | 2014-09-04T00:57:15Z | CONTRIBUTOR | null | Following on https://github.com/pydata/pandas/pull/8128, for certain `groupby` operations such as,
```
df.groupby(key)[col].transfrom('first')
```
it is necessary to use stable sort for group indices. As a result, `numpy.argsort` should be called with argument `kind='mergesort'`.
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} | 1 | 2014-08-30T07:46:45Z | 2014-08-30T17:07:39Z | 2014-08-30T12:17:50Z | CONTRIBUTOR | null | A documentation PR closes #8135.
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} | 9 | 2014-08-30T21:20:15Z | 2014-09-19T17:04:46Z | 2014-09-19T17:04:46Z | MEMBER | null | Is this a regression, or a delibarate decision? Compare dev docs (http://pandas-docs.github.io/pandas-docs-travis/visualization.html#basic-plotting-plot) with stable ones (http://pandas.pydata.org/pandas-docs/stable/visualization.html#basic-plotting-plot).
The xaxis labels (the date/time indications) are now rotated instead of horizontal, which I personally think is not an improvement.
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} | 8 | 2014-08-31T22:39:18Z | 2015-05-10T19:52:12Z | 2015-05-10T19:37:37Z | CONTRIBUTOR | null | Closes [#6528](https://github.com/pydata/pandas/issues/6528).
Currently series logical operations are bound to return a series with `dtype='bool'`. That being a good decision or not, it is good to have some internal consistency:
```
>>> a = pd.Series([nan, nan, nan])
>>> b = pd.Series([False, True, nan])
>>> a | b == b | a # not symmetric
0 True
1 False
2 True
dtype: bool
>>> a & b == a.astype(bool) & b.astype(bool) # not consistent w/ type casts
0 True
1 False
2 False
dtype: bool
```
With this patch, [all these tests pass.](https://github.com/pydata/pandas/pull/8151/files#diff-933180870dd152b775952fc54a99dba7R2371)
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} | 2 | 2014-09-01T03:55:23Z | 2014-09-04T00:29:43Z | 2014-09-04T00:29:43Z | NONE | null | It appears the `warnings` import is missing in `panel.py`
```
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: pd.__version__
Out[3]: '0.14.1'
In [4]: p = pd.Panel(np.random.rand(3, 3, 3))
In [5]: p.major_xs(1, copy=False)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-5-0b8025289379> in <module>()
----> 1 p.major_xs(1, copy=False)
/home/aido/.env/local/lib/python2.7/site-packages/pandas/core/panel.pyc in major_xs(self, key, copy)
715 """
716 if copy is not None:
--> 717 warnings.warn("copy keyword is deprecated, "
718 "default is to return a copy or a view if possible")
719
NameError: global name 'warnings' is not defined
```
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} | 38 | 2014-09-01T12:42:26Z | 2014-09-25T22:17:05Z | 2014-09-25T22:14:33Z | CONTRIBUTOR | null | This PR will collect the various fixups for Categorical.
See #8078 for the ToDos.
Closes: #8077 -> API/FIX: wrap result cut/qut in Series and make it ordered
Closes: #8074 -> Discussion
Closes: #8078 -> Todos
This has a (passing) unittest for #8138 but no code changes. Not sure what was the change which fixed the bug there...
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} | 7 | 2014-09-01T13:19:44Z | 2015-10-06T03:42:13Z | 2015-10-06T03:42:13Z | MEMBER | null | Say you have to parse some nicely ISO formatted date strings, you can just parse this with `todatetime` very fast. But if you were 'overcautious' and provided the `format="%Y-%m-%d %H:%M:%S"` for safety, this seems to be around 20 times slower.
Would it be possible to provide a fastpath for certain provided format strings (as already exists for `%Y%m%d` I think).
```
In [129]: s = pd.Series(pd.date_range('2000-01-01', periods=1000, freq='H'))
In [130]: s_as_dt_strings = s.apply(lambda x: x.strftime("%Y-%m-%dT%H:%M:%S.%f"))
In [131]: %timeit pd.to_datetime(s_as_dt_strings)
1000 loops, best of 3: 406 µs per loop
In [132]: %timeit pd.to_datetime(s_as_dt_strings, format="%Y-%m-%dT%H:%M:%S.%f")
100 loops, best of 3: 9.73 ms per loop
```
```
In [133]: s_as_dt_strings = s.apply(lambda x: x.strftime("%Y-%m-%d %H:%M:%S"))
In [134]: %timeit pd.to_datetime(s_as_dt_strings)
1000 loops, best of 3: 361 µs per loop
In [135]: %timeit pd.to_datetime(s_as_dt_strings, format="%Y-%m-%d %H:%M:%S")
100 loops, best of 3: 8.36 ms per loop
```
For non-standard formats, providing format _does_ give a big improvement:
```
In [136]: s_as_dt_strings = s.apply(lambda x: x.strftime("%Y/%m/%d %H:%M:%S"))
In [137]: %timeit pd.to_datetime(s_as_dt_strings)
10 loops, best of 3: 92.2 ms per loop
In [138]: %timeit pd.to_datetime(s_as_dt_strings, format="%Y/%m/%d %H:%M:%S")
100 loops, best of 3: 9.08 ms per loop
```
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} | 5 | 2014-09-01T14:37:59Z | 2014-09-03T23:29:08Z | 2014-09-03T19:49:45Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/7722
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
groupby_agg_builtins2 | 43.8553 | 2667.0587 | 0.0164 |
groupby_agg_builtins1 | 11.9447 | 118.6290 | 0.1007 |
```
on master:
```
In [4]: np.random.seed(27182)
In [5]: n = 100000
In [6]: df = DataFrame(np.random.randint(1, n / 100, (n, 3)),
...: columns=['jim', 'joe', 'jolie'])
In [7]: %timeit df.groupby('jim').agg([sum, min, max])
1 loops, best of 3: 204 ms per loop
In [8]: %timeit df.groupby(['jim', 'joe']).agg([sum, min, max])
1 loops, best of 3: 5.1 s per loop
In [9]: df.groupby(['jim']).apply(max).head() # this is broken
Out[9]:
jim
1 jolie
2 jolie
3 jolie
4 jolie
5 jolie
dtype: object
```
on branch:
```
In [9]: %timeit df.groupby('jim').agg([sum, min, max])
10 loops, best of 3: 20 ms per loop
In [10]: %timeit df.groupby(['jim', 'joe']).agg([sum, min, max])
10 loops, best of 3: 74.6 ms per loop
In [11]: df.groupby(['jim']).apply(max).head()
Out[11]:
jim joe jolie
jim
1 1 999 989
2 2 997 986
3 3 981 973
4 4 989 992
5 5 995 977
```
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} | 12 | 2014-09-01T15:16:22Z | 2018-07-06T22:43:15Z | 2018-07-06T22:43:05Z | NONE | null | I'm using the following code to import datetimes from a SQL Server database into Pandas:
`data = pd.read_sql('select top 10 timestamp from mytable',db)`
'MyTable' contains a column 'Timestamp', which is of type DateTime2.
If `db` is a pyodbc database connection this works fine, and my timestamps are returned as data type 'datetime64[ns]'. However if `db` an SQL Alchemy engine created using `create_engine('mssql+pyodbc://...')` then the timestamps returned in data are of type 'object' and cause problems later on in my code.
I'm using pandas version 0.14.1, pyodbc version 3.0.7 and SQL alchemy version 0.9.4. I'd like the default behaviour with the SQL Alchemy engine to be the same as pyodbc, i.e. to import all datetimes as datetime64[ns].
Originally from: http://stackoverflow.com/questions/25607775/datetime-import-using-pandas-sqlalchemy
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} | 3 | 2014-09-01T23:12:42Z | 2014-09-04T00:32:04Z | 2014-09-04T00:29:43Z | CONTRIBUTOR | null | closes #8152.
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} | 4 | 2014-09-02T10:00:44Z | 2016-04-26T15:17:03Z | 2016-04-26T15:17:03Z | MEMBER | null | from [SO](http://stackoverflow.com/questions/36804141/vectorized-construction-of-datetimeindex-in-pandas)
I didn't find an issue about this, but it has come up some times at stackoverflow: having columns with integers for year, month, day, hour, ..., how do you convert this to a datetime column/index ?
http://stackoverflow.com/questions/19350806/how-to-convert-columns-into-one-datetime-column-in-pandas
You have the typical solution of adding the columns: `pd.to_datetime((df['Y']*10000 + df['M']*100 + df['D']).astype('int'), format='%Y%m%d')`, and @unutbu added now a faster solution using numpy's different datetime64 resolutions to that question on SO.
I personally think this would be a nice addition to pandas to have a more native solution for this. But then we need to figure out a nice API. Or we keep it as is, but try to document it more (add as example to docs?)
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| null | 1 | 2014-09-02T13:27:18Z | 2015-10-20T22:40:13Z | 2015-10-20T22:40:13Z | CONTRIBUTOR | null | Hi,
I am using read_html to pull data from a table. Several of the columns are parsed as dates incorrectly. If I use parse_dates=False this has no effect. Using infer_types=False works, but it is going to be deprecated.
Sorry if I'm not reporting this correctly, new to GitHub ;-)
``` python
import pandas as pd
url = "http://www.ironman.com/triathlon/events/emea/ironman/france/results.aspx?p=1&rd=20050618#axzz38t1BwRL6"
dfs = pd.io.html.read_html(url, attrs = {'id': 'eventResults'}, parse_dates=False)#, infer_types=False)
print dfs[0]
```
---
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Darwin
OS-release: 13.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.13.1
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.1.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2014.3
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: 2.0.2
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.5
sqlalchemy: 0.9.4
lxml: 3.3.5
bs4: 4.3.1
html5lib: 0.999
bq: None
apiclient: None
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} | 2 | 2014-09-02T14:21:44Z | 2019-10-21T19:52:14Z | null | CONTRIBUTOR | null | 1. The docstring of the (non-member) `pivot()` function, http://pandas.pydata.org/pandas-docs/stable/generated/pandas.pivot.html#pandas.pivot, says `Produce ‘pivot’ table based on 3 columns of this DataFrame. Uses unique values from index / columns and fills with values.` But there is no DataFrame argument, and so no "this DataFrame". Is this an internal function that shouldn't be exposed? Or is the docstring wrong?
2. While the (non-member) `pivot_table()` supports specifying multiple columns for `columns`, so that the resulting table has multi-index columns, `DataFrame.pivot()` does not. Any reason it doesn't? I would have expected the two functions to behave similarly. Granted, the docstring for `DataFrame.pivot()` doesn't claim that it supports multiple columns for `columns`, so this isn't a bug, but it does seem inconsistent (and restrictive) vs. `pivot_table()`.
```
In [2]: from pandas import DataFrame, pivot_table
In [3]: df = DataFrame([['foo', 'ABC', 'A', 1],
...: ['foo', 'ABC', 'B', 2],
...: ['foo', 'XYZ', 'X', 3],
...: ['foo', 'XYZ', 'Y', 4],
...: ['bar', 'ABC', 'B', 5],
...: ['bar', 'XYZ', 'X', 6]],
...: columns=['FooBar', 'TLA', 'Letter', 'Number'])
In [4]: df
Out[4]:
FooBar TLA Letter Number
0 foo ABC A 1
1 foo ABC B 2
2 foo XYZ X 3
3 foo XYZ Y 4
4 bar ABC B 5
5 bar XYZ X 6
In [11]: pivot_table(df, index='FooBar', columns=['TLA', 'Letter'], values='Number')
Out[11]:
TLA ABC XYZ
Letter A B X Y
FooBar
bar NaN 5 6 NaN
foo 1 2 3 4
In [13]: df.pivot(index='FooBar', columns=['TLA', 'Letter'], values='Number')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-13-8585f7e09b0c> in <module>()
----> 1 df.pivot(index='FooBar', columns=['TLA', 'Letter'], values='Number')
C:\Python34\lib\site-packages\pandas\core\frame.py in pivot(self, index, columns, values)
3264 """
3265 from pandas.core.reshape import pivot
-> 3266 return pivot(self, index=index, columns=columns, values=values)
3267
3268 def stack(self, level=-1, dropna=True):
C:\Python34\lib\site-packages\pandas\core\reshape.py in pivot(self, index, columns, values)
357 indexed = Series(self[values].values,
358 index=MultiIndex.from_arrays([self[index],
--> 359 self[columns]]))
360 return indexed.unstack(columns)
361
C:\Python34\lib\site-packages\pandas\core\index.py in from_arrays(cls, arrays, sortorder, names)
2795 return Index(arrays[0], name=name)
2796
-> 2797 cats = [Categorical.from_array(arr) for arr in arrays]
2798 levels = [c.levels for c in cats]
2799 labels = [c.labels for c in cats]
C:\Python34\lib\site-packages\pandas\core\index.py in <listcomp>(.0)
2795 return Index(arrays[0], name=name)
2796
-> 2797 cats = [Categorical.from_array(arr) for arr in arrays]
2798 levels = [c.levels for c in cats]
2799 labels = [c.labels for c in cats]
C:\Python34\lib\site-packages\pandas\core\categorical.py in from_array(cls, data)
101 the unique values of `data`.
102 """
--> 103 return Categorical(data)
104
105 _levels = None
C:\Python34\lib\site-packages\pandas\core\categorical.py in __init__(self, labels, levels, name)
82 name = getattr(labels, 'name', None)
83 try:
---> 84 labels, levels = factorize(labels, sort=True)
85 except TypeError:
86 labels, levels = factorize(labels, sort=False)
C:\Python34\lib\site-packages\pandas\core\algorithms.py in factorize(values, sort, order, na_sentinel)
128 table = hash_klass(len(vals))
129 uniques = vec_klass()
--> 130 labels = table.get_labels(vals, uniques, 0, na_sentinel)
131
132 labels = com._ensure_platform_int(labels)
C:\Python34\lib\site-packages\pandas\hashtable.pyd in pandas.hashtable.PyObjectHashTable.get_labels (pandas\hashtable.c:13534)()
ValueError: Buffer has wrong number of dimensions (expected 1, got 2)
```
This is with Pandas v0.14.1.
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| null | 5 | 2014-09-02T15:58:01Z | 2014-09-02T16:18:07Z | 2014-09-02T16:17:08Z | CONTRIBUTOR | null | I was trying to figure out what's the most efficient way to create a dataframe out of a large numpy record array (I initially naively thought this could be done in a zero-copy way) and ran some simple tests:
---
``` python
n_columns = 2
n_records = int(15e6)
data = np.zeros((n_columns, n_records), np.int32)
arr = np.core.records.fromarrays(data)
%timeit -n1 -r1 df = pd.DataFrame(arr)
%timeit -n1 -r1 df = pd.DataFrame(arr, copy=False)
%timeit -n1 -r1 f0, f1 = arr['f0'].copy(), arr['f1'].copy()
f0, f1 = arr['f0'].copy(), arr['f1'].copy()
%timeit -n1 -r1 df = pd.DataFrame({'f0': f0, 'f1': f1})
%timeit -n1 -r1 df = pd.DataFrame({'f0': f0, 'f1': f1}, copy=False)
```
---
```
1 loops, best of 1: 2.47 s per loop
1 loops, best of 1: 2.42 s per loop
1 loops, best of 1: 48.2 ms per loop
1 loops, best of 1: 4.25 s per loop
1 loops, best of 1: 4.57 s per loop
```
---
I wonder what's the DataFrame constructor doing for several seconds straight even if it's provided with a materialized `ndarray` (note that copying both arrays takes < 50ms)? This is on v0.14.1.
P.S. Is there a more efficientway to make a dataframe out of a recarray?
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} | 19 | 2014-09-02T20:58:40Z | 2021-04-11T05:09:19Z | null | CONTRIBUTOR | null | - [ ] #13243 use df.index/df.columns names to automatically choose axis along which to broadcast
- [ ] #11373 Request for some kind of named arguments loc
- [ ] #4036 Partial Selection on MultiIndex: The need for empty slice support & dict indexing
What if we allowed the index of a dataframe to be referred to in the usual ways?
``` python
data = pd.read_table("...", index_col="id")
data.id # breaks
data["id"] # breaks
```
I find myself setting and resetting indices very often to join to a different dataframe or to pull in the values of the index to a subselection of the dataframe, etc. I figure this is because of how the data is stored under the hood, but wouldn't this be convenient?
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} | 4 | 2014-09-03T02:03:45Z | 2018-03-07T09:44:48Z | 2018-03-07T09:44:44Z | CONTRIBUTOR | null | Any reason `{Series,DataFrame}.dot()` are not listed under http://pandas.pydata.org/pandas-docs/stable/api.html?
They are documented:
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.dot.html
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.dot.html
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} | 32 | 2014-09-03T05:54:56Z | 2014-10-06T00:57:07Z | 2014-10-06T00:57:07Z | CONTRIBUTOR | null | this patch adds a return_type keyword argument to describe() to make it more
flexible to use on mixed-type dataframe. User can now select among returning
numeric, categorical, or both, as well as 'auto' (previous behaviour, default),
and 'same', which keep columns identical (useful, e.g. with groupby())
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} | 10 | 2014-09-03T10:47:12Z | 2015-10-22T13:26:30Z | 2015-10-22T13:26:30Z | NONE | null | xref #9270
With Python 2.7.6, pandas 0.13.1 and numpy 1.8.1, pytables 3.1.1, both 32 and 64bit (Python x,y and WinPython), I can load my hdf5 file.
With Python 3.4.1 64bit, pandas 0.14.1, numpy 1.8.2, pytables 3.1.1 (Anaconda3 2.0.1) I get the following error:
Traceback (most recent call last):
File "<ipython-input-2-fe436a8e76a5>", line 1, in <module>
test = pd.read_hdf('datafile.h5', 'data')
File "C:\Anaconda3\lib\site-packages\pandas\io\pytables.py", line 330, in read_hdf
return f(store, True)
File "C:\Anaconda3\lib\site-packages\pandas\io\pytables.py", line 322, in <lambda>
key, auto_close=auto_close, **kwargs)
File "C:\Anaconda3\lib\site-packages\pandas\io\pytables.py", line 669, in select
auto_close=auto_close).get_values()
File "C:\Anaconda3\lib\site-packages\pandas\io\pytables.py", line 1335, in get_values
results = self.func(self.start, self.stop)
File "C:\Anaconda3\lib\site-packages\pandas\io\pytables.py", line 658, in func
columns=columns, **kwargs)
File "C:\Anaconda3\lib\site-packages\pandas\io\pytables.py", line 2658, in read
ax = self.read_index('axis%d' % i)
File "C:\Anaconda3\lib\site-packages\pandas\io\pytables.py", line 2257, in read_index
_, index = self.read_index_node(getattr(self.group, key))
File "C:\Anaconda3\lib\site-packages\pandas\io\pytables.py", line 2385, in read_index_node
_unconvert_index(data, kind, encoding=self.encoding), **kwargs)
File "C:\Anaconda3\lib\site-packages\pandas\core\index.py", line 125, in **new**
result = DatetimeIndex(data, copy=copy, name=name, **kwargs)
File "C:\Anaconda3\lib\site-packages\pandas\tseries\index.py", line 301, in **new**
infer_dst=infer_dst)
File "tslib.pyx", line 2165, in pandas.tslib.tz_localize_to_utc (pandas\tslib.c:33574)
File "tslib.pyx", line 2082, in pandas.tslib._get_deltas (pandas\tslib.c:32187)
File "tslib.pyx", line 872, in pandas.tslib._get_utcoffset (pandas\tslib.c:16036)
AttributeError: 'numpy.bytes_' object has no attribute 'utcoffset'
The index in question is:
class 'pandas.tseries.index.DatetimeIndex'
[2013-04-03 00:00:00+02:00, ..., 2013-04-04 00:00:00+02:00]
Length: 8641, Freq: 10S, Timezone: Europe/Amsterdam
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} | 9 | 2014-09-03T14:14:27Z | 2015-08-06T12:15:47Z | 2015-08-06T11:55:12Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/8163
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| null | 3 | 2014-09-03T14:38:43Z | 2014-09-03T16:27:53Z | 2014-09-03T16:23:22Z | NONE | null | What should I do with this?
```
File "C:\Python27\lib\site-packages\pandas\io\pytables.py", line 914, in append
**kwargs)
File "C:\Python27\lib\site-packages\pandas\io\pytables.py", line 1273, in _write_to_group
s.write(obj=value, append=append, complib=complib, **kwargs)
File "C:\Python27\lib\site-packages\pandas\io\pytables.py", line 3589, in write
self.set_attrs()
File "C:\Python27\lib\site-packages\pandas\io\pytables.py", line 2887, in set_attrs
self.attrs.non_index_axes = self.non_index_axes
File "C:\Python27\lib\site-packages\tables\attributeset.py", line 455, in __setattr__
self._g__setattr(name, value)
File "C:\Python27\lib\site-packages\tables\attributeset.py", line 397, in _g__setattr
self._g_setattr(self._v_node, name, stvalue)
File "hdf5extension.pyx", line 704, in tables.hdf5extension.AttributeSet._g_setattr (tables\hdf5extension.c:6832)
tables.exceptions.HDF5ExtError: HDF5 error back trace
File "..\..\hdf5-1.8.11\src\H5A.c", line 254, in H5Acreate2
unable to create attribute
File "..\..\hdf5-1.8.11\src\H5A.c", line 503, in H5A_create
unable to create attribute in object header
File "..\..\hdf5-1.8.11\src\H5Oattribute.c", line 347, in H5O_attr_create
unable to create new attribute in header
File "..\..\hdf5-1.8.11\src\H5Omessage.c", line 224, in H5O_msg_append_real
unable to create new message
File "..\..\hdf5-1.8.11\src\H5Omessage.c", line 1945, in H5O_msg_alloc
unable to allocate space for message
File "..\..\hdf5-1.8.11\src\H5Oalloc.c", line 1142, in H5O_alloc
object header message is too large
End of HDF5 error back trace
Can't set attribute 'non_index_axes' in node:
/universe_liquid/alpha_adv_deciles/groups/raw/AAM_demean_groups/portfolio_returns/return_daily_benchmark (Group) u''.
```
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| null | 2 | 2014-09-03T15:33:00Z | 2014-09-04T16:31:00Z | 2014-09-04T16:31:00Z | NONE | null | Read same file inside a thread provokes a segmentation fault.
pandas==0.14.1
``` python
import pandas as pd
import threading
def thread():
'''
Stupid thread reading a DataFrame.
'''
store = pd.HDFStore('/tmp/df.h5')
dataframe = store['/data']
store.close()
def main():
'''
Main function.
'''
procs = list()
for _ in range(1, 100):
proc = threading.Thread(target=thread)
procs.append(proc)
proc.start()
# Wait for all worker processes to finish
for proc in procs:
proc.join()
```
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} | 0 | 2014-09-03T18:17:32Z | 2014-09-06T19:13:41Z | 2014-09-06T19:13:41Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/25648923/groupby-count-returns-different-values-for-pandas-dataframe-count-vs-describ)
something odd going on here:
```
vals = np.hstack((np.random.randint(0,5,(100,2)), np.random.randint(0,2,(100,2))))
df = pd.DataFrame(vals, columns=['a', 'b', 'c', 'd'])
df[df==2] = np.nan
df2 = df.copy()
df2['a'] = df2['a'].astype('float32')
df2['b'] = df2['b'].astype('float32')
```
```
df.groupby(['c', 'd']).count()
df2.groupby(['c','d']).count()
```
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} | 10 | 2014-09-03T18:47:48Z | 2014-09-22T16:36:11Z | 2014-09-13T19:27:40Z | CONTRIBUTOR | null | closes #5884, OLS does not correctly apply Newey-West corrections when clustering
is being used. Add an assertion to check for this.
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} | 4 | 2014-09-03T23:54:36Z | 2014-09-06T19:50:01Z | 2014-09-06T19:13:41Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/8169
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} | 5 | 2014-09-04T00:24:19Z | 2014-09-06T20:50:14Z | 2014-09-06T20:45:00Z | CONTRIBUTOR | null | Previously I had submitted https://github.com/pydata/pandas/pull/7853. Given that we are sorting indexers, for performance reasons I should have used `groupsort_indexer` instead of `np.argsort`.
on master:
```
In [7]: np.random.seed(2718281)
In [8]: n = 50000
In [9]: left = DataFrame(np.random.randint(1, n/500, (n, 2)),
...: columns=['jim', 'joe'])
In [10]: right = DataFrame(np.random.randint(1, n/500, (n, 2)),
....: columns=['jolie', 'jolia']).set_index('jolie')
In [11]: %timeit left.join(right, on='jim')
1 loops, best of 3: 3.53 s per loop
```
on branch:
```
In [13]: %timeit left.join(right, on='jim')
1 loops, best of 3: 2.46 s per loop
```
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} | 1 | 2014-09-04T00:44:14Z | 2014-09-05T06:29:08Z | 2014-09-05T06:29:08Z | CONTRIBUTOR | null | https://github.com/pydata/pandas/pull/7910
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} | 1 | 2014-09-04T15:47:50Z | 2016-09-19T21:07:02Z | 2016-09-19T21:07:02Z | NONE | null | In Pandas version 0.12, you could apply the Python built-in `divmod` function to a `Series` and an integer:
```
Enthought Canopy Python 2.7.6 | 64-bit | (default, Jun 4 2014, 16:42:26)
[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pd.__version__
'0.12.0'
>>> divmod(pd.Series(range(4)), 2)
(0 0
1 0
2 1
3 1
dtype: int64, 0 0
1 1
2 0
3 1
dtype: int64)
>>>
```
With version >= 0.13, it appears that this usage is no longer supported:
```
Enthought Canopy Python 2.7.6 | 64-bit | (default, Jun 4 2014, 16:42:26)
[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pd.__version__
'0.14.1'
>>> divmod(pd.Series(range(4)), 2)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: unsupported operand type(s) for divmod(): 'Series' and 'int'
```
Was this change intentional?
Some context: I was using this to read a climate datafile that had a 4-digit column holding combined month and day values. The original code looked something like: `month, day = divmod(df['MODA'], 100)`, but broke after upgrading to version 0.14.
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} | 2 | 2014-09-04T17:51:45Z | 2014-09-07T00:30:07Z | 2014-09-07T00:30:07Z | CONTRIBUTOR | null | While trying to produce a stacked bar plot which includes negative values, I found that if the dataframe contains NaN values the bar plot does not display correctly.
Specifically, this code:
``` python
df = pd.DataFrame([[10,20,5,40],[-5,5,20,30],[np.nan,-10,-10,20],[10,20,20,-40]], columns = ['A','B','C','D'])
df.plot(kind = 'bar', stacked=True); plt.show();
```
incorrectly produces this plot
Notice that at '2' on the x-axis, there should be a bar of size -10 for each of the 'B' and 'C' categories.
However, when I replace the NaN values with 0s by doing
``` python
df = pd.DataFrame([[10,20,5,40],[-5,5,20,30],[np.nan,-10,-10,20],[10,20,20,-40]], columns = ['A','B','C','D'])
df = df.fillna(0)
df.plot(kind = 'bar', stacked=True); plt.show();
```
then the plot displays correctly
This is clearly not a good behaviour. I suspect that this happens because the bars corresponding to the negative values are trying to use np.nan as their 'bottom' argument and thus not displaying at all, but I haven't investigated further.
It would be nice if area-style plots like this would either automatically replace NaN values with 0 or throw an error about NaN values present in the dataframe causing problems for the plotting functions.
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} | 1 | 2014-09-04T18:11:29Z | 2014-09-04T18:58:18Z | 2014-09-04T18:58:17Z | CONTRIBUTOR | null | related #2578
from [SO](http://stackoverflow.com/questions/25670760/copying-a-row-screws-up-pandas-column-data-types-how-to-avoid)
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} | 4 | 2014-09-04T18:36:23Z | 2014-09-07T00:30:08Z | 2014-09-07T00:30:07Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/8175
This makes barplot more consistent with how NaN's are handled in AreaPlots and PiePlots.
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} | 1 | 2014-09-05T02:24:42Z | 2017-05-10T11:04:19Z | 2017-05-10T11:04:19Z | CONTRIBUTOR | null | start of enhancing performance section should begin with tips for speeding up pandas, e.g. no apply, vectorization, pandas methods etc.
Initially I put in:
> This tutorial assumes you have refactored as much as possible in python, for example trying to remove for loops and making use of numpy vectorization, it’s always worth optimising in python first.
I think this should be more strongly worded.
_see [this question](http://stackoverflow.com/q/25676157/1240268), user should not jump to cython!_
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| null | 1 | 2014-09-05T04:18:18Z | 2015-04-29T15:34:45Z | 2014-09-05T06:29:08Z | CONTRIBUTOR | null | Closes #8173, release note for #7910
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} | 0 | 2014-09-05T09:30:58Z | 2014-09-09T07:55:59Z | 2014-09-09T07:55:57Z | MEMBER | null | Closes #7815
Adds a warning if the provided table name in `to_sql` and the actually used table name in the database do not match (eg converted to lower case due to case insensitivity settings of database, eg MySQL on Windows). When user is not aware of this, and keeps using the upper case table name, this leads to bugs in subsequent `to_sql` calls.
@maxgrenderjones
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} | 1 | 2014-09-05T13:56:08Z | 2014-09-17T19:33:59Z | 2014-09-17T19:33:59Z | CONTRIBUTOR | null | Very much an edge case:
``` python
In [17]: df = pd.DataFrame(np.random.randn(100, 4))
In [18]: df.loc[:, 0] = np.nan
In [19]: df.plot(kind='box')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-19-06443674b77d> in <module>()
----> 1 df.plot(kind='box')
/Users/tom/Envs/py3/lib/python3.4/site-packages/pandas/pandas/tools/plotting.py in plot_frame(frame, x, y, subplots, sharex, sharey, use_index, figsize, grid, legend, rot, ax, style, title, xlim, ylim, logx, logy, xticks, yticks, kind, sort_columns, fontsize, secondary_y, layout, **kwds)
2362 secondary_y=secondary_y, layout=layout, **kwds)
2363
-> 2364 plot_obj.generate()
2365 plot_obj.draw()
2366 return plot_obj.result
/Users/tom/Envs/py3/lib/python3.4/site-packages/pandas/pandas/tools/plotting.py in generate(self)
913 self._compute_plot_data()
914 self._setup_subplots()
--> 915 self._make_plot()
916 self._add_table()
917 self._make_legend()
/Users/tom/Envs/py3/lib/python3.4/site-packages/pandas/pandas/tools/plotting.py in _make_plot(self)
2140 kwds = self.kwds.copy()
2141
-> 2142 ret, bp = plotf(ax, y, column_num=0, **kwds)
2143 self.maybe_color_bp(bp)
2144 self._return_obj = ret
/Users/tom/Envs/py3/lib/python3.4/site-packages/pandas/pandas/tools/plotting.py in plotf(ax, y, column_num, **kwds)
2059 else:
2060 y = remove_na(y)
-> 2061 bp = ax.boxplot(y, **kwds)
2062
2063 if self.return_type == 'dict':
/Users/tom/Envs/py3/lib/python3.4/site-packages/matplotlib-1.5.x-py3.4-macosx-10.9-x86_64.egg/matplotlib/axes/_axes.py in boxplot(self, x, notch, sym, vert, whis, positions, widths, patch_artist, bootstrap, usermedians, conf_intervals, meanline, showmeans, showcaps, showbox, showfliers, boxprops, labels, flierprops, medianprops, meanprops, capprops, whiskerprops, manage_xticks)
3043 """
3044 bxpstats = cbook.boxplot_stats(x, whis=whis, bootstrap=bootstrap,
-> 3045 labels=labels)
3046 if flierprops is None:
3047 flierprops = dict(sym=sym)
/Users/tom/Envs/py3/lib/python3.4/site-packages/matplotlib-1.5.x-py3.4-macosx-10.9-x86_64.egg/matplotlib/cbook.py in boxplot_stats(X, whis, bootstrap, labels)
1963
1964 # medians and quartiles
-> 1965 q1, med, q3 = np.percentile(x, [25, 50, 75])
1966
1967 # interquartile range
/Users/tom/Envs/py3/lib/python3.4/site-packages/numpy/lib/function_base.py in percentile(a, q, axis, out, overwrite_input)
2818 axis = 0
2819
-> 2820 return _compute_qth_percentile(sorted, q, axis, out)
2821
2822 # handle sequence of q's without calling sort multiple times
/Users/tom/Envs/py3/lib/python3.4/site-packages/numpy/lib/function_base.py in _compute_qth_percentile(sorted, q, axis, out)
2824 if not isscalar(q):
2825 p = [_compute_qth_percentile(sorted, qi, axis, None)
-> 2826 for qi in q]
2827
2828 if out is not None:
/Users/tom/Envs/py3/lib/python3.4/site-packages/numpy/lib/function_base.py in <listcomp>(.0)
2824 if not isscalar(q):
2825 p = [_compute_qth_percentile(sorted, qi, axis, None)
-> 2826 for qi in q]
2827
2828 if out is not None:
/Users/tom/Envs/py3/lib/python3.4/site-packages/numpy/lib/function_base.py in _compute_qth_percentile(sorted, q, axis, out)
2854 # Use add.reduce in both cases to coerce data type as well as
2855 # check and use out array.
-> 2856 return add.reduce(sorted[indexer]*weights, axis=axis, out=out)/sumval
2857
2858 def trapz(y, x=None, dx=1.0, axis=-1):
ValueError: operands could not be broadcast together with shapes (0,) (2,)
```
Same result for `df.boxplot()`.
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| null | 2 | 2014-09-05T14:03:03Z | 2014-09-06T13:41:25Z | 2014-09-06T13:41:25Z | CONTRIBUTOR | null | This is inconsistent with the other plotting methods:
``` python
In [65]: df = pd.DataFrame(np.random.uniform(size=(100, 4)))
In [66]: df.loc[0, 0] = np.nan
In [67]: df.plot(kind='kde')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-67-9a78a7983298> in <module>()
----> 1 df.plot(kind='kde')
/Users/tom/Envs/py3/lib/python3.4/site-packages/pandas/pandas/tools/plotting.py in plot_frame(frame, x, y, subplots, sharex, sharey, use_index, figsize, grid, legend, rot, ax, style, title, xlim, ylim, logx, logy, xticks, yticks, kind, sort_columns, fontsize, secondary_y, layout, **kwds)
2362 secondary_y=secondary_y, layout=layout, **kwds)
2363
-> 2364 plot_obj.generate()
2365 plot_obj.draw()
2366 return plot_obj.result
/Users/tom/Envs/py3/lib/python3.4/site-packages/pandas/pandas/tools/plotting.py in generate(self)
913 self._compute_plot_data()
914 self._setup_subplots()
--> 915 self._make_plot()
916 self._add_table()
917 self._make_legend()
/Users/tom/Envs/py3/lib/python3.4/site-packages/pandas/pandas/tools/plotting.py in _make_plot(self)
1915 kwds['style'] = style
1916
-> 1917 artists = plotf(ax, y, column_num=i, **kwds)
1918 self._add_legend_handle(artists[0], label)
1919
/Users/tom/Envs/py3/lib/python3.4/site-packages/pandas/pandas/tools/plotting.py in plotf(ax, y, style, column_num, **kwds)
1960 def plotf(ax, y, style=None, column_num=None, **kwds):
1961 if LooseVersion(spv) >= '0.11.0':
-> 1962 gkde = gaussian_kde(y, bw_method=self.bw_method)
1963 else:
1964 gkde = gaussian_kde(y)
/Users/tom/Envs/py3/lib/python3.4/site-packages/scipy/stats/kde.py in __init__(self, dataset, bw_method)
186
187 self.d, self.n = self.dataset.shape
--> 188 self.set_bandwidth(bw_method=bw_method)
189
190 def evaluate(self, points):
/Users/tom/Envs/py3/lib/python3.4/site-packages/scipy/stats/kde.py in set_bandwidth(self, bw_method)
496 raise ValueError(msg)
497
--> 498 self._compute_covariance()
499
500 def _compute_covariance(self):
/Users/tom/Envs/py3/lib/python3.4/site-packages/scipy/stats/kde.py in _compute_covariance(self)
507 self._data_covariance = atleast_2d(np.cov(self.dataset, rowvar=1,
508 bias=False))
--> 509 self._data_inv_cov = linalg.inv(self._data_covariance)
510
511 self.covariance = self._data_covariance * self.factor**2
/Users/tom/Envs/py3/lib/python3.4/site-packages/scipy/linalg/basic.py in inv(a, overwrite_a, check_finite)
352
353 if check_finite:
--> 354 a1 = np.asarray_chkfinite(a)
355 else:
356 a1 = np.asarray(a)
/Users/tom/Envs/py3/lib/python3.4/site-packages/numpy/lib/function_base.py in asarray_chkfinite(a, dtype, order)
593 if a.dtype.char in typecodes['AllFloat'] and not np.isfinite(a).all():
594 raise ValueError(
--> 595 "array must not contain infs or NaNs")
596 return a
597
ValueError: array must not contain infs or NaNs
```
The default should be to drop missing observations.
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} | 1 | 2014-09-05T14:03:30Z | 2014-11-22T09:27:01Z | 2014-09-05T16:16:34Z | MEMBER | null | Closes #8081.
Also fixes a color problem with `c` kw found in #7780. (But should work with single color. It looks `errorbar` doesn't accept multiple colors).
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} | 64 | 2014-09-05T14:37:21Z | 2014-09-16T19:31:04Z | 2014-09-13T22:59:30Z | CONTRIBUTOR | null | closes #3009
closes #8187
closes #8209
closes #4533
closes #8190
closes #7869
closes #8214
This PR creates a new `timedelta` sub-class, the scalar `Timedelta`, very similar to how `Timestamp` sub-classes `datetime`. Further it creates a `TimedeltaIndex` modelled on `DatetimeIndex` that holds the underlying data (boxing is only on iteration).
- ToDo
- Document API changes:
- [x] `to_timedelta` return `TimedeltaIndex` rather than a `Series`
- [x] `to_timedelta` with scalar returns `Timedelta` (rather than `np.timedelta64`)
- [x] Boxing for `list(Series(...))` is now `Timedelta`
- [x] possible repr changes
- [x] `timedelta_range`
- [x] usage / examples
- [x] API
- Timedelta
- [x] conversions `to_timedelta/to_pytimedelta`
- TimedeltaIndex
- [x] conversions `to_pytimedelta`
- [x] what else needs boxing in Series?
- [x] fix formatters (Timedelta64Formatter; remove `repr_timedelta64` (no longer necessary)
- [x] create `timedelta_range` & fix freq / inferred_freq
- [x] fix remaining copied methods from DatetimeIndex
- more tests (of TimedeltaIndex / scalar)
- [x] scalar comparisons
- [x] index comparisons
- [x] getitem / partial string access
- [x] sql expecting `np.timedelta64` ? (currently skipping these 2 tests!!!!), see: https://github.com/jreback/pandas/commit/2743ef16f7dfe55ba63663fa65c5671031af94f1
- [x] validate series / td ops
- move methods of `TimedeltaIndex/DatetimeIndex`
- [x] move `DatetimeOpsMixin` to `tseries/base.py`?
- [x] `shift/take/order`
- [x] `slice_indexer`
- [x] `append/union/intersection`
- [x] comparbless?
- [x] `.dt` accessor are ok now
- [x] move `.td` to `.tdi`
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} | 0 | 2014-09-05T14:39:46Z | 2019-10-21T20:06:54Z | null | CONTRIBUTOR | null | read_csv to have a parse_timedelta to automatically apply the dtype, see #4650, #4684
original from #3009
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| null | 2 | 2014-09-05T16:04:42Z | 2017-05-04T19:07:32Z | 2017-05-04T19:07:32Z | CONTRIBUTOR | null | I'm trying to find a home for this function: https://gist.github.com/TomAugspurger/9156696
Output is shown [here](http://tomaugspurger.github.io/blog/2014/02/22/Visualizing%20Missing%20Data/).
Basically it highlights the locations of missing data.
Should it go
- Under `df.plot(kind='missing')`
- As a recipe in the cookbook
- Elsewhere (seaborn)
I've been talking with Michael from seaborn and he thinks it could be in scope for pandas since it's essentially a view on the DataFrame.
It's possible that I already posted about this, and we already made a decision, but I could remember and search didn't turn up anything.
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} | 6 | 2014-09-05T16:39:59Z | 2014-09-13T22:59:30Z | 2014-09-13T22:59:30Z | CONTRIBUTOR | null | ```
In [6]: s = pd.Series(pd.date_range(start='2014-01-01', periods=30, freq='d'))
In [7]: s.dt.tz_localize('US/Central')
```
Would return a Series with the same index whose values are Timestamps with the timezone set.
I haven't looked at this code too closely, so I'm not sure how difficult it would be and if it's doable for 0.15.
@hayd brought it up on SO: http://stackoverflow.com/questions/25656826/speeding-up-timestamp-operations/25657249?noredirect=1#comment40118386_25657249
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} | 9 | 2014-09-05T16:47:46Z | 2015-06-20T16:20:11Z | 2015-06-20T16:20:11Z | NONE | null | Problem description:
I have pandas dataframes with diverse data types and structures, including lists. The csv-writer saves the lists without trouble, but I prefer to also save in excel format for collaboration with my colleagues.
Currently the ExcelWriter returns a TypeError if the dataframe contains a list.
`TypeError: float() argument must be a string or a number, not 'list'`
It would be really great if ExcelWriter accepted lists and dictionaries and tuples.
Would it cause any problem to simply convert these data formats to strings before saving?
Sorry I’m not an experienced programmer, so I haven‘t made a pull request and attempted to improve the module myself.
Here is some code (Python 3.4, pandas 0.14.0) to describe the current situation.
```
import pandas as pd
#create a new dataframe that includes a list and a dict
title = pd.Series(['Toy Story (1995)',
'Jumanji (1995)',
'Grumpier Old Men (1995)',
'Waiting to Exhale (1995)',
'Father of the Bride Part II (1995)'], index = [0 ,1, 2, 3, 4], name='titles')
genre_as_list = pd.Series([['Animation', "Children's", 'Comedy'],
['Adventure', "Children's", 'Fantasy'],
['Comedy', 'Romance'],
['Comedy', 'Drama'],
['Comedy']], index = [0 ,1, 2, 3, 4], name='genre_as_list')
rating_as_dict = pd.Series([{'F':0.5,'M':0.6},
{'F':0.1,'M':0.2},
{'F':0.3,'M':0.3},
{'F':0.8,'M':0.8},
{'F':0.8,'M':0.6}], index = [0 ,1, 2, 3, 4], name='rating_as_dict')
df = pd.concat([title, genre_as_list, rating_as_dict], axis=1)
```
attempt to save the dataframe in excel
```
writer = pd.ExcelWriter('saved_df.xlsx')
df.to_excel(writer, sheet_name='Sheet1')
```
which will result in
`TypeError: float() argument must be a string or a number, not 'list'`
to save the data, it is necessary to convert the python lists or dicts to strings, separated by ", " or pipes "|"
```
df['genre_as_string'] = [str(i) for i in df['genre_as_list']]
df['rating_as_string'] = [str(i) for i in df['rating_as_dict']]
df['genre_pipes_string'] = ['|'.join(i) for i in df['genre_as_list']]
#in my ipython output, the difference is not actually visible between the lists and the stringlists
print('first item from list: %s\nfirst item from stringlist: %s' % (df['genre_as_list'][0][0],
df['genre_as_string'][0][0]))
```
After deleting any columns containing a python list or dict, I can now save without any problem.
```
df = df.drop(['genre_as_list','rating_as_dict'], axis=1)
writer = pd.ExcelWriter('saved_df.xlsx')
df.to_excel(writer, sheet_name='Sheet1')
writer.save()
```
If I load from the saved excel sheet, I can confirm that the data is intact
```
df_from_excel = pd.read_excel('saved_df.xlsx', sheetname='Sheet1')
#convert back to original python lists and dict, if desired
df_from_excel['genre_as_list_again'] = [eval(i) for i in df_from_excel['genre_as_string']]
df_from_excel['rating_as_dict_again'] = [eval(i) for i in df_from_excel['rating_as_string']]
df_from_excel.loc[0,'genre_as_list_again'][0]
```
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} | 2 | 2014-09-05T17:11:38Z | 2014-09-06T14:59:08Z | 2014-09-06T14:59:08Z | CONTRIBUTOR | null | Not sure if this is intentional / known.
```
In [58]: tz = pd.DatetimeIndex(start='2010-01-01', periods=10, freq='d', tz='US/Central')
In [59]: df = pd.DataFrame(np.random.randn(10, 2), columns=['A', 'B'])
In [60]: df['time'] = tz
In [62]: df.dtypes
Out[62]:
A float64
B float64
time object
dtype: object
In [63]: df.fillna(0).dtypes
Out[63]:
A float64
B float64
time datetime64[ns]
dtype: object
```
Notice that the dtype of the `time` column changes from `object` (column of `Timestamps`) to `datetime64[ns]`.
I think what's happening is the timestamps are being downcast:
```
In [64]: df.fillna(0, downcast=False).dtypes
Out[64]:
A float64
B float64
time object
dtype: object
```
The reason I worry about this as the default is because the timestamp information is lost.
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} | 1 | 2014-09-05T19:17:55Z | 2014-09-13T22:59:30Z | 2014-09-13T22:59:30Z | MEMBER | null | Right now the only long name is supports is "days" and it doesn't even abbreviations for "m" or "h" for minute or hour.
All the logic to do the conversions already exists, it just needs better parsing.
Related: #7611
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| null | 3 | 2014-09-05T20:23:23Z | 2015-10-20T22:42:08Z | 2015-10-20T22:42:08Z | NONE | null | I want to have thousands separators in Excel, but when I pass float_format="%,.2f" in I get an unsupported character error. I also tried "{0:,}". Am I missing something or is this unsupported?
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| null | 6 | 2014-09-05T21:03:49Z | 2014-09-06T20:50:00Z | 2014-09-06T20:50:00Z | NONE | null | Hi,
on MacOs 10.9,
with Python2.7.5
(ipython 2.2.0)
Numpy 1.8.2 and Pandas 0.14.1
(successfully) installed with "pip",
I got the following when I try to import Pandas
```
ValueError: numpy.dtype has the wrong size, try recompiling
```
then,
I installed pandas from github (uninstalling the pip packages before),
and this time I got this one:
```
ImportError: cannot import name hashtable
```
Any suggestion?
I'm not interested in downgrading Pandas
best,
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} | 4 | 2014-09-05T22:47:23Z | 2020-01-26T01:04:27Z | 2020-01-26T01:04:27Z | NONE | null | Related to this question:
http://stackoverflow.com/questions/25689558/pandas-bar-plot-specify-bar-color-by-column
Having the ability to natively pass in a dictionary of the column names and the desired color could be a nice addition to simplify specifying what color you want each line/bar to be.
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| null | 4 | 2014-09-05T23:38:55Z | 2014-09-06T00:36:49Z | 2014-09-06T00:14:36Z | CONTRIBUTOR | null | Currently `Series` has a `name` member, which is useful for retaining the name of the sliced `index`/`column` when the `Series` is produced by slicing a `DataFrame`.
It would be useful for `DataFrame` similarly to have a `name` member, so that when a `DataFrame` is produced by slicing a `Panel`, the name of the sliced index is retained.
And while you're at it, may as well add `name` to `Panel` and even `Panel4D`...
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| null | 4 | 2014-09-06T01:09:43Z | 2014-09-06T13:41:22Z | 2014-09-06T13:40:59Z | CONTRIBUTOR | null | I think the most sensible thing to do here is to drop NaN's, like histograms do, rather than filling 0's. The table in #8177 should be updated to reflect that NaN's are dropped, can do that myself after 8177 gets merged.
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| null | 2 | 2014-09-06T03:17:52Z | 2015-05-09T15:59:09Z | 2015-05-09T15:59:09Z | CONTRIBUTOR | null | Addresses #8139. Not sure how people feel about this one, in terms of adding clutter to the api, but it _was_ pretty easy to get working, so I thought I'd put this together to show what changes would need to be made. I might still need to go over `DataFrame.merge()` to see if the same arguments can be added for consistency.
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} | 8 | 2014-09-06T13:04:45Z | 2014-10-04T19:57:56Z | 2014-10-04T19:57:38Z | CONTRIBUTOR | null | From https://github.com/pydata/pandas/pull/8177#issuecomment-54711316, how should we handle NaNs in Pie Plots. Right now the documented behavior is to fill 0s and plot. This can result in some unattractive labeling depending on where the NaNs are at in the series.
``` python
In [11]: series = Series(3 * np.random.rand(8), index=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'], name='series')
In [15]: series.iloc[:4] = np.nan
In [16]: series.plot(kind='pie')
Out[16]: <matplotlib.axes._subplots.AxesSubplot at 0x11183e128>
```
This is actually the first case where I can see a use for a NaN-handling kwarg in `*.plot()` itself. You could reasonably want to
- Drop the NaNs (which I think should be the default)
- Fill 0s (shows they're missing, but makes the labeling confusing)
- Aggregate the NaNs and put those in a separate "missing" wedge that takes up some proportion of the pie.
I've got no idea how that proportion should be calculated (counts? relative to the sum or size?).
For now let's just decide if the default should change from filling 0s to filling dropping (with a couple releases of notice.)
cc @jorisvandenbossche @sinhrks
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| null | 9 | 2014-09-06T20:43:30Z | 2014-10-02T13:42:16Z | 2014-09-18T12:30:01Z | CONTRIBUTOR | null | The variable `n` is set to number of rows in dataframe, and then it overwritten with number of columns, but after that there is loop that should iterate over rows and it uses new value in `n` that holds number of columns already.
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} | 10 | 2014-09-06T22:13:38Z | 2016-04-17T14:00:03Z | 2016-04-17T14:00:03Z | NONE | null | Hello,
I'm trying to parse a file with the extension 'xls' which is not an Excel file but it's clearly an html file (open with a text editor it's clearly html code).
One of my columns uses the italian convention of using the comma instead of the dot before decimals: 5,5 instead of 5.5. I was hoping to parse it at least as string and replace commas with dots and convert the string to a float.
The problem is that the commas are completely ignored and instead of getting 5,5 or 7,04, I'm getting 55 and 704.
Is this known? Any idea on how to solve it?
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