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https://api.github.com/repos/pandas-dev/pandas/issues/2201 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2201/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2201/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2201/events | https://github.com/pandas-dev/pandas/pull/2201 | 8,223,174 | MDExOlB1bGxSZXF1ZXN0MjkzNzk3Mg== | 2,201 | PR: unicode, mostly | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2202 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2202/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2202/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2202/events | https://github.com/pandas-dev/pandas/issues/2202 | 8,232,664 | MDU6SXNzdWU4MjMyNjY0 | 2,202 | timeseries: shortcut to unique | {
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"followers_url": "https://api.github.com/users/wesm/... | 4 | 2012-11-09T10:24:18Z | 2012-11-09T23:52:59Z | 2012-11-09T18:08:06Z | CONTRIBUTOR | null | what do you think is it worthwhile to add a shortcur to get unique values of a index:
hours = nanops.unique1d(series.index.hour)
nanops.unique1d(series.index.days)
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https://api.github.com/repos/pandas-dev/pandas/issues/2203 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2203/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2203/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2203/events | https://github.com/pandas-dev/pandas/pull/2203 | 8,236,796 | MDExOlB1bGxSZXF1ZXN0Mjk0MzcwNQ== | 2,203 | link the info on scikits.timeseries | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2204 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2204/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2204/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2204/events | https://github.com/pandas-dev/pandas/pull/2204 | 8,236,829 | MDExOlB1bGxSZXF1ZXN0Mjk0MzcyMA== | 2,204 | set link target for faq site | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2205 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2205/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2205/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2205/events | https://github.com/pandas-dev/pandas/issues/2205 | 8,244,722 | MDU6SXNzdWU4MjQ0NzIy | 2,205 | Add option to disable pandas-style tick locator and formatter for plotting | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2206 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2206/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2206/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2206/events | https://github.com/pandas-dev/pandas/pull/2206 | 8,247,094 | MDExOlB1bGxSZXF1ZXN0Mjk0ODExOA== | 2,206 | ENH: eliminate _str() in favor of pprint_thing | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2207 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2207/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2207/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2207/events | https://github.com/pandas-dev/pandas/issues/2207 | 8,249,302 | MDU6SXNzdWU4MjQ5MzAy | 2,207 | MultiIndex with Heterogenous dtype | {
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I'm reading in a csv file:
`df = pd.read_csv('nc201052.dat', index_col=['FLOW', 'PERIOD', 'DECLARANT', 'PARTNER', 'PRODUCT_NC'], nrows=1000)`
```
df.head(5)
STAT_REGIME VALUE_1000ECU QUANTITY_TON SUP_QUANTITY
FLOW PERIOD D... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2209 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2209/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2209/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2209/events | https://github.com/pandas-dev/pandas/issues/2209 | 8,250,799 | MDU6SXNzdWU4MjUwNzk5 | 2,209 | Ability to handle fractional seconds in date_converters | {
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```
I have a 'second' column that contains microseconds like these:
In [52]: df.second.values
Out[52]:
array([ 4.50000000e-02, 4.50000000e-02, 4.50000000e-02, ...,
5.99920000e+01, 5.99920000e+01, 5.99920000e+01])
and had used this column to produce a DateTime index like this before:
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https://api.github.com/repos/pandas-dev/pandas/issues/2213 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2213/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2213/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2213/events | https://github.com/pandas-dev/pandas/issues/2213 | 8,252,166 | MDU6SXNzdWU4MjUyMTY2 | 2,213 | Specify format for strptime in to_datetime | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2214 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2214/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2214/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2214/events | https://github.com/pandas-dev/pandas/pull/2214 | 8,255,565 | MDExOlB1bGxSZXF1ZXN0Mjk1MjcwMw== | 2,214 | BUG: coerce ndarray dtype to object when comparing series | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2215 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2215/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2215/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2215/events | https://github.com/pandas-dev/pandas/issues/2215 | 8,255,863 | MDU6SXNzdWU4MjU1ODYz | 2,215 | integrate scatter plot into DataFrame/Series plot (or scatter) method | {
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"I've been searching the API/Internet to find the easiest way to make call plt.scatter() with a dataframe. I figured there would be a keyword if df.plot(kind='scatter') or df.scatter() but this seems not to be the case. I saw from the API that kind only accepts three arguments:
kind : {‘line’, ‘... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2216 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2216/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2216/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2216/events | https://github.com/pandas-dev/pandas/pull/2216 | 8,262,225 | MDExOlB1bGxSZXF1ZXN0Mjk1NTQyNQ== | 2,216 | Small tweaks to vb_suite | {
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will help make it work out of the box for more people.
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https://api.github.com/repos/pandas-dev/pandas/issues/2217 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2217/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2217/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2217/events | https://github.com/pandas-dev/pandas/issues/2217 | 8,267,497 | MDU6SXNzdWU4MjY3NDk3 | 2,217 | Can't slice and dice time series df (pandas 0.9) | {
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# Create a dataframe with dates as your index
data = [1,2,3,4,5,6,7,8,9,10]
idx = date_range('1/1/2012', periods=10)
df = DataFrame(data, index=idx, columns=['Revenue'])
df
df[datetime(2012,1,1)]
---
KeyError Traceback (most recent call last)
<ipython-input... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2218 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2218/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2218/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2218/events | https://github.com/pandas-dev/pandas/issues/2218 | 8,271,102 | MDU6SXNzdWU4MjcxMTAy | 2,218 | df with dupe cols should raise KeyError on accessing non-existent col via list | {
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from pandas import DataFrame
df=DataFrame([[1,2,3],[4,5,6]],columns=['a','a','b'])
try:
df[['baf']]
except KeyError:
pass
else:
self.fail("Dataframe failed to raise KeyError")
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2219 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2219/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2219/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2219/events | https://github.com/pandas-dev/pandas/issues/2219 | 8,271,120 | MDU6SXNzdWU4MjcxMTIw | 2,219 | df.iteritems() should yield Series even with non-unique column labels | {
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"followers_url": "https://api.github.com/users/wesm/... | 3 | 2012-11-11T12:21:29Z | 2012-11-13T23:26:18Z | 2012-11-13T23:26:18Z | NONE | null | ``` python
df=DataFrame([[1,2,3],[4,5,6]],columns=['a','a','b'])
for k,v in df.iteritems():
self.assertEqual(type(v),Series)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2220 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2220/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2220/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2220/events | https://github.com/pandas-dev/pandas/issues/2220 | 8,271,155 | MDU6SXNzdWU4MjcxMTU1 | 2,220 | 1 ** Sparse df with nans makes sp_values() produce strange results under df[] vs df.take([]) | {
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df = SparseDataFrame({ 'A' : [nan, 0, 1] }) # note : no error without nan
df = 1 ** df # note that 2 ** df works fine, also df ** 1
r1=df.take([0],1)['A']
r2=df['A']
assert len(r2.sp_values) == len(r1.sp_values)
```
- I'm not familiar with the block management code so I can't figure this out
with rea... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2221 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2221/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2221/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2221/events | https://github.com/pandas-dev/pandas/pull/2221 | 8,271,184 | MDExOlB1bGxSZXF1ZXN0Mjk1ODA3Mw== | 2,221 | fixes for #2218, #2219 | {
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afiact, that's a genuine issue.
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https://api.github.com/repos/pandas-dev/pandas/issues/2223 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2223/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2223/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2223/events | https://github.com/pandas-dev/pandas/pull/2223 | 8,271,313 | MDExOlB1bGxSZXF1ZXN0Mjk1ODExMQ== | 2,223 | CLN: use com._is_sequence instead of duplicating code | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2224 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2224/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2224/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2224/events | https://github.com/pandas-dev/pandas/pull/2224 | 8,274,181 | MDExOlB1bGxSZXF1ZXN0Mjk1ODg5Mw== | 2,224 | Unicode : change df.to_string() and friends to always return unicode objects | {
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**Note**: Although all the tests pass with minor fixes, this PR has an above-average chance of
breaking things for people who have relied on broken behaviour thus far.
`df.tidy_repr` combines several strings to produce a result. when one component is unicode
and other other is a non-ascii bytestring, it ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2225 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2225/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2225/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2225/events | https://github.com/pandas-dev/pandas/issues/2225 | 8,274,267 | MDU6SXNzdWU4Mjc0MjY3 | 2,225 | BUG: series tidy_repr UnicodeDecodeError | {
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a=pd.Series([u"\u05d0"]*1000)
a.name= 'title1'
repr(a)
pandas/pandas/core/series.py in _tidy_repr(self, max_vals)
869 name=False)
870 result = head + '\n...\n' + tail
--> 871 return '%s\n%s' % (result, self._repr_footer())
872
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https://api.github.com/repos/pandas-dev/pandas/issues/2226 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2226/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2226/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2226/events | https://github.com/pandas-dev/pandas/issues/2226 | 8,274,974 | MDU6SXNzdWU4Mjc0OTc0 | 2,226 | Better error message in pandas.core.index | {
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```
# Example from "Python for Data Analysys", page 148
import pandas as pd
import numpy as np
frame = pd.DataFrame(np.arange(12).reshape((4,3)),
index=[list... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2227 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2227/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2227/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2227/events | https://github.com/pandas-dev/pandas/issues/2227 | 8,275,001 | MDU6SXNzdWU4Mjc1MDAx | 2,227 | SparseDataFrame.icol(j) returns Series rather then SparseSeries | {
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In [38]: df=SparseDataFrame([[1,2],[4,5]],columns=['a','b'])
...: type(df.icol(0))
Out[38]: pandas.core.series.Series
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2228 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2228/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2228/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2228/events | https://github.com/pandas-dev/pandas/issues/2228 | 8,275,085 | MDU6SXNzdWU4Mjc1MDg1 | 2,228 | df.icol([0]) fails when df has non-unique column labels | {
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df=DataFrame([[1,2,3],[4,5,6]],columns=['a','b','b'])
df.icol([0])
pandas/pandas/core/index.pyc in get_indexer(self, target, method, limit)
747
748 if not self.is_unique:
--> 749 raise Exception('Reindexing only valid with uniquely valued Index '
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In [16]: s=SparseSeries.from_array(np.array([1,2,3]),index=Index(range(3)))
...: type(s)
...:
Out[16]: pandas.core.series.Series
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2230 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2230/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2230/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2230/events | https://github.com/pandas-dev/pandas/pull/2230 | 8,275,921 | MDExOlB1bGxSZXF1ZXN0Mjk1OTQ0Ng== | 2,230 | use boolean indexing via getitem to trigger masking; add inplace keyword to where | {
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changed method _getitem_ to use _mask_ directly (e.g. df.mask(df > 0) is equivalent semantically to df[df>0])
this would be a small API change as before df[df >0] returned a boolean np array
added inplace keyword to _where_ method (to update the dataframe in place, default is NOT to use inplace, an... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2231 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2231/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2231/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2231/events | https://github.com/pandas-dev/pandas/pull/2231 | 8,277,993 | MDExOlB1bGxSZXF1ZXN0Mjk2MDE0Ng== | 2,231 | BUG: Incorrect error message due to zero based levels. #2226 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2232 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2232/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2232/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2232/events | https://github.com/pandas-dev/pandas/issues/2232 | 8,280,254 | MDU6SXNzdWU4MjgwMjU0 | 2,232 | Inconsistent behaviour of to_period | {
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For example:
index = date_range('2000-1-1', periods=1000, freq='D', tz = 'Australia/Sy... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2233 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2233/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2233/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2233/events | https://github.com/pandas-dev/pandas/issues/2233 | 8,280,809 | MDU6SXNzdWU4MjgwODA5 | 2,233 | Strict mode for date parsing/conversion | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2234 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2234/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2234/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2234/events | https://github.com/pandas-dev/pandas/issues/2234 | 8,289,643 | MDU6SXNzdWU4Mjg5NjQz | 2,234 | Can't create a DataFrame from an empty Series | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2235 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2235/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2235/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2235/events | https://github.com/pandas-dev/pandas/issues/2235 | 8,298,016 | MDU6SXNzdWU4Mjk4MDE2 | 2,235 | prettier tick labelling etc for irregular timeseries | {
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"f... | 1 | 2012-11-12T18:10:36Z | 2018-06-27T18:06:49Z | 2018-06-27T18:06:49Z | CONTRIBUTOR | null | xref: http://stackoverflow.com/questions/13347288/how-do-i-get-nice-tick-labels-for-an-irregularly-sampled-time-series
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https://api.github.com/repos/pandas-dev/pandas/issues/2236 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2236/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2236/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2236/events | https://github.com/pandas-dev/pandas/issues/2236 | 8,302,533 | MDU6SXNzdWU4MzAyNTMz | 2,236 | Error converting DataFrame with duplicate columns to ndarray | {
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Trying to read Excel file. That part seems ok.
Originally, I was trying iteritems() for each row of the pandas dataframe, as the id_company had to be verified against a mysql database (code not included). Same/similar error message to putting it into a ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2237 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2237/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2237/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2237/events | https://github.com/pandas-dev/pandas/issues/2237 | 8,306,210 | MDU6SXNzdWU4MzA2MjEw | 2,237 | parallel_coordinates incorrect auto legend | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2012-11-12T22:43:41Z | 2016-10-12T23:03:19Z | 2012-11-14T19:36:35Z | CONTRIBUTOR | null | Just tried the latest version of the parallel coordinates plot, but the default legend appears to be incorrect: instead of just choosing the four expected groups, one legend entry is included per row, many of them set to "None".
To reproduce, follow steps in the [pandas guide](http://pandas.pydata.org/pandas-docs/stab... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2238 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2238/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2238/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2238/events | https://github.com/pandas-dev/pandas/issues/2238 | 8,322,184 | MDU6SXNzdWU4MzIyMTg0 | 2,238 | Aggregated datetime64 column converted to float | {
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It appears that when grouping and then aggregating a datetime column the dtype converts to float
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https://api.github.com/repos/pandas-dev/pandas/issues/2239 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2239/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2239/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2239/events | https://github.com/pandas-dev/pandas/issues/2239 | 8,331,141 | MDU6SXNzdWU4MzMxMTQx | 2,239 | win py3.3 packages? | {
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http://pypi.python.org/pypi/pandas/0.9.0
Thanks
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https://api.github.com/repos/pandas-dev/pandas/issues/2240 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2240/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2240/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2240/events | https://github.com/pandas-dev/pandas/issues/2240 | 8,340,063 | MDU6SXNzdWU4MzQwMDYz | 2,240 | cannot write resampled frame to excel TypeError: 'Period' object does not support indexing | {
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# this works for a dataframe with timeseries index
close.to_excel("temp.xlsx")
# this does not work. fails with error shown above
monthly=close.resample('M', kind='period')
monthly.to_excel("temp.xlsx")
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https://api.github.com/repos/pandas-dev/pandas/issues/2241 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2241/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2241/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2241/events | https://github.com/pandas-dev/pandas/pull/2241 | 8,341,283 | MDExOlB1bGxSZXF1ZXN0Mjk5MDQ1NQ== | 2,241 | Ignore terminal width when not running in an interactive shell | {
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gives you all the columns unles print_config.max_columns is set.
#1610.
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https://api.github.com/repos/pandas-dev/pandas/issues/2242 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2242/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2242/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2242/events | https://github.com/pandas-dev/pandas/pull/2242 | 8,344,947 | MDExOlB1bGxSZXF1ZXN0Mjk5MjI0Ng== | 2,242 | Panelnd | {
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( note - this superseeds the prior branch, FDPanel and this is 0.9rc1 compatible)
Panel4D is like a Panel object, but provides 4 dimensions
labels, items, major_axis, minor_axis
instead of using a dict of Panels to hold data, the Panel4D provides a convenient represenation in pandas space with named dim... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2243 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2243/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2243/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2243/events | https://github.com/pandas-dev/pandas/issues/2243 | 8,345,424 | MDU6SXNzdWU4MzQ1NDI0 | 2,243 | reset_index on DataFrame with PeriodIndex changes to ordinal representation | {
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Note this is due to PeriodIndex.values converting to array of ordinals
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https://api.github.com/repos/pandas-dev/pandas/issues/2244 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2244/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2244/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2244/events | https://github.com/pandas-dev/pandas/pull/2244 | 8,345,652 | MDExOlB1bGxSZXF1ZXN0Mjk5MjU4MA== | 2,244 | BUG: ExcelWriter raises exception on PeriodIndex #2240 | {
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otherwise roundtripping is hard and the output isn't all that useful for processing in Excel
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https://api.github.com/repos/pandas-dev/pandas/issues/2245 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2245/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2245/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2245/events | https://github.com/pandas-dev/pandas/issues/2245 | 8,356,255 | MDU6SXNzdWU4MzU2MjU1 | 2,245 | Exception during resample w/ tz | {
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idx = date_range('2001-09-20 15:59','2001-09-20 16:00', freq='T', tz='Australia/Sydney')
s = Series([1,2], index=idx)
s.resample('D')
## In [4]: s.resample('D')
ValueError Traceback (most recent call last)
<ipython-input-4-5ce76d689c21> in <module>()... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2246 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2246/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2246/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2246/events | https://github.com/pandas-dev/pandas/pull/2246 | 8,358,628 | MDExOlB1bGxSZXF1ZXN0Mjk5OTAwMg== | 2,246 | ENH: Import DataReader and Options into namespace | {
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s = Series()
s.tz_localize('UTC')
Q:\GAARD\Prod\Apps\Pandas64\4.36\pandas\core\series.pyc in tz_localize(self, tz, copy)
2730 localized : TimeSeries
2731 """
-> 2732 new_index = self.index.tz_localize(tz)
2733
2734 new_values = self.values
Attribut... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2249 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2249/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2249/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2249/events | https://github.com/pandas-dev/pandas/issues/2249 | 8,367,748 | MDU6SXNzdWU4MzY3NzQ4 | 2,249 | icol() should propegate fill_value for sparse data frames | {
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from pandas.sparse.api import SparseSeries,SparseDataFrame
from pandas import Series, DataFrame, bdate_range, Panel
data = {'A' : [0,1 ]}
iframe = SparseDataFrame(data, default_kind='integer')
print type(iframe['A'].sp_index)
print type(iframe.icol(0).sp_index)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2250 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2250/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2250/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2250/events | https://github.com/pandas-dev/pandas/pull/2250 | 8,367,788 | MDExOlB1bGxSZXF1ZXN0MzAwMzQ3Mg== | 2,250 | BUG: icol() should propegate fill_value for sparse data frames #2249 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2251 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2251/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2251/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2251/events | https://github.com/pandas-dev/pandas/issues/2251 | 8,367,855 | MDU6SXNzdWU4MzY3ODU1 | 2,251 | BUG: icol() doesn't preserve index type on sparse DataFrames | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2012-11-14T20:48:59Z | 2012-11-15T00:35:25Z | 2012-11-15T00:35:25Z | NONE | null | ``` python
from pandas.sparse.api import SparseSeries,SparseDataFrame
from pandas import Series, DataFrame, bdate_range, Panel
data = {'A' : [0,1 ]}
iframe = SparseDataFrame(data, default_kind='integer')
print type(iframe['A'].sp_index)
print type(iframe.icol(0).sp_index)
<type 'pandas._sparse.IntIndex'>
<type 'pandas... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2252 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2252/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2252/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2252/events | https://github.com/pandas-dev/pandas/issues/2252 | 8,372,735 | MDU6SXNzdWU4MzcyNzM1 | 2,252 | Losing Nanosecond precision upon conversion to DatetimeIndex | {
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I am experiencing a problem when converting from a TimeSeries to a DatetimeIndex. In the code below, I create a TimeSeries of nanosecond precision TimeStamps. I then convert it to a DatetimeIndex and lose the nanosecond precision (but maintain the microseconds). This appears to be a bug as the resulting elements ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2253 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2253/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2253/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2253/events | https://github.com/pandas-dev/pandas/pull/2253 | 8,374,500 | MDExOlB1bGxSZXF1ZXN0MzAwNjg0NQ== | 2,253 | CLN: df.iteritems() can use self.icol() | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2254 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2254/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2254/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2254/events | https://github.com/pandas-dev/pandas/pull/2254 | 8,375,852 | MDExOlB1bGxSZXF1ZXN0MzAwNzQ5MQ== | 2,254 | update 0.9.1 whatsnew to add where/mask commentary | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2012-11-15T02:03:33Z | 2014-06-26T19:47:36Z | 2012-11-15T03:14:09Z | CONTRIBUTOR | null | update whatsnew docs for where/mask (prob could be added to main docs at some point)
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https://api.github.com/repos/pandas-dev/pandas/issues/2255 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2255/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2255/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2255/events | https://github.com/pandas-dev/pandas/issues/2255 | 8,386,578 | MDU6SXNzdWU4Mzg2NTc4 | 2,255 | dtype=object not obeyed and integer converted to float | {
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"followers_url": "https://api.github.com/users/wesm/... | 0 | 2012-11-15T13:23:22Z | 2012-11-24T00:32:19Z | 2012-11-24T00:32:19Z | CONTRIBUTOR | null | reading http://pandas.pydata.org/pandas-docs/dev/gotchas.html it implies that using dtype=object should preserve the integer, passing the data via a numpy array preserves the type as I would like:
```
In [1]: import pandas
In [2]: pandas.__version__
Out[2]: '0.9.1'
In [3]: data = [(6260L, 20302010L), (6262L, None)]
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2256 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2256/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2256/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2256/events | https://github.com/pandas-dev/pandas/pull/2256 | 8,388,237 | MDExOlB1bGxSZXF1ZXN0MzAxMzMzNQ== | 2,256 | Fixes - Block.repr unicode and docstring tweak | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2257 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2257/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2257/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2257/events | https://github.com/pandas-dev/pandas/issues/2257 | 8,388,541 | MDU6SXNzdWU4Mzg4NTQx | 2,257 | Regression issue with concat of panels in 0.9.1 | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2012-11-15T14:40:30Z | 2012-11-19T05:14:07Z | 2012-11-19T05:14:07Z | CONTRIBUTOR | null | concat of panels with the same items and minor_axis (but different major_axis) on axis=2 worked in 0.8.1, but fails in 0.9.1
```
In [1]: import numpy as np
In [2]: import pandas
In [3]: pandas.__version__
Out[3]: '0.9.1'
In [4]: def make_panel():
...: index = 5
...: cols = 3
...: r... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2258 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2258/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2258/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2258/events | https://github.com/pandas-dev/pandas/issues/2258 | 8,394,468 | MDU6SXNzdWU4Mzk0NDY4 | 2,258 | Memory leak when subsetting and using timeit.Timer | {
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```
from timeit import Timer
def test():
for i in range(1000000):
s = pd.Series(np.random.randn(150))
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https://api.github.com/repos/pandas-dev/pandas/issues/2259 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2259/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2259/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2259/events | https://github.com/pandas-dev/pandas/issues/2259 | 8,395,131 | MDU6SXNzdWU4Mzk1MTMx | 2,259 | icol([0]) fails with non-unique, integer columns | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2012-11-15T18:02:29Z | 2012-12-14T02:41:34Z | 2012-12-14T02:41:34Z | NONE | null | ``` python
In [14]: df=pd.DataFrame([[1,2,3],[4,5,6]],columns=[1,1,2])
In [15]: df.icol([0])
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-15-bb76389fbd62> in <module>()
----> 1 df.icol([0])
/home/... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2260 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2260/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2260/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2260/events | https://github.com/pandas-dev/pandas/issues/2260 | 8,396,373 | MDU6SXNzdWU4Mzk2Mzcz | 2,260 | Series append function drops timezone information | {
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Code to reproduce:
import numpy as np
import pandas as pd
rng1 = pd.date_range('1/1/2011 01:00', periods=1, freq='H')
rng2 = pd.date_range('1/1/2011 02:00', periods=1, freq='H')
ts1 = pd.Series(np.random.randn(len(rng1)), index=rng... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2261 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2261/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2261/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2261/events | https://github.com/pandas-dev/pandas/pull/2261 | 8,398,048 | MDExOlB1bGxSZXF1ZXN0MzAxNzg1Ng== | 2,261 | pytables selection enhancements (to close GH #1966) | {
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1. added **str** (to do **repr**)
2. row removal in tables is much faster if rows are consecutive
3. added Term class, refactored Selection (this is backdwards compatible)
Term is a concise way of specifying conditions for queries, e.g.
```
Term(dict(field = 'index', op = '>', val... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2262 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2262/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2262/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2262/events | https://github.com/pandas-dev/pandas/issues/2262 | 8,398,112 | MDU6SXNzdWU4Mzk4MTEy | 2,262 | DataFrame.reset_index() loses timezone | {
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After the round-trip, the datetime.datetime column becomes a datetime64[ns] column, which can be considered a feature, not a bug. The times are correctly c... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2263 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2263/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2263/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2263/events | https://github.com/pandas-dev/pandas/issues/2263 | 8,398,382 | MDU6SXNzdWU4Mzk4Mzgy | 2,263 | read_csv dateparser empty date's are assigned today's date | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2012-11-15T19:20:13Z | 2012-11-28T00:43:03Z | 2012-11-28T00:43:03Z | NONE | null | Hi,
``` python
import StringIO
s = StringIO.StringIO("Date, test\n2012-01-01, 1\n,2")
pd.read_csv(s, parse_dates=["Date"])
Date test
0 2012-01-01 00:00:00 1
1 2012-11-15 00:00:00 2
```
second row should be None.
is this the default behaviour?
this is a quick fix for me
``` python
from date... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2264 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2264/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2264/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2264/events | https://github.com/pandas-dev/pandas/pull/2264 | 8,400,210 | MDExOlB1bGxSZXF1ZXN0MzAxODkzNA== | 2,264 | Pytables selection enhancement & docs update for HDF5 tables | {
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2. row removal in tables is much faster if rows are consecutive
3. added Term class, refactored Selection (this is backwards compatible)
Term is a concise way of specifying conditions for queries, e.g.
```
Term(dict(field = 'index', op = '>', value = '20121114'))
Term(... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2265 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2265/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2265/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2265/events | https://github.com/pandas-dev/pandas/pull/2265 | 8,402,259 | MDExOlB1bGxSZXF1ZXN0MzAxOTk1NA== | 2,265 | BUG: loses nano precision when converting Timestamp objects #2252 | {
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"followers_url": "https://api.github.com/users/wesm/... | 0 | 2012-11-15T21:19:02Z | 2014-06-22T14:35:35Z | 2012-11-23T21:11:39Z | CONTRIBUTOR | null | quick and dirty: add nanoseconds after conversion to datetimestruct #2252
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https://api.github.com/repos/pandas-dev/pandas/issues/2266 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2266/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2266/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2266/events | https://github.com/pandas-dev/pandas/issues/2266 | 8,402,394 | MDU6SXNzdWU4NDAyMzk0 | 2,266 | diff() behavior causes memory problems | {
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"followers_url": "https://api.github.com/users/wesm/... | 3 | 2012-11-15T21:23:40Z | 2016-10-12T23:03:20Z | 2012-11-17T02:57:28Z | NONE | null | diff(n) with a negative n used to be a recipe for obtaining a forward-looking diff. Now it causes memory problems.
The change was introduced in commit 06f74e5d06c4766ec59564374640c4758a72409d and "improved" in commit 0d6fb1b381ef3882ceb766fecb0e28a5d5c2bd2bw . The first commit produces unexpected results silently. Th... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2267 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2267/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2267/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2267/events | https://github.com/pandas-dev/pandas/issues/2267 | 8,413,152 | MDU6SXNzdWU4NDEzMTUy | 2,267 | Allow TimeSeries to be initialized using a data vector of type Series | {
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``` python
from pandas import Series, TimeSeries, date_range
foo = date_range('2012-1-1', '2012-1-4')
bar = Series(range(0,4))
```
Currently (as of pandas 0.9.1), initializing a `TimeSeries` object using `data` of type `Series` produces unexpected results---all data values are conv... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2268 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2268/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2268/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2268/events | https://github.com/pandas-dev/pandas/issues/2268 | 8,425,347 | MDU6SXNzdWU4NDI1MzQ3 | 2,268 | Duplicate row indices (bootstrapped dataframes) | {
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"f... | 5 | 2012-11-16T17:00:04Z | 2016-12-29T11:24:30Z | 2016-12-29T11:24:30Z | NONE | null | I'm running into issues using bootstrap samples of the rows of a DataFrame. Example:
<pre>
import pandas
import numpy
numpy.random.seed(1)
df=pandas.DataFrame({'a':numpy.random.randn(20),'b':numpy.random.randn(20)})
nrows=df.shape[0]
# make a bootstrap sample of the rows (nrows indices with replacement)
boot_indxs=n... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2269 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2269/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2269/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2269/events | https://github.com/pandas-dev/pandas/issues/2269 | 8,425,419 | MDU6SXNzdWU4NDI1NDE5 | 2,269 | In read_csv, header=0 and passed names should discard first row | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2270 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2270/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2270/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2270/events | https://github.com/pandas-dev/pandas/issues/2270 | 8,425,490 | MDU6SXNzdWU4NDI1NDkw | 2,270 | usecols conflicts with leading columns handling in read_csv | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2012-11-16T17:04:34Z | 2012-11-18T18:53:34Z | 2012-11-18T18:53:34Z | MEMBER | null | E.g. 3-column file, `usecols=[1, 2]`
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https://api.github.com/repos/pandas-dev/pandas/issues/2271 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2271/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2271/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2271/events | https://github.com/pandas-dev/pandas/pull/2271 | 8,425,975 | MDExOlB1bGxSZXF1ZXN0MzAzMTc1Mw== | 2,271 | ENH: pprint_thing escapes tabs. close #2038 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2272 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2272/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2272/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2272/events | https://github.com/pandas-dev/pandas/issues/2272 | 8,428,085 | MDU6SXNzdWU4NDI4MDg1 | 2,272 | datetime64 ns representation error in Series repr | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2273 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2273/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2273/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2273/events | https://github.com/pandas-dev/pandas/issues/2273 | 8,429,456 | MDU6SXNzdWU4NDI5NDU2 | 2,273 | Significant performance degradation in 0.9.1 for SparseDataFrame methods like to_dense() and save() and for arithmetic operations | {
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```
import pandas as pd
pd.__version__
```
> '0.9.0'
```
barra_industry_exposures
```
> <class 'pandas.core.frame.DataFrame'>
> MultiIndex: 253738 entries, (20061229, '00036110') to (20120928, 'Y8564W10')
> Data columns:
> MINING_METALS 253738 non-null values
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2274 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2274/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2274/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2274/events | https://github.com/pandas-dev/pandas/pull/2274 | 8,431,533 | MDExOlB1bGxSZXF1ZXN0MzAzNDY5Mw== | 2,274 | Added support for specifying colors in parallel_plot() | {
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"followers_url": "https://api.github.com/users/wesm/... | 6 | 2012-11-16T20:52:32Z | 2012-11-19T00:27:59Z | 2012-11-19T00:11:39Z | CONTRIBUTOR | null | Sometimes the random colors chosen in parallel_plot are very unfortunate, so I thought it would be nice to provide an option to pass a list of colors in.
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https://api.github.com/repos/pandas-dev/pandas/issues/2275 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2275/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2275/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2275/events | https://github.com/pandas-dev/pandas/issues/2275 | 8,433,239 | MDU6SXNzdWU4NDMzMjM5 | 2,275 | qtconsole + _repr_html_ bug | {
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I'm not sure this behaviour is right. When you're in the qtconsole, `_repr_html_` should check the size of the qtconsole window and not t... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2276 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2276/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2276/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2276/events | https://github.com/pandas-dev/pandas/pull/2276 | 8,433,282 | MDExOlB1bGxSZXF1ZXN0MzAzNTYwOA== | 2,276 | ENH: add encoding/decoding error handling | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2012-11-16T21:58:47Z | 2014-06-12T13:19:13Z | 2012-11-17T03:50:06Z | CONTRIBUTOR | null | When encoding/decoding strings with errors allow you to pass error handling strings. This works the same as error handling for other encode/decode functions. Defaults to 'strict', but you can pass 'ignore', 'replace'.
Extends work done in #1706.
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https://api.github.com/repos/pandas-dev/pandas/issues/2277 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2277/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2277/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2277/events | https://github.com/pandas-dev/pandas/issues/2277 | 8,437,124 | MDU6SXNzdWU4NDM3MTI0 | 2,277 | Series.reset_index not working with "inplace=True" | {
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``` python
ts_a = pandas.Series([1]*3+[2]*3+[3]*4, range(10))
ts_b = pandas.Series([2]*3+[3]*3+[1]*4, range(10))
ts_c ... | {
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"followers_url": "https://api.github.com/users/wesm/... | 0 | 2012-11-17T02:32:14Z | 2012-11-22T20:34:04Z | 2012-11-22T20:34:04Z | MEMBER | null | Cartesian product problem; unstacks relative to hypothetical possibilities instead of observed combinations. I'm claiming this unless someone else wants to look inside the reshape code
```
import pandas as pd
import numpy as np
# Generate Long File & Test Pivot
NUM_ROWS = 1000000
df = pd.DataFrame({'A' : np.random.r... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2279 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2279/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2279/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2279/events | https://github.com/pandas-dev/pandas/pull/2279 | 8,446,482 | MDExOlB1bGxSZXF1ZXN0MzA0MTAzMA== | 2,279 | Easy vbench testing of HEAD against a known-good baseline | {
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if no one else's...
Her'es some example output:
```
groupby_apply_dict_return 33.20 33.26 1.00
frame_reindex_axis1 2.32 2.32 1.00
frame_fillna_inplace 18.34 18.31 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2280 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2280/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2280/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2280/events | https://github.com/pandas-dev/pandas/issues/2280 | 8,452,058 | MDU6SXNzdWU4NDUyMDU4 | 2,280 | test_transform_broadcast failure on ubuntu 13.04 | {
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I'm not sure if this is a bug in the test or if some underlying component may have changed to cause rounding errors when there were non before.
a possible reason might be numpy 1.7 because the test works fine in ubuntu 12.10 with numpy 1.6
see also... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2281 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2281/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2281/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2281/events | https://github.com/pandas-dev/pandas/issues/2281 | 8,452,787 | MDU6SXNzdWU4NDUyNzg3 | 2,281 | Assigning PeriodIndex to DataFrame columns should box period objects | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2282 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2282/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2282/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2282/events | https://github.com/pandas-dev/pandas/issues/2282 | 8,453,121 | MDU6SXNzdWU4NDUzMTIx | 2,282 | Ideas about random sampling | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2283 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2283/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2283/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2283/events | https://github.com/pandas-dev/pandas/pull/2283 | 8,453,911 | MDExOlB1bGxSZXF1ZXN0MzA0MzMxMQ== | 2,283 | ENH: google analytics integration using oauth2 | {
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@wesm, mainly just want a quick review of the testing suite to make sure it skips the test in the right situations.
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https://api.github.com/repos/pandas-dev/pandas/issues/2284 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2284/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2284/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2284/events | https://github.com/pandas-dev/pandas/pull/2284 | 8,454,855 | MDExOlB1bGxSZXF1ZXN0MzA0MzYwNQ== | 2,284 | API: fillna method argument should be None by default and raise on both ... | {
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"followers_url": "https://api.github.com/users/wesm/... | 3 | 2012-11-18T22:28:48Z | 2014-06-12T07:17:06Z | 2012-11-30T23:46:12Z | CONTRIBUTOR | null | ...value and method #2027
@wesm what do you think of the API change? If you're ok with it I'll propagate to DataFrame and Panel.
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https://api.github.com/repos/pandas-dev/pandas/issues/2285 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2285/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2285/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2285/events | https://github.com/pandas-dev/pandas/issues/2285 | 8,456,671 | MDU6SXNzdWU4NDU2Njcx | 2,285 | Replace with regular expression | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2286 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2286/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2286/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2286/events | https://github.com/pandas-dev/pandas/issues/2286 | 8,457,040 | MDU6SXNzdWU4NDU3MDQw | 2,286 | Additional parameterization for groupby.agg | {
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"f... | 3 | 2012-11-19T00:43:13Z | 2017-05-04T13:37:41Z | 2017-05-04T13:37:41Z | CONTRIBUTOR | null | more explicit control over whether the agg func is applied per column or over each group DataFrame.
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https://api.github.com/repos/pandas-dev/pandas/issues/2287 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2287/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2287/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2287/events | https://github.com/pandas-dev/pandas/pull/2287 | 8,458,517 | MDExOlB1bGxSZXF1ZXN0MzA0NDU5MA== | 2,287 | Support for customizing parallel_plot() x axis tickmarks | {
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"followers_url": "https://api.github.com/users/wesm/... | 0 | 2012-11-19T03:26:26Z | 2012-12-11T20:34:24Z | 2012-12-11T20:34:24Z | CONTRIBUTOR | null | If the columns are already numeric, then you can simply use "use_columns=True" to have x-axis scaled according to those values. Otherwise the "xticks" parameter can be used to manually specify xticks to use.
Feedback or suggestions are welcome!
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https://api.github.com/repos/pandas-dev/pandas/issues/2288 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2288/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2288/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2288/events | https://github.com/pandas-dev/pandas/issues/2288 | 8,459,442 | MDU6SXNzdWU4NDU5NDQy | 2,288 | Replace all usages of assert keyword/function with AssertionErrors | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2289 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2289/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2289/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2289/events | https://github.com/pandas-dev/pandas/issues/2289 | 8,470,044 | MDU6SXNzdWU4NDcwMDQ0 | 2,289 | Add "day of month" (e.g., 15th of month) frequencies, easier extension? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2290 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2290/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2290/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2290/events | https://github.com/pandas-dev/pandas/issues/2290 | 8,474,627 | MDU6SXNzdWU4NDc0NjI3 | 2,290 | Dependency on weird statsmodels import causes large slowdown | {
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Within statsmodels/tools/data.py, there's a piece of code in the function "_is_using_larry" that tries an import of the la module and walks the Python path looking for the... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2291 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2291/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2291/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2291/events | https://github.com/pandas-dev/pandas/pull/2291 | 8,479,713 | MDExOlB1bGxSZXF1ZXN0MzA1NDQyNg== | 2,291 | BUG: dtype=object should stop conversion from object in frame constructo... | {
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don't call maybe_convert_objects in _convert_object_array if dtype is object.
Note that it also stops other type conversions too.
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https://api.github.com/repos/pandas-dev/pandas/issues/2292 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2292/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2292/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2292/events | https://github.com/pandas-dev/pandas/issues/2292 | 8,481,713 | MDU6SXNzdWU4NDgxNzEz | 2,292 | sum over objects | {
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"followers_url": "https://api.github.com/users/wesm/... | 5 | 2012-11-19T20:25:46Z | 2012-11-27T02:37:27Z | 2012-11-27T02:37:27Z | CONTRIBUTOR | null | Was surprised by this. I don't recall seeing this behavior before. I guess there are some object types that you do want to try to add together? Still seems like this could lead to problems
```
[~/statsmodels/statsmodels-skipper/statsmodels/sandbox/examples/]
[20]: import statsmodels.api as sm
[~/statsmodels/statsmode... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2293 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2293/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2293/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2293/events | https://github.com/pandas-dev/pandas/pull/2293 | 8,482,316 | MDExOlB1bGxSZXF1ZXN0MzA1NTczNw== | 2,293 | BUG: remove asserts from core #2288 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2294 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2294/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2294/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2294/events | https://github.com/pandas-dev/pandas/issues/2294 | 8,483,279 | MDU6SXNzdWU4NDgzMjc5 | 2,294 | pandas export_to_excel (time stamp in tuple) | {
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import pandas as pd
import StringIO
s = StringIO.StringIO(
"""Date,ticker,type,value
2001-01-01,x,close,12.2
2001-01-01,x,open ,12.1
2001-01-01,y,close,12.2
2001-01-01,y,open ,12.1
2001-02-01,x,close,12.2
2001-02-01,x,open ,12.1
2001-02-01,y,close,12.2
2001-02-01,y,open ,12.1
2001-03-01,x,close,12.2
2001-03-... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2295 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2295/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2295/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2295/events | https://github.com/pandas-dev/pandas/issues/2295 | 8,485,825 | MDU6SXNzdWU4NDg1ODI1 | 2,295 | Error setting multiple columns via hierarchical indexing in DataFrame | {
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```
for i in xrange(len(reg_results)):
name = names[i]
reg_result = reg_results[i]
betas[name] = reg_result._beta_raw
```
> ---
>
> AssertionError Traceback (most recent call last)
> <ipython-input-26-86f8dc5d0623> in <module>()
> 5 print(... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2296 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2296/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2296/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2296/events | https://github.com/pandas-dev/pandas/issues/2296 | 8,486,974 | MDU6SXNzdWU4NDg2OTc0 | 2,296 | bug in csv_reader? | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2012-11-19T23:17:27Z | 2012-11-20T04:54:46Z | 2012-11-20T04:53:42Z | NONE | null | Some previously-functioning code of mine broke this morning after upgrading to the newest Ubuntu package of Pandas v. 0.9.2. It seems to be a bug in the upgraded read_csv parser. Beyond that, the error message (below) is fairly unhelpful to the uninitiated. Maybe this is a bug, or maybe the new version uses differen... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2297 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2297/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2297/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2297/events | https://github.com/pandas-dev/pandas/pull/2297 | 8,487,061 | MDExOlB1bGxSZXF1ZXN0MzA1ODAyNA== | 2,297 | ENH: DataFrame.from_records takes iterator #1794 | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2012-11-19T23:20:40Z | 2012-11-20T19:29:28Z | 2012-11-20T19:29:28Z | CONTRIBUTOR | null | @wesm are there other cases you can think of we should add as test case?
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https://api.github.com/repos/pandas-dev/pandas/issues/2298 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2298/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2298/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2298/events | https://github.com/pandas-dev/pandas/issues/2298 | 8,497,822 | MDU6SXNzdWU4NDk3ODIy | 2,298 | read_csv fails for UTF-16 with BOM (maybe also other encodings with BOM) and skiprows | {
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```
��Name Ad performance report
Type Ad
Frequency One time
Date range Custom date range
Dates Sep 19, 2012-Nov 19, 2012... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2299 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2299/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2299/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2299/events | https://github.com/pandas-dev/pandas/issues/2299 | 8,499,482 | MDU6SXNzdWU4NDk5NDgy | 2,299 | Retrieving large frames with sparse data from hdf5 - 'NoneType' object is not iterable error | {
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"followers_url": "https://api.github.com/users/wesm/... | 3 | 2012-11-20T10:30:45Z | 2014-09-19T12:20:26Z | 2012-11-26T01:40:39Z | NONE | null | I have been using pandas within my scripts for some time now, especially to store large data sets in an easily accessible way. I have stumbled upon this problem a couple of days ago and have not been able to solve it so far.
The problem is that after I store a huge data frame into an hdf5 file, when I later load it ba... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2300 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2300/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2300/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2300/events | https://github.com/pandas-dev/pandas/issues/2300 | 8,505,571 | MDU6SXNzdWU4NTA1NTcx | 2,300 | BinGrouper AttributeError when returning a Series from DataFrame.apply | {
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"followers_url": "https://api.github.com/users/wesm/... | 0 | 2012-11-20T14:09:25Z | 2012-12-07T20:48:03Z | 2012-12-07T20:48:03Z | CONTRIBUTOR | null | http://nbviewer.ipython.org/4118124/
``` python
import pandas as pd
from pandas.tseries.resample import TimeGrouper
N = 1000
ind = pd.date_range(start="2000-01-01", freq="D", periods=N)
df = pd.DataFrame({'open':1, 'close':2}, index=ind)
tg = TimeGrouper('M')
grouper = tg.get_grouper(df)
# Errors
df.groupby(grouper).... | {
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