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"description": "RC1 ANN went out on 2013-12-6: https://groups.google.com/forum/#!topic/pydata/OHutLByJvK0\r\nTwo weeks, clear all reported bugs then call it on 2013-12-20. \r\n(@y-p)\r\n\r\nsince we've had some bugs reported at a high enough rate, extending rc by one\r\nweek until 2013-12-27. \r\n(@y-p)\r\n\r\nBumping to clear debian sparc\r\n2013-12-30. \r\n(@y-p)",
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} | 12 | "2012-06-20T12:48:14Z" | "2014-06-16T17:17:45Z" | "2013-09-26T00:58:05Z" | CONTRIBUTOR | null | {
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https://api.github.com/repos/pandas-dev/pandas/issues/1502 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1502/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1502/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1502/events | https://github.com/pandas-dev/pandas/issues/1502 | 5,174,312 | MDU6SXNzdWU1MTc0MzEy | 1,502 | Performance of adding columns to DataFrame | {
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} | 4 | "2012-06-20T16:10:06Z" | "2019-10-08T09:28:42Z" | "2016-01-14T21:35:33Z" | NONE | null | I found a performance issue involving modifying `DataFrame` objects. Adding columns where a value is present for every value of the index (e.g., `len(column) == len(index)`) is really, really slow.
``` python
import numpy as np
import pandas
import time
values = np.random.randn(100)
t0 = time.time()
columns = [values for i in range(10000)]
df = pandas.DataFrame(np.column_stack(columns),
columns=range(len(columns)),
index=values)
print 'A took %.3f seconds' % (time.time() - t0)
t0 = time.time()
df = pandas.DataFrame(index=values)
for i in range(10000):
df[i] = values
print 'B took %.3f seconds' % (time.time() - t0)
```
Running it on my laptop shows that the first version (doing all the work in `__init__`) is much faster:
```
A took 0.070 seconds
B took 10.717 seconds
```
The `cProfile` results show hotspots involving the index:
```
ncalls tottime percall cumtime percall filename:lineno(function)
13410 2.716 0.000 2.716 0.000 {method 'astype' of 'numpy.ndarray' objects}
12246 2.024 0.000 2.024 0.000 {numpy.core.multiarray.concatenate}
22274 1.443 0.000 1.501 0.000 index.py:277(__contains__)
10555 1.103 0.000 1.103 0.000 {method 'copy' of 'numpy.ndarray' objects}
555 0.639 0.001 3.711 0.007 internals.py:823(_consolidate_inplace)
555 0.429 0.001 3.072 0.006 internals.py:1370(_consolidate)
11135 0.299 0.000 8.921 0.001 internals.py:892(insert)
11141 0.217 0.000 0.217 0.000 {pandas.lib.is_integer_array}
121164 0.163 0.000 0.163 0.000 {numpy.core.multiarray.array}
25105 0.140 0.000 3.493 0.000 index.py:75(__new__)
342808 0.107 0.000 0.107 0.000 {isinstance}
1691 0.106 0.000 0.115 0.000 index.py:211(is_unique)
11138 0.096 0.000 0.096 0.000 {pandas.lib.list_to_object_array}
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1503 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1503/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1503/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1503/events | https://github.com/pandas-dev/pandas/issues/1503 | 5,178,757 | MDU6SXNzdWU1MTc4NzU3 | 1,503 | Improve repr for weekly Periods | {
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} | 2 | "2012-06-20T19:39:56Z" | "2012-07-13T01:35:24Z" | "2012-07-13T01:35:24Z" | NONE | null | It's totally possible I don't understand how to_timestamp should be used with a Period. But, I have a hunch there is a bug here.
```
In : p = Period('01-Jan-2012', 'W')
In : p.to_timestamp(how='E')
Out: <Timestamp: 2012-01-01 00:00:00>
In : p.to_timestamp('H', how='E')
Out: <Timestamp: 2012-01-01 23:00:00>
```
(found in 0.8b2)
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} | 2 | "2012-06-20T21:29:22Z" | "2012-07-12T16:50:50Z" | "2012-07-12T16:50:35Z" | NONE | null | See #50
Using a `min-heap` and `max-heap` to implement the `rolling_min` and `rolling_max` functions seems to be about 30% faster than the current implementation.
Something sort of like this:
``` python
from heapq import heappush, heapify
from numpy import empty, nan
cimport numpy as np
cdef extern from "math.h":
bint isnan(double x)
cdef np.float64_t NaN = nan
def rolling_min(np.ndarray[np.float64_t] arg, int window):
cdef Py_ssize_t N = len(arg)
cdef np.ndarray[np.double_t] output = empty(N, dtype=float)
cdef list min_heap = []
for i from 0 <= i < N:
x = arg[i]
if isnan(x):
min_heap = []
output[i] = x
else:
heappush(min_heap, arg[i])
if len(min_heap) < window:
output[i] = NaN
else:
output[i] = min_heap[0]
x = arg[i+1-window]
min_heap.remove(x)
heapify(min_heap)
return output
def rolling_max(np.ndarray[np.float64_t] arg, int window):
cdef Py_ssize_t N = len(arg)
cdef np.ndarray[np.double_t] output = empty(N, dtype=float)
cdef list min_heap = []
for i from 0 <= i < N:
x = arg[i]
if isnan(x):
min_heap = []
output[i] = x
else:
heappush(min_heap, -arg[i])
if len(min_heap) < window:
output[i] = NaN
else:
output[i] = -min_heap[0]
x = -arg[i+1-window]
min_heap.remove(x)
heapify(min_heap)
return output
```
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} | 0 | "2012-06-21T11:10:29Z" | "2012-06-21T23:02:54Z" | "2012-06-21T23:02:54Z" | NONE | null | ```
from pandas import *
x = DataFrame([(1,3,4.5,6.5), (1,3,4.5,6.5)])
print x
print x.dtypes
for row in x.itertuples():
print row
0 1 2 3
0 1 3 4.5 6.5
1 1 3 4.5 6.5
0 int64
1 int64
2 float64
3 float64
(0, 1.0, 3.0, 4.5, 6.5)
(1, 1.0, 3.0, 4.5, 6.5)
```
mixed dtype row leaves ints untouched
```
rom pandas import *
x = DataFrame([(1,3,4.5,6.5,'a'), (1,3,4.5,6.5, 'b')])
print x
print x.dtypes
for row in x.itertuples():
print row
0 1 2 3 4
0 1 3 4.5 6.5 a
1 1 3 4.5 6.5 b
0 int64
1 int64
2 float64
3 float64
4 object
(0, 1, 3, 4.5, 6.5, 'a')
(1, 1, 3, 4.5, 6.5, 'b')
```
```
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1506 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1506/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1506/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1506/events | https://github.com/pandas-dev/pandas/issues/1506 | 5,191,592 | MDU6SXNzdWU1MTkxNTky | 1,506 | DataFrame.boxplot() NaN handling | {
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} | 0 | "2012-06-21T12:25:26Z" | "2012-06-22T14:29:11Z" | "2012-06-22T14:29:11Z" | CONTRIBUTOR | null | In case of NaN, `DataFrame.boxplot()` produces unexpected plot (see below df2, column A boxplot).
It is matplotlib related (using version 1.1.0).
``` python
In [248]: df = pandas.DataFrame(np.random.randn(1000,2), columns=['A', 'B'])
In [249]: df.describe()
Out[249]:
A B
count 1000.000000 1000.000000
mean 0.015119 -0.000871
std 1.010431 1.028899
min -2.751259 -2.959695
25% -0.707412 -0.684895
50% 0.025767 -0.000795
75% 0.724173 0.757402
max 3.885804 2.821532
In [250]: df2 = pandas.concat([df, df], ignore_index=True)
In [251]: df2['A'][1000::] = np.NaN
In [252]: df2.describe()
Out[252]:
A B
count 1000.000000 2000.000000
mean 0.015119 -0.000871
std 1.010431 1.028641
min -2.751259 -2.959695
25% -0.707412 -0.684895
50% 0.025767 -0.000795
75% 0.724173 0.757402
max 3.885804 2.821532
In [253]: df.boxplot();figure();df2.boxplot();
```
[screenshot](https://lh4.googleusercontent.com/-7EplwgT9ltk/T-MUlkumNxI/AAAAAAAAANg/lQPskQy56XQ/s800/issue1506_screenshot1.png)
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} | 0 | "2012-06-21T12:53:48Z" | "2012-06-29T00:12:49Z" | "2012-06-29T00:12:49Z" | CONTRIBUTOR | null | matplotlib `boxplot()` does not create a new figure by default (as `hist()` does).
Since `DataFrameGroupby.boxplot()` relays boxplot to `DataFrame.boxplot()`, one ends up with one messy plot.
See [screenshot](https://lh6.googleusercontent.com/-Ax6O9Be-VLQ/T-MYeSyB7VI/AAAAAAAAANs/fGzupMhexw8/s583/issue1507_screenshot1.png)
Either `DataFrameGroupby.boxplot()` should not relay to `DataFrame.boxplot()` and handle this or other option is to change behavior of `DataFrame.boxplot()` and open a new figure before calling matplotlib boxplot.
``` python
In [300]: tuples = [t for t in itertools.product(list('abcdefghijkl'), range(4))]
In [301]: index = pandas.MultiIndex.from_tuples(tuples, names=['lvl0', 'lvl1'])
In [302]: df = pandas.DataFrame(np.random.randn(len(index),5), columns=list('ABCDE'), index=index)
In [303]: grouped = df.groupby(level=1)
In [304]: grouped.boxplot()
Out[304]:
lvl1
0 {'medians': [<matplotlib.lines.Line2D object at 0
1 {'medians': [<matplotlib.lines.Line2D object at 0
2 {'medians': [<matplotlib.lines.Line2D object at 0
3 {'medians': [<matplotlib.lines.Line2D object at 0
```
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} | 0 | "2012-06-21T15:53:31Z" | "2012-11-02T16:56:11Z" | "2012-11-02T16:56:11Z" | CONTRIBUTOR | null | Currently na_option is unused in rank(). It would be nice to have at last "top" or "bottom" along with "keep" (it's actually useful as some published algorithms need this feature).
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} | 4 | "2012-06-22T03:40:12Z" | "2012-07-19T19:35:43Z" | "2012-07-19T19:35:43Z" | CONTRIBUTOR | null | need to get all the lines and labels and pass to the same axes
maybe need to have ax and twinx keep references to each other
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} | 1 | "2012-06-22T15:03:54Z" | "2020-05-03T21:04:04Z" | "2020-05-03T21:04:03Z" | CONTRIBUTOR | null | 1. explicit: user supplies timedelta defining the maximum distance between points that should be interpolated.
2. implicit: computes GCF for all unique timedeltas in the index and use that to get the regularized frequency.
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} | 1 | "2012-06-22T17:22:40Z" | "2012-06-28T22:40:21Z" | "2012-06-28T22:40:21Z" | CONTRIBUTOR | null | ``` python
s = Series([0, -1, 0, 1, -3])
ind, label = cut(s, [-4, 0, 1], retbins=True, labels=False)
# label [-4 0 1]
# ind array([0, 0, 0, 1, 0])
s = Series([0, -1, 0, 1, -3])
ind, label = cut(s, [0, 1], retbins=True, labels=False)
# label [0 1]
# ind array([ nan, nan, nan, 1., nan])
# Should the 1 be a 0?
```
I recall that there always used to be an implicit out of range level. It seems like the second example still acts like the (-inf, 0] exists.
I could test the ind for nan to handle this, but wanted to make sure it wasn't a bug.
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} | 2 | "2012-06-22T17:38:58Z" | "2013-05-18T00:51:49Z" | "2013-05-18T00:45:46Z" | CONTRIBUTOR | null | Note to self. I think it makes sense to move or copy the Stata file handling from statsmodels to pandas rather than having a return_pandas option in statsmodels. DataFrame.from_dta would be helpful.
https://github.com/statsmodels/statsmodels/blob/master/statsmodels/iolib/foreign.py
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} | 2 | "2012-06-22T18:06:09Z" | "2012-06-22T19:27:26Z" | "2012-06-22T19:26:59Z" | NONE | null | ```
In [2]: df = DataFrame({'a': [1,2,3], 'b': [1,2,3], 'c': [4,5,6]})
In [3]: df.set_index(['a','b'], inplace=True)
Out[3]:
c
a b
1 1 4
2 2 5
3 3 6
In [5]: dropped = df.drop((1,1))
In [6]: dropped
Out[6]:
c
2 2 5
3 3 6
```
MultiIndex is gone! (2,2) and (3,3) are now plain tuples
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https://api.github.com/repos/pandas-dev/pandas/issues/1514 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1514/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1514/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1514/events | https://github.com/pandas-dev/pandas/issues/1514 | 5,221,508 | MDU6SXNzdWU1MjIxNTA4 | 1,514 | Signal Processing in pandas | {
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} | [] | closed | false | null | [] | null | 2 | "2012-06-22T18:49:43Z" | "2012-06-22T19:50:13Z" | "2012-06-22T19:50:13Z" | NONE | null | I'm interested in using pandas in a signal processing context (for analysis of neuroscience data). I want to make use of the great time series capabilities of pandas, but it seems focused on a different scale of time than in neuroscience. It should work on any timescale though. It's a bit strange to have a `time` column be a `datetime` object in which there are changes in the microsecond, so it'd be good to have an easier interface for micro-timescale time series objects. Additionally, it looks like seconds is the lowest timescale currently supported by pandas.
I see that there are `nanoseconds` and `datetime64` branches, and a few other timeseries changes in the pipeline -- is this kind of timescale issue being looked at right now? If so, how can I get involved in it? If not, any pointers in implementing this kind of change?
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} | [] | closed | false | null | [] | null | 1 | "2012-06-23T00:16:16Z" | "2015-07-29T15:00:35Z" | "2012-06-25T22:40:18Z" | NONE | null | After using .take on a dataframe and getting a subset of rows, the new dataframe has an incorrect index. This confuses iterrows:
In [20]: df = pandas.DataFrame(np.random.rand(5,2))
In [21]: df
Out[21]:
0 1
0 0.854891 0.303209
1 0.460207 0.661508
2 0.534319 0.626693
3 0.504076 0.693567
4 0.628285 0.597230
In [23]: df.take(np.flatnonzero(df[0]>0.5))
Out[23]:
0 1
0 0.854891 0.303209
2 0.534319 0.626693
3 0.504076 0.693567
4 0.628285 0.597230
In [24]: df.take(np.flatnonzero(df[0]>0.5)).index
Out[24]: Int64Index([0, 2, 3, 4])
In [26]: for (i,x) in df.take(np.flatnonzero(df[0]>0.5)).iterrows(): print i
0
2
3
4
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} | 0 | "2012-06-23T00:55:47Z" | "2014-07-15T12:46:59Z" | "2012-06-25T14:13:02Z" | CONTRIBUTOR | null | ... #1494
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} | 0 | "2012-06-23T23:20:17Z" | "2016-10-12T23:03:02Z" | "2012-06-25T14:41:03Z" | MEMBER | null | I presume this is due to conversion to PeriodIndex. Needs to be fixed for 0.8.0
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} | 0 | "2012-06-24T14:22:36Z" | "2012-06-25T22:32:57Z" | "2012-06-25T22:32:57Z" | MEMBER | null | Moving issue here:
http://stackoverflow.com/questions/11175213/pandas-read-csv-input-local-datetime-strings-tz-convert-to-utc
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} | 0 | "2012-06-24T19:11:41Z" | "2014-07-08T14:50:35Z" | "2012-06-25T14:40:58Z" | CONTRIBUTOR | null | Plotting both fixed frequency and irregular time series on same subplot fails
#1517
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} | 0 | "2012-06-24T20:31:56Z" | "2016-10-12T23:03:03Z" | "2012-06-25T13:58:59Z" | NONE | null | I will update with a self contained example if I manage to find one (please tell me if necessary).
```
print df.dtypes
0 int64
1 int64
2 object
3 object
4 object
5 object
6 datetime64[ns]
7 float64
8 int64
9 int64
10 int64
11 float64
12 float64
13 bool
14 bool
15 int64
16 object
17 int64
18 int64
19 int64
20 int64
21 int64
22 int64
23 float64
24 float64
25 float64
26 float64
27 float64
28 float64
29 float64
30 float64
31 float64
32 float64
33 float64
34 float64
35 float64
36 float64
37 float64
38 float64
39 float64
40 float64
41 float64
42 float64
43 float64
44 float64
45 float64
46 float64
47 float64
48 float64
49 int64
50 int64
51 float64
52 float64
53 int64
Length: 54
store = HDFStore('data.h5')
store['last_three_months'] = df
store.close()
store = HDFStore('data.h5')
test = store['last_three_months']
print test.dtypes
0 datetime64[ns]
1 datetime64[ns]
2 object
3 object
4 object
5 object
6 datetime64[ns]
7 datetime64[ns]
8 datetime64[ns]
9 datetime64[ns]
10 datetime64[ns]
11 datetime64[ns]
12 datetime64[ns]
13 datetime64[ns]
14 datetime64[ns]
15 datetime64[ns]
16 object
17 datetime64[ns]
18 datetime64[ns]
19 datetime64[ns]
20 datetime64[ns]
21 datetime64[ns]
22 datetime64[ns]
23 datetime64[ns]
24 datetime64[ns]
25 datetime64[ns]
26 datetime64[ns]
27 datetime64[ns]
28 datetime64[ns]
29 datetime64[ns]
30 datetime64[ns]
31 datetime64[ns]
32 datetime64[ns]
33 datetime64[ns]
34 datetime64[ns]
35 datetime64[ns]
36 datetime64[ns]
37 datetime64[ns]
38 datetime64[ns]
39 datetime64[ns]
40 datetime64[ns]
41 datetime64[ns]
42 datetime64[ns]
43 datetime64[ns]
44 datetime64[ns]
45 datetime64[ns]
46 datetime64[ns]
47 datetime64[ns]
48 datetime64[ns]
49 datetime64[ns]
50 datetime64[ns]
51 datetime64[ns]
52 datetime64[ns]
53 datetime64[ns]
Length: 54
```
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} | 0 | "2012-06-24T20:48:33Z" | "2014-06-16T17:26:53Z" | "2012-06-25T13:58:46Z" | CONTRIBUTOR | null | #1520
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} | 1 | "2012-06-24T23:10:36Z" | "2014-06-18T09:47:42Z" | "2012-06-25T13:57:50Z" | CONTRIBUTOR | null | {
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} | 1 | "2012-06-25T14:01:22Z" | "2014-06-14T00:46:22Z" | "2012-06-25T16:36:01Z" | CONTRIBUTOR | null | This patch will allow me to sleep better at night. Thoughts?
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} | 2 | "2012-06-25T19:10:43Z" | "2016-10-12T23:03:03Z" | "2012-07-13T02:54:20Z" | CONTRIBUTOR | null | this might be obvious to others but I couldn't find a way to do a simple X-Y plot where two columns of values correspond to X and Y locations. If this is already implemented I would be happy to write an example for the documentation.
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https://api.github.com/repos/pandas-dev/pandas/issues/1529 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1529/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1529/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1529/events | https://github.com/pandas-dev/pandas/issues/1529 | 5,261,399 | MDU6SXNzdWU1MjYxMzk5 | 1,529 | Parse Excel from in-memory file object | {
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} | 11 | "2012-06-25T22:54:25Z" | "2012-08-20T23:16:04Z" | "2012-06-28T22:30:46Z" | NONE | null | We came across a situation where we had a file object representing Excel data (came from HTTP POST but I'm thinking it could also come from MongoDB for example), and would've liked to pass it directly to Pandas to parse (vs saving it to disk and passing path to Pandas).
Could this be possible?
I saw that xlrd had `file_contents` as a possible argument of `open_workbook`:
https://github.com/python-excel/xlrd/blob/master/xlrd/__init__.py#L385
Maybe `ExcelFile` in Pandas could take `path_or_buffer` as argument, and pass along the correct one to xlrd.
https://github.com/pydata/pandas/blob/master/pandas/io/parsers.py#L1133
Don't know if that could work for openpyxl also.
Thoughts?
Thanks!
Nicolas
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https://api.github.com/repos/pandas-dev/pandas/issues/1530 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1530/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1530/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1530/events | https://github.com/pandas-dev/pandas/issues/1530 | 5,263,776 | MDU6SXNzdWU1MjYzNzc2 | 1,530 | build issues on osx 10.6.8 | {
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when I try installing with pip or building from source (latest from git and latest stable release), i get error messages to the tune of:
```
python setup.py build
...
building 'pandas._algos' extension
creating build/temp.macosx-10.6-universal-2.6
creating build/temp.macosx-10.6-universal-2.6/pandas
creating build/temp.macosx-10.6-universal-2.6/pandas/src
gcc-4.2 -fno-strict-aliasing -fno-common -dynamic -DNDEBUG -g -fwrapv -Os -Wall -Wstrict-prototypes -DENABLE_DTRACE -arch i386 -arch ppc -arch x86_64 -pipe -I/Library/Python/2.6/site-packages/numpy-2.0.0.dev_20101104-py2.6-macosx-10.6-universal.egg/numpy/core/include -I/System/Library/Frameworks/Python.framework/Versions/2.6/include/python2.6 -c pandas/src/generated.c -o build/temp.macosx-10.6-universal-2.6/pandas/src/generated.o
In file included from pandas/src/generated.c:282:
pandas/src/numpy_helper.h:47: warning: function declaration isn’t a prototype
In file included from pandas/src/generated.c:284:
pandas/src/datetime/np_datetime.h:110: error: expected declaration specifiers or ‘...’ before ‘NPY_CASTING’
In file included from pandas/src/generated.c:285:
pandas/src/datetime/np_datetime_strings.h:46: error: expected declaration specifiers or ‘...’ before ‘NPY_CASTING’
pandas/src/datetime/np_datetime_strings.h:84: error: expected declaration specifiers or ‘...’ before ‘NPY_CASTING’
pandas/src/generated.c: In function ‘__Pyx_GetBuffer’:
pandas/src/generated.c:61217: warning: unused variable ‘getbuffer_cobj’
pandas/src/generated.c: In function ‘__Pyx_ReleaseBuffer’:
pandas/src/generated.c:61257: warning: unused variable ‘releasebuffer_cobj’
In file included from pandas/src/generated.c:282:
pandas/src/numpy_helper.h:47: warning: function declaration isn’t a prototype
In file included from pandas/src/generated.c:284:
pandas/src/datetime/np_datetime.h:110: error: expected declaration specifiers or ‘...’ before ‘NPY_CASTING’
In file included from pandas/src/generated.c:285:
pandas/src/datetime/np_datetime_strings.h:46: error: expected declaration specifiers or ‘...’ before ‘NPY_CASTING’
pandas/src/datetime/np_datetime_strings.h:84: error: expected declaration specifiers or ‘...’ before ‘NPY_CASTING’
pandas/src/generated.c: In function ‘__Pyx_GetBuffer’:
pandas/src/generated.c:61217: warning: unused variable ‘getbuffer_cobj’
pandas/src/generated.c: In function ‘__Pyx_ReleaseBuffer’:
pandas/src/generated.c:61257: warning: unused variable ‘releasebuffer_cobj’
In file included from pandas/src/generated.c:282:
pandas/src/numpy_helper.h:47: warning: function declaration isn’t a prototype
In file included from pandas/src/generated.c:284:
pandas/src/datetime/np_datetime.h:110: error: expected declaration specifiers or ‘...’ before ‘NPY_CASTING’
In file included from pandas/src/generated.c:285:
pandas/src/datetime/np_datetime_strings.h:46: error: expected declaration specifiers or ‘...’ before ‘NPY_CASTING’
pandas/src/datetime/np_datetime_strings.h:84: error: expected declaration specifiers or ‘...’ before ‘NPY_CASTING’
pandas/src/generated.c: In function ‘__Pyx_GetBuffer’:
pandas/src/generated.c:61217: warning: unused variable ‘getbuffer_cobj’
pandas/src/generated.c: In function ‘__Pyx_ReleaseBuffer’:
pandas/src/generated.c:61257: warning: unused variable ‘releasebuffer_cobj’
lipo: can't open input file: /var/folders/zL/zLXn-4ccH9WTIkX1lKghn++++TI/-Tmp-//ccRz3AFI.out (No such file or directory)
error: command 'gcc-4.2' failed with exit status 1
```
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} | 10 | "2012-06-26T14:14:25Z" | "2012-06-28T23:10:39Z" | "2012-06-28T20:04:37Z" | CONTRIBUTOR | null | Create an array of datetime.time() objects as an index for a DataFrame, and plotting will be wrong, and the index is not of type TimeSeries. Convert that array (using combine()) into an array of datetime.datetime() objects, and it works.
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} | 1 | "2012-06-26T15:31:03Z" | "2012-06-28T18:21:47Z" | "2012-06-28T18:21:47Z" | NONE | null | With pandas from the current master branch and python-3.2.2, I'm getting a seg fault with the following minimal example:
```
df1 = pandas.DataFrame({'a': [1, 1], 'b': [1, 2], 'x': [1, 2]})
df2 = pandas.DataFrame({'a': [2, 2], 'b': [1, 2], 'y': [1, 2]})
df1 = df1.set_index(['a', 'b'])
df2 = df2.set_index(['a', 'b'])
df1.join(df2)
Process Python segmentation fault
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1533 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1533/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1533/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1533/events | https://github.com/pandas-dev/pandas/issues/1533 | 5,277,114 | MDU6SXNzdWU1Mjc3MTE0 | 1,533 | Assigning to a sliced a Panel | {
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} | 4 | "2012-06-26T16:06:41Z" | "2013-10-04T15:28:52Z" | "2012-06-29T14:39:49Z" | CONTRIBUTOR | null | Suppose I have:
a=Panel(items=[1,2,3],major_axis=[11,22,33],minor_axis=[111,222,333])
b=DataFrame(randn(2,3),index=[111,333],columns=[1,2,3])
I get an error when trying to assign b to a slice of a:
## a.ix[:,22,[111,333]]=b
ValueError Traceback (most recent call last)
<ipython-input-34-a593ce0f28a0> in <module>()
----> 1 a.ix[:,22,[111,333]]=b
/usr/local/lib/python2.7/site-packages/pandas/core/indexing.pyc in **setitem**(self, key, value)
66 indexer = self._convert_to_indexer(key)
67
---> 68 self._setitem_with_indexer(indexer, value)
69
70 def _convert_tuple(self, key):
/usr/local/lib/python2.7/site-packages/pandas/core/indexing.pyc in _setitem_with_indexer(self, indexer, value)
101 if isinstance(indexer, tuple):
102 indexer = _maybe_convert_ix(*indexer)
--> 103 self.obj.values[indexer] = value
104
105 def _getitem_tuple(self, tup):
ValueError: array is not broadcastable to correct shape
The slice of a and b look of same shape to me, so I do not understand the broadcasting error.
When debugging _setitem_with_indexer() I notice that the indexer returns a 2x3 array instead of 3x2, so if I do:
a.ix[:,22,[111,333]]=b.T
then it works. Is this a bug or the intended behaviour?
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https://api.github.com/repos/pandas-dev/pandas/issues/1534 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1534/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1534/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1534/events | https://github.com/pandas-dev/pandas/issues/1534 | 5,281,155 | MDU6SXNzdWU1MjgxMTU1 | 1,534 | MultiIndex.from_tuples issue | {
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} | 0 | "2012-06-26T19:02:59Z" | "2012-06-28T18:10:31Z" | "2012-06-28T18:10:31Z" | MEMBER | null | from mailing list cc @ruidc
```
import pandas
mix = pandas.MultiIndex.from_tuples([('1a', '2a'), ('1a', '2b'), ('1a', '2c'), ('1b', '2d')])
df = pandas.DataFrame([[1,2,3,4],[5,6,7,8]], columns=mix)
#... manipulate columns here ... but not required to raise error here
df.columns = pandas.MultiIndex.from_tuples(df.columns) #df.columns.values works fine
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Python27\lib\site-packages\pandas\core\index.py", line 1538, in from_tuples
arrays = list(lib.tuples_to_object_array(tuples).T)
File "inference.pyx", line 687, in pandas.lib.tuples_to_object_array (pandas\src\tseries.c:97580)
IndexError: Out of bounds on buffer access (axis 0)
Is this a miss-use of from_tuples?
```
```
import pandas
mix = pandas.MultiIndex.from_tuples([('1a', '2a'), ('1a', '2b'), ('1a', '2c')])
df = pandas.DataFrame([[1,2],[3,4],[5,6]], index=mix)
s = pandas.Series({(1,1): 1, (1,2): 2})
#df['new'] = s.to_dict() # Nice AssertionError about length mismatch
df['new'] = s #bad kaboom on rc2, loads NaNs on b2 as expected
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 1712, in __setitem__
self._set_item(key, value)
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 1751, in _set_item
value = self._sanitize_column(key, value)
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 1778, in _sanitize_column
value = value.reindex(self.index).values
File "C:\Python27\lib\site-packages\pandas\core\series.py", line 2044, in reindex
level=level, limit=limit)
File "C:\Python27\lib\site-packages\pandas\core\index.py", line 791, in reindex
limit=limit)
File "C:\Python27\lib\site-packages\pandas\core\index.py", line 722, in get_indexer
indexer = self._engine.get_indexer(target)
File "engines.pyx", line 245, in pandas.lib.IndexEngine.get_indexer (pandas\src\tseries.c:105931)
File "hashtable.pyx", line 751, in pandas.lib.PyObjectHashTable.lookup (pandas\src\tseries.c:21174)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1535 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1535/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1535/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1535/events | https://github.com/pandas-dev/pandas/pull/1535 | 5,281,633 | MDExOlB1bGxSZXF1ZXN0MTY1NTg5NA== | 1,535 | ENH: implement DataFrameGroupBy.boxplot(), close #1507 | {
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} | 1 | "2012-06-26T19:28:48Z" | "2014-07-15T10:14:51Z" | "2012-06-29T00:16:09Z" | CONTRIBUTOR | null | I noticed this was marked for milestone 0.9.0, so a pull request iso push myself.
I know plenty of goodies are on it`s way for plotting, so this might be a temporary solution (it is not my intention to run in the way of @orbitfold).
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https://api.github.com/repos/pandas-dev/pandas/issues/1536 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1536/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1536/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1536/events | https://github.com/pandas-dev/pandas/issues/1536 | 5,285,782 | MDU6SXNzdWU1Mjg1Nzgy | 1,536 | index_col parser bug | {
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} | 0 | "2012-06-26T22:55:11Z" | "2012-06-27T00:31:43Z" | "2012-06-27T00:31:43Z" | MEMBER | null | ```
In [10]: !cat book_scripts/io/ex2.csv
1,2,3,4,hello
5,6,7,8,world
9,10,11,12,foo
In [11]: names
Out[11]: ['a', 'b', 'c', 'd', 'message']
In [12]: read_csv('book_scripts/io/ex2.csv', names=names, index_col=['message'])
Out[12]:
a b c d
message
hello 1 2 3 4
world 5 6 7 8
foo 9 10 11 12
In [13]: read_csv('book_scripts/io/ex2.csv', names=names, index_col='message')
Out[13]:
a b c d
hello 1 2 3 4
world 5 6 7 8
foo 9 10 11 12
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1537 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1537/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1537/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1537/events | https://github.com/pandas-dev/pandas/issues/1537 | 5,294,266 | MDU6SXNzdWU1Mjk0MjY2 | 1,537 | Problem when setting values based on MultiIndex subset | {
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} | 6 | "2012-06-27T10:40:43Z" | "2012-06-29T14:40:56Z" | "2012-06-29T00:01:13Z" | NONE | null | ```
from pandas import *
test = read_csv('/home/arthur/transform_issue.csv')
x =test.groupby(['A','B','C'])['revenues'].first().index
test.set_index(['A','B','C'], inplace=True)
test.ix[x]['revenues']= 999.99
print test.ix[x].shape
print test[test.revenues==999.99]
(127, 5)
Empty DataFrame
Columns: array([D, E, week, revenues, orders], dtype=object)
Index: array([], dtype=object)
```
I then tried setting via df[col][indexes] which simply crashed my session without any exception
```
test = read_csv('/home/arthur/transform_issue.csv')
x =test.groupby(['A','B','C'])['revenues'].first().index
test.set_index(['A','B','C'], inplace=True)
test['revenues'][x]= 999.99
print test.ix[x].shape
print test[test.revenues==999.99]
```
I sent the csv via email to [email protected]
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https://api.github.com/repos/pandas-dev/pandas/issues/1540 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1540/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1540/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1540/events | https://github.com/pandas-dev/pandas/issues/1540 | 5,299,600 | MDU6SXNzdWU1Mjk5NjAw | 1,540 | DataFrame.plot doesn't use kwarg color properly | {
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} | [] | closed | false | null | [] | null | 0 | "2012-06-27T15:23:58Z" | "2013-12-04T00:43:09Z" | "2012-06-28T22:16:49Z" | NONE | null | When we use df.plot(kind='bar), each bar has a different color and it's ok, but not if we use df.plot(kind='bar, color='rgbc') for example, or each custom colormap, because all bars in a particular group have the same color.
A quick workaround, in plotting.py, line 716, replace
if 'color' not in kwds:
kwds['color'] = colors[i % len(colors)]
by
if 'color' not in kwds:
kwds['color'] = colors[i % len(colors)]
else:
kwds['color'] = kwds['color'][i % len(kwds['color'])]
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} | 3 | "2012-06-27T17:27:44Z" | "2014-06-18T22:33:22Z" | "2012-06-28T20:04:21Z" | CONTRIBUTOR | null | re #1531
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} | 4 | "2012-06-27T20:29:22Z" | "2016-06-14T21:22:16Z" | "2016-06-14T21:22:16Z" | NONE | null | Any change of getting bi-monthly (1st and 15th) of the month, or bi-weekly (every two weeks) supported?
I could probably do this.
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} | 0 | "2012-06-28T13:41:11Z" | "2012-06-28T17:53:57Z" | "2012-06-28T17:53:57Z" | MEMBER | null | http://stackoverflow.com/questions/11237557/typeerror-on-read-csv-working-in-pandas-7-error-in-8-0rc2-possible-dependen
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https://api.github.com/repos/pandas-dev/pandas/issues/1545 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1545/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1545/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1545/events | https://github.com/pandas-dev/pandas/issues/1545 | 5,328,065 | MDU6SXNzdWU1MzI4MDY1 | 1,545 | Can not pip install git+: generated.c: No such file or directory | {
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<snip>
gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -fPIC -I/local/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/src/generated.c -o build/temp.linux-i686-2.7/pandas/src/generated.o
gcc: error: pandas/src/generated.c: No such file or directory
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} | 1 | "2012-06-28T19:26:36Z" | "2012-06-28T20:28:56Z" | "2012-06-28T20:28:56Z" | NONE | null | I got the following message while running nosetests pandas:
C:\Python27\lib\site-packages\pandas\core\algorithms.py:125: RuntimeWarning: tp_compare didn't return -1 or -2 for exception
sorter = uniques.argsort()
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} | 1 | "2012-06-29T09:17:25Z" | "2012-06-29T14:54:50Z" | "2012-06-29T14:54:50Z" | NONE | null | Because of the way local variables are used in read_cvs, setting a tracing routine will fail at that point. Here is a test case with an arbitrarily simple tracing routine:
```
def trace_memory_usage(frame, event, arg):
return trace_memory_usage
import sys
sys.settrace(trace_memory_usage)
if __name__ == '__main__':
from pandas.io.parsers import read_csv
print read_csv('dummy.txt')
```
this fails with a traceback
```
Traceback (most recent call last):
File "test2.py", line 10, in <module>
print read_csv('dummy.txt')
File "/volatile/fabian/envs/p26/lib/python2.6/site-packages/pandas/io/parsers.py", line 226, in read_csv
return _read(TextParser, filepath_or_buffer, kwds2)
File "/volatile/fabian/envs/p26/lib/python2.6/site-packages/pandas/io/parsers.py", line 185, in _read
parser = cls(f, **kwds)
TypeError: __init__() got an unexpected keyword argument 'sep'
```
Some real-world utilities that are affected because of this are profile (but not cProfile) and memory_profiler.
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} | 3 | "2012-06-29T09:30:33Z" | "2012-06-29T14:40:29Z" | "2012-06-29T14:40:29Z" | CONTRIBUTOR | null | the call added in https://github.com/pydata/pandas/commit/61aac3d0a98f748fd9fd4df9df0ef4bd8849f9b5
added an import of tseries.converter from tools\plotting.py
which in turn loads matplotlib:
from matplotlib import pylab
in rc2, the following worked:
import pandas
import matplotlib
matplotlib.use('qt4agg')
in trunk:
WARNING C:\Python27\lib\site-packages\matplotlib__init__.py:921: UserWarning: This call to matplotlib.use() has no effect
because the the backend has already been chosen;
matplotlib.use() must be called _before_ pylab, matplotlib.pyplot,
or matplotlib.backends is imported for the first time.
if warn: warnings.warn(_use_error_msg)
which leads to the default matplotlib backend being used
on first inspection it would appear that the line:
from matplotlib import pylab
in tseries/converter.py is not required as pylab does not appear to be used.
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} | 1 | "2012-06-29T10:11:20Z" | "2014-07-06T07:39:56Z" | "2012-06-29T14:40:21Z" | CONTRIBUTOR | null | not sure how to craft a test for this, especially given as we may not be able to presume which backends are present on a system, nor does there seem to be an easy way to find out, but local run of the above code ceased raising the warning.
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} | 3 | "2012-06-30T04:39:12Z" | "2012-07-12T16:15:24Z" | "2012-07-12T16:14:55Z" | CONTRIBUTOR | null | Test output: http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120629-win-amd64-py3.2/pandas_test.txt
Test environment: http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120629-win-amd64-py3.2/versions.txt
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https://api.github.com/repos/pandas-dev/pandas/issues/1551 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1551/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1551/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1551/events | https://github.com/pandas-dev/pandas/issues/1551 | 5,370,036 | MDU6SXNzdWU1MzcwMDM2 | 1,551 | intersection between DatetimeIndex does not perform element-wise intersection | {
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} | 3 | "2012-06-30T20:46:32Z" | "2012-07-13T17:53:25Z" | "2012-07-12T14:42:38Z" | NONE | null | When I have two dataframes, df1 and df2, and df1 is missing an index of df2, performing the intersection only seems to compute matching slices -- but doesn't take into account missing inner-elements. For example, below, see that `2010-07-14 00:00:00` is missing from df1, but in df2. The resulting index intersection, however, contains that index.
``` python
>>> df1.index
<class 'pandas.tseries.index.DatetimeIndex'>
[2010-05-07 00:00:00, ..., 2012-06-29 00:00:00]
Length: 542, Freq: None, Timezone: None
>>> for x in df1.index[40:47]:
... print x
...
...
2010-07-06 00:00:00
2010-07-07 00:00:00
2010-07-08 00:00:00
2010-07-09 00:00:00
2010-07-12 00:00:00
2010-07-13 00:00:00
2010-07-15 00:00:00
>>> df2.index
<class 'pandas.tseries.index.DatetimeIndex'>
[2000-07-14 00:00:00, ..., 2012-06-29 00:00:00]
Length: 3010, Freq: None, Timezone: None
>>> for x in df2.index[2510:2517]:
... print x
...
...
2010-07-09 00:00:00
2010-07-12 00:00:00
2010-07-13 00:00:00
2010-07-14 00:00:00
2010-07-15 00:00:00
2010-07-16 00:00:00
2010-07-19 00:00:00
>>> x = df1.index.intersection(df2.index)
>>> for i in x[40:48]:
... print i
...
2010-07-06 00:00:00
2010-07-07 00:00:00
2010-07-08 00:00:00
2010-07-09 00:00:00
2010-07-12 00:00:00
2010-07-13 00:00:00
2010-07-14 00:00:00
2010-07-15 00:00:00
```
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} | 1 | "2012-07-01T01:11:41Z" | "2016-10-12T23:03:03Z" | "2012-07-12T15:20:54Z" | NONE | null | Due to _infer_columns() consuming 1 line when names provided:
```
read_csv(StringIO('1,2'), names=['a', 'b'], sep=None))
Traceback (most recent call last):
...
StopIteration
```
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} | 1 | "2012-07-01T10:47:35Z" | "2012-11-02T21:34:07Z" | "2012-11-02T21:34:07Z" | CONTRIBUTOR | null | The page http://pandas.pydata.org/developers.html contains the roadmap.
Please strike items through after a release has implemented a feature stated.
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} | 3 | "2012-07-01T10:48:39Z" | "2012-09-11T19:36:44Z" | "2012-09-11T19:36:44Z" | CONTRIBUTOR | null | http://pandas.pydata.org/pandas-docs/stable/io.html#io-dialect
to
http://docs.python.org/library/csv.html#csv.Dialect
using intersphinx
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http://pandas.pydata.org/pandas-docs/dev/whatsnew.html
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$ python3 setup.py build
$ python3 setup.py install
Everything appears to run normally, but when I run
import pandas
in IDLE, it tells me there is no pandas module. What could be the problem.
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} | 1 | "2012-07-02T16:51:51Z" | "2012-07-11T23:45:32Z" | "2012-07-11T23:45:32Z" | NONE | null | MultiIndex does not behave as expected anymore on 0.8.0 (I'm on Python 3.2.3). In particular, I get a strange error when I pass in an np.int64 object (i.e. the same type as the first level of the index). I hope the following testcase is clear, tried to stick close to the MultiIndex example in the docs.
Code:
```
from pandas import Series
import numpy as np
arrays = [[0, 0, 1, 1, 2, 2, 3, 3],
[0, 1, 0, 1, 0, 1, 0, 1]]
s = Series(np.random.randn(8), index=arrays)
print(s[0]) # Works as expected
print(s[np.int64(0)]) # Fails strangely
```
Error:
```
In [6]: print(s[0]) # Works as expected
0 -0.420831
1 -1.818968
In [7]: print(s[np.int64(0)]) # Fails strangely
-1.81896761059---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-7-29c87a86dbe4> in <module>()
----> 1 print(s[np.int64(0)]) # Fails strangely
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
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```
Hi guys,
I am using the new release 0.8.0. Below is what I observed. My numpy
version if 1.6.2. Please help. thx.
In [3]: from pandas import Series
In [4]: from datetime import datetime
In [5]: s = Series().set_value(datetime(2001,1,1),
1.).set_value(datetime(2001,1,2),float('nan'))
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/yihao/YH_Backups/YH_Work/lyh/gss/python/gss_main/<ipython-input-5-5c2e9c89865a>
in <module>()
----> 1 s = Series().set_value(datetime(2001,1,1),
1.).set_value(datetime(2001,1,2),float('nan'))
/usr/local/lib/python2.7/dist-packages/pandas/core/series.pyc in
set_value(self, label, value)
741 return self
742 except KeyError:
--> 743 new_index = np.concatenate([self.index.values, [label]])
744 new_values = np.concatenate([self.values, [value]])
745 return Series(new_values, index=new_index, name=self.name)
TypeError: invalid type promotion
```
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} | 0 | "2012-07-03T14:09:57Z" | "2012-07-12T00:04:27Z" | "2012-07-12T00:04:27Z" | MEMBER | null | This throws an exception
http://stackoverflow.com/questions/11306167/pandas-slicing-on-a-timeseries-seems-inconsistent-with-list-slicing
```
import pandas as pd
import datetime
times = [datetime.datetime(2000, 1, 1) + datetime.timedelta(minutes=i) for i in range(1000000)]
df = pd.TimeSeries(range(1000000), times)
df.ix[datetime.datetime(1900,1,1):datetime.datetime(2100,1,1)]
```
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} | 1 | "2012-07-03T14:37:18Z" | "2012-07-13T01:30:12Z" | "2012-07-13T01:30:12Z" | CONTRIBUTOR | null | from pydata mailing list:
just wondering if there is a more convenient way to convert the new Period /
PeriodIndex classes to datetime objects. As well as converting into lists of
tuples to insert into sql databases.
For example:
import pandas as pd
prng = pd.period_range('2012-01', periods = 12, freq = 'M')
prd = pd.Period.now('B')
ts = pd.Series(range(12), index = prng)
# could we have a shortcut to_pydatetime method on the Period class?
dt = prd.to_timestamp().to_pydatetime() # pandas 0.8 way
# desired: dt = prd.to_pydatetime()
# ditto for PeriodIndex
dtrng = prng.to_timestamp().to_pydatetime() # pandas 0.8 way
# desired: dtrng = prng.to_pydatetime()
# converting time series to list of (datetime, value) tuples for inserting into
# sql database. This is what .tolist() returned in the old scikits.timeseries
# module
sql_tuples = zip(ts.index.to_timestamp().to_pydatetime(), ts.tolist()) # pandas 0.8 way
# desired: sql_tuples = ts.tolist() # or ts.to_sqltuples(), etc
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} | 0 | "2012-07-03T18:35:56Z" | "2014-07-10T09:20:29Z" | "2012-07-11T15:50:13Z" | CONTRIBUTOR | null | dict or list of string #1559
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} | 0 | "2012-07-03T19:13:42Z" | "2014-06-29T20:34:22Z" | "2012-07-11T17:12:02Z" | CONTRIBUTOR | null | If by is specified, boxplot was ignoring the given axes.
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} | 0 | "2012-07-03T20:56:22Z" | "2014-06-19T00:11:37Z" | "2012-07-11T17:48:52Z" | CONTRIBUTOR | null | http://pandasplotting.blogspot.com/2012/07/radviz.html
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```
In [1]: import pandas
In [3]: import StringIO
In [4]: pandas.read_csv(StringIO.StringIO("a,b\n0,NA\n"), na_values={}, index_col=0)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/bbloniarz/src/pandas-git/<ipython-input-4-f407e87fa8a9> in <module>()
----> 1 pandas.read_csv(StringIO.StringIO("a,b\n0,NA\n"), na_values={}, index_col=0)
/home/bbloniarz/src/pandas-git/pandas/io/parsers.py in read_csv(filepath_or_buffer, sep, dialect, header, index_col, names, skiprows, na_values, thousands, comment, parse_dates, keep_date_col, dayfirst, date_parser, nrows, iterator, chunksize, skip_footer, converters, verbose, delimiter, encoding, squeeze)
234 kwds['delimiter'] = sep
235
--> 236 return _read(TextParser, filepath_or_buffer, kwds)
237
238 @Appender(_read_table_doc)
/home/bbloniarz/src/pandas-git/pandas/io/parsers.py in _read(cls, filepath_or_buffer, kwds)
189 return parser
190
--> 191 return parser.get_chunk()
192
193 @Appender(_read_csv_doc)
/home/bbloniarz/src/pandas-git/pandas/io/parsers.py in get_chunk(self, rows)
758 if not self._has_complex_date_col and self.index_col is not None:
759 index = self._get_simple_index(zipped_content)
--> 760 index = self._agg_index(index)
761 else:
762 index = Index(np.arange(len(content)))
/home/bbloniarz/src/pandas-git/pandas/io/parsers.py in _agg_index(self, index, try_parse_dates)
878 if try_parse_dates and self._should_parse_dates(self.index_col):
879 index = self._conv_date(index)
--> 880 index, na_count = _convert_types(index, self.na_values)
881 index = Index(index, name=self.index_name)
882 if self.verbose and na_count:
/home/bbloniarz/src/pandas-git/pandas/io/parsers.py in _convert_types(values, na_values)
1065 result = lib.maybe_convert_numeric(values, na_values, False)
1066 except Exception:
-> 1067 na_count = lib.sanitize_objects(values, na_values, False)
1068 result = values
1069
/home/bbloniarz/src/pandas-git/pandas/lib.so in pandas.lib.sanitize_objects (pandas/src/tseries.c:96732)()
TypeError: Argument 'na_values' has incorrect type (expected set, got dict)
```
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} | 1 | "2012-07-04T16:08:40Z" | "2012-07-11T23:08:45Z" | "2012-07-11T23:08:29Z" | CONTRIBUTOR | null | pandas.Series([10.0], index=pandas.MultiIndex.from_tuples([(2, 3)])).sum(level=0) #level=None works, series length > 1 works
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} | 3 | "2012-07-04T16:51:48Z" | "2012-07-11T17:05:19Z" | "2012-07-11T16:02:52Z" | NONE | null | I asked for a way to do it on http://stackoverflow.com/questions/11264307/adding-levels-to-multiindex-removing-without-losing
similar to reset_index I want to be able to drop a level from a MultiIndex that then enters the columns again. similarly I want to be able to move a column to the MultiIndex.
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https://api.github.com/repos/pandas-dev/pandas/issues/1570 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1570/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1570/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1570/events | https://github.com/pandas-dev/pandas/issues/1570 | 5,431,637 | MDU6SXNzdWU1NDMxNjM3 | 1,570 | monthly periods prior to dec-1969 off by one year | {
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} | 0 | "2012-07-04T17:22:04Z" | "2016-10-12T23:03:04Z" | "2012-07-07T12:33:11Z" | NONE | null | <pre>
# version 0.8.0 of pandas
# version 1.6.1 of numpy
# windows XP 32-bit
>>> import pandas as pd
>>> pd.Period('1969-12', 'M')
Period('Dec-1969', 'M')
>>> pd.Period('1969-11', 'M')
Period('Nov-1970', 'M')
</pre>
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https://api.github.com/repos/pandas-dev/pandas/issues/1571 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1571/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1571/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1571/events | https://github.com/pandas-dev/pandas/issues/1571 | 5,460,694 | MDU6SXNzdWU1NDYwNjk0 | 1,571 | to_datetime poor performance parsing string datetimes | {
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} | 9 | "2012-07-06T08:22:42Z" | "2012-07-13T11:01:05Z" | "2012-07-13T02:47:43Z" | NONE | null | Hi,
I want to convert to datetime64 a Series that contains datetimes as strings. The format is '%Y-%m-%d %H:%M:%S' ('2012-07-06 10:05:58', for instance).
Casting the strings array into a datetime64 array in numpy (or using Series.astype("datetime64")) is fast, but it transforms the datetimes according to the local timezone, which is not the behavior I want in this case. Pandas to_datetime function does the parsing right, but it is much slower.
However, it is also possible to do the parsing right and fast with numpy by appending the "+0000" timezone suffix to every string before parsing/casting to datetime64. So I wonder, is there any reason why to_datetime() runs much slower than this approach?
Thanks and regards.
Some sample code to illustrate the issue:
``` python
import os
import time
import numpy as np
from datetime import datetime, timedelta
from pandas import DatetimeIndex, Series, to_datetime
from pandas.tseries.offsets import Minute
DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S"
# Decorator to execute code in a faked GMT timezone
def GMT_Timezone(func):
def wrapper(*args, **kwargs):
# Set timezone to GMT
previous_tz = time.tzname[0]
os.environ['TZ'] = 'GMT'
time.tzset()
# Execute function
result = func(*args, **kwargs)
# Set timezone to previous state
os.environ['TZ'] = previous_tz
time.tzset()
return result
return wrapper
def generate_test_data():
start_date = datetime(2012, 1, 1)
end_date = datetime(2012, 1, 31)
minutes = DatetimeIndex(start=start_date, end=end_date, freq=Minute())
str_minutes = Series(minutes.map(lambda d: d.strftime(DATETIME_FORMAT)))
return str_minutes
def simple_casting(data):
return data.astype("datetime64")
def shifting_afterwards(data):
casted = data.astype("datetime64")
shifted = DatetimeIndex(casted) - timedelta(seconds=time.altzone)
return Series(shifted)
def concat_gmt_tz(data):
data = np.add(data, "+0000")
return data.astype("datetime64")
def using_to_datetime(data):
return to_datetime(data)
@GMT_Timezone
def faking_tz(data):
return data.astype("datetime64")
if __name__ == '__main__':
test_data = generate_test_data()
# Some aproaches to parse the datetime string
using_to_dt = using_to_datetime(test_data)
simple = simple_casting(test_data)
shifted = shifting_afterwards(test_data)
faked_tz = faking_tz(test_data)
concat_tz = concat_gmt_tz(test_data)
# Simple casting does not work. During the process, numpy converts the date
# into GMT according to the local timezone.
assert not (simple == using_to_dt).all(), "Fails if run from GMT timezone."
# Shifting after casting does not work either, the problem being that
# we should shift using a different timezone depending on the date we are
# parsing (summer vs winter) -> more complex + error-prone + slower
assert not (shifted == using_to_dt).all()
# Faking the timezone to be GMT avoids datetime transformations, so it seems
# to work, although since we are messing with environmental variables
# it gets risky (multithreading, other side-effects?)
assert (faked_tz == using_to_dt).all()
assert (concat_tz == using_to_dt).all()
# Now to performance
from timeit import Timer
ITERATIONS = 5
t = Timer("using_to_datetime(test_data)",
"from __main__ import generate_test_data, using_to_datetime;" + \
"test_data = generate_test_data()")
t_using_to_dt = t.timeit(ITERATIONS) / ITERATIONS
t = Timer("faking_tz(test_data)",
"from __main__ import generate_test_data, faking_tz;" + \
"test_data = generate_test_data()")
t_faking_tz = t.timeit(ITERATIONS) / ITERATIONS
t = Timer("concat_gmt_tz(test_data)",
"from __main__ import generate_test_data, concat_gmt_tz;" + \
"test_data = generate_test_data()")
t_concat_tz = t.timeit(ITERATIONS) / ITERATIONS
print "to_datetime():", t_using_to_dt
print "faking tz:", t_faking_tz
# to_datetime() ~90 times slower than .astype("datetime64")
print "Ratio:", t_using_to_dt / t_faking_tz
print
print "to_datetime():", t_using_to_dt
print "concat tz:", t_concat_tz
# to_datetime() ~270 times slower than concat tz + .astype("datetime64")
print "Ratio:", t_using_to_dt / t_concat_tz
```
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} | 1 | "2012-07-06T12:39:44Z" | "2012-07-11T23:55:40Z" | "2012-07-11T23:55:08Z" | NONE | null | If I have a DataFrame `df` with homogenous type, then `DataFrame(df)` returns a view on the original dataframe. `DataFrame(df, dtype=current_type)` should be identical; but, instead, it makes an unnecessary copy.
```
>>> import pandas
>>> pandas.__version__
'0.8.0'
>>> df = pandas.DataFrame([[1, 2]])
>>> df
0 1
0 1 2
>>> df[0].dtype
dtype('int64')
>>> view = pandas.DataFrame(df)
>>> view
0 1
0 1 2
>>> view[0][0] = 100
>>> view
0 1
0 100 2
>>> df
0 1
0 100 2
>>> should_be_view = pandas.DataFrame(df, dtype=df[0].dtype)
>>> should_be_view
0 1
0 100 2
>>> should_be_view[0][0] = 99
>>> should_be_view
0 1
0 99 2
>>> df
0 1
0 100 2
```
The same thing seems to happen in the input is an ndarray -- `DataFrame(arr)` returns a view, `DataFrame(arr, dtype=arr.dtype)` returns a copy.
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} | 9 | "2012-07-06T13:33:16Z" | "2017-11-28T08:59:05Z" | "2017-11-28T08:59:05Z" | CONTRIBUTOR | null | pandas.DatetimeIndex(start=datetime.datetime(2012, 2, 28), freq=pandas.offsets.YearEnd(month=2), periods=10)
works, but not
pandas.DatetimeIndex(start=datetime.datetime(2012, 2, 20), freq=pandas.offsets.MonthEnd(day=20), periods=10)
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https://api.github.com/repos/pandas-dev/pandas/issues/1574 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1574/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1574/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1574/events | https://github.com/pandas-dev/pandas/issues/1574 | 5,469,225 | MDU6SXNzdWU1NDY5MjI1 | 1,574 | Negative indexing into Series in 0.8.0 | {
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} | [] | closed | false | null | [] | null | 0 | "2012-07-06T16:54:09Z" | "2012-07-06T16:58:10Z" | "2012-07-06T16:58:10Z" | NONE | null | Something changed between 0.5.0 and 0.8.0 that has disallowed using negative indexes as you would into a sequence. For example:
In [3]: ser = pandas.Series( ['A','B','C'] )
```
In [4]: ser
Out[4]:
0 A
1 B
2 C
In [5]: ser[1]
Out[5]: 'B'
In [6]: ser[-1]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-6-3cbe0b873a9e> in <module>()
----> 1 ser[-1]
c:\python27\lib\site-packages\pandas\core\series.pyc in __getitem__(self, key)
427 def __getitem__(self, key):
428 try:
--> 429 return self.index.get_value(self, key)
430 except InvalidIndexError:
431 pass
c:\python27\lib\site-packages\pandas\core\index.pyc in get_value(self, series, k
ey)
639 """
640 try:
--> 641 return self._engine.get_value(series, key)
642 except KeyError, e1:
643 if len(self) > 0 and self.inferred_type == 'integer':
c:\python27\lib\site-packages\pandas\lib.pyd in pandas.lib.IndexEngine.get_value
(pandas\src\tseries.c:104257)()
c:\python27\lib\site-packages\pandas\lib.pyd in pandas.lib.IndexEngine.get_value
(pandas\src\tseries.c:104085)()
c:\python27\lib\site-packages\pandas\lib.pyd in pandas.lib.IndexEngine.get_loc (
pandas\src\tseries.c:104794)()
c:\python27\lib\site-packages\pandas\lib.pyd in pandas.lib.Int64HashTable.get_it
em (pandas\src\tseries.c:15561)()
c:\python27\lib\site-packages\pandas\lib.pyd in pandas.lib.Int64HashTable.get_it
em (pandas\src\tseries.c:15515)()
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1575 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1575/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1575/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1575/events | https://github.com/pandas-dev/pandas/issues/1575 | 5,469,227 | MDU6SXNzdWU1NDY5MjI3 | 1,575 | Negative indexing into Series in 0.8.0 | {
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} | [] | closed | false | null | [] | null | 2 | "2012-07-06T16:54:10Z" | "2012-07-06T18:35:21Z" | "2012-07-06T18:35:21Z" | NONE | null | Something changed between 0.5.0 and 0.8.0 that has disallowed using negative indexes as you would into a sequence. For example:
In [3]: ser = pandas.Series( ['A','B','C'] )
```
In [4]: ser
Out[4]:
0 A
1 B
2 C
In [5]: ser[1]
Out[5]: 'B'
In [6]: ser[-1]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-6-3cbe0b873a9e> in <module>()
----> 1 ser[-1]
c:\python27\lib\site-packages\pandas\core\series.pyc in __getitem__(self, key)
427 def __getitem__(self, key):
428 try:
--> 429 return self.index.get_value(self, key)
430 except InvalidIndexError:
431 pass
c:\python27\lib\site-packages\pandas\core\index.pyc in get_value(self, series, k
ey)
639 """
640 try:
--> 641 return self._engine.get_value(series, key)
642 except KeyError, e1:
643 if len(self) > 0 and self.inferred_type == 'integer':
c:\python27\lib\site-packages\pandas\lib.pyd in pandas.lib.IndexEngine.get_value
(pandas\src\tseries.c:104257)()
c:\python27\lib\site-packages\pandas\lib.pyd in pandas.lib.IndexEngine.get_value
(pandas\src\tseries.c:104085)()
c:\python27\lib\site-packages\pandas\lib.pyd in pandas.lib.IndexEngine.get_loc (
pandas\src\tseries.c:104794)()
c:\python27\lib\site-packages\pandas\lib.pyd in pandas.lib.Int64HashTable.get_it
em (pandas\src\tseries.c:15561)()
c:\python27\lib\site-packages\pandas\lib.pyd in pandas.lib.Int64HashTable.get_it
em (pandas\src\tseries.c:15515)()
```
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} | 0 | "2012-07-06T21:53:23Z" | "2012-07-11T22:29:32Z" | "2012-07-11T22:29:32Z" | CONTRIBUTOR | null | In [18]: idx
Out[18]:
MultiIndex
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(0.34279706720294867, -0.7021022763765951)
(1.4611933034915323, -0.9892166873678242)
(-1.8708717491582252, 1.0296013286162102)
In [19]: len(idx.tolist())
Out[19]: 0
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} | 0 | "2012-07-06T22:28:26Z" | "2014-06-13T17:28:33Z" | "2012-07-11T16:02:42Z" | CONTRIBUTOR | null | #1569
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```
>>> import pandas
>>> pandas.__version__
'0.8.0'
>>> s = pandas.Series([1, 2, 3], index=[4, 5, 6], name="NAME")
>>> s
4 1
5 2
6 3
Name: NAME
>>> pandas.Series(s)
4 1
5 2
6 3
```
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} | 8 | "2012-07-07T16:25:13Z" | "2013-09-13T12:34:28Z" | "2013-09-13T12:34:28Z" | CONTRIBUTOR | null | From pystatsmodels mailing list:
I am trying to use the plot method of series in pandas 8.0 to graph minutely data that is several years long. This can take 30 seconds to display because of the processing of the datetime index. I was thinking instead of using matplotlibs plot_date function for large series which seems to be a lot faster (even though its datetick functionality isnt as nice as pandas)The problem is that it uses an index a vector of floats representing fractional days since 010101. Is there a easy and fast way to convert a datetimeindex into the appropriate numpy array of floats? Maybe by hacking into the numpy array of datetime64s?
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} | 7 | "2012-07-07T20:22:59Z" | "2012-07-12T22:34:57Z" | "2012-07-12T22:34:57Z" | NONE | null | Hi I was feeding pandas (0.8.0rc2) with dates and found some errors. The
amounts from following csv file are grouped by date, but the sums for some days
are wrong:
2011-02-02 resulting: 0 correct: 40
2011-08-21 resulting: 3 correct: 133
2012-10-22 resulting: 157 correct: 27
This is the script I am running:
``` python
import sys
import pandas
f=pandas.read_csv(sys.stdin, index_col=1, parse_dates=True)
f.sort()
f=f.resample('D', how='sum')
f['amount'] = f['amount'].fillna(0)
f.to_csv(sys.stdout)
```
Maybe I'm using the time series methods in a wrong way.
The file with data is not too long, it is hosted here: https://raw.github.com/danse/sparkles/master/cleaned.csv
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} | 0 | "2012-07-08T16:38:23Z" | "2012-07-21T14:01:59Z" | "2012-07-21T14:01:59Z" | MEMBER | null | inspired by: http://stackoverflow.com/questions/11337437/convert-object-to-daterange/11384801#11384801
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} | 0 | "2012-07-08T16:44:45Z" | "2012-07-11T20:52:56Z" | "2012-07-11T20:52:56Z" | MEMBER | null | Case 1 (i'll look for the other)
http://stackoverflow.com/questions/11329611/python-pandas-to-panel-from-dataframe-returns-weird-numbers-for-binary-variab
pm10 = pds.read_csv('pm10.csv', index_col = [0,1], parse_dates=True)
panel_exog = pm10.to_panel()['pass_ind']
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https://api.github.com/repos/pandas-dev/pandas/issues/1583 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1583/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1583/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1583/events | https://github.com/pandas-dev/pandas/issues/1583 | 5,488,292 | MDU6SXNzdWU1NDg4Mjky | 1,583 | DataFrame.to_csv with no "leading comma" option | {
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} | 0 | "2012-07-08T18:52:59Z" | "2012-09-18T15:31:28Z" | "2012-09-18T15:31:28Z" | MEMBER | null | cf http://stackoverflow.com/questions/11105728/pivoting-a-dataframe-in-pandas-for-output-to-csv
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https://api.github.com/repos/pandas-dev/pandas/issues/1584 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1584/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1584/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1584/events | https://github.com/pandas-dev/pandas/issues/1584 | 5,488,501 | MDU6SXNzdWU1NDg4NTAx | 1,584 | Add option to disable adjustment factor in EWMA | {
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} | 0 | "2012-07-08T22:35:49Z" | "2012-07-11T22:25:00Z" | "2012-07-11T22:25:00Z" | NONE | null | Example:
``` python
sdf = pandas.SparseDataFrame(index=[0, 1, 2], columns=['a', 'b','c'])
sdf[['a', 'b']]
/usr/lib64/python2.7/site-packages/pandas/sparse/frame.pyc in __getitem__(self, item)
321 try:
322 # unsure about how kludgy this is
--> 323 s = self._series[item]
324 s.name = item
325 return s
TypeError: unhashable type: 'list'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1586 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1586/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1586/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1586/events | https://github.com/pandas-dev/pandas/issues/1586 | 5,490,062 | MDU6SXNzdWU1NDkwMDYy | 1,586 | Unordered duplicate index doesn't yield scalar value | {
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} | 1 | "2012-07-08T23:41:04Z" | "2012-07-11T20:59:59Z" | "2012-07-11T20:59:59Z" | MEMBER | null | ```
In [13]: obj = Series(range(5), index=['c', 'a', 'a', 'b', 'b'])
In [14]: obj['c']
Out[14]: c 0
```
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} | 1 | "2012-07-09T14:24:45Z" | "2016-10-12T23:03:04Z" | "2012-07-13T16:49:01Z" | NONE | null | I have a problem for time series with multiple time stamps.
This is the data file:
06/01/04 10:11:46 1.2735 1.2737
06/01/04 10:11:47 1.2735 1.2737
06/01/04 10:11:48 1.2736 1.2738
06/01/04 10:11:50 1.2736 1.2738
06/01/04 10:11:50 1.2735 1.2737
raw_data = read_csv('bug_data',header=None, parse_dates=[[0,1]], sep=' ', index_col=0, dayfirst=True)
oldest=raw_data.index[-1]
then:
raw_data[:oldest]
fails with TypeError: unsupported operand type(s) for +: 'slice' and 'int'
as does
raw_data.ix[:oldest]
raw_data[oldest:]
does work, whereas
raw_data.ix[oldest:]
fails with IndexError: invalid slice
Cutting out the last entry things work again, e.g.
raw_data[:-1].ix[oldest:]
raw_data[:-1].ix[:oldest]
or
raw_data[:-1][oldest:]
raw_data[:-1][:oldest]
a (or the ?) get_slice-routines return a slice in these cases.
get_loc gives back a slice when there are non-unique indexes, not sure if
that routine(s) should be fixed or slice_locs
my workaround (?) is catching this in slice_loc but I guess the get_loc's should be fixed instead
--- /home/jgarcke/Prog/Packages/pandas-git/pandas/core/index.py
2012-06-26 14:10:12.107724998 +0200
+++ index.py 2012-06-26 16:47:19.917653332 +0200
@@ -1039,7 +1039,11 @@
beg_slice = 0
else:
try:
- beg_slice = self.get_loc(start)
- if isinstance(self.get_loc(start), slice):
- beg_slice = self.get_loc(start).start
- else:
- beg_slice = self.get_loc(start)
except KeyError:
if self.is_monotonic:
beg_slice = self.searchsorted(start, side='left')
@@ -1050,7 +1054,11 @@
end_slice = len(self)
else:
try:
- end_slice = self.get_loc(end) + 1
- if isinstance(self.get_loc(end), slice):
- end_slice = self.get_loc(end).stop + 1
- else:
- end_slice = self.get_loc(end) + 1
except KeyError:
if self.is_monotonic:
end_slice = self.searchsorted(end, side='right')
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} | 0 | "2012-07-09T19:47:42Z" | "2012-07-09T20:19:52Z" | "2012-07-09T20:19:52Z" | MEMBER | null | This blows up inside the Cython binning method:
```
dates = date_range('4/16/2012 20:00', periods=5000000, freq='s')
ts = Series(randn(len(dates)), index=dates)
ts.resample('d')
```
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} | 0 | "2012-07-09T21:26:43Z" | "2012-07-11T21:50:46Z" | "2012-07-11T21:50:46Z" | MEMBER | null | int64 should alias to generic, or perhaps generate a specialized int64 version
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} | 2 | "2012-07-09T22:09:39Z" | "2013-12-04T00:43:09Z" | "2012-07-11T17:09:52Z" | NONE | null | It seems unless a DataFrame is completely fresh and it's index hasn't been manipulated in anyway ( such as being a result of a group by ) then set_index() doesn't quite work.
```
In [1]: import pandas
In [2]: pandas.__version__
Out[2]: '0.8.0'
In [3]: df = pandas.DataFrame([ {'val':0,'key':'a'},{'val':1,'key':'b'},{'val':2,'key':'c'} ] )
In [4]: df
Out[4]:
key val
0 a 0
1 b 1
2 c 2
In [5]: df.set_index('key')
Out[5]:
val
key
a 0
b 1
c 2
In [6]: df2 = df.select(lambda indx:indx>=1)
In [8]: df2
Out[8]:
key val
1 b 1
2 c 2
```
so far so good
```
In [9]: df2.set_index('key')
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-9-366937e9485c> in <module>()
----> 1 df2.set_index('key')
c:\python27\lib\site-packages\pandas\core\frame.pyc in set_index(self, keys, dro
p, inplace, verify_integrity)
2306 arrays.append(level)
2307
-> 2308 index = MultiIndex.from_arrays(arrays, names=keys)
2309
2310 if verify_integrity and not index.is_unique:
c:\python27\lib\site-packages\pandas\core\index.pyc in from_arrays(cls, arrays,
sortorder, names)
1504 if len(arrays) == 1:
1505 name = None if names is None else names[0]
-> 1506 return Index(arrays[0], name=name)
1507
1508 cats = [Categorical.from_array(arr) for arr in arrays]
c:\python27\lib\site-packages\pandas\core\index.pyc in __new__(cls, data, dtype,
copy, name)
102 if dtype is None:
103 if (lib.is_datetime_array(subarr)
--> 104 or lib.is_datetime64_array(subarr)
105 or lib.is_timestamp_array(subarr)):
106 from pandas.tseries.index import DatetimeIndex
c:\python27\lib\site-packages\pandas\lib.pyd in pandas.lib.is_datetime64_array (
pandas\src\tseries.c:90487)()
c:\python27\lib\site-packages\pandas\core\series.pyc in __getitem__(self, key)
427 def __getitem__(self, key):
428 try:
--> 429 return self.index.get_value(self, key)
430 except InvalidIndexError:
431 pass
c:\python27\lib\site-packages\pandas\core\index.pyc in get_value(self, series, k
ey)
639 """
640 try:
--> 641 return self._engine.get_value(series, key)
642 except KeyError, e1:
643 if len(self) > 0 and self.inferred_type == 'integer':
c:\python27\lib\site-packages\pandas\lib.pyd in pandas.lib.IndexEngine.get_value
(pandas\src\tseries.c:104257)()
c:\python27\lib\site-packages\pandas\lib.pyd in pandas.lib.IndexEngine.get_value
(pandas\src\tseries.c:104085)()
c:\python27\lib\site-packages\pandas\lib.pyd in pandas.lib.IndexEngine.get_loc (
pandas\src\tseries.c:104794)()
c:\python27\lib\site-packages\pandas\lib.pyd in pandas.lib.Int64HashTable.get_it
em (pandas\src\tseries.c:15561)()
c:\python27\lib\site-packages\pandas\lib.pyd in pandas.lib.Int64HashTable.get_it
em (pandas\src\tseries.c:15515)()
KeyError: 0L
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1591 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1591/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1591/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1591/events | https://github.com/pandas-dev/pandas/issues/1591 | 5,512,106 | MDU6SXNzdWU1NTEyMTA2 | 1,591 | Cannot resample with 'D' and tz aware timeseries | {
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} | 1 | "2012-07-09T23:29:55Z" | "2012-07-11T21:12:50Z" | "2012-07-11T21:12:40Z" | NONE | null | ``` python
from pandas import *
dr = date_range(start='2012-5-1', end='2012-6-1')
ts = Series(range(len(dr)), dr)
ts_utc = ts.tz_localize('UTC')
ts_local = ts_utc.tz_convert('America/Los_Angeles')
ts_local.resample('D')
```
yields..
``` traceback
/env/lib/python2.7/site-packages/pandas/tseries/resample.pyc in _get_time_bins(self, axis)
97 # a little hack
98 trimmed = False
---> 99 if len(binner) > 2 and binner[-2] == axis[-1] and self.closed == 'right':
100 binner = binner[:-1]
101 trimmed = True
/env/lib/python2.7/site-packages/pandas/lib.so in pandas.lib._Timestamp.__richcmp__ (pandas/src/tseries.c:33751)()
Exception: Cannot compare tz-naive and tz-aware timestamps
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1592 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1592/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1592/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1592/events | https://github.com/pandas-dev/pandas/pull/1592 | 5,514,465 | MDExOlB1bGxSZXF1ZXN0MTc1MjY3NQ== | 1,592 | BUG: DataFrame.set_index bug | {
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} | 0 | "2012-07-10T02:28:40Z" | "2014-06-15T10:47:23Z" | "2012-07-11T17:09:23Z" | CONTRIBUTOR | null | calls .values as necessary when getting Index components
#1590
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} | 2 | "2012-07-10T02:46:45Z" | "2012-07-10T14:16:48Z" | "2012-07-10T14:16:43Z" | NONE | null | Minimal example:
```
In [10]: df1 = pandas.DataFrame({'x': ['a', 'b'], 'y': ['A', 'A'], 'z': [1,1]}).set_index(['x', 'y'])
In [11]: df2 = pandas.DataFrame({'x': ['a'], 'y': ['B'], 'z': [1]}).set_index(['x', 'y'])
In [12]: df3 = df1.append(df2)
In [13]: df3
Out[13]:
z
x y
a A 1
b A 1
a B 1
In [14]: df3.drop('a')
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/home/me/<ipython-input-14-d69e3ae69a9d> in <module>()
----> 1 df3.drop('a')
/home/me/.local/lib64/python3.2/site-packages/pandas-0.8.1.dev_e1ff90c-py3.2-linux-x86_64.egg/pandas/core/generic.py in drop(self, labels, axis, level)
298 new_axis = axis.drop(labels, level=level)
299 else:
--> 300 new_axis = axis.drop(labels)
301
302 return self.reindex(**{axis_name : new_axis})
/home/me/.local/lib64/python3.2/site-packages/pandas-0.8.1.dev_e1ff90c-py3.2-linux-x86_64.egg/pandas/core/index.py in drop(self, labels, level)
1671 inds.append(loc)
1672 else:
-> 1673 inds.extend(list(range(loc.start, loc.stop)))
1674
1675 return self.delete(inds)
AttributeError: 'numpy.ndarray' object has no attribute 'start'
```
There isn't a duplicate index, so I think it should work.
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} | 12 | "2012-07-10T12:05:52Z" | "2013-12-04T00:43:09Z" | "2012-07-19T19:10:01Z" | NONE | null | I am doing an exemple from http://pandas.pydata.org/pandas-docs/stable/timeseries.html
rng = pandas.period_range('1/1/2011', periods=500, freq='M')
---
DateParseError Traceback (most recent call last)
/home/users/pawel/AGH/mgr/dane/gazowe/<ipython-input-4-a54f4f8b58b5> in <module>()
----> 1 rng = pandas.period_range('1/1/2011', periods=500, freq='M')
/usr/lib64/python2.7/site-packages/pandas/tseries/period.pyc in period_range(start, end, periods, freq)
1064 """
1065 return PeriodIndex(start=start, end=end, periods=periods,
-> 1066 freq=freq)
1067
1068 def _period_rule_to_timestamp_rule(freq, how='end'):
/usr/lib64/python2.7/site-packages/pandas/tseries/period.pyc in **new**(cls, data, ordinal, freq, start, end, periods, copy, name, year, month, quarter, day, hour, minute, second)
533 fields = [year, month, quarter, day, hour, minute, second]
534 data, freq = cls._generate_range(start, end, periods,
--> 535 freq, fields)
536 else:
537 ordinal, freq = cls._from_arraylike(data, freq)
/usr/lib64/python2.7/site-packages/pandas/tseries/period.pyc in _generate_range(cls, start, end, periods, freq, fields)
551 raise ValueError('Can either instantiate from fields '
552 'or endpoints, but not both')
--> 553 subarr, freq = _get_ordinal_range(start, end, periods, freq)
554 elif field_count > 0:
555 y, mth, q, d, h, minute, s = fields
/usr/lib64/python2.7/site-packages/pandas/tseries/period.pyc in _get_ordinal_range(start, end, periods, freq)
925
926 if start is not None:
--> 927 start = Period(start, freq)
928 if end is not None:
929 end = Period(end, freq)
/usr/lib64/python2.7/site-packages/pandas/tseries/period.pyc in **init**(self, value, freq, ordinal, year, month, quarter, day, hour, minute, second)
98 value = str(value)
99
--> 100 dt, freq = _get_date_and_freq(value, freq)
101
102 elif isinstance(value, datetime):
/usr/lib64/python2.7/site-packages/pandas/tseries/period.pyc in _get_date_and_freq(value, freq)
382 def _get_date_and_freq(value, freq):
383 value = value.upper()
--> 384 dt, _, reso = parse_time_string(value, freq)
385
386 if freq is None:
/usr/lib64/python2.7/site-packages/pandas/tseries/tools.pyc in parse_time_string(arg, freq)
217 return ret, parsed, reso # datetime, resolution
218 except Exception, e:
--> 219 raise DateParseError(e)
220
221 def _try_parse_monthly(arg):
DateParseError: 'str' object has no attribute 'read'
Works fine with python-3.2.
python-2.7.3, numpy-1.6.2, scipy-0.10.1, pandas 0.8.0
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} | 1 | "2012-07-10T15:47:56Z" | "2012-07-11T15:45:59Z" | "2012-07-11T15:45:59Z" | NONE | null | For example:
A B
0 1 NaN
1 1 NaN
2 1 NaN
3 NaN 1
4 NaN 1
5 NaN 1
In DataFrame.corr() it only checks for valid.all(). It needs to check for valid.any() also.
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} | 2 | "2012-07-10T16:08:49Z" | "2012-07-11T22:16:42Z" | "2012-07-11T22:16:42Z" | CONTRIBUTOR | null | I would suggest that the observed behaviour is not the expected when passing a how=sequence of functions in resample.
I would like to get a dataframe back where each column corresponds to applying each function to each time interval.
If you insert a value into the series, for example at 2012-06-12 01:01:00, you get that behaviour.
In [54]: stamps = [p for p in pd.DatetimeIndex(freq='60Min',start=pd.datetime(2012,6,12),periods=4)]
In [55]: stamps
Out[55]:
[Timestamp: 2012-06-12 00:00:00,
Timestamp: 2012-06-12 01:00:00,
Timestamp: 2012-06-12 02:00:00,
Timestamp: 2012-06-12 03:00:00]
In [56]: series = pd.TimeSeries([1,2,3,4], index=stamps)
In [57]: series
Out[57]:
2012-06-12 00:00:00 1
2012-06-12 01:00:00 2
2012-06-12 02:00:00 3
2012-06-12 03:00:00 4
In [58]: series.resample( '20Min', how=(np.mean,np.sum) )
Out[58]:
2012-06-12 00:00:00 1
2012-06-12 00:20:00 NaN
2012-06-12 00:40:00 NaN
2012-06-12 01:00:00 2
2012-06-12 01:20:00 NaN
2012-06-12 01:40:00 NaN
2012-06-12 02:00:00 3
2012-06-12 02:20:00 NaN
2012-06-12 02:40:00 NaN
2012-06-12 03:00:00 4
Freq: 20T
In [59]:
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if empty arrays are passed in, kendalltau leads to max recursion depth exceeded in mergesort, so must check valid.any before valid.all case.
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} | 0 | "2012-07-10T17:09:09Z" | "2014-06-29T10:04:10Z" | "2012-07-11T15:45:40Z" | CONTRIBUTOR | null | #1595
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} | 1 | "2012-07-10T18:21:26Z" | "2015-02-18T15:41:44Z" | "2012-07-13T03:11:47Z" | CONTRIBUTOR | null | from stackoverflow:
I am trying to create a plot waith an x range of e.g. 500 milliseconds.
rng = date_range(s,periods=500,freq="U")
df = DataFrame(randn(500),index=rng,columns=["A"])
to plot column A:
df["A"].plot()
The whole plot will be squeezed into a single spike because the x range is defined from Jan-2011 until Jul-2014.
Is there a way to change this?
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} | 0 | "2012-07-10T18:30:48Z" | "2012-07-11T22:26:36Z" | "2012-07-11T22:26:36Z" | CONTRIBUTOR | null | In [18]: period_range('1/1/2012', periods=500, freq='U')
Out[18]:
<class 'pandas.tseries.period.PeriodIndex'>
freq: D
[01-Jan-2012, ..., 14-May-2013]
length: 500
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} | https://api.github.com/repos/pandas-dev/pandas/issues/1600/timeline | null | null | null |
Subsets and Splits