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} | 6 | "2012-05-07T14:21:18Z" | "2018-01-01T21:20:54Z" | "2012-07-12T21:36:26Z" | MEMBER | null | ```
(Pdb) result
A B C D
foo 2 3 4 5
bar 7 8 9 10
baz 12 13 14 15
qux 12 13 14 15
foo 12 13 14 15
bar 12 13 14 15
(Pdb) result.ix['bar']
*** Exception: Reindexing only valid with uniquely valued Index objects
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1202 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1202/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1202/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1202/events | https://github.com/pandas-dev/pandas/issues/1202 | 4,455,611 | MDU6SXNzdWU0NDU1NjEx | 1,202 | Business frequencies error in tsplot | {
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} | 1 | "2012-05-07T14:57:33Z" | "2012-05-08T14:52:47Z" | "2012-05-08T14:52:47Z" | MEMBER | null | Should alias these to their calendar frequencies for compatibility, or something
```
/home/wesm/code/pandas/pandas/tseries/plotting.py in tsplot(series, plotf, *args, **kwargs)
77 # Convert DatetimeIndex to PeriodIndex
78 if isinstance(series.index, DatetimeIndex):
---> 79 idx = series.index.to_period(freq=freq)
80 series = Series(series.values, idx, name=series.name)
81
/home/wesm/code/pandas/pandas/tseries/index.pyc in to_period(self, freq)
528 freq = self.freq
529
--> 530 return PeriodIndex(self.values, freq=freq)
531
532 def order(self, return_indexer=False, ascending=True):
/home/wesm/code/pandas/pandas/tseries/period.pyc in __new__(cls, data, freq, start, end, periods, copy, name)
516 freq = freq.freq
517 else:
--> 518 freq = _freq_mod.get_standard_freq(freq)
519
520 if data is None:
/home/wesm/code/pandas/pandas/tseries/frequencies.pyc in get_standard_freq(freq)
454 return get_offset_name(freq)
455
--> 456 code, stride = get_freq_code(freq)
457 return _get_freq_str(code, stride)
458
/home/wesm/code/pandas/pandas/tseries/frequencies.pyc in get_freq_code(freqstr)
64
65 base, stride = _base_and_stride(freqstr)
---> 66 code = _period_str_to_code(base)
67
68 return code, stride
/home/wesm/code/pandas/pandas/tseries/frequencies.pyc in _period_str_to_code(freqstr)
646 return _period_code_map[freqstr]
647 except:
--> 648 alias = _period_alias_dict[freqstr]
649 try:
650 return _period_code_map[alias]
KeyError: 'BM'
```
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} | 1 | "2012-05-07T15:03:09Z" | "2019-01-01T17:24:29Z" | "2019-01-01T17:24:29Z" | MEMBER | null | Related to #216
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https://api.github.com/repos/pandas-dev/pandas/issues/1204 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1204/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1204/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1204/events | https://github.com/pandas-dev/pandas/issues/1204 | 4,457,665 | MDU6SXNzdWU0NDU3NjY1 | 1,204 | Improve parser performance when handling special characters | {
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} | 2 | "2012-05-07T16:53:04Z" | "2012-11-28T01:11:25Z" | "2012-11-28T01:11:25Z" | CONTRIBUTOR | null | thousands separator and comments (#962) processing is slow right now.
at least comments should be preprocessed
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} | 4 | "2012-05-07T19:24:40Z" | "2012-07-21T16:51:11Z" | "2012-07-21T16:51:10Z" | NONE | null | Hi,
The logic for Series.interpolate assumes the indexes are equally spaced. With a floating point index this is not the desired interpolation. For example:
``` python
x1 = np.array([0, 0.25, 0.77, 1.2, 1.4, 2.6, 3.1])
y1 = np.array([0, 1.1, 0.5, 1.5, 1.2, 2.1, 2.4])
x2 = np.array([0, 0.25, 0.66, 1.0, 1.2, 1.4, 3.1])
y2 = np.array([0, 0.2, 0.8, 1.1, 2.2, 0.1, 2.4])
df1 = DataFrame(data=y1, index=x1, columns=['A'])
df1.plot(marker='o')
df2 = DataFrame(data=y2, index=x2, columns=['A'])
df2.plot(marker='o')
df3=df1 - df2
df3.plot(marker='o')
print df3
def resample(signals):
aligned_x_vals = reduce(lambda s1, s2: s1.index.union(s2.index), signals)
return map(lambda s: s.reindex(aligned_x_vals).apply(Series.interpolate), signals)
sig1, sig2 = resample([df1, df2])
sig3 = sig1 - sig2
plt.plot(df1.index, df1.values, marker='D')
plt.plot(sig1.index, sig1.values, marker='o')
plt.grid()
plt.figure()
plt.plot(df2.index, df2.values, marker='o')
plt.plot(sig2.index ,sig2.values, marker='o')
plt.grid()
```
I expect sig1 and sig2 to have more points than df1 and df2 but with the values interpolated. There are a few points that are not overlapping because it is assumed they are equally spaced. In my opinion if the index is a floating point the user wants to interpolate by the index's value and don't assume they are equally spaced. It should do something like this:
``` python
import numpy as np
from pandas import *
def interpolate(serie):
try:
inds = np.array([float(d) for d in serie.index])
except ValueError:
inds = np.arange(len(serie))
values = serie.values
invalid = isnull(values)
valid = -invalid
firstIndex = valid.argmax()
valid = valid[firstIndex:]
invalid = invalid[firstIndex:]
inds = inds[firstIndex:]
result = values.copy()
result[firstIndex:][invalid] = np.interp(inds[invalid], inds[valid],
values[firstIndex:][valid])
return Series(result, index=serie.index, name=serie.name)
```
Thanks
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} | 1 | "2012-05-07T19:55:16Z" | "2012-05-08T14:44:07Z" | "2012-05-08T14:44:07Z" | MEMBER | null | Currently there's a single column special case. Does this actually impact performance?
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} | 1 | "2012-05-07T19:58:28Z" | "2012-05-19T19:24:51Z" | "2012-05-19T19:24:19Z" | CONTRIBUTOR | null | These are in lots of places. Need a round of docs clean up after full support for non-unique has been built in
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} | 14 | "2012-05-07T22:54:31Z" | "2014-07-04T09:18:53Z" | "2012-05-12T18:12:12Z" | CONTRIBUTOR | null | I've tried to isolate pandas usage of the datetime64 from the aspects of the numpy
API which would be nice to evolve. Pandas wasn't directly using this API for
functionality or interoperability, but more as a consequence of borrowing some
of the functional datetime operations from the numpy codebase.
As a result of doing this, some datetime64-related bugs were exposed in numpy
master, and I've submitted pull requests to fix them.
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} | 0 | "2012-05-08T00:32:18Z" | "2012-05-08T14:31:25Z" | "2012-05-08T14:31:25Z" | CONTRIBUTOR | null | This raises an Exception:
DataFrame(randn(10, 5), columns=period_range('1/1/2000', periods=5))
/pandas/pandas/core/frame.pyc in _apply_standard(self, func, axis, ignore_failures)
3209 try:
3210 if hasattr(e, 'args'):
-> 3211 e.args = e.args + ('occurred at index %s' % str(k),)
3212 except NameError:
3213 # no k defined yet
UnboundLocalError: local variable 'k' referenced before assignment
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} | 1 | "2012-05-08T08:53:00Z" | "2014-06-13T02:46:22Z" | "2012-05-14T15:06:32Z" | CONTRIBUTOR | null | Finally I got around to implement this properly in pandas, and I added also a conversion to R matrices if need be. Notice that this doesn't handle MultiIndex and similar functionalities, but since I don't use them I'm not sure how they'd fit into R data frames.
The biggest drawback here is the use of an intermediate, but other solutions would probably involve hacking around the rpy2 internals (so far all the alternative solutions I found where worse, performance-wise).
Two (smallish) unit tests are included. This code has also been extensively used in production where I work.
All other unit tests pass after this change.
closes #350
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} | 0 | "2012-05-08T12:26:28Z" | "2012-05-14T14:12:30Z" | "2012-05-14T14:12:30Z" | CONTRIBUTOR | null | From pystatsmodels mailing list:
"I've got an excel file created from Paste Values that when read with pandas.ExcelFile returns int 42 for #N/A values.
Looking at http://stackoverflow.com/questions/4928629/xlrd-excel-script-converting-n-a-to-42 it would seem I'd need to be able to access the xlrd cell type rather than the value to interpret as NaN. Is there a way to do this?"
As suggested by the stackoverflow article, we should use the error_text_from_code dict in the xlrd module to convert error codes to the proper text.
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http://slendrmeans.wordpress.com/2012/04/14/will-it-python-machine-learning-for-hackers-chapter-1-part-1-loading-the-data
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(Pdb) grouped
value
a b c
0 foo 3.169106
b 1 bar 1.060375
(Pdb) grouped.index
MultiIndex([('a', 0, 'foo'), ('b', 1, 'bar')], dtype=object)
```
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} | 1 | "2012-05-08T21:43:31Z" | "2012-05-12T22:03:20Z" | "2012-05-12T22:03:20Z" | NONE | null | It didn't seem necessary that a grid of histogram plots should be square. For example, if you have 6 columns to plot, a 2x3 grid makes more sense than 3x3 with an empty row. Likewise with 12 it should give you 3x4, and so on.
I added a simple way to allow this. The number of columns will not exceed the number of rows by more than 1 (e.g., you will not get a 2x5 grid if you have 10 columns).
For a quick test:
``` python
df = pandas.DataFrame(np.random.randn(200, 6))
df.hist()
```
Note: it might be desirable to allow more rows than columns for plots in portrait mode. That could easily be a keyword option.
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} | 1 | "2012-05-08T23:29:38Z" | "2012-05-08T23:49:03Z" | "2012-05-08T23:49:03Z" | MEMBER | null | #
## FAIL: test_reindex_pad (pandas.tests.test_series.TestSeries)
Traceback (most recent call last):
File "C:\Program Files (x86)\Jenkins\workspace\pandas-windows-test-py27\pandas\tests\test_series.py", line 2263, in test_reindex_pad
assert_series_equal(reindexed, expected)
File "C:\Program Files (x86)\Jenkins\workspace\pandas-windows-test-py27\pandas\util\testing.py", line 128, in assert_series_equal
assert(left.dtype == right.dtype)
AssertionError
---
Ran 1762 tests in 58.531s
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} | 1 | "2012-05-09T01:30:56Z" | "2020-04-03T03:52:13Z" | null | MEMBER | null | cc @johnmyleswhite
http://www.johnmyleswhite.com/notebook/2012/05/03/cumplyr-extending-the-plyr-package-to-handle-cross-dependencies/
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```
In [108]: df = pandas.DataFrame(numpy.arange(12).reshape(3, 4))
In [109]: df
Out[109]:
0 1 2 3
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
In [110]: df.irow(slice(0, df.shape[0]))
Out[110]:
0 1 2 3
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
In [111]: df.irow(slice(0, -1))
Out[111]:
0 1 2 3
0 0 1 2 3
1 4 5 6 7
```
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} | 4 | "2012-05-11T09:16:21Z" | "2014-06-17T07:00:41Z" | "2012-05-14T14:12:24Z" | CONTRIBUTOR | null | I can't test the unit tests as I don't have a build environment.
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} | 1 | "2012-05-11T17:17:34Z" | "2014-06-26T16:23:45Z" | "2012-05-14T14:52:32Z" | CONTRIBUTOR | null | per #1186
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},
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"due_on": "2012-06-12T07:00:00Z",
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} | 0 | "2012-05-12T16:33:05Z" | "2012-05-12T19:41:52Z" | "2012-05-12T19:41:52Z" | MEMBER | null | ```
In [1]: Period('2007Q2', freq='Q-JUN')
Out[1]: Period('2007Q4', 'Q-JUN')
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1229 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1229/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1229/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1229/events | https://github.com/pandas-dev/pandas/issues/1229 | 4,548,501 | MDU6SXNzdWU0NTQ4NTAx | 1,229 | Series.repeat does not do the right thing | {
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} | 0 | "2012-05-12T18:15:41Z" | "2012-05-12T19:46:37Z" | "2012-05-12T19:46:37Z" | MEMBER | null | ```
In [4]: Series(np.array([tm.rands(10) for _ in xrange(10)], dtype='O'))
Out[4]:
0 uU7IsfsSR0
1 8so3eV8AAA
2 ITSfW3Ce68
3 WNDTb9pMg0
4 FCmWYJyWri
5 70hQk6Hy4b
6 19gZtoizil
7 CTvpYNkem8
8 rMZijLdFL9
9 yVd35ga8Ap
In [5]: Series(np.array([tm.rands(10) for _ in xrange(10)], dtype='O')).repeat(10)
Out[5]:
0 oP0SHbkuc5
1 oP0SHbkuc5
2 oP0SHbkuc5
3 oP0SHbkuc5
4 oP0SHbkuc5
5 oP0SHbkuc5
6 oP0SHbkuc5
7 oP0SHbkuc5
8 oP0SHbkuc5
9 oP0SHbkuc5
Length: 100
```
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|
https://api.github.com/repos/pandas-dev/pandas/issues/1231 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1231/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1231/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1231/events | https://github.com/pandas-dev/pandas/issues/1231 | 4,549,193 | MDU6SXNzdWU0NTQ5MTkz | 1,231 | Resampling of minutely / secondly periods -> 5 minute, etc. | {
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} | 0 | "2012-05-12T20:29:16Z" | "2012-05-12T21:40:51Z" | "2012-05-12T21:40:51Z" | MEMBER | null | May not work properly, need tests
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https://api.github.com/repos/pandas-dev/pandas/issues/1232 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1232/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1232/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1232/events | https://github.com/pandas-dev/pandas/issues/1232 | 4,550,879 | MDU6SXNzdWU0NTUwODc5 | 1,232 | Storing time zones HDFStore | {
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} | 0 | "2012-05-13T04:36:58Z" | "2012-05-14T03:55:26Z" | "2012-05-14T03:55:26Z" | MEMBER | null | Need to be careful to not break backwards compat (don't REQUIRE that time zone be stored in the hdf5 file)
```
In [10]: ts
Out[10]:
2000-01-01 00:00:00-05:00 1.673428
2000-01-01 00:00:01-05:00 -0.647638
2000-01-01 00:00:02-05:00 0.185806
2000-01-01 00:00:03-05:00 -1.181972
2000-01-01 00:00:04-05:00 1.172915
2000-01-01 00:00:05-05:00 -1.058382
2000-01-01 00:00:06-05:00 0.716881
2000-01-01 00:00:07-05:00 -1.077554
2000-01-01 00:00:08-05:00 -1.347873
2000-01-01 00:00:09-05:00 -1.283497
2000-01-01 00:00:10-05:00 -0.628599
2000-01-01 00:00:11-05:00 1.681750
2000-01-01 00:00:12-05:00 -0.876274
2000-01-01 00:00:13-05:00 -0.137497
2000-01-01 00:00:14-05:00 1.199150
...
2000-01-02 03:46:25-05:00 -1.449709
2000-01-02 03:46:26-05:00 1.333123
2000-01-02 03:46:27-05:00 -0.306314
2000-01-02 03:46:28-05:00 0.040174
2000-01-02 03:46:29-05:00 0.823003
2000-01-02 03:46:30-05:00 -0.721399
2000-01-02 03:46:31-05:00 -2.812037
2000-01-02 03:46:32-05:00 0.721936
2000-01-02 03:46:33-05:00 -1.538009
2000-01-02 03:46:34-05:00 0.726892
2000-01-02 03:46:35-05:00 0.336684
2000-01-02 03:46:36-05:00 1.128328
2000-01-02 03:46:37-05:00 0.106069
2000-01-02 03:46:38-05:00 -1.167883
2000-01-02 03:46:39-05:00 -1.695608
Freq: S, Length: 100000
In [11]: store['bar']
Out[11]:
2000-01-01 05:00:00 1.673428
2000-01-01 05:00:01 -0.647638
2000-01-01 05:00:02 0.185806
2000-01-01 05:00:03 -1.181972
2000-01-01 05:00:04 1.172915
2000-01-01 05:00:05 -1.058382
2000-01-01 05:00:06 0.716881
2000-01-01 05:00:07 -1.077554
2000-01-01 05:00:08 -1.347873
2000-01-01 05:00:09 -1.283497
2000-01-01 05:00:10 -0.628599
2000-01-01 05:00:11 1.681750
2000-01-01 05:00:12 -0.876274
2000-01-01 05:00:13 -0.137497
2000-01-01 05:00:14 1.199150
...
2000-01-02 08:46:25 -1.449709
2000-01-02 08:46:26 1.333123
2000-01-02 08:46:27 -0.306314
2000-01-02 08:46:28 0.040174
2000-01-02 08:46:29 0.823003
2000-01-02 08:46:30 -0.721399
2000-01-02 08:46:31 -2.812037
2000-01-02 08:46:32 0.721936
2000-01-02 08:46:33 -1.538009
2000-01-02 08:46:34 0.726892
2000-01-02 08:46:35 0.336684
2000-01-02 08:46:36 1.128328
2000-01-02 08:46:37 0.106069
2000-01-02 08:46:38 -1.167883
2000-01-02 08:46:39 -1.695608
Freq: S, Length: 100000
In [12]: store['bar'].index
Out[12]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2000-01-01 05:00:00, ..., 2000-01-02 08:46:39]
Length: 100000, Freq: S, Timezone: None
```
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} | 0 | "2012-05-15T18:55:07Z" | "2014-06-25T18:40:22Z" | "2012-05-15T20:31:57Z" | CONTRIBUTOR | null | Series/DataFrame.replace using np.putmask
if np.nan is supplied, replace will mimic fillna (e.g., replace nan, inf or any value that is true if passed to isnull)
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} | 1 | "2012-05-15T19:49:03Z" | "2012-05-19T16:29:46Z" | "2012-05-19T16:29:46Z" | MEMBER | null | ``````
# Save to hdf5.
kw = dict(table=True, compression='zlib')
store = pandas.HDFStore('ADCP_series.h5', 'w')
store.put('ADCP', ADCP, **kw)
store.put('Ancillary', Anc, **kw)
store.close()
On my Enthought Python Distribution I can load it fine (also with my previous setup),
pandas.__version__
Out[2]: '0.6.1'
tables.__version__
Out[4]: '2.3.1'
But on pandas dev,
pandas.__version__
Out[8]: '0.8.0.dev'
I get the following:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
----> 1 ADCP = f['ADCP']
/usr/lib/python2.7/site-packages/pandas/io/pytables.pyc in __getitem__(self, key)
181
182 def __getitem__(self, key):
--> 183 return self.get(key)
184
185 def __setitem__(self, key, value):
/usr/lib/python2.7/site-packages/pandas/io/pytables.pyc in get(self, key)
280 try:
281 group = getattr(self.handle.root, key)
--> 282 return self._read_group(group)
283 except AttributeError:
284 raise
/usr/lib/python2.7/site-packages/pandas/io/pytables.pyc in _read_group(self, group, where)
792 kind = _LEGACY_MAP.get(kind, kind)
793 handler = self._get_handler(op='read', kind=kind)
--> 794 return handler(group, where)
795
796 def _read_series(self, group, where=None):
/usr/lib/python2.7/site-packages/pandas/io/pytables.pyc in _read_wide_table(self, group, where)
552
553 def _read_wide_table(self, group, where=None):
--> 554 return self._read_panel_table(group, where)
555
556 def _write_index(self, group, key, index):
/usr/lib/python2.7/site-packages/pandas/io/pytables.pyc in _read_panel_table(self, group, where)
843
844 if len(unique(key)) == len(key):
--> 845 sorter, _ = lib.groupsort_indexer(key, J * K)
846
847 # the data need to be sorted
/usr/lib/python2.7/site-packages/pandas/_tseries.so in pandas._tseries.groupsort_indexer (pandas/src/tseries.c:49539)()
ValueError: Buffer dtype mismatch, expected 'int64_t' but got 'long'```
``````
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} | 1 | "2012-05-15T21:30:56Z" | "2012-05-19T20:15:34Z" | "2012-05-19T20:15:34Z" | NONE | null | In [91]: from pandas import DataFrame
In [92]: a = DataFrame([1,2, None, 3, 4])
In [93]: a
Out[93]:
0
0 1
1 2
2 None
3 3
4 4
In [94]: a.apply(lambda x: str(x[0]), axis=1)
Out[94]:
0 1
1 2
2 None
3 3
4 4
In [95]: a.apply(lambda x: int(x[0]), axis=1)
Out[95]:
0 1
1 2
2 NaN
3 NaN
4 NaN
---
I was expecting elements 3 and 4 to still evaluate properly in this case to something like this:
0 1
1 2
2 NaN
3 3
4 4
i am using windows xp 32 bit with pandas 0.7.1 on python 2.7.2
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} | 1 | "2012-05-15T22:34:15Z" | "2014-06-20T00:57:07Z" | "2012-05-19T17:18:02Z" | CONTRIBUTOR | null | nan handling using a special pandas null object
#557
inplace keyword
#805
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} | 1 | "2012-05-16T20:54:55Z" | "2012-06-21T17:04:03Z" | "2012-06-21T17:04:03Z" | CONTRIBUTOR | null | many modules need many more test cases
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} | 0 | "2012-05-16T21:53:31Z" | "2012-05-19T17:18:48Z" | "2012-05-19T17:18:48Z" | CONTRIBUTOR | null | As mentioned in #1098.
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} | 0 | "2012-05-17T22:36:39Z" | "2012-05-19T18:50:56Z" | "2012-05-19T18:50:56Z" | CONTRIBUTOR | null | In [66]: ts = tm.makeTimeSeries()
## In [67]: ts.at_time(ts.index[2])
IndexError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-67-324c3adda78b> in <module>()
----> 1 ts.at_time(ts.index[2])
/home/chang/Dropbox/git/pandas/pandas/core/series.py in at_time(self, time, tz, asof)
2676 """
2677 from pandas.tseries.resample import values_at_time
-> 2678 return values_at_time(self, time, tz=tz, asof=asof)
2679
2680 def tz_convert(self, tz, copy=True):
/home/chang/Dropbox/git/pandas/pandas/tseries/resample.py in values_at_time(obj, time, tz, asof)
363
364 mus = _time_to_microsecond(time)
--> 365 indexer = lib.values_at_time(obj.index.asi8, mus)
366 indexer = com._ensure_platform_int(indexer)
367 return obj.take(indexer)
/home/chang/Dropbox/git/pandas/pandas/_tseries.so in pandas._tseries.values_at_time (pandas/src/tseries.c:38356)()
IndexError: Out of bounds on buffer access (axis 0)
> /home/chang/Dropbox/git/pandas/datetime.pyx(1155)pandas._tseries.values_at_time (pandas/src/tseries.c:38356)()
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} | 5 | "2012-05-18T13:08:05Z" | "2012-05-30T22:40:24Z" | "2012-05-23T21:25:45Z" | CONTRIBUTOR | null | Right now date conversion is post-processing so the index has already been constructed by then. We need to make it so you can use the newly generated columns as the index
(maybe as simple as a call to set_index)?
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} | 0 | "2012-05-18T13:08:41Z" | "2012-05-21T18:30:36Z" | "2012-05-21T18:30:36Z" | CONTRIBUTOR | null | Have option to remove the original columns used to parse the combined date columns
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} | 4 | "2012-05-18T13:10:36Z" | "2012-05-24T18:08:03Z" | "2012-05-21T18:31:18Z" | CONTRIBUTOR | null | Have option to return a Series if there's only one column
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https://api.github.com/repos/pandas-dev/pandas/issues/1254 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1254/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1254/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1254/events | https://github.com/pandas-dev/pandas/issues/1254 | 4,646,144 | MDU6SXNzdWU0NjQ2MTQ0 | 1,254 | HDFStore.get(non_existing_key) raises NoSuchNodeError | {
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} | 1 | "2012-05-18T17:31:16Z" | "2012-07-12T21:44:13Z" | "2012-07-12T21:43:53Z" | CONTRIBUTOR | null | Here is clipped traceback. I guess you need to catch NoSuchNodeError also. Or maybe it is better to ask pytables to make NoSuchNodeError a subclass of AttributeError? I am using PyTables 2.3.1 and 0.7.3.
```
/.../local/lib/python2.7/site-packages/pandas/io/pytables.pyc in get(self, key)
258 """
259 try:
--> 260 group = getattr(self.handle.root, key)
261 return self._read_group(group)
262 except AttributeError:
/.../src/tables/tables/group.pyc in __getattr__(self, name)
824 self._g_addChildrenNames()
825 return myDict[name]
--> 826 return self._f_getChild(name)
827
828
/.../src/tables/tables/group.pyc in _f_getChild(self, childname)
695 self._g_checkOpen()
696
--> 697 self._g_checkHasChild(childname)
698
699 childPath = joinPath(self._v_pathname, childname)
/.../src/tables/tables/group.pyc in _g_checkHasChild(self, name)
434 raise NoSuchNodeError(
435 "group ``%s`` does not have a child named ``%s``"
--> 436 % (self._v_pathname, name))
437 return node_type
438
NoSuchNodeError: group ``/`` does not have a child named ``power_change_9``
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1255 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1255/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1255/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1255/events | https://github.com/pandas-dev/pandas/issues/1255 | 4,647,761 | MDU6SXNzdWU0NjQ3NzYx | 1,255 | Improve Series.interpolate to use index values | {
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```
x1 = np.array([0, 0.25, 0.77, 1.2, 1.4, 2.6, 3.1])
y1 = np.array([0, 1.1, 0.5, 1.5, 1.2, 2.1, 2.4])
x2 = np.array([0, 0.25, 0.66, 1.0, 1.2, 1.4, 3.1])
y2 = np.array([0, 0.2, 0.8, 1.1, 2.2, 0.1, 2.4])
df1 = DataFrame(data=y1, index=x1, columns=['A'])
df1.plot(marker='o')
df2 = DataFrame(data=y2, index=x2, columns=['A'])
df2.plot(marker='o')
df3=df1 - df2
df3.plot(marker='o')
print df3
def resample(signals):
aligned_x_vals = reduce(lambda s1, s2: s1.index.union(s2.index), signals)
return map(lambda s: s.reindex(aligned_x_vals).apply(Series.interpolate), signals)
sig1, sig2 = resample([df1, df2])
sig3 = sig1 - sig2
plt.plot(df1.index, df1.values, marker='D')
plt.plot(sig1.index, sig1.values, marker='o')
plt.grid()
plt.figure()
plt.plot(df2.index, df2.values, marker='o')
plt.plot(sig2.index ,sig2.values, marker='o')
plt.grid()
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1256 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1256/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1256/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1256/events | https://github.com/pandas-dev/pandas/issues/1256 | 4,647,887 | MDU6SXNzdWU0NjQ3ODg3 | 1,256 | Vectorized "crossing" computation | {
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} | 3 | "2012-05-18T19:21:24Z" | "2021-04-11T01:49:34Z" | null | MEMBER | null | http://stackoverflow.com/questions/10475488/calculating-crossing-intercept-points-of-a-series-or-dataframe
```
def cross(series, cross=0, direction='cross'):
"""
Given a Series returns all the index values where the data values equal
the 'cross' value.
Direction can be 'rising' (for rising edge), 'falling' (for only falling
edge), or 'cross' for both edges
"""
# Find if values are above or bellow yvalue crossing:
above=series.values > cross
below=np.logical_not(above)
left_shifted_above = above[1:]
left_shifted_below = below[1:]
x_crossings = []
# Find indexes on left side of crossing point
if direction == 'rising':
idxs = (left_shifted_above & below[0:-1]).nonzero()[0]
elif direction == 'falling':
idxs = (left_shifted_below & above[0:-1]).nonzero()[0]
else:
rising = left_shifted_above & below[0:-1]
falling = left_shifted_below & above[0:-1]
idxs = (rising | falling).nonzero()[0]
# Calculate x crossings with interpolation using formula for a line:
x1 = series.index.values[idxs]
x2 = series.index.values[idxs+1]
y1 = series.values[idxs]
y2 = series.values[idxs+1]
x_crossings = (cross-y1)*(x2-x1)/(y2-y1) + x1
return x_crossings
# Test it out:
time = [0, 0.1, 0.21, 0.31, 0.40, 0.49, 0.51, 0.6, 0.71, 0.82, 0.93]
voltage = [1, -1, 1.1, -0.9, 1, -1, 0.9,-1.2, 0.95, -1.1, 1.11]
df = DataFrame(data=voltage, index=time, columns=['voltage'])
x_crossings = cross(df['voltage'])
y_crossings = np.zeros(x_crossings.shape)
plt.plot(time, voltage, '-ob', x_crossings, y_crossings, 'or')
plt.grid(True)
```
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} | 0 | "2012-05-18T22:00:20Z" | "2012-05-19T20:12:40Z" | "2012-05-19T20:12:40Z" | MEMBER | null | ```
In [5]: means_by_hour_of_day = training_data.groupby('hour')[target_names].mean()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/wesm/Dropbox/book/svn/data/air_quality/<ipython-input-5-141d964fe4bb> in <module>()
----> 1 means_by_hour_of_day = training_data.groupby('hour')[target_names].mean()
/home/wesm/code/pandas/pandas/core/groupby.pyc in __getitem__(self, key)
1663 as_index=self.as_index)
1664 else:
-> 1665 if key not in self.obj: # pragma: no cover
1666 raise KeyError(str(key))
1667 # kind of a kludge
/home/wesm/code/pandas/pandas/core/frame.pyc in __contains__(self, key)
566 def __contains__(self, key):
567 """True if DataFrame has this column"""
--> 568 return key in self.columns
569
570 #----------------------------------------------------------------------
/home/wesm/code/pandas/pandas/core/index.pyc in __contains__(self, key)
263
264 def __contains__(self, key):
--> 265 hash(key)
266 # work around some kind of odd cython bug
267 try:
/home/wesm/code/pandas/pandas/core/index.pyc in __hash__(self)
271
272 def __hash__(self):
--> 273 return hash(self.view(np.ndarray))
274
275 def __setitem__(self, key, value):
TypeError: unhashable type: 'numpy.ndarray'
```
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} | 0 | "2012-05-19T16:08:09Z" | "2012-05-20T02:57:41Z" | "2012-05-20T02:57:41Z" | MEMBER | null | ```
In [20]: rng = date_range('1/1/2000','12/31/2000')
In [21]: ts = Series(np.random.randn(len(rng)), index=rng)
In [22]: ts.resample('M', kind='period')
Out[22]:
Jan-2000 0.280017
Feb-2000 0.010740
Mar-2000 -0.175855
Apr-2000 -0.080775
May-2000 0.253388
Jun-2000 0.072629
Jul-2000 0.136193
Aug-2000 -0.039670
Sep-2000 -0.300413
Oct-2000 -0.200501
Nov-2000 0.037753
Dec-2000 -0.053187
Jan-2001 NaN
Freq: M
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1262 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1262/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1262/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1262/events | https://github.com/pandas-dev/pandas/issues/1262 | 4,661,437 | MDU6SXNzdWU0NjYxNDM3 | 1,262 | pandas should not import scipy automatically | {
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>>> import pandas
>>> import sys
>>> 'scipy' in sys.modules
True
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1263 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1263/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1263/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1263/events | https://github.com/pandas-dev/pandas/pull/1263 | 4,667,179 | MDExOlB1bGxSZXF1ZXN0MTM5MTA3OQ== | 1,263 | ENH: Add JSON export option for DataFrame (take 2) | {
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} | 13 | "2012-05-21T09:05:51Z" | "2014-06-13T10:11:17Z" | "2012-05-24T00:20:33Z" | CONTRIBUTOR | null | Second attempt for JSON pull request (original #1226) for issue #631.
All tests pass apart from the two below, tested on 64 bit OSX and 32 bit Ubuntu.
Time stamp JSON encoding/decoding in test_frame and test_series fails. I haven't looked into this too much, as Wes mentioned the work with timestamps was still ongoing but this appears to occur because the code always operates on the underlying numpy array rather than the pandas objects and the numpy array returns a bad Python date object to be encoded.
For numpy 1.6
``` python
(Pdb) series.index[0]
Timestamp(2000, 1, 3, 0, 0)
(Pdb) series.index.values[0]
1970-01-11 232:00:00
(Pdb) ujson.encode(series.index) # encoder operates on series.index.values
'[1699200,864000,950400,1036800,1123200,1382400,1468800,1555200,1641600,1728000,1065600,1152000,1238400,1324800,1411200,1670400,1756800,921600,1008000,1094400,1353600,1440000,1526400,1612800,1699200,1036800,1123200,1209600,1296000,1382400]'
```
For numpy 1.7 the situation is improved and the encoding is correct (although the timestamps are in nanoseconds):
``` python
(Pdb) series.index[0]
Timestamp(2000, 1, 3, 0, 0)
(Pdb) series.index.values[0]
numpy.datetime64('2000-01-03T00:00:00.000000000+0000')
(Pdb) ujson.encode(series.index)
'[946857600000000000,946944000000000000,947030400000000000,947116800000000000,947203200000000000,947462400000000000,947548800000000000,947635200000000000,947721600000000000,947808000000000000,948067200000000000,948153600000000000,948240000000000000,948326400000000000,948412800000000000,948672000000000000,948758400000000000,948844800000000000,948931200000000000,949017600000000000,949276800000000000,949363200000000000,949449600000000000,949536000000000000,949622400000000000,949881600000000000,949968000000000000,950054400000000000,950140800000000000,950227200000000000]'
```
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} | 0 | "2012-05-21T17:52:47Z" | "2012-05-21T18:41:33Z" | "2012-05-21T18:41:33Z" | MEMBER | null | ```
In [7]: tips.groupby('sex').agg({'tip' : {'mean' : 'mean', 'std' : 'std'}, 'size' : 'sum'})
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/home/wesm/code/pandas/<ipython-input-7-850d34319c48> in <module>()
----> 1 tips.groupby('sex').agg({'tip' : {'mean' : 'mean', 'std' : 'std'}, 'size' : 'sum'})
/home/wesm/code/pandas/pandas/core/groupby.pyc in agg(self, func, *args, **kwargs)
278 See docstring for aggregate
279 """
--> 280 return self.aggregate(func, *args, **kwargs)
281
282 def _iterate_slices(self):
/home/wesm/code/pandas/pandas/core/groupby.pyc in aggregate(self, arg, *args, **kwargs)
1430 colg = SeriesGroupBy(obj[col], selection=col,
1431 grouper=self.grouper)
-> 1432 result[col] = colg.aggregate(agg_how)
1433 keys.append(col)
1434
/home/wesm/code/pandas/pandas/core/groupby.pyc in aggregate(self, func_or_funcs, *args, **kwargs)
1185
1186 if hasattr(func_or_funcs,'__iter__'):
-> 1187 ret = self._aggregate_multiple_funcs(func_or_funcs)
1188 else:
1189 cyfunc = _intercept_cython(func_or_funcs)
/home/wesm/code/pandas/pandas/core/groupby.pyc in _aggregate_multiple_funcs(self, arg)
1216 else:
1217 # list of functions
-> 1218 columns = [func.__name__ for func in arg]
1219 arg = zip(columns, arg)
1220
AttributeError: 'str' object has no attribute '__name__'
In [8]: tips.groupby('sex').agg({'tip' : {'mean' : 'mean', 'std' : 'std'}, 'size' : ['sum']})
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/home/wesm/code/pandas/<ipython-input-8-6961866fbf6c> in <module>()
----> 1 tips.groupby('sex').agg({'tip' : {'mean' : 'mean', 'std' : 'std'}, 'size' : ['sum']})
/home/wesm/code/pandas/pandas/core/groupby.pyc in agg(self, func, *args, **kwargs)
278 See docstring for aggregate
279 """
--> 280 return self.aggregate(func, *args, **kwargs)
281
282 def _iterate_slices(self):
/home/wesm/code/pandas/pandas/core/groupby.pyc in aggregate(self, arg, *args, **kwargs)
1430 colg = SeriesGroupBy(obj[col], selection=col,
1431 grouper=self.grouper)
-> 1432 result[col] = colg.aggregate(agg_how)
1433 keys.append(col)
1434
/home/wesm/code/pandas/pandas/core/groupby.pyc in aggregate(self, func_or_funcs, *args, **kwargs)
1185
1186 if hasattr(func_or_funcs,'__iter__'):
-> 1187 ret = self._aggregate_multiple_funcs(func_or_funcs)
1188 else:
1189 cyfunc = _intercept_cython(func_or_funcs)
/home/wesm/code/pandas/pandas/core/groupby.pyc in _aggregate_multiple_funcs(self, arg)
1216 else:
1217 # list of functions
-> 1218 columns = [func.__name__ for func in arg]
1219 arg = zip(columns, arg)
1220
AttributeError: 'str' object has no attribute '__name__'
In [9]: tips.groupby('sex').agg({'tip' : {'mean' : 'mean', 'std' : 'std'}, 'size' : {'sum' : 'sum'}})
Out[9]:
tip size
std mean sum
sex
Female 1.159495 2.833448 214
Male 1.489102 3.089618 413
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1278 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1278/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1278/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1278/events | https://github.com/pandas-dev/pandas/issues/1278 | 4,682,483 | MDU6SXNzdWU0NjgyNDgz | 1,278 | DataFrame.describe fails for dtype=datetime64 | {
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} | 0 | "2012-05-22T00:47:34Z" | "2013-12-04T00:43:19Z" | "2012-05-23T21:28:26Z" | MEMBER | null | ```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/wesm/Dropbox/book/svn/data/haiti/<ipython-input-165-efa692752119> in <module>()
----> 1 data.describe()
/home/wesm/code/pandas/pandas/core/frame.pyc in describe(self, percentile_width)
3772 for column in numdata.columns:
3773 series = self[column]
-> 3774 destat.append([series.count(), series.mean(), series.std(),
3775 series.min(), series.quantile(lb), series.median(),
3776 series.quantile(ub), series.max()])
/home/wesm/code/pandas/pandas/core/series.pyc in f(self, axis, dtype, out, skipna, level)
258 if level is not None:
259 return self._agg_by_level(shortname, level=level, skipna=skipna)
--> 260 return nanop(self.values, skipna=skipna)
261 f.__name__ = shortname
262 return f
/home/wesm/code/pandas/pandas/core/nanops.pyc in f(values, axis, skipna, **kwds)
41 result = alt(values, axis=axis, skipna=skipna, **kwds)
42 except Exception:
---> 43 result = alt(values, axis=axis, skipna=skipna, **kwds)
44
45 return result
/home/wesm/code/pandas/pandas/core/nanops.pyc in _nanmean(values, axis, skipna)
77 np.putmask(values, mask, 0)
78
---> 79 the_sum = _ensure_numeric(values.sum(axis))
80 count = _get_counts(mask, axis)
81
ValueError: could not find a matching type for add.reduce, requested type has type code 'M'
```
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Port-au-Prince 184
Jacmel 45
Pétionville 33
Leogane 24
Léogâne 22
index : Index([Port-au-Prince, Jacmel, Pétionville, Leogane, Léogâne], dtype=object)
```
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} | 0 | "2012-05-22T02:02:01Z" | "2014-06-15T07:01:31Z" | "2012-05-23T21:50:51Z" | CONTRIBUTOR | null | simple implementations, maybe include non-overlapping option in the future? #1271
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} | 2 | "2012-05-22T07:37:10Z" | "2012-05-23T21:48:58Z" | "2012-05-23T21:40:22Z" | CONTRIBUTOR | null | When converting a mixed_type DataFrame to a R matrix (not a data.frame), everything is casted to string vectors, because a matrix can't be heterogeneously typed, to my knowledge.
I have two possible solutions I can send as pull request, and I would like to ask which is better:
1. raise ValueError for mixed type dataframes
2. raise a warning
Given that most matrices needed in R are the numeric ones, I would go for option 1.
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https://api.github.com/repos/pandas-dev/pandas/issues/1283 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1283/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1283/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1283/events | https://github.com/pandas-dev/pandas/pull/1283 | 4,685,830 | MDExOlB1bGxSZXF1ZXN0MTM5OTQ5NA== | 1,283 | BUG: Raise TypeError when converting mixed-type DataFrames, use proper NA types, close #1282 | {
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1. The use of improper NA types in R, which can cause issues when converting to matrices (NA_Logical gets converted to 0)
2. Improper casting when converting a mixed-type DataFrame to matrix, which will cause all sorts of problems: thus mixed types DataFrame raise a TypeError exception now.
Added a bit to the unit tests so that I know the thing works, but these (for pandas 0.9) needs to be ported to unittest/nose so that it can be run with the other tests.
#1282
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} | 1 | "2012-05-23T00:11:44Z" | "2014-06-25T07:17:11Z" | "2012-05-23T21:18:53Z" | CONTRIBUTOR | null | adds amount of padding equal to the difference between len(x) and len(x.decode('utf-8')). #1279
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} | 1 | "2012-05-23T10:17:25Z" | "2013-12-04T00:58:09Z" | "2012-05-29T00:12:10Z" | CONTRIBUTOR | null | Doing GroupBy.agg(numpy.size) or agg(len) is roughly twice as fast as GroupBy.size(), which could constitute a temporary improvement to a temporary solution (there's a "TODO: better impl" in size() ).
Just wanted to put it out there.
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} | 3 | "2012-05-23T15:23:32Z" | "2012-09-18T14:50:27Z" | "2012-09-18T14:50:27Z" | CONTRIBUTOR | null | From mailing list:
This is something that surprised me.
When reading from csv the values "True" and "False" are converted to bool, while "TRUE" and "FALSE"' to object
Using a converter {...: np.bool} I could take it into account.
Is it useful to take more possibilities into account knowing that there are a lot of possibilies True/False, Yes/No, ....?
In [19]: data = '''A;B
....: True;False
....: False;True'''
In [20]: df = pd.read_csv(StringIO(data), sep=';')
In [22]: df.dtypes
Out[22]:
A bool
B bool
In [23]: data = '''A;B
....: TRUE;FALSE
....: FALSE;TRUE'''
In [24]: df = pd.read_csv(StringIO(data), sep=';')
In [25]: df.dtypes
Out[25]:
A object
B object
In [27]: df = pd.read_csv(StringIO(data), sep=';', converters={'A':np.bool})
In [28]: df.dtypes
Out[28]:
A bool
B object
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} | 1 | "2012-05-23T15:25:30Z" | "2012-07-13T00:49:00Z" | "2012-07-13T00:49:00Z" | CONTRIBUTOR | null | Not just for date parsing but for various data conversions (converting yes/no to bool, converting integer values to string rep, etc.)
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} | 1 | "2012-05-23T16:48:59Z" | "2012-05-29T00:31:16Z" | "2012-05-29T00:31:16Z" | MEMBER | null | cc @orbitfold
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} | 1 | "2012-05-23T19:40:22Z" | "2012-06-11T17:44:36Z" | "2012-06-11T17:42:33Z" | CONTRIBUTOR | null | ## In [6]: DataFrame.from_records( [(1,2,3)], columns=['a','b','a'])
IndexError Traceback (most recent call last)
/home/adam/bin/jtds/<ipython-input-6-879cfff3d748> in <module>()
----> 1 DataFrame.from_records( [(1,2,3)], columns=['a','b','a'])
/home/adam/code/pandas/pandas/core/frame.pyc in from_records(cls, data, index, exclude, columns, names, coerce_float)
760 result_index = np.arange(len(data))
761
--> 762 return cls(sdict, index=result_index, columns=columns)
763
764 def to_records(self, index=True):
/home/adam/code/pandas/pandas/core/frame.pyc in **init**(self, data, index, columns, dtype, copy)
301 mgr = self._init_mgr(data, index, columns, dtype=dtype, copy=copy)
302 elif isinstance(data, dict):
--> 303 mgr = self._init_dict(data, index, columns, dtype=dtype)
304 elif isinstance(data, ma.MaskedArray):
305 mask = ma.getmaskarray(data)
/home/adam/code/pandas/pandas/core/frame.pyc in _init_dict(self, data, index, columns, dtype)
391
392 # segregates dtypes and forms blocks matching to columns
--> 393 blocks = form_blocks(homogenized, axes)
394
395 # consolidate for now
/home/adam/code/pandas/pandas/core/internals.pyc in form_blocks(data, axes)
1125
1126 if len(int_dict):
-> 1127 int_block = _simple_blockify(int_dict, items, np.int64)
1128 blocks.append(int_block)
1129
/home/adam/code/pandas/pandas/core/internals.pyc in _simple_blockify(dct, ref_items, dtype)
1154
1155 def _simple_blockify(dct, ref_items, dtype):
-> 1156 block_items, values = _stack_dict(dct, ref_items, dtype)
1157 # CHECK DTYPE?
1158 if values.dtype != dtype: # pragma: no cover
/home/adam/code/pandas/pandas/core/internals.pyc in _stack_dict(dct, ref_items, dtype)
1187 stacked = np.empty(shape, dtype=dtype)
1188 for i, item in enumerate(items):
-> 1189 stacked[i] = _asarray_compat(dct[item])
1190
1191 # stacked = np.vstack([_asarray_compat(dct[k]) for k in items])
IndexError: index out of bounds
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} | 0 | "2012-05-23T20:43:23Z" | "2014-07-04T01:56:05Z" | "2012-05-23T21:25:39Z" | CONTRIBUTOR | null | #1251 and #1294
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} | 4 | "2012-05-23T21:22:06Z" | "2017-01-06T12:12:59Z" | "2017-01-06T12:12:53Z" | CONTRIBUTOR | null | > In pandas 0.8.0, the time series plotting will be much improved (see
> attached example plot)
> What I'd love to see is an option to switch the reference time value off for aggregated time series.
Examples:
- I have an average of wind speeds created from 5 years of data --> plotting a year would not make sense
- daily maximum temperatures recoded during a single month --> depending on the case, showing the reference month or not could be desirable.
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