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} | 3 | 2015-07-03T16:05:33Z | 2016-02-27T17:52:43Z | 2016-02-27T17:52:43Z | NONE | null | Hi all,
I can't find any documentation that says this should happen, so I think it's a bug. But maybe something's happening that I don't understand. When I do a simple operation (adding 1 to a slice), suddenly the dtype of the columns changes from uint32 to int64.
Any ideas why this is happening? Bug?
Thanks
Make a sample dataframe. Columns are dtype uint32.
```
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({'a':[0, 1, 1], 'b':[100, 200, 300]}, dtype='uint32')
In [3]: df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 3 entries, 0 to 2
Data columns (total 2 columns):
a 3 non-null uint32
b 3 non-null uint32
dtypes: uint32(2)
memory usage: 48.0 bytes
```
Take a slice of a column. Adding 1 to that slice still results in dtype uint32.
```
In [4]: ix = df['a'] == 1
In [5]: z = df.loc[ix, 'b']
In [6]: z + 1
Out[6]:
1 201
2 301
Name: b, dtype: uint32
```
But, if I modify that slice in the original dataframe, suddenly both columns of the dataframe are int64.
```
In [7]: df.loc[ix, 'b'] = z + 1
In [8]: df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 3 entries, 0 to 2
Data columns (total 2 columns):
a 3 non-null int64
b 3 non-null int64
dtypes: int64(2)
memory usage: 72.0 bytes
```
I've seen this in 0.16, 0.16.1, and 0.16.2.
```
In [9]: pd.__version__
Out[9]: '0.16.2'
```
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| null | 7 | 2015-07-03T21:21:02Z | 2015-07-03T22:54:58Z | 2015-07-03T21:54:16Z | NONE | null | Is there a hashtable based sort function in pandas to sort integer interchangeable dtype (dense) in O(n) time?
Not sure if the feature is the one mentioned in the link below. It's called groupsort.
http://wesmckinney.com/blog/tag/pandas/
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} | 0 | 2015-07-03T23:03:17Z | 2015-07-28T15:28:20Z | 2015-07-28T15:28:20Z | MEMBER | null | Related to #10501, but not the same. `groupby` can accept `Categorical` and `sort` keyword.
```
df = pd.DataFrame({'A': [1, 2, 3 ,4], 'B': [5, 6, 7, 8]})
# OK
df.groupby(pd.Categorical(['A', 'B', 'A', 'B'])).groups
# {'A': [0, 2], 'B': [1, 3]}
# OK
df.groupby(pd.Categorical(['A', 'B', 'A', 'B']), sort=False).groups
# {'A': [0, 2], 'B': [1, 3]}
```
If `Categorical` has datetime-like categories, groupby fails if `sort=False` is specified.
```
# OK
df.groupby(pd.Categorical(pd.DatetimeIndex(['2011', '2012', '2011', '2012']))).groups
# {numpy.datetime64('2011-01-01T09:00:00.000000000+0900'): [0, 2],
# numpy.datetime64('2012-01-01T09:00:00.000000000+0900'): [1, 3]}
# NG
df.groupby(pd.Categorical(pd.DatetimeIndex(['2011', '2012', '2011', '2012'])), sort=False).groups
# ValueError: items in new_categories are not the same as in old categories
```
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} | 2 | 2015-07-04T00:01:37Z | 2016-05-06T07:38:12Z | 2015-07-06T12:01:50Z | CONTRIBUTOR | null | See PR below. Includes tests and release doc edits.
https://github.com/pydata/pandas/pull/10384
I'm just having a little problem with merging a simple two line conflict. I'm in the process of learning, so be gentle =P
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} | 7 | 2015-07-04T01:05:22Z | 2015-08-11T11:08:53Z | 2015-08-10T16:27:46Z | MEMBER | null | Closes #10373. Also, this is based on #10500 and #10501.
The fix also did some refactoring related to `assert_index_equal`. Added `check_exact` and `check_less_precise`, and moved logics from `assert_series_equal` and `assert_frame_equal` for cleanups.
Followings are the list of tested and what the output looks like
## Index
- Shape (size)
- Dtype
- Values
- Metadata
```
tm.assert_index_equal(pd.Index([1, 2, 3]), pd.Index([1, 2, 3, 4]))
# AssertionError: Index are different
#
# Index length are different
# [left]: 3, Int64Index([1, 2, 3], dtype='int64')
# [right]: 4, Int64Index([1, 2, 3, 4], dtype='int64')
tm.assert_index_equal(pd.Index([1, 2, 3]), pd.Index([1, 2, 5]))
# AssertionError: Index are different
#
# Index values are different (33.33333 %)
# [left]: Int64Index([1, 2, 3], dtype='int64')
# [right]: Int64Index([1, 2, 5], dtype='int64')
tm.assert_index_equal(pd.Index([1, 2, 3]), pd.Index([1, 2, 3], name='x'))
# AssertionError: Index are different
#
# Attribute "names" are different
# [left]: [None]
# [right]: [u'x']
```
## Series
- Shape (size)
- Dtype
- Index (same as above)
- Values
- Metadata (same as above)
```
tm.assert_series_equal(pd.Series([1, 2]), pd.Series([1, 2, 3]))
# AssertionError: Series are different
#
# Series length are different
# [left]: 2, Int64Index([0, 1], dtype='int64')
# [right]: 3, Int64Index([0, 1, 2], dtype='int64')
tm.assert_series_equal(pd.Series([1, 2, 3]), pd.Series([1, 2, 4]))
# AssertionError: Series are different
#
# Series values are different (33.33333 %)
# [left]: [1, 2, 3]
# [right]: [1, 2, 4]
```
## DataFrame
- Shape (size)
- Dtype
- Index (same as above)
- Column (almost same as above, but different summary)
- Values
```
tm.assert_frame_equal(pd.DataFrame([[1, 2], [3, 4]]), pd.DataFrame([[1, 2, 3], [4, 5, 6]]))
# AssertionError: DataFrame are different
#
# DataFrame shape (number of columns) are different
# [left]: 2, Int64Index([0, 1], dtype='int64')
# [right]: 3, Int64Index([0, 1, 2], dtype='int64')
tm.assert_frame_equal(pd.DataFrame([[1, 2], [3, 4]]), pd.DataFrame([[1, 2], [3, 4], [5, 6]]))
# AssertionError: DataFrame are different
#
# DataFrame shape (number of rows) are different
# [left]: 2, Int64Index([0, 1], dtype='int64')
# [right]: 3, Int64Index([0, 1, 2], dtype='int64')
tm.assert_frame_equal(pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B']), pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b']))
# AssertionError: DataFrame.columns are different
#
# DataFrame.columns values are different (100.0 %)
# [left]: Index([u'A', u'B'], dtype='object')
# [right]: Index([u'a', u'b'], dtype='object')
tm.assert_frame_equal(pd.DataFrame([[1, 2], [3, 4]]), pd.DataFrame([[1, 2], [3, 5]]))
# AssertionError: DataFrame.iloc[1, :] are different
#
# DataFrame.iloc[1, :] values are different (50.0 %)
# [left]: [2, 4]
# [right]: [2, 5]
```
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} | 6 | 2015-07-04T06:14:30Z | 2015-07-28T21:02:20Z | 2015-07-28T15:28:20Z | MEMBER | null | Closes #10505.
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} | 1 | 2015-07-04T11:42:17Z | 2015-07-06T12:59:35Z | 2015-07-06T12:41:42Z | MEMBER | null | Should not input pandas version manually to avoid any mistake.
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} | 24 | 2015-07-04T13:22:26Z | 2016-01-24T19:06:48Z | 2015-11-10T12:12:36Z | NONE | null | Hi,
I'm attempting to install pandas in a virtualenv on a FreeBSD system with Python 3.4.3. I can't see that I'm missing any obvious steps or dependancies. I get an error in a C module when importing pandas if I install pandas from pip, from a release tarball and with the repo (same error every time) that would imply something pretty major is missing. numpy, scipy and scikit-learn all install without any issues in the same venv. Example of the error:
``` bash
(venv)hostname% pip install --upgrade pandas
Collecting pandas
Requirement already up-to-date: pytz>=2011k in ./venv/lib/python3.4/site-packages (from pandas)
Requirement already up-to-date: numpy>=1.7.0 in ./venv/lib/python3.4/site-packages (from pandas)
Requirement already up-to-date: python-dateutil>=2 in ./venv/lib/python3.4/site-packages (from pandas)
Requirement already up-to-date: six>=1.5 in ./venv/lib/python3.4/site-packages (from python-dateutil>=2->pandas)
Installing collected packages: pandas
Successfully installed pandas-0.16.2
(venv)hostname% python
Python 3.4.3 (default, Jun 18 2015, 17:00:30)
[GCC 4.2.1 Compatible FreeBSD Clang 3.4.1 (tags/RELEASE_34/dot1-final 208032)] on freebsd10
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas
Traceback (most recent call last):
File "/home/someuser/venv/lib/python3.4/site-packages/pandas/__init__.py", line 7, in <module>
from pandas import hashtable, tslib, lib
ImportError: /home/someuser/venv/lib/python3.4/site-packages/pandas/hashtable.so: Undefined symbol "get_c_string"
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/someuser/venv/lib/python3.4/site-packages/pandas/__init__.py", line 13, in <module>
"extensions first.".format(module))
ImportError: C extension: /home/someuser/venv/lib/python3.4/site-packages/pandas/hashtable.so: Undefined symbol "get_c_string" not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext --inplace' to build the C extensions first.
>>>
```
When installing from a tarbal or the a checkout of the repo where setup.py is accessible and following the above suggestion (`python setup.py build_ext --inplace`) it compiles without issue it does not resolve the issue.
Some system information (print_versions.py taken from a repo checkout):
``` bash
(venv)hostname% python print_versions.py
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: FreeBSD
OS-release: 10.1-RELEASE-p10
machine: amd64
processor: amd64
byteorder: little
LC_ALL: None
LANG: None
pandas: None
nose: 1.3.7
Cython: 0.22.1
numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.4
bottleneck: 1.0.0
tables: None
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
```
Any pointers would be appreciated, thanks.
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} | 20 | 2015-07-04T18:33:13Z | 2019-07-18T10:51:49Z | 2019-07-18T10:51:49Z | NONE | null | While building up a DataFrame in several steps, I found it difficult to add a new "perpendicular" column, i.e. a column that adds another dimension to already existing columns. To solve this problem I got the idea that this may be done in two steps:
1. Add a new column whose values at each cell is a python list of the values the new column takes on.
2. Unlistify the column thereby creating a new row for each element in the above lists.
I.e. I propose two new DataFrame methods, listify() and unlistify().
`listify(df, column)`: Takes as input a dataframe and the name of a column. It will do a groupby of the df for all columns except column and generate a single row where the values in the column cell is a list of the column `column` values.
`unlistify(df, column)`: Takes as input a dataframe and the name of a column. It will iterate over the values of the contents of `column` for each row and generate a new row for each value.
The functions may be expanded to support multiple columns. `listify()` may e.g. support a post processing function, that will be applied on the list.
The following python code illustrates these two functions. But obviously the functionality may be implemented more efficienctly on the C-level.
``` python
#!/usr/bin/python
import pandas as pd
def listify(df, column):
matches = [i for i,n in enumerate(df.columns)
if n==column]
if len(matches)==0:
raise Exception('Failed to find column named ' + column +'!')
if len(matches)>1:
raise Exception('More than one column named ' + column +'!')
old_index = df.index
col_idx = matches[0] + len(old_index.shape) # Since we will reset the index
column_names = list(df.index.names) + list(df.columns)
gb_cols = [c for c in column_names
if c!= column]
# Helper function to generate the squashed dataframe
def fnc(d):
row = list(d.values[0])
return pd.DataFrame([row[:col_idx]
+ [[v[col_idx] for v in list(d.values)]]
+ row[col_idx+1:]])
return (df
.reset_index()
.groupby(gb_cols)
.apply(fnc)
.rename(columns = lambda i : column_names[i])
.set_index(old_index.names)
)
def unlistify(df, column):
matches = [i for i,n in enumerate(df.columns)
if n==column]
if len(matches)==0:
raise Exception('Failed to find column named ' + column +'!')
if len(matches)>1:
raise Exception('More than one column named ' + column +'!')
col_idx = matches[0]
# Helper function to expand and repeat the column col_idx
def fnc(d):
row = list(d.values[0])
bef = row[:col_idx]
aft = row[col_idx+1:]
col = row[col_idx]
z = [bef + [c] + aft for c in col]
return pd.DataFrame(z)
col_idx += len(df.index.shape) # Since we will push reset the index
index_names = list(df.index.names)
column_names = list(index_names) + list(df.columns)
return (df
.reset_index()
.groupby(level=0,as_index=0)
.apply(fnc)
.rename(columns = lambda i :column_names[i])
.set_index(index_names)
)
# Examples of how to listify and unlistify a column.
df = pd.DataFrame([[11,range(5),10],
[22,range(3),20]],
columns = ['A','B','C']).set_index('C')
print 'org'
print df
print '--'
df = unlistify(df,'B')
print 'unlistify(df,B)'
print df
print '--'
df = listify(df,'B')
print 'listify(df,B)'
print df
```
The corresponding output:
```
org
A B
C
10 11 [0, 1, 2, 3, 4]
20 22 [0, 1, 2]
--
unlistify(df,B)
A B
C
10 11 0
10 11 1
10 11 2
10 11 3
10 11 4
20 22 0
20 22 1
20 22 2
--
listify(df,B)
A B
C
10 11 [0, 1, 2, 3, 4]
20 22 [0, 1, 2]
```
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} | 14 | 2015-07-04T23:29:46Z | 2015-07-28T22:13:59Z | 2015-07-28T21:45:17Z | MEMBER | null | Closes #10427.
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} | 12 | 2015-07-05T23:26:35Z | 2015-08-02T21:31:20Z | 2015-08-02T21:26:30Z | CONTRIBUTOR | null | Closes #10451
I made a call here in response to my questions on #10451 and hopefully people like it. I made it clear that "precision" refers to places after the decimal, not significant figures, and changed the default value to match so that for many pandas users no change would be detected. I updated the Options docs and also What's New. For tests I basically updated the precision setting to the new semantics, so that the expected strings wouldn't need to change.
The one question I have is the code that computes `too_long`. This compares the longest formatted string against what looks to be an arbitrary constant of "number of digits + 5". Changing the 5 to a 4 or 6 doesn't trip up any unit tests. If it's desired, this could be increased by 1 as the "new" digits value is effectively 1 less than what the old value in terms of its effects on formatting, so the 5 would need to change to a 6 to maintain behavior. I could write tests on this as well.
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| null | 1 | 2015-07-06T10:09:46Z | 2015-07-06T10:31:52Z | 2015-07-06T10:31:30Z | NONE | null | Whilst answering this question: http://stackoverflow.com/questions/31242021/python-3-4-pandas-dataframe-not-responding-to-ordered-dictionary
I had look at this and found the error is in index.py line 5746 on pandas 0.16.2:
```
def _union_indexes(indexes):
if len(indexes) == 0:
raise AssertionError('Must have at least 1 Index to union')
if len(indexes) == 1:
result = indexes[0]
if isinstance(result, list):
result = Index(sorted(result)) #<---- here
return result
```
minimal example:
```
In [38]:
import pandas as pd
from collections import OrderedDict
d = OrderedDict([('XXX', OrderedDict([('B', 1), ('A', 2)]))])
pd.DataFrame(d)
Out[38]:
XXX
A 2
B 1
```
The same thing occurs if you do:
```
pd.DataFrame.from_dict(d)
```
Note that passing 'orient='index'' preserves the order:
```
In [40]:
pd.DataFrame.from_dict(d, orient='index')
Out[40]:
B A
XXX 1 2
```
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} | 1 | 2015-07-06T10:39:16Z | 2015-07-10T12:57:18Z | 2015-07-10T12:57:18Z | NONE | null | Exception stack message:
```
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-21-06190a246c08> in <module>()
2 d3_df = pd.read_csv(d3_file, sep='|', skipinitialspace=True, header=None,
3 names=d3_col_names, index_col=False, dtype=d3_dtypes,
----> 4 parse_dates=['timestamp'])
/Users/vs791j/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/io/parsers.pyc in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, na_fvalues, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, float_precision, nrows, iterator, chunksize, verbose, encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format, skip_blank_lines)
472 skip_blank_lines=skip_blank_lines)
473
--> 474 return _read(filepath_or_buffer, kwds)
475
476 parser_f.__name__ = name
/Users/vs791j/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/io/parsers.pyc in _read(filepath_or_buffer, kwds)
258 return parser
259
--> 260 return parser.read()
261
262 _parser_defaults = {
/Users/vs791j/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/io/parsers.pyc in read(self, nrows)
719 raise ValueError('skip_footer not supported for iteration')
720
--> 721 ret = self._engine.read(nrows)
722
723 if self.options.get('as_recarray'):
/Users/vs791j/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/io/parsers.pyc in read(self, nrows)
1168
1169 try:
-> 1170 data = self._reader.read(nrows)
1171 except StopIteration:
1172 if nrows is None:
pandas/parser.pyx in pandas.parser.TextReader.read (pandas/parser.c:7544)()
pandas/parser.pyx in pandas.parser.TextReader._read_low_memory (pandas/parser.c:7784)()
pandas/parser.pyx in pandas.parser.TextReader._read_rows (pandas/parser.c:8617)()
pandas/parser.pyx in pandas.parser.TextReader._convert_column_data (pandas/parser.c:9928)()
pandas/parser.pyx in pandas.parser.TextReader._convert_tokens (pandas/parser.c:10536)()
pandas/parser.pyx in pandas.parser.TextReader._convert_with_dtype (pandas/parser.c:11390)()
Exception: Integer column has NA values
```
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} | 17 | 2015-07-06T11:37:01Z | 2015-11-12T23:43:50Z | 2015-07-23T11:37:59Z | CONTRIBUTOR | null | closes #9882
This extends the Stata reader to handle version 118 (stata 14) format files.
It correctly handles the test file posted by @makmanalp. I don't have stata14 now to generate additional test files, but I'm not sure that it's necessary.
There is one point I'm not sure about, relating to the way that strls are indexed by a weird composite of a short and a 6 byte integer. The stata docs aren't clear on how this 8 byte composite should (or shouldn't) be byteswapped when the file and local endianness don't match. We currently byteswap it in this case (inheriting from our dta117 code) but I'm not sure that this is correct.
I can confirm that it works when the file and local machine are both little endian, which covers the most common situation.
I haven't looked at the writer much, it seems to always write version 114 files.
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} | 2 | 2015-07-06T17:46:15Z | 2020-12-06T22:11:18Z | 2019-12-22T16:56:11Z | CONTRIBUTOR | null | As mentioned in #4052. [ReadStat](https://github.com/WizardMac/ReadStat) is an MIT-licensed C library for reading and writing R, SAS, Stata, and SPSS binary files. [Haven](https://github.com/hadley/haven) uses it.
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} | 1 | 2015-07-06T18:27:37Z | 2015-07-07T15:53:59Z | 2015-07-07T15:53:55Z | CONTRIBUTOR | null | The HDF5 documentation "Notes & Caveats" defines a PyTables NaturalNameWarning vaguely, and incorrectly states that a natural name may not begin with an underscore. I clarified the definition of a natural identifier. The relevant regex is defined [here](https://github.com/PyTables/PyTables/blob/2fde39957c7b5263dd4d0b11aa797900250f16bc/tables/path.py#L39) in the PyTables source.
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} | 6 | 2015-07-06T19:44:22Z | 2019-01-26T15:19:38Z | 2015-07-06T20:19:07Z | CONTRIBUTOR | null | ``` python
def foo(x):
print "hello"
_ = pd.DataFrame({'a':[1]}).groupby(["a"]).apply(foo)
```
```
hello
hello
```
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} | 1 | 2015-07-06T21:15:58Z | 2015-07-07T10:19:46Z | 2015-07-07T10:19:46Z | CONTRIBUTOR | null | closes #10154
This addresses the original issue and another discovered with the test cases.
Please review the tests to be sure you agree with the assertions.
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} | 5 | 2015-07-06T22:58:51Z | 2020-12-30T13:50:13Z | 2020-12-30T13:50:13Z | NONE | null | `DF.loc['A', :, 1]` returns a DataFrame with the full MultiIndex if the second level has more than one entry, and a truncated index if the second level has only one entry (same as, e.g., `DF.loc['A',0,1]`). Is this the intended behavior?
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} | 3 | 2015-07-07T07:57:57Z | 2015-07-07T11:23:26Z | 2015-07-07T09:28:41Z | CONTRIBUTOR | null | Hello,
I noticed
```
df_ohlc['high'].sort(ascending=False)
```
raises
```
ValueError: This Series is a view of some other array, to sort in-place you must create a copy
```
which seems to mean that, by default, sort uses by default `inplace=True`
but according to
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sort.html
```
inplace : boolean, default False
```
Here is a minimal example:
```
import pandas as pd
import numpy as np
N = 1000
rng = pd.date_range('1/1/2011', periods=N, freq='H')
s = pd.Series(np.random.random(N), index=rng)
df_ohlc = s.resample('1D', how='ohlc')
df_ohlc['high'].sort(ascending=False)
```
raises `ValueError: This Series is a view of some other array, to sort in-place you must create a copy`
```
df_ohlc['high'].sort(ascending=False, inplace=True)
```
raises same exception
but
```
df_ohlc['high'].sort(ascending=False, inplace=False)
```
doesn't raises exception
Kind regards
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} | 6 | 2015-07-07T13:52:27Z | 2021-01-06T09:18:38Z | 2018-11-13T22:36:42Z | NONE | null | As far as I can tell, both methods of reading an Excel sheet (through ExcelFile and read_excel) do not have the option to avoid mangling duplicate columns, which exists for read_csv (https://github.com/pydata/pandas/issues/3468).
I have an Excel file that looks like this:
```
foo foo bar bar baz baz
A B A B A B
123 456 789 12 345 678
901 234 567 890 123 456
789 12 345 678 901 234
```
I initially encountered this problem while trying to find a workaround for not being able to specify a multirow-header (#4679). Reading in this Excel file with header = 0 (default) mangles the column names:
```
>>> df = pd.read_excel("dupe_cols.xlsx", header = 0)
>>> print df
foo foo.1 bar bar.1 baz baz.1
0 A B A B A B
1 123 456 789 12 345 678
2 901 234 567 890 123 456
3 789 12 345 678 901 234
```
Unlike `read_csv`, `read_excel` does not have the option to avoid mangling duplicate columns (using ExcelFile.parse works the same as far as I can see). Specifying header = None in `read_excel` and then assigning the column names to the first row will effectively allow you to avoid mangling the column names. You could also read in the Excel sheet with header = None, save a .csv with header = False and index = False, then read that csv and specify mangle_dupe_cols = False to get the dataframe you want. (Incidentally, this also allows you to specify multiple rows as the header, which is the behavior I was originally trying to emulate.)
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| null | 7 | 2015-07-07T18:14:18Z | 2015-07-07T22:01:48Z | 2015-07-07T22:01:48Z | CONTRIBUTOR | null | I found this behavior confusing.
``` python
df = pd.DataFrame(dict(a=["a", "a", "a", "b", "b", "b"],
x=[True, True, False, True, np.nan, False]))
```
This works fine:
``` python
df.x.mean()
0.59999999999999998
```
But this fails:
``` python
df.groupby("a").x.mean()
---------------------------------------------------------------------------
DataError Traceback (most recent call last)
<ipython-input-34-867769d888c3> in <module>()
----> 1 df.groupby("a").x.mean()
/Users/mwaskom/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc in mean(self)
698 """
699 try:
--> 700 return self._cython_agg_general('mean')
701 except GroupByError:
702 raise
/Users/mwaskom/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc in _cython_agg_general(self, how, numeric_only)
1101
1102 if len(output) == 0:
-> 1103 raise DataError('No numeric types to aggregate')
1104
1105 return self._wrap_aggregated_output(output, names)
DataError: No numeric types to aggregate
```
Happy to be kicked over to StackOverflow, but I cannot make heads or tails of this behavior.
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| null | 3 | 2015-07-07T18:38:34Z | 2015-07-08T04:44:49Z | 2015-07-07T19:18:50Z | CONTRIBUTOR | null | This came up during the SciPy 2015 tutorial.
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| null | 6 | 2015-07-07T19:28:17Z | 2020-10-02T23:24:44Z | 2020-10-02T23:24:43Z | NONE | null | Today I tried to read json data from an url that checks the Accept-Header for 'application/json',
and only delivers json if this tag is higher ranked than 'text/html'.
See: [Flask: Handling Accept Headers](http://flask.pocoo.org/snippets/45/)
It seems, that the pandas request in '_url_open' has no such header at all.
This works:
``` python
import requests
import pandas
url = "http://localhost:5000/foo"
r = requests.get(url, headers={'Accept': 'application/json'})
data = pandas.DataFrame(r.json())
print(data.head())
```
This does not, and I checked the header in the flask webapp of the received request, it is empty:
``` python
import pandas
url = "http://localhost:5000/foo"
data = pandas.read_json(url)
print(data.head())
```
It would make sense if the `read_json` had `application/json` in its Accept-Header, correct?
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} | 10 | 2015-07-08T04:04:17Z | 2015-07-18T13:30:47Z | 2015-07-18T13:30:43Z | CONTRIBUTOR | null | To close #9618
Note I modified `encode`, so it's not backward compatible.
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} | 2 | 2015-07-08T06:06:14Z | 2015-07-08T09:29:06Z | 2015-07-08T09:29:06Z | NONE | null | ```
import pandas as pd
index = pd.date_range("2015-01-01", "2015-10-1", freq="W-SUN")
s = pd.Series(index)
print index.dtype
print s.dtype
print index.tz_localize("utc").dtype
print s.dt.tz_localize("utc").dtype
```
here is the output:
```
datetime64[ns]
datetime64[ns]
datetime64[ns]
object
```
`s.dt.tz_localize("utc")` is an array of `Timestamp` objects instead of int64 with `datetime64[ns]` dtype.
Here is `show_versions()` output:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.16.2
nose: 1.3.4
Cython: 0.21.2
numpy: 1.9.1
scipy: 0.15.0
statsmodels: 0.6.1
IPython: 3.1.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.3
pytz: 2014.10
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.3
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.7.3
lxml: None
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/10529 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10529/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10529/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10529/events | https://github.com/pandas-dev/pandas/pull/10529 | 93,770,462 | MDExOlB1bGxSZXF1ZXN0Mzk0Njg2OTA= | 10,529 | BUG : read_csv() twice decodes stream on URL file #10424 | {
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} | 3 | 2015-07-08T11:53:41Z | 2015-10-25T15:29:19Z | 2015-10-25T15:29:12Z | NONE | null | As described in #10424 , reading non-utf-8 csv files from URL leads to decoding problems, i.e. a decoding may first be made in io.common.get_filepath_or_buffer() when file is URL.
Modification done makes this function read stream from URL without decoding (this is done later, at the same place as for local files).
It's also used by io.common.read_stata(), io.common.read_json() and io.common.read_msgpack(). Similar problems reading stata and msgpack URL files may also be solved using this modification.
Thanks for reviewing.
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} | 3 | 2015-07-08T16:20:46Z | 2018-09-27T13:46:13Z | 2015-08-15T22:41:03Z | CONTRIBUTOR | null | ``` python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.normal(size=(100,4)))
df.index = pd.timedelta_range(start='0s', periods=100, freq='s')
print df.resample('10s',base=5)
```
The resampled index is still aligned to 0.
The base argument seems to never be used, it doesn't raise any error when passing `base=1223456879`, or `base='gjhmljpouj'`, or even `base=df`
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} | 6 | 2015-07-08T18:24:19Z | 2015-07-21T10:34:53Z | 2015-07-21T10:34:53Z | NONE | null | Just like it says in the subject. Here's an example:
``` python
In [216]: pd.version.version
Out[216]: '0.16.2'
In [217]: df = pd.DataFrame(np.random.randint(10,size=(10000,5)),columns=list('abcde'))
In [218]: df.head()
Out[218]:
a b c d e
0 2 6 1 4 0
1 5 2 6 8 5
2 0 8 7 3 1
3 9 2 6 3 1
4 4 8 6 8 6
In [219]: ddf = pd.get_dummies(df,columns=df.columns,sparse=True)
In [220]: ddf.head()
Out[220]:
a_0 a_1 a_2 a_3 a_4 a_5 a_6 a_7 a_8 a_9 ... e_0 e_1 e_2 e_3 \
0 0 NaN 1 NaN 0 0 NaN NaN NaN 0 ... 1 0 NaN NaN
1 0 NaN 0 NaN 0 1 NaN NaN NaN 0 ... 0 0 NaN NaN
2 1 NaN 0 NaN 0 0 NaN NaN NaN 0 ... 0 1 NaN NaN
3 0 NaN 0 NaN 0 0 NaN NaN NaN 1 ... 0 1 NaN NaN
4 0 NaN 0 NaN 1 0 NaN NaN NaN 0 ... 0 0 NaN NaN
e_4 e_5 e_6 e_7 e_8 e_9
0 NaN 0 0 NaN NaN NaN
1 NaN 1 0 NaN NaN NaN
2 NaN 0 0 NaN NaN NaN
3 NaN 0 0 NaN NaN NaN
4 NaN 0 1 NaN NaN NaN
[5 rows x 50 columns]
In [221]: type(ddf)
Out[221]: pandas.core.frame.DataFrame
In [222]: hasattr(ddf,'density')
Out[222]: False
In [223]: ddf = ddf.to_sparse()
In [224]: type(ddf)
Out[224]: pandas.sparse.frame.SparseDataFrame
In [225]: ddf.density
Out[225]: 0.1
```
I notice the NaN encoding in the DataFrame returned by `get_dummies` when `sparse=True` but the datatype is not sparse. Is this expected behavior?
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| null | 1 | 2015-07-09T06:54:14Z | 2015-07-09T06:59:54Z | 2015-07-09T06:59:39Z | NONE | null | I have two dataframes.
sharesdb:
```
PARTICIPANTNAME 2015-06-28
```
0 THE HONGKONG AND SHANGHAI BANKING 765945421
1 THE HONGKONG AND SHANGHAI BANKING 398945696
tmpdb:
```
PARTICIPANTNAME 2015-06-29
```
0 THE HONGKONG AND SHANGHAI BANKING 766433989
1 THE HONGKONG AND SHANGHAI BANKING 398817122
Then I run the following:
pd.merge(sharesdb, tmpdb, on='PARTICIPANTNAME', how='outer')
The result is:
```
PARTICIPANTNAME 2015-06-28 2015-06-29
```
0 THE HONGKONG AND SHANGHAI BANKING 765945421 766433989
1 THE HONGKONG AND SHANGHAI BANKING 765945421 398817122
2 THE HONGKONG AND SHANGHAI BANKING 398945696 766433989
3 THE HONGKONG AND SHANGHAI BANKING 398945696 398817122
However, what I expected should be:
```
PARTICIPANTNAME 2015-06-28 2015-06-29
```
0 THE HONGKONG AND SHANGHAI BANKING 765945421 766433989
1 THE HONGKONG AND SHANGHAI BANKING 398945696 398817122
Is it a bug?
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| null | 1 | 2015-07-09T10:14:49Z | 2021-04-18T07:14:55Z | null | CONTRIBUTOR | null | When we call the interpolate function based on time, it would be useful to be able to give a limit expressed as a timedelta (instead of a number of row).
Use case
- I've a time series measurement of temperature in function of time. The measurements are not performed at a constant frequency
- I want to interpolate missing data if the interpolation period is less than 1 hour.
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Options to read them as string?
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} | 5 | 2015-07-09T18:14:28Z | 2015-07-21T10:34:24Z | 2015-07-21T10:34:24Z | CONTRIBUTOR | null | Fixes #10531 .
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} | 1 | 2015-07-09T18:16:36Z | 2016-04-18T19:55:34Z | 2016-04-18T19:55:34Z | CONTRIBUTOR | null | Concatenating two sparse series does not return sparse data structures as expected:
```
In [1]: import pandas as pd
In [2]: ts = pd.Series([0, 1, 1, 2, 3, 0 ,0 ,0])
In [3]: sts = ts.to_sparse()
In [4]: print type(pd.concat([sts, sts], axis=0))
<class 'pandas.core.series.Series'>
In [5]: print type(pd.concat([sts, sts], axis=1))
<class 'pandas.core.frame.DataFrame'>
```
The above _does_ work correctly for SparseDataFrames.
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} | 2 | 2015-07-09T19:00:52Z | 2015-07-14T15:52:23Z | 2015-07-14T15:52:23Z | NONE | null | ```
>>> pd.Timestamp('2014-06')
Timestamp('2014-06-01 00:00:00')
>>> pd.Timestamp('06-2014')
Timestamp('2014-06-09 00:00:00')
```
When parsing a string "YYYY-MM", the day defaults to the 1st of the month (the behavior I would expect, consistent with defaulting hour minute and second to 0), while "MM-YYYY" defaults the day to today's day. If the string format is specified manually as in `pd.to_datetime('06-2014',format='%m-%Y')`, the day consistently defaults to the first of the month.
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| null | 6 | 2015-07-09T22:43:39Z | 2015-08-20T13:05:10Z | 2015-08-20T13:05:10Z | NONE | null | Merging two data frames, the first using the index, and the second using a column,with a left join has an unusual behavior. Logically, the merged Dataframe should keep the index column, and discard the right Dataframe's join column(a right join logically has the reverse argument). Here is an illustrative example:
``` python
import pandas as pd
df1 = pd.DataFrame({'a':['bb','zz','dd'],'g':[10,20,30]})
df1.set_index(inplace=True)
df2 = pd.DataFrame({'a1':['aa','bb','zz','qq','tt','dd'],'b':[4,2,7,8,6,3]})
dfj = pd.merge(df1,df2,how='left',left_index=True,right_on='a1')
print(dfj)
g a1 b
1 10 bb 2
2 20 zz 7
5 30 dd 3
```
This deletes the index column, and outputs the right data frame's column. I am not sure why this would be desirable over 'a' remaining the index, and only column b being added to the outputted data frame.
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} | 6 | 2015-07-09T23:45:37Z | 2020-09-26T22:59:04Z | 2020-09-26T22:59:04Z | CONTRIBUTOR | null | Shows up whenver you have multiple blocks of the same type in a dataframe.
Int example:
```
In [1]: a = pd.Series([1,2,3])
In [2]: b = pd.Series([4,5,6])
In [3]: df1 = pd.DataFrame({'a': a, 'b': b})
In [4]: df2 = pd.DataFrame({'a': a}).assign(b=b)
In [5]: df1._data.blocks
Out[5]: (IntBlock: slice(0, 2, 1), 2 x 3, dtype: int64,)
In [6]: df2._data.blocks
Out[6]:
(IntBlock: slice(0, 1, 1), 1 x 3, dtype: int64,
IntBlock: slice(1, 2, 1), 1 x 3, dtype: int64)
In [7]: df1
Out[7]:
a b
0 1 4
1 2 5
2 3 6
In [8]: df2
Out[8]:
a b
0 1 4
1 2 5
2 3 6
In [9]: df1.shift(1, axis=1)
Out[9]:
a b
0 NaN 1
1 NaN 2
2 NaN 3
In [10]: df2.shift(1, axis=1)
Out[10]:
a b
0 NaN NaN
1 NaN NaN
2 NaN NaN
```
Or with different data types:
```
In [2]: df = pd.DataFrame({'a': [1,2,3], 'b': ['A', 'B', 'C']})
In [3]: df
Out[3]:
a b
0 1 A
1 2 B
2 3 C
In [4]: df.shift(1, axis=1)
Out[4]:
a b
0 NaN NaN
1 NaN NaN
2 NaN NaN
```
I expect a similar issue pops up with panels and ndframes when shifting across the axis where dtypes can change.
Perhaps this can be fixed by having shift move the column labels rather than the data here?
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} | 5 | 2015-07-10T00:11:38Z | 2020-08-11T11:14:03Z | null | CONTRIBUTOR | null | DataFrame.drop_duplicates can be useful to 'sparsify' a frame, requiring less
memory/storage. However, it doesn't handle the case where a value later reverts
to an earlier value. For example:
```
In [2]: df = pd.DataFrame(index=pd.date_range('20020101', periods=5, freq='D'),
data={'poll_support': [0.3, 0.4, 0.4, 0.4, 0.3]})
In [3]: df
Out[3]:
poll_support
2002-01-01 0.3
2002-01-02 0.4
2002-01-03 0.4
2002-01-04 0.4
2002-01-05 0.3
In [4]: df.drop_duplicates()
Out[4]:
poll_support
2002-01-01 0.3
2002-01-02 0.4
```
Would be ideal to be able to do something like:
```
In [4]: df.drop_duplicates(consecutive=True)
Out[4]:
poll_support
2002-01-01 0.3
2002-01-02 0.4
2002-01-05 0.3
```
This should also be a much faster operation, since you only have to compare each
row with its successor, rather with all other rows.
You can achieve something like this with some shift trickery:
```
In [5]: s1 = df.shift(1)
In [6]: different = (s1 != df) & (s1.notnull() | df.notnull())
In [7]: df.drop(df.index[~different.any(axis=1)], axis=0)
Out[7]:
poll_support
2002-01-01 0.3
2002-01-02 0.4
2002-01-05 0.3
```
But this is somewhat cumbersome, and allocating the intermediate shifted
frame can be slow (particularly if done via a groupby with a lot of groups).
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} | 1 | 2015-07-10T02:57:13Z | 2015-07-10T12:57:14Z | 2015-07-10T12:57:06Z | CONTRIBUTOR | null | Made a quick change to the `Exception` raised in `_convert_with_dtype` per the issue raised in #10515.
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} | 3 | 2015-07-10T20:04:16Z | 2015-07-11T20:07:32Z | 2015-07-11T16:47:31Z | CONTRIBUTOR | null | Notes:
- I did not use `is_platform_xxx` on `pandas/util/clipboard.py` because this module is a fork. So I guess better not deviate from the original, right?
- I added the helper `_skip_if_windows` function in `pandas/util/testing.py`, I added an underscore before `skip` to maintain consistency with other `skip` functions (although none of them should have this underscore...)
> I think we could use an update on the wiki on the same
I didnt find any references on the wiki or docs.
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} | 14 | 2015-07-11T04:41:10Z | 2015-08-15T22:41:23Z | 2015-08-15T22:41:23Z | CONTRIBUTOR | null | closes #10530
I'm not 100% sure that I implemented this per the documentation or that my tests are complete but I'd be glad to make any necessary changes.
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| null | 1 | 2015-07-11T08:22:44Z | 2015-07-11T13:08:36Z | 2015-07-11T13:08:36Z | CONTRIBUTOR | null | Creating an empty Series with `dtype` as a string-type results in an instance with `dtype=object`.
``` python
import pandas as pd
dtype = 'S1'
ps = pd.Series([], dtype=dtype)
assert ps.dtype == dtype, "Series dtype = %r; requested dtype = %r" % (ps.dtype, dtype)
```
```
Traceback (most recent call last):
File "issue_2.py", line 5, in <module>
assert ps.dtype == dtype, "Series dtype = %r; requested dtype = %r" % (ps.dtype, dtype)
AssertionError: Series dtype = dtype('O'); requested dtype = 'S1'
```
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| null | 1 | 2015-07-11T08:41:40Z | 2015-07-11T13:04:03Z | 2015-07-11T13:03:53Z | NONE | null | When working with a subclass of `Series` or a `DataFrame` the type is not maintained.
``` py
In [1]: import pandas as pd
In [2]: class Subclassed(pd.Series):
...: pass
...:
In [3]: type(Subclassed(range(10)))
Out[3]: __main__.Subclassed
In [4]: type(Subclassed(range(10)) + Subclassed(range(10)))
Out[4]: pandas.core.series.Series
In [5]: pd.__version__
Out[5]: '0.16.2'
```
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} | 0 | 2015-07-11T14:09:15Z | 2015-07-20T21:19:08Z | 2015-07-20T21:19:08Z | MEMBER | null | ```
import pandas as pd
# scalar (OK)
s = 1
pd.eval('s', engine='numexpr')
# 1L
pd.eval('s', engine='python')
#1
# array
a = np.array([1])
# OK
pd.eval('a', engine='python')
# array([1])
# NG
pd.eval('a', engine='numexpr')
# 1L
```
Internally, we can distinguish them using returned shape.
```
import numexpr as ne
ne.evaluate('s')
# array(1L)
ne.evaluate('a')
# array([1])
ne.evaluate('s').shape
# ()
ne.evaluate('a').shape
# (1,)
```
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} | 1 | 2015-07-11T15:28:16Z | 2015-08-14T18:40:52Z | 2015-08-14T18:40:52Z | CONTRIBUTOR | null | When a `Series` is of length one, I would expect `iloc[[-1]]` to produce the same result as `iloc[[0]]`. However `iloc[[-1]]` produces an error, even though `iloc[[0]]` and `iloc[-1]` work.
```
In [178]: import pandas as pd
In [179]: pd.Series(['a', 'b'], index=['A', 'B']).iloc[[-1]]
Out[179]:
B b
dtype: object
In [180]: pd.Series(['a'], index=['A']).iloc[[-1]] # BUG: This should return the same as [181].
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-180-47228385ee31> in <module>()
----> 1 pd.Series(['a'], index=['A']).iloc[[-1]]
C:\Python34\lib\site-packages\pandas\core\indexing.py in __getitem__(self, key)
1187 return self._getitem_tuple(key)
1188 else:
-> 1189 return self._getitem_axis(key, axis=0)
1190
1191 def _getitem_axis(self, key, axis=0):
C:\Python34\lib\site-packages\pandas\core\indexing.py in _getitem_axis(self, key, axis)
1463
1464 # validate list bounds
-> 1465 self._is_valid_list_like(key, axis)
1466
1467 # force an actual list
C:\Python34\lib\site-packages\pandas\core\indexing.py in _is_valid_list_like(self, key, axis)
1402 l = len(ax)
1403 if len(arr) and (arr.max() >= l or arr.min() <= -l):
-> 1404 raise IndexError("positional indexers are out-of-bounds")
1405
1406 return True
IndexError: positional indexers are out-of-bounds
In [181]: pd.Series(['a'], index=['A']).iloc[[0]]
Out[181]:
A a
dtype: object
In [182]: pd.Series(['a'], index=['A']).iloc[-1]
Out[182]: 'a'
In [183]: pd.__version__
Out[183]: '0.16.2'
```
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} | 3 | 2015-07-11T18:45:44Z | 2015-08-17T11:09:50Z | 2015-08-17T11:09:50Z | NONE | null | xref #4623
Reading this file produces the expected behavior:
```
1,2,3
3,8,3
#comment
5,5,5
```
```
In [52]: pandas.read_csv('commentBugTest.csv',comment='#')
Out[52]:
1 2 3
0 3 8 3
1 5 5 5
```
However, if the last line in the file is a comment, you get unexpected NaNs:
```
1,2,3
3,8,3
#comment
```
```
In [51]: pandas.read_csv('commentBugTest2.csv',comment='#')
Out[51]:
1 2 3
0 3 8 3
1 NaN NaN NaN
```
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} | 7 | 2015-07-11T20:12:55Z | 2021-11-15T19:03:25Z | null | NONE | null | So here's my setup (using pandas 0.16.2):
``` python
>>> midx = pd.MultiIndex.from_product([['bar', 'baz', 'foo', 'qux'], ['one', 'two']],names=['first','second'])
>>> df = pd.DataFrame(np.random.randint(10,size=(8,8)),index=midx)
>>> df
0 1 2 3 4 5 6 7
first second
bar one 0 5 5 5 6 2 6 8
two 2 6 9 0 3 6 7 9
baz one 9 0 9 9 2 5 7 4
two 4 8 1 2 9 2 8 1
foo one 2 7 3 6 5 5 5 2
two 3 4 6 2 7 7 1 2
qux one 0 8 5 9 5 5 7 3
two 7 4 0 7 3 6 8 6
```
I recently found that I can select multiple levels by indexing with a tuple of tuples
```
>>> df.loc[( ('bar','baz'), ), :]
0 1 2 3 4 5 6 7
first second
bar one 0 5 5 5 6 2 6 8
two 2 6 9 0 3 6 7 9
baz one 9 0 9 9 2 5 7 4
two 4 8 1 2 9 2 8 1
```
Or even select at multiple depths of levels
```
>>> df.loc[( ('bar','baz'), ('one',) ), :]
0 1 2 3 4 5 6 7
first second
bar one 0 5 5 5 6 2 6 8
baz one 9 0 9 9 2 5 7 4
```
The issue is this: if I add any levels to the index tuple that don't exist in the dataframe, pandas drops them silently
```
>>> df.loc[( ('bar','baz','xyz'), ('one',) ), :]
0 1 2 3 4 5 6 7
first second
bar one 0 5 5 5 6 2 6 8
baz one 9 0 9 9 2 5 7 4
```
It seems to me like this should raise an exception since
1. The shape of the dataframe that is returned in this instance is not what you'd expect
2. There's no way to unambiguously fill the returned dataframe with NaNs where a level didn't exist (as is done in the case where there is only a single level index)
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} | 4 | 2015-07-11T20:56:41Z | 2019-10-06T01:58:55Z | 2019-10-06T01:58:55Z | NONE | null | ...perhaps this is the desired behavior, but it is not at all what I expected and had me very confused for a good while as I tried to figure out why I suddenly couldn't change values in the DataFrame I was using like I always had before.
Below is an MWE. Basically what happens is that if you assign a value to a row that did not previously exist, it get assigned to the row of that column, but then the DataFrame still seems to use the original column. That means that updating a value for a row that was in the original table doesn't update it in the DataFrame afterwards.
```
>>> import pandas as pd
>>> from numpy import zeros
>>> df = pd.DataFrame(data=zeros([2,3]), index=['row1', 'row2'], columns=['col1' ,'col2', 'col3'])
>>> df
col1 col2 col3
row1 0 0 0
row2 0 0 0
>>> df['col1']['row1'] = 1
>>> df
col1 col2 col3
row1 1 0 0
row2 0 0 0
>>> df['col1']['row3'] = 2
>>> df
col1 col2 col3
row1 1 0 0
row2 0 0 0
>>> df['col1']
row1 1
row2 0
row3 2
Name: col1, dtype: float64
>>> df['col1']['row2'] = 3
>>> df
col1 col2 col3
row1 1 0 0
row2 0 0 0
>>> df['col1']
row1 1
row2 3
row3 2
Name: col1, dtype: float64
```
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| null | 1 | 2015-07-11T21:10:52Z | 2015-07-11T21:23:46Z | 2015-07-11T21:23:46Z | MEMBER | null | Like R's `stringsAsFactors`, it is nice to create `category` when read data. Currently, it raises `TypeError`.
```
import pandas as pd
import StringIO
pd.read_csv(StringIO.StringIO("""A, B
a, c
b, d"""), dtype={'A': 'category'})
# TypeError: data type "category" not understood
```
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} | 3 | 2015-07-11T21:18:36Z | 2021-06-12T00:42:34Z | null | MEMBER | null | All of the following should be equivalent:
```
In [21]: df = pd.DataFrame({'a': [1], 'b': [2], 'c': [3]}).set_index(['a', 'b'])
In [22]: df.loc[1]
Out[22]:
c
b
2 3
In [23]: df.loc[(1,), slice(None)]
Out[23]:
c
b
2 3
In [24]: df.loc[(1, slice(None)), slice(None)]
Out[24]:
c
a b
1 2 3
```
In the last output, there should no longer be a level `a`.
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} | 2 | 2015-07-11T21:36:54Z | 2020-12-07T08:17:00Z | 2015-07-13T02:40:09Z | NONE | null | In a subclass of pandas objects, pickling an object doesn't serialize properties of an instance of that subclass, even if the attribute has been added to `_metadata`. It would be hard to override this behavior entirely in the subclass, because it will require updates to `__getstate__` and `__setstate__`, and probably also an addition or subclass of `BlockManager`. It would be nice if the `_metadata` serialization was handled in the base pandas class.
Example:
```
class SubDataFrame(DataFrame):
_metadata = ['my_data']
@property
def _constructor(self):
return SubDataFrame
sdf = SubDataFrame()
sdf.my_data = 'foo'
sdf.to_pickle('tmp.pkl')
new_sdf = read_pickle('tmp.pkl')
new_sdf.my_data
```
raises `AttributeError: 'SubDataFrame' object has no attribute 'my_data'`
(edited original example for correctness)
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} | 1 | 2015-07-12T13:55:16Z | 2015-07-12T13:58:18Z | null | NONE | null | Hi all,
I observed now several times that `pivot_table()` requires a lot of memory when I wanted to reshape a `Data.Frame` in table format (id, name, value) into matrix format (index=id, columns=name, values=value):
``` python
print(d)
# id sample value
1 A 1
2 A 2
1 B 10
2 B 20
..... (many rows)
e = pd.pivot_table(d, index='id', columns='sample', values='value')
print(e)
A B
1 1 2
2 10 20
```
Is this a known issue? Is there a workaround?
For data maintenance, it's often times better to store large data in table format, and convert them to matrix format on demand. The R `reshape` and `gather` functions (http://goo.gl/isx1bv) from the dplyr package seem to be faster and require less memory than `pivot_table` and `melt`.
Cheers,
Christof
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| null | 2 | 2015-07-12T19:57:47Z | 2015-07-13T15:31:06Z | 2015-07-13T11:56:05Z | MEMBER | null | @jonathanrocher can follow up with a specific example
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} | 22 | 2015-07-12T20:06:31Z | 2020-05-25T21:46:18Z | null | MEMBER | null | In my discussion with Jonathan and others and at the SciPy sprints, we agreed that it would be really nice to expose some minimal tools for manipulating and view the internal pandas blocks system. For example, it should be possible to:
1. manually consolidate blocks
2. view a representation of the internal blocking of a dataframe (via matplotlib?)
It's not so much that we want to create and use blocks directly, but that we want to make it easier to understand the internal data model and make performance with more predictable.
At the same time, we would like to disable automatic consolidation of blocks in the DataFrame constructor and when inserting new columns. Consolidation is certainly a useful feature, but it is currently not always possible to even predict when it will happen.
Most users never notice or care about consolidation. Power users (concerned about memory or performance) are at least as likely to find it frustrating as helpful, so we should make this something that they can trigger explicitly (as part of the blocks API). This would make it possible to create dataframes while guaranteeing that none of the data is copied (#9216).
cc @jonathanrocher @sinhrks @jreback @cpcloud @TomAugspurger @ARF1 @quicknir
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} | 6 | 2015-07-12T20:17:27Z | 2015-07-13T11:31:29Z | 2015-07-13T02:40:09Z | NONE | null | Fixes #10553
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} | 2 | 2015-07-12T21:35:36Z | 2015-07-20T21:19:12Z | 2015-07-20T21:19:08Z | MEMBER | null | Closes #10546. Used `assert_numpy_atray_equivalent` to guarantee array comparison, not scalar. This should be prior to #10542 and being changed to use `assert_numpy_array_equal` in #10542.
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} | 2 | 2015-07-12T21:46:02Z | 2017-05-23T19:36:28Z | 2017-05-23T19:36:28Z | NONE | null | pd.read_excel() takes "sheetname" as an argument.
pd.DataFrame.to_excel() takes "sheet_name" as an argument.
For consistency, I think one ought to be changed to match the other.
On my windows installation of pandas 0.16.2, I modified to_excel to take sheetname rather than sheet_name. All I had to do was locate frame.py within the core directory. to_excel() is defined here, and sheet_name is written twice in the function and once in the docstring.
Very new to github so if ideas like these are more appropriately filed through PRs I apologize.
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} | 1 | 2015-07-13T05:12:40Z | 2016-04-03T14:20:30Z | 2016-04-03T14:20:30Z | CONTRIBUTOR | null | ``` python
In [2]: pd.__version__
Out[2]: '0.16.2-123-gdf1f5cf'
In [3]: pd.options.display.max_rows = 3
In [4]: pd.Series(randn(3)).to_sparse()
Out[4]:
0 1.100684
1 -0.924482
2 -0.106069
dtype: float64
BlockIndex
Block locations: array([0], dtype=int32)
Block lengths: array([3], dtype=int32)
In [5]: pd.Series(randn(4)).to_sparse()
Out[5]: ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
...
TypeError: cannot concatenate a non-NDFrame object
```
Set `max_rows=3` for demonstration, but occurs also for the default `max_rows=60`.
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} | 3 | 2015-07-13T18:12:06Z | 2018-07-06T20:21:07Z | 2017-07-10T21:07:20Z | NONE | null | This involves incorrect pickling of Pandas Panel values. From what I've seen, it occurs when new values of a different data type are assigned to a Pandas Panel axis. Everything functions well until the panel is pickled. When the pickle is read the old values appear. In the example below floats are reassigned as objects, but when the pickle is loaded back up, it contains the old float values. Also noted:
- does not happen with DataFrames
- does not happen if the new values are of the same data type as the old ones (in that case id() of the Series is the same)
- happens whenever data type is changed (in the snippet below float to str but happens in reverse too).
``` python
import pandas as pd
import numpy as np
import pickle
wp = pd.Panel(np.random.randn(2,3,2),
items=['Item1', 'Item2'],
major_axis=[1,2,3],
minor_axis=['float', 'string'])
print wp['Item1']
wp['Item1']['string'] = 'blue'
print wp['Item1']
with open('test_pickle', 'wb') as f:
pickle.dump(wp, f)
wp2 = pickle.load( open('test_pickle', 'rb') )
print wp2['Item1']
```
UPDATE: It's not a pickle problem. For example, a transpose operation on the Panel also erases the new values and brings back the old ones.
``` python
wp=wp.transpose(2,0,1)
wp=wp.transpose(1,2,0)
print wp['Item1']
```
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} | 4 | 2015-07-14T12:52:35Z | 2015-08-20T20:33:14Z | 2015-08-20T20:33:14Z | CONTRIBUTOR | null | xref https://github.com/pydata/pandas/issues/9794
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} | 2 | 2015-07-14T13:26:08Z | 2016-02-02T19:53:54Z | 2016-02-02T19:53:46Z | MEMBER | null | ``` python
In [10]: s = pd.Series(np.arange(1000.0), name='foo')
In [11]: s.name
Out[11]: 'foo'
In [12]: pd.rolling_mean(s, 30).name is None
Out[12]: True
In [13]: pd.__version__
Out[13]: '0.16.2'
```
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} | 7 | 2015-07-14T13:59:54Z | 2015-08-20T13:23:56Z | 2015-08-20T13:23:56Z | CONTRIBUTOR | null | I'd like to remove `bind_method` from [here](https://github.com/pydata/pandas/blob/master/pandas/compat/__init__.py#L149-171) and [here](https://github.com/pydata/pandas/blob/master/pandas/core/common.py#L116-138), as I don't see any benefit over `setattr(cls, name, func)`. Is there any?
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} | 11 | 2015-07-14T14:50:06Z | 2018-02-13T21:01:32Z | 2016-08-12T15:44:54Z | NONE | null | xref addl example in #13650
combine_first() loses timezone information for datetime columns
```
dts1 = pd.date_range('20150101','20150105',tz='UTC')
df1 = pd.DataFrame({'DATE':dts1})
dts2 = pd.date_range('20150103','20150105',tz='UTC')
df2 = pd.DataFrame({'DATE':dts2})
df = df1.combine_first(df2)
df.DATE[0].tz # this shows up as None
```
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} | 29 | 2015-07-14T16:34:04Z | 2015-09-03T13:45:36Z | 2015-09-03T13:41:10Z | CONTRIBUTOR | null | I've found myself doing a lot of `DataFrame.to_latex` because I'm using pandas to write an academic paper.
I'm constantly messing about with the number of decimal places displayed by doing `np.round(df, 2)` so thought this flexible `round`, with different numbers of decimals per column, should be part of the `DataFrame` API (I'm surprised there isn't already such a piece of functionality.)
Here is an example:
```
In [9]: df = pd.DataFrame(np.random.random([10, 3]), columns=['a', 'b', 'c'])
In [10]: df
Out[10]:
a b c
0 0.761651 0.430963 0.440312
1 0.094071 0.242381 0.149731
2 0.620050 0.462600 0.194143
3 0.614627 0.692106 0.176523
4 0.215396 0.888180 0.380283
5 0.492990 0.200268 0.067020
6 0.804531 0.816366 0.065751
7 0.751224 0.037474 0.884083
8 0.994758 0.450143 0.808945
9 0.373180 0.537589 0.809112
In [11]: df.round(dict(b=2, c=4))
Out[11]:
a b c
0 0.761651 0.43 0.4403
1 0.094071 0.24 0.1497
2 0.620050 0.46 0.1941
3 0.614627 0.69 0.1765
4 0.215396 0.89 0.3803
5 0.492990 0.20 0.0670
6 0.804531 0.82 0.0658
7 0.751224 0.04 0.8841
8 0.994758 0.45 0.8089
9 0.373180 0.54 0.8091
```
You can also round by column number:
```
In [12]: df.round([1, 2, 3])
Out[12]:
a b c
0 0.8 0.43 0.440
1 0.1 0.24 0.150
2 0.6 0.46 0.194
3 0.6 0.69 0.177
4 0.2 0.89 0.380
5 0.5 0.20 0.067
6 0.8 0.82 0.066
7 0.8 0.04 0.884
8 1.0 0.45 0.809
9 0.4 0.54 0.809
```
and any columns which are not explicitly rounded are unaffected:
```
In [13]: df.round([1])
Out[13]:
a b c
0 0.8 0.430963 0.440312
1 0.1 0.242381 0.149731
2 0.6 0.462600 0.194143
3 0.6 0.692106 0.176523
4 0.2 0.888180 0.380283
5 0.5 0.200268 0.067020
6 0.8 0.816366 0.065751
7 0.8 0.037474 0.884083
8 1.0 0.450143 0.808945
9 0.4 0.537589 0.809112
```
Non-integer values raise a `TypeError`, as might be expected:
```
In [15]: df.round({'a':1.2})
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-15-6f51d3fd917d> in <module>()
----> 1 df.round({'a':1.2})
/home/rob/Dropbox/PhD/pandas/pandas/core/frame.py in round(self, places)
1467
1468 if isinstance(places, dict):
-> 1469 new_cols = [col for col in _dict_round(self, places)]
1470 else:
1471 new_cols = [col for col in _list_round(self, places)]
/home/rob/Dropbox/PhD/pandas/pandas/core/frame.py in _dict_round(df, places)
1455 for col in df:
1456 try:
-> 1457 yield np.round(df[col], places[col])
1458 except KeyError:
1459 yield df[col]
/usr/local/lib/python2.7/dist-packages/numpy/core/fromnumeric.pyc in round_(a, decimals, out)
2646 except AttributeError:
2647 return _wrapit(a, 'round', decimals, out)
-> 2648 return round(decimals, out)
2649
2650
/home/rob/Dropbox/PhD/pandas/pandas/core/series.pyc in round(self, decimals, out)
1209
1210 """
-> 1211 result = _values_from_object(self).round(decimals, out=out)
1212 if out is None:
1213 result = self._constructor(result,
TypeError: integer argument expected, got float
```
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} | 24 | 2015-07-14T17:43:09Z | 2015-07-17T14:46:09Z | 2015-07-17T13:58:58Z | CONTRIBUTOR | null | xref, #1079
numpy 1.10 shows a deprecation warning for this. this should eliminate a bunch of these.
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} | 27 | 2015-07-14T18:12:46Z | 2015-08-20T21:36:48Z | 2015-08-20T20:33:14Z | CONTRIBUTOR | null | (Even though they cannot be read back in)
Closes #10564
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} | 1 | 2015-07-14T18:40:03Z | 2016-03-23T17:56:45Z | 2016-03-23T17:56:45Z | CONTRIBUTOR | null | For example,
``` python
>>> import pandas as pd
>>> df1 = pd.DataFrame(data=[[1, None], [2, None]], columns=['a', 'b'])
>>> df2 = pd.DataFrame(data=[[3, None], [4, None]], columns=['a', 'b'])
>>> df1
a b
0 1 None
1 2 None
>>> df1.dtypes
a int64
b object
dtype: object
>>> df2
a b
0 3 None
1 4 None
>>> df2.dtypes
a int64
b object
dtype: object
>>> pd.concat([df1, df2])
a b
0 1 NaN
1 2 NaN
0 3 NaN
1 4 NaN
>>> pd.concat([df1, df2]).dtypes
a int64
b object
dtype: object
```
I have found that this is a direct result of line 4102-4103 in core/internals.py
``` python
4101 # create the result
4102 if 'object' in upcast_classes:
4103 return np.dtype(np.object_), np.nan
4104 elif 'bool' in upcast_classes:
4105 if has_none_blocks:
4106 return np.dtype(np.object_), np.nan
4107 else:
4108 return np.dtype(np.bool_), None
4109 elif 'category' in upcast_classes:
4110 return com.CategoricalDtype(), np.nan
4111 elif 'float' in upcast_classes:
4112 return np.dtype(np.float64), np.nan
4113 elif 'datetime' in upcast_classes:
4114 return np.dtype('M8[ns]'), tslib.iNaT
4115 elif 'timedelta' in upcast_classes:
4116 return np.dtype('m8[ns]'), tslib.iNaT
4117 else: # pragma
4118 raise AssertionError("invalid dtype determination in get_concat_dtype")
```
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| null | 8 | 2015-07-14T22:21:28Z | 2015-09-15T01:12:12Z | 2015-09-14T19:41:30Z | NONE | null | Google's libraries support Python 3 since version 1.4.0, so we could add support for Python 3 in read_gbq method.
Also it is not always useful to see "Job not yet complete" printings, so I've added optional parameter that allows disable it.
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| null | 2 | 2015-07-15T00:25:18Z | 2015-07-15T02:24:44Z | 2015-07-15T02:24:44Z | CONTRIBUTOR | null | With master I currently get no error at compilation but then at import:
ImportError: /lib64/libc.so.6: version `GLIBC_2.14' not found
With 0.16.2 this does not happen. Is GLIBC_2.14 absolutely required for some new functionality? Why does the bubble not burst during `python setup.py install` then?
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} | 4 | 2015-07-15T01:33:44Z | 2018-05-18T07:49:34Z | 2018-05-18T07:49:34Z | NONE | null | First, here's the example DataFrame
``` python
0 1 2 3 4 5 6 7
first second third
bar one three 4 9 7 8 5 0 7 8
four 6 8 1 5 9 9 1 7
two three 7 5 2 6 7 8 5 9
four 0 8 8 5 3 5 8 3
baz one three 6 0 8 0 0 9 8 8
four 9 3 2 0 2 7 4 9
two three 0 3 7 7 4 3 7 0
four 6 3 2 8 3 9 7 8
foo one three 6 7 3 7 3 0 3 6
four 5 8 0 8 1 5 1 5
two three 2 0 8 2 8 1 8 3
four 9 0 2 7 0 6 8 3
qux one three 1 2 5 5 0 7 0 1
four 6 6 7 0 0 4 5 3
two three 1 8 2 8 7 5 7 5
four 1 1 3 8 8 6 0 3
```
For `DataFrame`s with `MultiIndex`ed rows, pandas allows this type of indexing
`df.loc[('foo','bar'), ('one','two'), ('three','four')]`
To be taken to mean
`df.loc[(('foo','bar'), ('one','two'), ('three','four')), :]`
But this type of indexing is ambiguous in the case when the number of indexing tuples is 2 since
`df.loc[('foo','bar'), ('one','two')]`
could mean incomplete indexing as in
`df.loc[(('foo','bar'), ('one','two')),:]`
or row,column indexing as in
`df.loc[(('foo','bar'),), (('one','two'),)]`
I appreciate that there is already a warning for this in the [documentation](http://pandas.pydata.org/pandas-docs/stable/advanced.html#using-slicers), but I wonder if the functionality is worth the complications it adds to the code/docs.
Personally, I would suggest offloading the responsibility of complete indexing on a MultiIndex DataFrame to the user (obviously this doesn't apply to `Series` as they are 1d so to speak). This would take away the _minor_ syntactical convenience of not specifying the column index, but it simplifies the code and gives the user only one way to index on a MultiIndex DataFrame (which makes usage less confusing).
The consequence to the user in the specific case of selecting multiple levels of a row-MultiIndex on a DataFrame is that instead of writing
`df.loc['foo','one']`
they would have to write
`df.loc[('foo','one'), :]`
And, in the syntactically worst case, instead of writing
`df.loc[('foo','bar'), ('one','two'), ('three','four')]`
they would have to write
`df.loc[(('foo','bar'), ('one','two'), ('three','four')), :]`
I'm fairly new to pandas (don't think I started using it until v0.16), so I realize I may be missing the bigger picture. If so, enlighten me!
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| null | 7 | 2015-07-15T02:42:07Z | 2021-04-18T07:20:32Z | null | CONTRIBUTOR | null | The [asof docs](http://pandas.pydata.org/pandas-docs/version/0.15.1/generated/pandas.Index.asof.html) state that the method should _return the most recent label up to and including the passed label_. For period indexes, this doesn't seem to be happening - if I supply a period of a weekend date to a weekday series, it returns the Monday, rather than the Friday.
If I do the same on a DatetimeIndex, it behaves as expected
``` python
In [50]:
dates
Out[50]:
<class 'pandas.tseries.period.PeriodIndex'>
[1990-05-07, ..., 2015-07-13]
Length: 6568, Freq: B
In [51]:
dates_ts=dates.to_timestamp()
dates_ts
Out[51]:
<class 'pandas.tseries.index.DatetimeIndex'>
[1990-05-07, ..., 2015-07-13]
Length: 6568, Freq: None, Timezone: None
In [52]:
dates.asof('2015-07-11')
Out[52]:
Period('2015-07-13', 'B')
In [53]:
dates_ts.asof('2015-07-11')
Out[53]:
Timestamp('2015-07-10 00:00:00')
```
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} | 5 | 2015-07-15T04:31:58Z | 2015-07-21T06:09:49Z | 2015-07-21T05:27:32Z | CONTRIBUTOR | null | closes #9266
Tried replicating the test case provided in the original issue. Let me know if it needs any changes or fixes.
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} | 14 | 2015-07-15T06:38:56Z | 2015-07-18T11:20:49Z | 2015-07-18T11:20:37Z | CONTRIBUTOR | null | The `CParser` implementation for `pd.read_csv` ignores argument `dtype` if the input is empty. This pull request fixes this so that a DataFrame with the expected column types is returned.
``` Python
import pandas as pd
import cStringIO as stringio
data, dtype = 'a,b', 'i'
df = pd.read_csv(stringio.StringIO(data), dtype={'a': dtype})
assert df.dtypes[0].kind == dtype, "df.types[0].kind = %r, dtype = %r" % (df.dtypes[0].kind, dtype)
```
``
Traceback (most recent call last):
File "issue_1.py", line 9, in <module>
assert df.dtypes[0].kind == dtype, "df.types[0].kind = %r, dtype = %r" % (df.dtypes[0].kind, dtype)
AssertionError: df.types[0].kind = 'O', dtype = 'i'
```
```
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| null | 7 | 2015-07-15T08:49:10Z | 2021-04-18T07:21:10Z | null | NONE | null | Was trying to upsample a DataFrame by non-integer amount, then compare the two. When trying to plot the second DF, pandas tries to allocate a lot of memory, and finally throws MemoryError after a few seconds.
Minimal working example
``` python
import pandas as pd
# Create data for 288 seconds
index = pd.date_range(start='2015-07-13 12:18:47', freq='S', periods=288)
df = pd.DataFrame(range(288), index=index)
# Upsample to 500 samples
td = (df.index[-1] - df.index[0])/499
# Pandas does not allow interpolation when upsampling, so resort to bfill :(
df2 = df.resample(td, fill_method='bfill')
# Let's compare them!
ax = df.plot()
df2.plot(ax=ax) # BOOM! Allocates too much memory and crashes
```
Using pandas 0.16.2 in WinPython 2.7.10.1 x64. pd.show_versions()
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US
pandas: 0.16.2
nose: 1.3.7
Cython: 0.22.1
numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: 3.2.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.4
bottleneck: None
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.5
pymysql: None
psycopg2: None
```
(I should also note that upsamping to arbitrary time index seems hopeless in pandas, as it does not allow interpolation between values near the sampling point, but that's a different issue).
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| null | 13 | 2015-07-15T10:53:12Z | 2020-03-31T04:58:07Z | null | NONE | null | Not sure if it is OK to make the request here, but here you go.
Can a feature be added to exponential smoothing where
alpha = time decay = (time_now - time_previous)/time_scale;
where time_scale and the difference are given in a specific unit eg milli/micro/nanoseconds.
This would really speed up temporally irregular time series analysis. I do understand that there are many ways to derive alpha in irregular time series but this one is I hope reasonably generic neat feature.
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| null | 10 | 2015-07-15T11:13:26Z | 2015-07-15T13:05:05Z | 2015-07-15T12:02:09Z | NONE | null | DataFrame.resample() is not accurate, at least when using time delta as rule.
This is because it does not interpolate between values when up-sampling, nor does aggregation take into account the exact sampling point when down-sampling. When a new sampling point falls between two original samples:
- when up-sampling, the only option is to select one of them or leave NaN (fill_method)
- when down-sampling, simple aggregators such as mean do not consider the distance between the new sampling point to the others. When trying to pass an aggregator function, pandas does not tell it exactly where the new sample point falls either.
Consider a simple example which generates three linear series in a DataFrame of length 10 seconds with exactly 10 points.
### Down-sampling
Let's try down-sampling to 6 points (over the same length of 10 seconds) using DataFrame.resample() and using scipy.interpolate.interp1d().
Black dots are the original samples, colored lines and X's are the resampled data.
First, DataFrame.resample (left figure) actually **misses** the original points entirely.
Second, note the distortion near 09:00:06 and 09:00:07 where the slope of the interpolated data is simply wrong. This is exactly the sort of problem that happens when the exact position of the new sample is not taken into account.
Compare to the scipy.interpolation.interpolate (right figure) which interpolates the original (and correct) lines exactly.
### Up-sampling
Let's try up-sampling to 16 points (over the same length of 10 seconds).
Again, we see inaccuracies and "stairs", and again, simple linear interpolation with scipy produces the correct result.
### Code
The following code reproduces the above figures. This code uses how='mean' for resample(). Using fill_method='bfill' or 'ffill' gives even worse results.
``` python
import pandas as pd
from scipy.interpolate import interp1d
import numpy as np
import matplotlib.pylab as pl
n = 10
x = pd.date_range(start='2015-07-13 9:00:00', freq='S', periods=n)
y = np.vstack([range(n), range(n+1,1+2*n), range(1,2*n+1,2)]).T
df = pd.DataFrame(y, index=x)
def PandasResample(df, length):
td = (df.index[-1] - df.index[0]) / (length-1)
return df.resample(td, how='mean').interpolate() # Handle NaNs when upsampling
def CorrectResample(df, length):
time_delta = (df.index[-1] - df.index[0]) / (length-1)
new_index = pd.date_range(start=df.index[0], end=df.index[-1], freq=time_delta)
f = interp1d(df.index.astype('i8'), df.values.T)
return pd.DataFrame(data=f(new_index.astype('i8')).T, index=new_index)
def PlotComparison(a, b, ax, title):
a.plot(ax=ax, style='ko', legend=False)
b.plot(ax=ax, style='x-', title=title)
def TryLength(new_length):
dfa = PandasResample(df, new_length)
dfb = CorrectResample(df, new_length)
fig, ax = pl.subplots(1,2, figsize=(12,6))
PlotComparison(df, dfa, ax[0], 'DataFrame.resample')
PlotComparison(df, dfb, ax[1], 'scipi.interpolate')
pl.suptitle('Resample to %d' % new_length)
pl.savefig('resampled_%d.png' % new_length)
TryLength(6)
TryLength(16)
pl.show()
```
I'm using pandas 0.16.2 from WinPython 2.7.10.1 x64.
I can provide show_versions() if needed, but this issue is long enough already :)
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} | 1 | 2015-07-15T11:36:53Z | 2015-08-18T11:09:49Z | 2015-08-18T11:09:49Z | CONTRIBUTOR | null | as discussed on https://groups.google.com/forum/#!topic/pydata/ngROaML_hLI
encoding does not seem to be respected on reading a msgpack, below i am expecting to get back what
I put in as utf8
```
In [17]: s
Out[17]: u'\u2019'
In [18]: s = pd.Series({'a' : u"\u2019" })
In [19]: s.values[0]
Out[19]: u'\u2019'
In [20]: pd.read_msgpack(s.to_msgpack(encoding='utf8')).values[0]
Out[20]: u'\xe2\x80\x99'
```
in stepping through, part of the problem seems to be that in the call to unpack on https://github.com/pydata/pandas/blob/master/pandas/io/packers.py#L134 that there is no encoding argument passed and so it defaults to latin1 in https://github.com/pydata/pandas/blob/master/pandas/io/packers.py#L558
changing L134 to :
```
l = list(unpack(fh, **kwargs))
```
and passing the encoding like:
```
pandas.read_msgpack(m, encoding='utf8')
```
makes it work for me, however i don't have en environment set up to submit this as a pull request via GH, and we're still using 0.14.1 due to compatibility issues.
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} | 7 | 2015-07-15T13:27:06Z | 2021-04-18T07:21:55Z | null | NONE | null | I'v found two examples of pivot_table which unexpectedly fail but for different reasons:
``` python
NUM_ROWS = 51364452
NUM_INDEX = 34262015
NUM_COLUMNS = 1732
df = pd.DataFrame({'A' : np.random.randint(NUM_INDEX, size=NUM_ROWS),
'B' : np.random.randint(NUM_COLUMNS, size=NUM_ROWS),
'C' : np.random.randn(NUM_ROWS)})
df_pivoted = df.pivot_table(index='A', columns='B', values='C', margins=False)
```
Error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Anaconda\lib\site-packages\pandas\tools\pivot.py", line 117, in pivot_table
table = agged.unstack(to_unstack)
File "C:\Anaconda\lib\site-packages\pandas\core\frame.py", line 3605, in unstack
return unstack(self, level)
File "C:\Anaconda\lib\site-packages\pandas\core\reshape.py", line 394, in unstack
return _unstack_multiple(obj, level)
File "C:\Anaconda\lib\site-packages\pandas\core\reshape.py", line 294, in _unstack_multiple
unstacked = dummy.unstack('__placeholder__')
File "C:\Anaconda\lib\site-packages\pandas\core\frame.py", line 3605, in unstack
return unstack(self, level)
File "C:\Anaconda\lib\site-packages\pandas\core\reshape.py", line 398, in unstack
return _unstack_frame(obj, level)
File "C:\Anaconda\lib\site-packages\pandas\core\reshape.py", line 438, in _unstack_frame
value_columns=obj.columns)
File "C:\Anaconda\lib\site-packages\pandas\core\reshape.py", line 98, in __init__
self._make_selectors()
File "C:\Anaconda\lib\site-packages\pandas\core\reshape.py", line 132, in _make_selectors
mask = np.zeros(np.prod(self.full_shape), dtype=bool)
ValueError: negative dimensions are not allowed
```
Second example (I have just changed the input values):
``` python
NUM_ROWS = 5e7
NUM_INDEX = 3e7
NUM_COLUMNS = 2e3
df = pd.DataFrame({'A' : np.random.randint(NUM_INDEX, size=NUM_ROWS),
'B' : np.random.randint(NUM_COLUMNS, size=NUM_ROWS),
'C' : np.random.randn(NUM_ROWS)})
df_pivoted = df.pivot_table(index='A', columns='B', values='C', margins=False)
```
Error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Anaconda\lib\site-packages\pandas\tools\pivot.py", line 117, in pivot_table
table = agged.unstack(to_unstack)
File "C:\Anaconda\lib\site-packages\pandas\core\frame.py", line 3605, in unstack
return unstack(self, level)
File "C:\Anaconda\lib\site-packages\pandas\core\reshape.py", line 394, in unstack
return _unstack_multiple(obj, level)
File "C:\Anaconda\lib\site-packages\pandas\core\reshape.py", line 294, in _unstack_multiple
unstacked = dummy.unstack('__placeholder__')
File "C:\Anaconda\lib\site-packages\pandas\core\frame.py", line 3605, in unstack
return unstack(self, level)
File "C:\Anaconda\lib\site-packages\pandas\core\reshape.py", line 398, in unstack
return _unstack_frame(obj, level)
File "C:\Anaconda\lib\site-packages\pandas\core\reshape.py", line 438, in _unstack_frame
value_columns=obj.columns)
File "C:\Anaconda\lib\site-packages\pandas\core\reshape.py", line 98, in __init__
self._make_selectors()
File "C:\Anaconda\lib\site-packages\pandas\core\reshape.py", line 133, in _make_selectors
mask.put(selector, True)
IndexError: index 1421936250 is out of bounds for axis 0 with size 1421935744
```
The example works fine if you reduce the input values.
```
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 45 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.16.2
nose: 1.3.7
Cython: 0.22.1
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 3.2.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.4
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: 0.9
apiclient: None
sqlalchemy: 1.0.5
pymysql: None
psycopg2: None
```
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} | 5 | 2015-07-15T15:32:38Z | 2015-09-01T19:38:53Z | 2015-09-01T19:38:53Z | CONTRIBUTOR | null | ``` python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.normal(size=(10,4)))
df.index = pd.timedelta_range(start='0s', periods=10, freq='s')
print(df.loc['0s':,:])
```
raises a `ValueError: invalid resolution`, while `print(df.loc['5s':,:])` works.
Workaround : `print(df.loc[pd.Timedelta('0s'):,:])`
```
INSTALLED VERSIONS
------------------
commit: 6246cc1bb5149863de09b470530b69f7e22cad87
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-57-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
pandas: 0.16.2-138-g6246cc1
nose: 1.3.1
Cython: 0.20.1post0
numpy: 1.8.2
scipy: 0.13.3
statsmodels: None
IPython: 1.2.1
sphinx: 1.2.2
patsy: None
dateutil: 2.4.1
pytz: 2013b
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
```
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| null | 3 | 2015-07-15T17:06:21Z | 2015-07-15T19:32:55Z | 2015-07-15T19:32:55Z | NONE | null | What causes Pandas 'unstack' to output a series instead of a Dataframe?
import pandas as pd;
events = pd.DataFrame.from_csv('/Users/davidlaxer/gdelt/events/20150613.export.CSV', sep='\t', index_col='GLOBALEVENTID')
e = events.unstack()
type(e)
Out[129]:
pandas.core.series.Series
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| null | 1 | 2015-07-15T18:12:54Z | 2015-07-15T18:22:31Z | 2015-07-15T18:22:18Z | CONTRIBUTOR | null | ``` Python
import pandas as pd
import numpy as np
data = {'a': np.empty(0)}
df = pd.DataFrame(data)
print df
rows, cols = data.values(), data.keys()
print rows, cols
df = pd.DataFrame(rows, columns=cols)
```
```
Empty DataFrame
Columns: [a]
Index: []
[array([], dtype=float64)] ['a']
Traceback (most recent call last):
File "issue_2.py", line 12, in <module>
df = pd.DataFrame(rows, columns=cols)
File "/home/tjerk/dev/pandas/pandas/core/frame.py", line 251, in __init__
arrays, columns = _to_arrays(data, columns, dtype=dtype)
File "/home/tjerk/dev/pandas/pandas/core/frame.py", line 4951, in _to_arrays
dtype=dtype)
File "/home/tjerk/dev/pandas/pandas/core/frame.py", line 5010, in _list_to_arrays
coerce_float=coerce_float)
File "/home/tjerk/dev/pandas/pandas/core/frame.py", line 5068, in _convert_object_array
'columns' % (len(columns), len(content)))
AssertionError: 1 columns passed, passed data had 0 columns
```
## INSTALLED VERSIONS
commit: e51d5cf1b09554ceb182ca71cc02b716277a5a08
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-57-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.16.2-138-g2b801b6
nose: 1.3.7
Cython: 0.22.1
numpy: 1.9.2
scipy: None
statsmodels: None
IPython: 3.2.1
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.4
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.0
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
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} | 1 | 2015-07-15T19:55:50Z | 2020-01-03T23:23:44Z | 2020-01-03T23:23:44Z | NONE | null | From [SO](http://stackoverflow.com/questions/31439314/axis-argument-to-loc-to-interpret-the-passed-slicers-on-a-axis-1)
The documentation suggests:
```
You can also specify the axis argument to .loc to interpret the passed slicers on a single axis.
see http://pandas.pydata.org/pandas-docs/stable/advanced.html#using-slicers
```
However I get an error trying to slice along the column index.
```
import pandas as pd
import numpy as np
cols= [(yr,m) for yr in [2014,2015] for m in [7,8,9,10]]
df = pd.DataFrame(np.random.randint(1,100,(10,8)),index=tuple('ABCDEFGHIJ'))
df.columns =pd.MultiIndex.from_tuples(cols)
print df.head()
2014 2015
7 8 9 10 7 8 9 10
A 68 51 6 48 24 3 4 85
B 79 75 68 62 19 40 63 45
C 60 15 32 32 37 95 56 38
D 4 54 81 50 13 64 65 13
E 78 21 84 1 83 18 39 57
#This does not work as expected
print df.loc(axis=1)[(2014,9):(2015,8)]
#AssertionError: Start slice bound is non-scalar
#but an arbitrary transpose and changing axis works!
df = df.T
print df.loc(axis=0)[(2014,9):(2015,8)]
A B C D E F G H I J
2014 9 6 68 32 81 84 60 83 39 94 93
10 48 62 32 50 1 84 18 14 92 33
2015 7 24 19 37 13 83 69 31 91 69 90
8 3 40 95 64 18 8 32 93 16 25
#So I could always assign the slice and re-transpose.
#That though feels like a hack and the axis=1 setting should have worked.
df = df.loc(axis=0)[(2014,9):(2015,8)]
df = df.T
print df
2014 2015
9 10 7 8
A 64 98 99 87
B 43 36 22 84
C 32 78 86 66
D 67 8 34 73
E 83 54 96 33
F 18 83 36 71
G 13 25 76 8
H 69 4 99 84
I 3 52 50 62
J 67 60 9 49
```
The canonical way to do this is:
```
In [6]: df.loc()[:,(2014,9):(2015,8)]
Out[6]:
2014 2015
9 10 7 8
A 26 2 44 69
B 41 7 5 1
C 8 27 23 22
D 54 72 81 93
E 18 23 54 7
F 11 81 37 83
G 60 38 59 29
H 3 95 89 96
I 6 9 77 9
J 90 92 10 32
```
So prob a bug
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} | 0 | 2015-07-15T20:24:36Z | 2015-07-17T13:05:06Z | 2015-07-17T13:05:06Z | CONTRIBUTOR | null | These are currently converted to object (and back) in `_concat_compat`. But if they are the same dtype (same categoriies/ordering), then this can be short-circutied.
```
In [17]: s = Series(list('aabbcd')*1000000).astype('category')
In [18]: result = pd.concat([s,s])
In [19]: result2 = Series(pd.Categorical(pd.concat([s.cat.codes,s.cat.codes]),s.cat.categories,fastpath=True))
In [20]: result = pd.concat([s,s],ignore_index=True)
In [21]: result2 = Series(pd.Categorical(pd.concat([s.cat.codes,s.cat.codes],ignore_index=True),s.cat.categories,fastpath=True))
In [22]: result.equals(result2)
Out[22]: True
In [23]: %timeit pd.concat([s,s],ignore_index=True)
1 loops, best of 3: 658 ms per loop
In [24]: %timeit Series(pd.Categorical(pd.concat([s.cat.codes,s.cat.codes],ignore_index=True),s.cat.categories,fastpath=True))
10 loops, best of 3: 52.3 ms per loop
```
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| null | 4 | 2015-07-15T21:09:03Z | 2015-07-15T22:10:40Z | 2015-07-15T21:10:58Z | NONE | null | http://stackoverflow.com/questions/31435632/mul-broadcast-levels-from-multi-index
Attempting to use a multiply operation with a multi index.
```
import pandas as pd
import numpy as np
d = {'Alpha': [1,2,3,4,5,6,7,8,9]
,'Beta':tuple('ABCDEFGHI')
,'C': np.random.randint(1,10,9)
,'D': np.random.randint(100,200,9)
}
df = pd.DataFrame(d)
df.set_index(['Alpha','Beta'],inplace=True)
df = df.stack() #it's now a series
df.index.names = df.index.names[:-1] + ['Gamma']
ser = pd.Series(data = np.random.rand(9))
ser.index = pd.MultiIndex.from_tuples(zip(range(1,10),np.repeat('C',9)))
ser.index.names = ['Alpha','Gamma']
print df
print ser
foo = df.mul(ser,axis=0,level = ['Alpha','Gamma'])
So my dataframe which became a series looks like
Alpha Beta Gamma
1 A C 7
D 188
2 B C 7
D 110
3 C C 2
D 124
4 D C 4
D 153
5 E C 9
D 178
6 F C 6
D 196
7 G C 1
D 156
8 H C 1
D 184
9 I C 3
D 169
And my series looks like
Alpha Gamma
1 C 0.8731
2 C 0.6347
3 C 0.4688
4 C 0.5623
5 C 0.4944
6 C 0.5234
7 C 0.9946
8 C 0.7815
9 C 0.1219
```
In my multiple operation I want to broadcast on index levels 'Alpha' and 'Gamma'
but i get this error message TypeError: Join on level between two MultiIndex objects is ambiguous
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} | 5 | 2015-07-15T21:09:35Z | 2016-02-16T16:30:00Z | 2015-07-15T23:37:23Z | CONTRIBUTOR | null | Fix small issues in convert_objects doc
[skip ci]
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} | 5 | 2015-07-15T21:23:28Z | 2015-09-03T13:09:57Z | 2015-09-03T13:09:57Z | CONTRIBUTOR | null | `NaT` is included in result of `groupby` method `first` while `NaN`. I am expecting that first should skip both `NaN` and `NaT` and include the first value where `pandas.isnull` is False.
Demonstration of the inconsistency. (note that both `NaT` and `NaN` in the data frame are produced by `np.nan`, the difference is that the d_t column contains date values).
``` Python
import numpy as np
import pandas as pd
from datetime import datetime as dt
testFrame=DataFrame({'IX':['A','A'],'num':[np.nan,100],'d_t':[np.nan,dt.now()]})
```
Resulting data frame:
```
IX d_t num
0 A NaT NaN
1 A 2015-07-15 22:47:10.635 100
```
Grouping this data frame on the `IX` column and executing the `first` method results in this data frame which shows the inconsistency between the `d_t` and `num` columns.
```
testFrame.groupby('IX').first()
```
Resulting dataframe:
```
d_t num
IX
A NaT 100
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.16.2
nose: 1.3.4
Cython: 0.22
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 3.0.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.4
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.6.7
lxml: 3.4.2
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 0.9.9
pymysql: None
psycopg2: None
```
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} | 4 | 2015-07-15T21:27:40Z | 2015-07-17T21:03:49Z | 2015-07-17T21:03:49Z | MEMBER | null | Not look into detail because I'm out, filenames seem to be changed.
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
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| null | 4 | 2015-07-15T21:29:31Z | 2015-07-15T22:05:32Z | 2015-07-15T22:05:32Z | CONTRIBUTOR | null | How would people feel about me adding a "select_all" option to duplicates / duplicated that returns all duplicated rows, not just all but first or all but last?
I often check to see if I have duplicates by some primary key, and if I do, I then want to look at ALL rows with the same primary key. Right now, duplicates / duplicated won't do so and I need to use the following hack:
```
df[df.duplicated('key',take_last=True) | df.duplicated('key')]
```
It seems I"m not the only person with this issue:
http://stackoverflow.com/questions/26244309/how-to-analyze-all-duplicate-entries-in-this-pandas-dataframe
http://stackoverflow.com/questions/14657241/how-do-i-get-a-list-of-all-the-duplicate-items-using-pandas-in-python
http://stackoverflow.com/questions/23667369/drop-all-duplicate-rows-in-python-pandas
Happy to write PR if people support.
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https://api.github.com/repos/pandas-dev/pandas/issues/10593 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10593/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10593/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10593/events | https://github.com/pandas-dev/pandas/pull/10593 | 95,303,608 | MDExOlB1bGxSZXF1ZXN0NDAwNzY2NTk= | 10,593 | DOC-10371 Add note regarding supported interpolation methods for Series/DF | {
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} | 8 | 2015-07-15T22:24:57Z | 2015-07-16T08:28:28Z | 2015-07-15T23:36:26Z | CONTRIBUTOR | null | Closes #10371
Suggested improvement for https://github.com/pydata/pandas/issues/10371
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} | 3 | 2015-07-15T22:39:19Z | 2015-07-15T22:50:20Z | 2015-07-15T22:47:55Z | CONTRIBUTOR | null | It would be nice to see more resampling examples in the Tutorials section [here](http://pandas.pydata.org/pandas-docs/stable/timeseries.html).
Some examples I am working on for this:
- example using custom `how` function
- example showing how to use `base`
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| null | 1 | 2015-07-15T23:19:19Z | 2015-07-15T23:46:46Z | 2015-07-15T23:46:46Z | NONE | null | It never refers to `self`, and is useful all on its own.
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} | 3 | 2015-07-15T23:27:50Z | 2015-07-28T21:43:15Z | 2015-07-28T21:43:15Z | CONTRIBUTOR | null | ``` python
In [27]: period_index=pd.PeriodIndex(start='2015-01-01',end='2015-03',freq='B')
period_index
Out[27]: <class 'pandas.tseries.period.PeriodIndex'>
[2015-01-01, ..., 2015-03-02]
Length: 43, Freq: B
In [28]: period_index.difference(period_index)
Out[28]: Index([], dtype='object')
```
I think this should return an empty PeriodIndex object, not an empty Index object.
This happens because if there is an empty set as a result of `difference`, the object doesn't check its type before creating an empty version of itself: https://github.com/pydata/pandas/blob/v0.16.0/pandas/core/index.py#L1360. Generally I've seen a better construction for that line be `type(self)([])`.
I'm happy to make this PR, although I'm not sure whether I'm missing something on the intention. If I'm not, should this be executed by adding something in Index's `difference` method, or overriding that method in `PeriodIndex`?
A method that removed items from the index would avoid any subclass-specific code, but the drop method also has some odd behavior:
``` python
In [25]:
period_index.drop(period_index)
Out[25]:
Int64Index([], dtype='int64')
```
So if you're creating a new object, you'd need to check the freq of the PeriodIndex too, given an empty PeriodIndex constructor needs a freq. Something like `type(self)([], freq=self.freq, name=self.name)`. Are there cases for other subclasses of Index?
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} | 15 | 2015-07-15T23:38:26Z | 2015-07-17T13:05:22Z | 2015-07-17T13:05:06Z | MEMBER | null | closes #10587
before (current master):
``` python
In [1]: s = pd.Series(list('aabbcd')*1000000).astype('category')
In [2]: timeit pd.concat([s,s])
1 loops, best of 3: 573 ms per loop
```
after (this PR):
``` python
In [1]: s = pd.Series(list('aabbcd')*1000000).astype('category')
In [2]: timeit pd.concat([s,s])
10 loops, best of 3: 30.1 ms per loop
```
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} | 4 | 2015-07-16T00:19:18Z | 2016-06-24T00:01:47Z | 2016-06-24T00:01:47Z | CONTRIBUTOR | null | http://docs.travis-ci.com/user/migrating-from-legacy/
trivial I think to remove the `sudo`. but prob have to change some stuff (as we
use _some_ apt stuff)
currently we externally cache, so that would have to be figure out (at a later date is ok)
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} | 8 | 2015-07-16T00:25:13Z | 2015-07-24T07:34:42Z | 2015-07-24T07:34:42Z | CONTRIBUTOR | null | Resolves #10596
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} | 1 | 2015-07-16T09:08:28Z | 2015-09-06T17:14:50Z | 2015-09-06T17:14:50Z | MEMBER | null | cc @bashtage
The deprecation warning is triggered, but the value of the keyword argument should also be adapted so it still works with the new code I think (from a failure in the docs)
```
In [1]: from datetime import datetime
In [2]: s = pd.Series([datetime(2001,1,1,0,0), 'foo', 1.0, 1,
...: pd.Timestamp('20010104'), '20010105'], dtype='O')
In [5]: s.convert_objects(convert_dates='coerce')
c:\users\vdbosscj\scipy\pandas-joris\pandas\util\decorators.py:81: FutureWarning
: the 'convert_dates' keyword is deprecated, use 'datetime' instead
warnings.warn(msg, FutureWarning)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-5-b962b638ac86> in <module>()
----> 1 s.convert_objects(convert_dates='coerce')
c:\users\vdbosscj\scipy\pandas-joris\pandas\util\decorators.pyc in wrapper(*args
, **kwargs)
86 else:
87 kwargs[new_arg_name] = new_arg_value
---> 88 return func(*args, **kwargs)
89 return wrapper
90 return _deprecate_kwarg
c:\users\vdbosscj\scipy\pandas-joris\pandas\util\decorators.pyc in wrapper(*args
, **kwargs)
86 else:
87 kwargs[new_arg_name] = new_arg_value
---> 88 return func(*args, **kwargs)
89 return wrapper
90 return _deprecate_kwarg
c:\users\vdbosscj\scipy\pandas-joris\pandas\util\decorators.pyc in wrapper(*args
, **kwargs)
86 else:
87 kwargs[new_arg_name] = new_arg_value
---> 88 return func(*args, **kwargs)
89 return wrapper
90 return _deprecate_kwarg
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\generic.py in convert_objects(s
elf, datetime, numeric, timedelta, coerce, copy)
2468 timedelta=timedelta,
2469 coerce=coerce,
-> 2470 copy=copy)).__finalize__(self)
2471
2472 #-------------------------------------------------------------------
---
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\internals.py in convert(self, *
*kwargs)
3459 """ convert the whole block as one """
3460 kwargs['by_item'] = False
-> 3461 return self.apply('convert', **kwargs)
3462
3463 @property
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\internals.py in apply(self, f,
axes, filter, do_integrity_check, **kwargs)
2467 copy=align_copy)
2468
-> 2469 applied = getattr(b, f)(**kwargs)
2470
2471 if isinstance(applied, list):
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\internals.py in convert(self, d
atetime, numeric, timedelta, coerce, copy, by_item)
1493 timedelta=timedelta,
1494 coerce=coerce,
-> 1495 copy=copy
1496 ).reshape(self.values.shape)
1497 blocks.append(make_block(values,
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\common.py in _possibly_convert_
objects(values, datetime, numeric, timedelta, coerce, copy)
1897 """ if we have an object dtype, try to coerce dates and/or numbers "
""
1898
-> 1899 conversion_count = sum((datetime, numeric, timedelta))
1900 if conversion_count == 0:
1901 import warnings
TypeError: unsupported operand type(s) for +: 'int' and 'str'
```
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} | 20 | 2015-07-16T15:00:18Z | 2016-02-16T16:30:00Z | 2015-09-06T17:14:50Z | CONTRIBUTOR | null | Fix to temporary allow passing 'coerce' to variables
closes #10601
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