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} | 28 | "2013-04-19T12:31:45Z" | "2014-01-15T02:57:34Z" | "2014-01-15T02:57:34Z" | CONTRIBUTOR | null | http://stackoverflow.com/questions/16103238/pandas-timedelta-in-days/16104567#16104567
Its easy to think about, e.g. if no dates in a particular column have time values, then can print them w/o the time values (e.g. just 2001-01-01 maybe), I think this would be nice as the default (maybe add an option for it?)
```
In [17]: df
Out[17]:
age today diff years
0 2001-01-01 00:00:00 2013-04-19 00:00:00 4491 days, 00:00:00 12.304110
1 2004-06-01 00:00:00 2013-04-19 00:00:00 3244 days, 00:00:00 8.887671
```
Better?
```
In [17]: df
Out[17]:
age today diff years
0 2001-01-01 2013-04-19 4491 days 12.304110
1 2004-06-01 2013-04-19 3244 days 8.887671
```
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} | 36 | "2013-04-19T16:01:53Z" | "2016-06-20T10:54:47Z" | "2014-02-04T21:44:22Z" | CONTRIBUTOR | null | ## ENH: Linked Datasets (RDF)
- This is very much a meta ticket.
- There are a number of bare links here.
- They are for documentation
(UPDATE: see https://github.com/westurner/pandas-rdf/issues/1)
### Use Case
So I:
- retrieved some data
- from somewhere
- about a certain #topic
- perfomed analysis
- with certain transformations and aggregations
- with certain versions of certain tools
- confirmed/rejected a [null] hypothesis
and I want to share my findings so that others can find, review, repeat, reproduce, and verify (confirm/reject) a given conclusion.
### User Story
As a data analyst, I would like to **share** or **publish** `Series`, `DataFrame`s, `Panel`s, and `Panel4D`s as structured, hierarchical, RDF linked data ("DataSet").
### Status Quo: Pandas IO
> How do I go from a [CSV] to a DataFrame to something shareable with a URL?
http://pandas.pydata.org/pandas-docs/dev/io.html
- http://pandas.pydata.org/pandas-docs/dev/dsintro.html
- https://github.com/pydata/pandas/blob/master/pandas/core/format.py
- https://github.com/pydata/pandas/blob/master/pandas/rpy/common.py
.
- Series (1D)
- index
- data
- NumPy datatypes
- DataFrame (2D)
- index
- column(s)
- NumPy datatypes
- Panel (3D)
- Panel4D (4D)
**Read** or **parse** a data format into a DataSet:
- `pandas.read_*`
- `read_clipboard`
- `read_csv`
- `read_excel`
- `read_fwf`
- `read_gbq`
- `read_hdf`
- `read_html`
- `read_json`
- `read_msgpack`
- `read_pickle`
- `read_sql`
- `read_stata`
- `read_table`
- `pandas.HDFStore`
- https://pandas.pydata.org/docs/dev/io.html#hdf5-pytables
**Add** metadata:
- [ ] Add RDF metadata (RDFa, JSONLD)
**Save** or **serialize** a DataSet into a data format:
- `pandas.DataFrame.`
- `to_csv`
- `to_dict`
- `to_excel`
- `to_gbq`
- `to_html`
- `to_latex`
- `to_panel`
- `to_period`
- `to_records`
- `to_sparse`
- `to_sql`
- `to_stata`
- `to_string`
- `to_timestamp`
- `to_wide`
- [ ] to_ RDF
- [ ] to_ CSVW
- [ ] to_ HTML + RDFa
- [ ] to_ JSONLD
- [ ] create a JSONLD @context
**Share** or **publish** a serialized DataSet with the internet:
- Email Attachment (Table in a PDF)
- opendatahandbook.org
- project-open-data.github.io
- FTP, SFTP, RSYNC, NFS
- HTML web upload form with metadata form fields
- CLI tool
- Version Control: Git, Hg, Svn
- challenge: 'large' files ("binary blobs") in VCS systems
- HTTP API: Object Storage (~LDP)
- `GET/POST /container/filename.csv` # [.json|.xml|.xls|.rdf|.html]
- challenge: indexing metadata from a separate document / named graph
- `GET/POST to`/container/filename.csv`
- Push to CKAN
- http://docs.ckan.org/en/latest/api.html
- http://docs.ckan.org/en/latest/linked-data-and-rdf.html
- https://github.com/okfn/ckan
- Host DataSet metadata
- `python -m SimpleHTTPServer 8088`
- e.g. http://datasets.schema-labs.appspot.com/about Indexes http://schema.org/Dataset s
### Implementation
What changes would be needed for Pandas core to support this workflow?
- `.meta` schema
- `to_rdf` for Series, DataFrames, Panels, and Panel4Ds
- `read_rdf` for Series, DataFrames, Panels, and Panel 4Ds
- ~`@datastep` process decorators
- ~`DataSet`
- ~`DataCatalog` of precomputed aggregations/views/slices.
- Units support (`.meta`?)
#### `.meta` schema
It's easy enough to serialize a dict and a table to naieve RDF.
For interoperability, it would be helpful to standardize with a common
set of terms/symbols/structures/schema for describing
the tabular, hierarchical data which pandas is designed to handle.
There is currently no standard method for storing columnar metadata
within Pandas (e.g. in `.meta['columns'][colname]['schema']`, or as a **JSON-LD `@context`**).
##### Ontology Resources
- http://www.w3.org/TR/rdf-schema/ (`rdfs:`)
- http://www.w3.org/TR/owl-overview/ (`owl:`)
- http://www.w3.org/TR/sparql11-query/#sparqlDefinition
- http://lov.okfn.org
- http://prefix.cc
##### CSV2RDF (`csvw`)
- http://www.w3.org/ns/csvw
- https://github.com/w3c/csvw
- https://w3c.github.io/csvw/
##### W3C PROV (`prov:`)
- http://www.w3.org/TR/prov-primer/#intuitive-overview-of-prov
- http://www.w3.org/TR/prov-o/
- http://www.w3.org/2011/prov/wiki/ProvImplementations
- **https://pypi.python.org/pypi/prov**
- http://prov.readthedocs.org/en/latest/usage.html
##### schema.org (`schema:`)
- http://schema.org
- http://www.w3.org/wiki/WebSchemas
- http://schema.rdfs.org/
- https://schema.org/docs/full.html :
- schema:Dataset -- A body of structured information describing some topic(s) of interest.
- [schema:Thing, schema:CreativeWork]
- distribution -- A downloadable form of this dataset, at a specific location, in a specific format (DataDownload)
- spatial, temporal
- catalog -- A data catalog which contains a dataset (DataCatalog)
- schema:DataCatalog -- collection of Datasets
- [schema:Thing, schema:CreativeWork]
- dataset -- A dataset contained in a catalog. (Dataset)
- schema:DataDownload -- A dataset in downloadable form.
- [schema:Thing, schema:CreativeWork]
- contentSize
- contentURL
- uploadDate
##### W3C RDF Data Cube (`qb:`)
- http://www.w3.org/TR/vocab-data-cube/
- http://www.w3.org/2011/gld/wiki/Data_Cube_Vocabulary#The_history_of_Data_Cube.2C_SDMX-RDF_and_SCOVO
- http://www.w3.org/TR/vocab-data-cube/#vocab-reference :
- qb:DataSet -- a collection of observations, possibly organized into various slices, conforming to some common dimensional structure
- qb:Slice -- a subset of a DataSet defined by fixing a subset of the dimensional values.
- qb:Observation -- a single observation in the cube, may have one or more associated measured values.
- qb:dataset -- data set of which this observation is a part.
- qb:ObservationGroup -- a, possibly arbitrary, group of observations.
- qb:observation -- an observation contained within this slice of the data set.
- qb:Slice -- a subset of a DataSet defined by fixing a subset of the dimensional values, component properties on the Slice.
- [Components, Properties, Dimensions, Attributes, Measures]
#### `to_rdf`
http://pandas.pydata.org/pandas-docs/dev/io.html
Arguments:
- [ ] output `fmt`
- [ ] JSON-LD: compaction
.
- [ ] `Series.meta`
- [ ] `Series.to_rdf()`
- [ ] `DataFrame.meta`
- [ ] `DataFrame.to_rdf()`
- [ ] `Panel.meta`
- [ ] `Panel.to_rdf()`
- [ ] `Panel4D.meta`
- [ ] `Panel4D.to_rdf()`
#### `read_rdf`
http://pandas.pydata.org/pandas-docs/dev/remote_data.html
- [ ] `Series.read_rdf()`
- [ ] `DataFrame.read_rdf()`
- [ ] `Panel.read_rdf()`
- [ ] `Panel4D.read_rdf()`
Arguments to `read_rdf` would need to describe which dimensions of data to
read into 1D/2D/3D/4D form.
#### @datastep / PROV
- [ ] Objective: Additive journal of transformations
- [ ] Link to source script(s) URIs
- [ ] Decorator for annotating data transformations with metadata.
- [ ] Generate PROV metadata for data transformations
[Ten Simple Rules for Reproducible Computational Research](http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003285) (3, 4, 5, 7, 8, 10)
#### DataCatalog
A collection of Datasets.
- [ ] `DataCatalog = {that=df1, this=df1.group().apply(), also_this=df2]`
- 'this is an aggregation of that'
- 'this' has a URI
- 'that' has a URI
- What if there is no metadata for df2?
#### Units support
- [ ] Series.meta
- [ ] DataFrame.column.meta
- NumPy [, PyTables]
- http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html
- https://pint.readthedocs.org/en/latest/
- http://pythonhosted.org/quantities/
RDF Datatypes
- http://en.wikipedia.org/wiki/ISO_8601
- http://www.w3.org/TR/xmlschema-2/#decimal
- http://schema.org/Date
- http://schema.org/DateTime
- http://schema.org/Float
- http://schema.org/Quantity
- **https://github.com/RDFLib/rdflib**
- `from rdflib.namespace import XSD, RDF, RDFS`
- `from rdflib import URIRef, Literal`
- https://github.com/RDFLib/rdflib-sqlalchemy (SQLAlchemy)
JSON-LD (RDF in JSON)
- https://github.com/digitalbazaar/pyld (JSON-LD)
- https://github.com/RDFLib/rdflib-jsonld (JSON-LD)
### Linked Data Primer
Linked Data Abstractions
- Graphs are represented as triples of (s,p,o)
- Subject, Predicate, Object
- Queries are patterns with ?references
- `graph.triples((None, None, None))`
- `SELECT ?s, ?p, ?o WHERE { ?s ?p ?o };`
- subjects are linked to objects by predicates
- subjects and predicate are identified by URI 'keys'
URIs and URLs
- a URI is like a URL
- usually, we expect URLs to be 'dereferencable` HTTP URIs
- HTTP GET http://en.wikipedia.org/
- a URI may start with a different URI prefix
- `urn:`
- `uuid:`
SQL and Linked Data
- there exist standard mappings for whole SQL tablesets
- rdb2rdf
- similar to application scaffolding
- ACL support adds complexity
- virtuoso supports SQL and RDF and SPARQL
- standard mappings
- virtuoso powers http://dbpedia.org/
- dbpedia.org has a high degree of centrality
- http://lod-cloud.net/
- rdflib-sqlalchemy maps RDF onto SQL tables
- fairly inefficiently, when compared to native triplestores
Named Graphs
- Quads: (g, s, p, o)
- g: sometimes called the 'context' of a triple
- Metadata about `GRAPH ?g`
- Multiple named graphs in one file: TriX, TriG
Linked Data Formats
- [ ] NTriples
- [ ] RDF/XML
- [ ] TriX
- [ ] Turtle, N3
- [ ] TriG
- [ ] JSON-LD
Choosing Schema
- [ ] XSD, RDF, RDFS, DCTERMS
- Which schema is most popular?
- Which schema is a best fit for the data?
- Which schema will search engines index for us?
- What do the queries look like?
- Years Later... What is OWL?
- Why would we start with RDFS now?
#### Linked Data Process, Provenance, and Schema
DataSets have [implicit] URIs:
```
http://example.com/datasets/#<key>
```
**Shared** or **published** DataSets have URLs:
```
http://ckan.example.org/datasets/<key>
```
DataSets are about certain things:
```
e.g. URIs for #Tags, Categories, Taxonomy, Ontology
```
DataSets are derived from somewhere, somehow:
- where and how was it downloaded? (digital sense)
- how was it collected? (process control sense)
Datasets have structure:
- Tabular, Hierarchical
- 1D, 2D, 3D, 4D
- Graph-based
- Chains
- Flows
- Schema
**5 ★ Open Data**
**http://5stardata.info/**
**http://www.w3.org/TR/ld-glossary/#x5-star-linked-open-data**
> ☆ Publish data on the Web in any format (e.g., PDF, JPEG) accompanied by an explicit Open License (expression of rights).
> ☆☆ Publish structured data on the Web in a machine-readable format (e.g., XML).
> ☆☆☆ Publish structured data on the Web in a documented, non-proprietary data format (e.g., CSV, KML).
> ☆☆☆☆ Publish structured data on the Web as RDF (eg Turtle, RDFa, JSON-LD, SPARQL)
> ☆☆☆☆☆ In your RDF, have the identifiers be links (URLs) to useful data sources.
https://en.wikipedia.org/wiki/Linked_Data
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} | 9 | "2013-04-20T19:45:26Z" | "2016-09-29T22:44:20Z" | "2016-09-29T22:29:46Z" | NONE | null | Hi there,
I have a ginormous problem right now.
When doing simple operations, a python interpreter subprocess keeps running in the background (even when my application finished) and eats a lot of CPU (25%) continually, and forever.
It seems that whenever I do some simple operations, such as:
```
df / serie2
df.div(serie2)
df - serie2
df - 2
df.apply(minus)
df ** 2
```
where minus is:
```
def minus(serie):
serie - serie2
```
But the following operations do not produce the bug:
```
df * (1.0/serie2)
df * serie2**-1
df * df
```
And any operation using \* is typically not affected by the bug.
The bug is that if the above operations are present in my code, it launches an instance of Python and then this instance never goes away, and keeps using 25 percent of my CPU!
I am using:
Windows 7 x64
Python 2.7
Pandas 0.11 rc1
Numpy 1.7.1
IPython 0.13.2
What is weird is that I have a hard time reproducing it outside of my application (ie: directly inside IPython with simplified code), so I can't really post anything here.
Another thing to note is that when this bug happens compared to when it doesn't (using the workarounds I outlined above by replacing buggy operations by non buggy operations, and having the same result at the end), not only a python subprocess keeps running in the background, but also while my application is running, my app use a LOT more CPU and memory (CPU ~ 60% when buggy, ~10-20% when not buggy).
I'm still trying to debug the problem, but it seems it can't be profiled (because it's not a loop due to my application, my application ends correctly, and then the Python sub-process keeps processing something but I can't know what!).
If someone has an idea to help me debugging I would gladly hear your ideas. Meanwhile this is posted here as a reference if anyone else gets a similar problem.
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} | 1 | "2013-04-21T14:38:28Z" | "2013-12-04T00:57:16Z" | "2013-05-30T16:39:25Z" | CONTRIBUTOR | null | related to #3418
should auto generate class methods for the io
and delay the imports until actually called
(including html, excel, csv, hdf, dta)
deprecate these methods as well:
https://mail.google.com/mail/u/0/?shva=1#inbox/13ec1cd9b60c5bda
at the very least should deprecate:
```
from_csv -> pd.read_csv
from_dta -> pd.read_dta
```
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b = pandas.Series([3,4])
c = pandas.DataFrame( {'a':a, 'b':b})
d = pandas.DataFrame( {'a':a, 'b':b})
e = pandas.Panel({1365455239396772380:c, 1365455239396772380:d})
In [21]: e[1365455239396772380]
Out[21]:
a b
0 NaN NaN
1 NaN NaN
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} | 4 | "2013-04-22T03:39:32Z" | "2013-11-27T18:31:21Z" | "2013-11-27T18:31:21Z" | CONTRIBUTOR | null | I haven't looked under the hood yet, but it doesn't look like you can grab multiple series from FRED data at the moment. This works
```
from pandas.io.data import DataReader
merge = lambda x,y : x.merge(y, left_index=True, right_index=True)
dta = reduce(merge, [DataReader(i, "fred") for i in ["AKUR", "ALUR","GAUR"]])
```
but this just hangs and then returns an empty DataFrame
```
dta = DataReader(["AKUR", "ALUR","GAUR"], "fred")
```
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} | 10 | "2013-04-22T04:52:37Z" | "2013-05-09T00:43:14Z" | "2013-05-09T00:43:14Z" | CONTRIBUTOR | null | Similar to #3416, failing in 0.11
Calling astype(object) when the dtype is datetime64 and there are NaT values, sets them to 2262-04-10 00:12:43.145224
I would have thought they should be set to None.
See attached test case.
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} | 3 | "2013-04-22T08:24:40Z" | "2013-09-25T15:10:49Z" | "2013-09-25T15:10:49Z" | NONE | null | I don't know if it is desirable, but I think it would be nice to be able to include an infinity value as the endpoint to the bins in pandas cut function.
```
pd.cut([10, 20], [1, 5, np.inf], right=False)
```
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} | 1 | "2013-04-22T13:41:21Z" | "2014-03-30T09:25:39Z" | "2013-05-08T21:52:21Z" | CONTRIBUTOR | null | see #3414
The resulting dtype is wrong (should be datetime64[ns]),
and should either raise on dtype='datetime64' or deal with it in Series
```
In [1]: dates = [
...: np.datetime64(datetime.date(2013, 1, 1)),
...: np.datetime64(datetime.date(2013, 1, 2)),
...: np.datetime64(datetime.date(2013, 1, 3)),
...: ]
In [2]: s = pd.Series(dates, dtype='datetime64')
In [3]: s.ix[0] = np.nan
In [4]: print s
0 NaT
1 2013-01-02 00:00:00
2 2013-01-03 00:00:00
dtype: datetime64[us]
```
This should force conversion on the np.datetime64 (regardless of there dtype)
```
In [22]: pd.Series(dates)
Out[22]:
0 2013-01-01 00:00:00
1 2013-01-02 00:00:00
2 2013-01-03 00:00:00
dtype: object
```
Clearly incorrect interpret of np.datetime64 dtypes as well (its interpreting them as M8[ns], not converting
to)
```
In [25]: pd.Series(dates,dtype='M8[ns]')
Out[25]:
0 1970-01-16 16:56:38.400000
1 1970-01-16 16:58:04.800000
2 1970-01-16 16:59:31.200000
dtype: datetime64[ns]
```
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} | 3 | "2013-04-22T16:52:27Z" | "2013-08-27T00:20:23Z" | "2013-08-27T00:20:23Z" | NONE | null | The following code starts from a 9 by 2 DataFrame with 3 groups, no index. When applying a function to each group that returns a 1 by 2 DataFrame, an index is introduced that doesn't seem to make sense, and the as_index=False option is ignored:
```
df = pd.DataFrame({
'a' : [1,1,1,2,2,2,3,3,3],
'b' : [1,2,3,4,5,6,7,8,9],
})
def f(x):
if x.a[:1] == 2:
# In the documentation there is nothing that indicates how one could
# return an 'empty group', ie nothing, from a function suitable for an apply.
# Is this solution adequate?
return pd.DataFrame({})
return pd.DataFrame({'mean' : [x.b.mean()], 'std' : [x.b.std()]})
grouped = df.groupby('a', as_index=False).apply(f)
grouped.index
MultiIndex
[(1, 0), (3, 0)]
```
In short: a MultiIndex is introduced, when ideally I would have expected no index at all, and in the worst case a non-hierarchical Index corresponding to the groups 1 and 3.
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} | 4 | "2013-04-22T17:23:43Z" | "2013-09-30T13:07:28Z" | "2013-09-30T13:07:28Z" | NONE | null | Using pandas 0.10.1.
I read the docs, but didn't see any explanation of why this would be true. pandas.read_csv() works exactly as you'd expect, but pandas.DataFrame.from_csv() is different. Looks like the latter method assumes you're probably dealing with time series data, so it sets defaults parameters to automatically convert integers to dates. I disagree that this is desired, but even if it is, why would it be true for the later method but not the former? Why shouldn't both methods assume the same default assumptions?
Create a CSV like this:
a,b
1,4
2,3
Now this does exactly what you'd expect:
p = pandas.read_csv(your_csv_file)
But this converts the first column into a data. Almost certainly not what you'd expect:
p = pandas.DataFrame.from_csv(your_csv_file)
There is an optional parameter on the second method "parse_dates" which is default False. If you add that flag, then the second method works just like the first. But why the inconsistency? I'd expect this method to default to acting just like the other one.
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} | 0 | "2013-04-22T18:06:15Z" | "2013-04-30T04:15:23Z" | "2013-04-24T22:39:54Z" | NONE | null | ```
Exception RuntimeError: 'maximum recursion depth exceeded while calling a Python object'
in <type 'exceptions.RuntimeError'> ignored
```
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} | 1 | "2013-04-22T20:53:51Z" | "2014-06-14T00:34:08Z" | "2013-04-22T21:02:26Z" | CONTRIBUTOR | null | My first PR...
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] | closed | false | null | [] | null | 3 | "2013-04-22T22:10:07Z" | "2013-05-10T10:40:08Z" | "2013-05-10T10:40:08Z" | NONE | null | Consider the following data:
``` python
In [36]: index = pd.MultiIndex.from_arrays([['a','a','b','b','c','c'], [1,2,1,2,1,2]], names=['first','second'])
In [37]: data = pd.DataFrame(np.random.rand(len(index),2), index=index, columns=['A','B'])
In [38]: data
Out[38]:
A B
first second
a 1 0.320885 0.794688
2 0.062344 0.808103
b 1 0.789837 0.956867
2 0.129658 0.461514
c 1 0.237009 0.975737
2 0.779002 0.692922
```
Now, I can do slicing on the first index as:
``` python
In [40]: data.ix[['a','b']]
Out[40]:
A B
first second
a 1 0.320885 0.794688
2 0.062344 0.808103
b 1 0.789837 0.956867
2 0.129658 0.461514
```
I can also select a particular first, and second index as
``` python
In [41]: data.ix['a',1]
Out[41]:
A 0.320885
B 0.794688
Name: (a, 1), dtype: float64
```
However, if I try to take an arbitrary slice through the first and second index, it doesn't work, but something unexpected, it selects a column!
``` python
In [42]: data.ix[['a','b'],1]
Out[42]:
first second
a 1 0.794688
2 0.808103
b 1 0.956867
2 0.461514
Name: B, dtype: float64
```
when I was expecting
``` python
A B
first
a 0.320885 0.794688
b 0.789837 0.956867
```
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} | 1 | "2013-04-23T00:07:55Z" | "2013-05-08T21:51:34Z" | "2013-05-08T21:51:34Z" | CONTRIBUTOR | null | http://stackoverflow.com/questions/16157939/pandas-read-csv-fills-empty-values-with-string-nan/16158762#16158762
The convert_objects(convert_dates='force') should really just to
to_datetime()....
```
In [1]: In [1]: data = """
...: ...: value date id
...: ...: 0 2 2013-3-1 a
...: ...: 1 3 2013-3-1 b
...: ...: 2 4 2013-3-1 c
...: ...: 3 5 nan d
...: ...: 4 6 2013-3-1 d
...: ...: """
In [2]: In [15]: df = pd.read_csv(StringIO(data[1:]),index_col=0,sep='\s+')
In [3]: df.dtypes
Out[3]:
...: float64
value float64
date object
id object
dtype: object
In [4]: In [24]: pd.to_datetime(df['date'])
Out[4]:
...: 2013-03-01 00:00:00
...: 2013-03-01 00:00:00
...: 2013-03-01 00:00:00
...: NaT
...: 2013-03-01 00:00:00
...: NaT
Name: date, dtype: datetime64[ns]
In [5]: df['date'].convert_objects(convert_dates=True)
Out[5]:
...: 2013-3-1
...: 2013-3-1
...: 2013-3-1
...: NaN
...: 2013-3-1
...: None
Name: date, dtype: object
```
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http://nbviewer.ipython.org/5439745
Version: '0.11.0.dev-9167213'
Expected outcome: Same as `pd.concat([df1, df2], axis=1)`
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} | 1 | "2013-04-23T00:46:29Z" | "2013-12-04T00:57:16Z" | "2013-05-08T21:51:55Z" | CONTRIBUTOR | null | http://stackoverflow.com/questions/16158795/cant-convert-dates-to-datetime64
prob should raise if u try to convert to something other than object or datetime64[ns]
otherwise can have weird things happen
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} | 10 | "2013-04-23T02:11:49Z" | "2018-07-06T22:16:25Z" | "2018-07-06T22:16:11Z" | NONE | null | #### TODO
- [ ] dicts with many keys should be summarized (Update: I can no longer remember the use case for dicts
printed via pprint_thing).
- [ ] summarization should match numpy [a.a.a ... b b b] not [a a a ...]
- [ ] add np.set_printoptions(edgeitems) analogue.
<del>
- [ ] options.display.max_seq_items should have a default value != None #3391, #5120 , #5629
</del>
via https://github.com/pydata/pandas/pull/5753
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} | 1 | "2013-04-23T02:54:28Z" | "2013-07-22T20:31:51Z" | "2013-07-22T20:31:10Z" | NONE | null | #3164 was merged (for diff reasons).
enable on travis, enable some tests, and kick the tires
a little bit.
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} | 4 | "2013-04-23T09:03:17Z" | "2013-09-25T16:23:43Z" | "2013-08-24T00:37:13Z" | NONE | null | similar to #3416 (also failing in 0.11)
Using pandas 0.10.1 and numpy 1.6.1:
```
>>> import pandas; import numpy
>>> s = pandas.Series(pandas.date_range('2013-01-01', periods=3))
>>> d = numpy.datetime64('2013-01-02')
>>> s > d
0 False
1 False
2 True
>>> d < s
0 True
1 True
2 True
>>> s.dtype
dtype('datetime64[ns]')
>>> d.dtype
dtype('datetime64[us]')
```
All other comparison operators also give expected results with operands (s,d), and seem to compare them as unscaled integers when the order is (d,s).
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} | 12 | "2013-04-23T12:11:00Z" | "2014-01-10T21:49:03Z" | "2014-01-10T12:10:12Z" | NONE | null | Noted by @karmel in #3422, sphinx supports syntax highlighting/code
quoting in code (ReST) docstrings using double colon + new line:
``` rst
For example::
make_axis_dummies(panel, axis='major',
transform=lambda d: d.weekday())
```
sphinx 'autodoc' will pick these up and make code look nice.
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} | 7 | "2013-04-23T12:52:18Z" | "2013-04-30T04:16:24Z" | "2013-04-23T13:16:37Z" | NONE | null | **edit:** totally misguided.
Sphinx has great support for method signature metadata in docstrings,
there are python standars for ReST docstrings, and tooling to leverage
and generate the docstrings.
<del>In practice pandas uses an ad-hoc "visual" format for docstrings, which
confuses sphinx and impedes automation.
Major cleanup all over.
</del>
http://www.python.org/dev/peps/pep-0257/
http://www.python.org/dev/peps/pep-0287/
**edit:** numpydoc is in use by pandas, unbeknownst to me previously.
#2916, #3422
Bad:
```
"""
Parameters
----------
key : for use with a lock
Returns
-------
res : 3.5% annualized
"""
```
Better:
```
"""
:param key: for use with a lock
:returns: 3.5% annualized
"""
```
Much Better:
```
"""Subject
some text
a code example::
I.am.code(is=True)
:param key: for use with a lock
:type key: str
:returns: 3.5% annualized
:rtype: float
"""
```
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} | 1 | "2013-04-23T13:49:59Z" | "2013-09-22T00:36:35Z" | "2013-09-22T00:36:35Z" | CONTRIBUTOR | null | in #2893
we changed the groupby behavior to return a series if a simple index is passed in and only have 1 group (rather than return a mi)
I hit this regression as I doing several groupby's on different input, all of which yield multiple groups, except for 1, so I result with a bunch of frames and 1 series
in 0.10.1 this would yield all frames.
so the question is should the automatic magic of 2893 always happen?
e.g. I guess that should create an option to not always reduce the input dims
so my example, shoudl result1 == DataFrame ?
```
In [6]: df1 = DataFrame(dict(A = range(4), B = 0))
In [7]: def func(dataf):
...: return Series({ dataf.name : 1})
...:
In [8]: result1 = df1.groupby("B").apply(func)
In [9]: result2 = df1.groupby("A").apply(func)
In [10]: result1
Out[10]:
B
0 0 1
Name: 0, dtype: int64
In [11]: result2
Out[11]:
A
0 0 1
1 1 1
2 2 1
3 3 1
dtype: int64
```
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} | 7 | "2013-04-23T14:10:36Z" | "2018-03-26T00:26:15Z" | "2018-03-26T00:26:15Z" | CONTRIBUTOR | null | I happened to have an older verion of numpy 1.6.2 MKL installed on a windows machines
very intermittently (I think you hae to be using a really big frame)
sometimes I got a:
OMP : Error # 134 Cannot set thread affinity mask
which I think is caused by numexpr operations
see this
http://software.intel.com/en-us/forums/topic/279406
I am not sure its a real problem, because it only affects some versions of MKL, upgrading numpy (or even using a newer version of 1.6.2) seems to fix it..
also can be programatically disabled by
`pandas.core.expressions.set_use_numexpr(False)`
just a FYI
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} | 15 | "2013-04-23T15:52:43Z" | "2020-06-19T11:17:51Z" | "2013-05-13T22:51:49Z" | NONE | null | Should raise on a passed list to `value`
The results from the fillna() method are very strange when the value parameter is given a list.
For example, using a simple example DataFrame:
> df = pandas.DataFrame({'A': [numpy.nan, 1, 2], 'B': [10, numpy.nan, 12], 'C': [[20, 21, 22], [23, 24, 25], numpy.nan]})
> df
> A B C
> 0 NaN 10 [20, 21, 22]
> 1 1 NaN [23, 24, 25]
> 2 2 12 NaN
>
> df.fillna(value=[100, 101, 102])
> A B C
> 0 100 10 [20, 21, 22]
> 1 1 101 [23, 24, 25]
> 2 2 12 102
So it appears the values in the list are used to fill the 'holes' in order, if the list has the same length as number of holes. But if the the list is shorter than the number of holes, the behavior changes to using only the first value in the list:
> df.fillna(value=[100, 101])
> A B C
> 0 100 10 [20, 21, 22]
> 1 1 100 [23, 24, 25]
> 2 2 12 100
If the list is longer than the number of holes, you get something even more odd:
> df.fillna(value=[100, 101, 102, 103])
> A B C
> 0 100 10 [20, 21, 22]
> 1 1 100 [23, 24, 25]
> 2 2 12 102
If you specify provide a dict that specifies the fill values by column, the values from the list are used within that column only:
> df.fillna(value={'C': [100, 101]})
> A B C
> 0 NaN 10 [20, 21, 22]
> 1 1 NaN [23, 24, 25]
> 2 2 12 100
Since it's not always practical to know the number of NaN values a priori, or to customize the length of the value list to match it, this is problematic. Furthermore, some desired values get over-interpreted and cannot be used:
For example, if you want to actually replace all NaN instances in a single column with the same list (either empty or non-empty), I can't figure out how to do it:
> df.fillna(value={'C': [[100,101]]})
> A B C
> 0 NaN 10 [20, 21, 22]
> 1 1 NaN [23, 24, 25]
> 2 2 12 100
Indeed, if you specify the empty list nothing is filled:
> df.fillna(value={'C': list()})
> A B C
> 0 NaN 10 [20, 21, 22]
> 1 1 NaN [23, 24, 25]
> 2 2 12 NaN
But a dict works fine:
> f.fillna(value={'C': {0: 1}})
> A B C
> 0 NaN 10 [20, 21, 22]
> 1 1 NaN [23, 24, 25]
> 2 2 12 {0: 1}
>
> df.fillna(value={'C': dict()})
> A B C
> 0 NaN 10 [20, 21, 22]
> 1 1 NaN [23, 24, 25]
> 2 2 12 {}
So it appears the fillna() is making a lot of decisions about how the fill values should be applied, and certain desired outcomes can't be achieved because it's being too 'clever'.
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``` python
df = DataFrame([["2003-1-1", "1"]], columns=["a", "b"])
conv = df.convert_objects(convert_numeric=True, convert_dates="coerce")
conv.b.dtype == dtype('<M8[ns]')
```
I think date and numeric conversion should be independent between columns and dependent between rows, since DataFrames are column oriented. Maybe numeric conversion should be tried first and then if a `TypeError` or some other more appropriate exception is caught then try date conversion.
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} | 2 | "2013-04-23T21:05:03Z" | "2014-07-02T07:30:22Z" | "2013-04-24T12:49:50Z" | CONTRIBUTOR | null | fixes #3437
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} | 13 | "2013-04-23T21:12:36Z" | "2018-08-17T07:20:12Z" | "2018-08-17T07:20:12Z" | NONE | null | ```
.../pandas$ nosetests -v --with-doctest -I .*test
/.../.virtualenvs/myenv/local/lib/python2.7/site-packages/scikits/statsmodels/__init__.py:2: UserWarning: scikits.statsmodels namespace is deprecated and will be removed in 0.5, please use statsmodels instead
warnings.warn('scikits.statsmodels namespace is deprecated and will be '
/.../.virtualenvs/myenv/local/lib/python2.7/site-packages/scikits/statsmodels/regression/__init__.py:2: UserWarning: scikits.statsmodels namespace is deprecated and will be removed in 0.5, please use statsmodels instead
warnings.warn('scikits.statsmodels namespace is deprecated and will be '
Doctest: pandas.compat.scipy.percentileofscore ... ok
Doctest: pandas.compat.scipy.rankdata ... FAIL
Doctest: pandas.compat.scipy.scoreatpercentile ... FAIL
Doctest: pandas.core.algorithms.quantile ... ok
Doctest: pandas.core.common.iterpairs ... FAIL
Doctest: pandas.core.common.split_ranges ... ok
Doctest: pandas.core.format.EngFormatter.__call__ ... FAIL
Doctest: pandas.core.frame.DataFrame.__init__ ... FAIL
Doctest: pandas.core.frame.DataFrame.apply ... FAIL
Doctest: pandas.core.frame.DataFrame.combine_first ... FAIL
Doctest: pandas.core.frame.DataFrame.merge ... FAIL
Doctest: pandas.core.frame.DataFrame.pivot ... FAIL
Doctest: pandas.core.frame.DataFrame.reindex ... FAIL
Doctest: pandas.core.frame.DataFrame.reindex_axis ... FAIL
Doctest: pandas.core.frame.DataFrame.set_index ... FAIL
Doctest: pandas.core.frame.DataFrame.sort ... FAIL
Doctest: pandas.core.frame.DataFrame.sort_index ... FAIL
Doctest: pandas.core.frame.DataFrame.stack ... FAIL
Doctest: pandas.core.frame.DataFrame.to_excel ... FAIL
Doctest: pandas.core.frame.DataFrame.unstack ... FAIL
Doctest: pandas.core.frame.DataFrame.xs ... FAIL
Doctest: pandas.core.generic.PandasObject.groupby ... FAIL
Doctest: pandas.core.groupby.NDFrameGroupBy.transform ... FAIL
Doctest: pandas.core.groupby.SeriesGroupBy.aggregate ... FAIL
Doctest: pandas.core.groupby.SeriesGroupBy.transform ... FAIL
Doctest: pandas.core.index.Index.diff ... FAIL
Doctest: pandas.core.index.Index.get_indexer ... FAIL
Doctest: pandas.core.index.MultiIndex.get_indexer ... FAIL
Doctest: pandas.core.indexing._SeriesIndexer ... FAIL
Doctest: pandas.core.panel.WidePanel.transpose ... FAIL
Doctest: pandas.core.panel.panel_index ... FAIL
Doctest: pandas.core.reshape._Unstacker ... FAIL
Doctest: pandas.core.reshape.lreshape ... FAIL
Doctest: pandas.core.reshape.melt ... FAIL
Doctest: pandas.core.series.Series.map ... FAIL
Doctest: pandas.core.series.Series.rename ... FAIL
Doctest: pandas.core.series.Series.sort_index ... FAIL
Doctest: pandas.core.series.Series.unstack ... FAIL
Doctest: pandas.core.strings.StringMethods ... FAIL
Failure: ImportError (No module named httplib2) ... ERROR
Doctest: pandas.io.data.Options ... FAIL
Doctest: pandas.io.data.Options.get_call_data ... FAIL
Doctest: pandas.io.data.Options.get_options_data ... FAIL
Doctest: pandas.io.data.Options.get_put_data ... FAIL
Failure: ImportError (No module named httplib2) ... ERROR
Doctest: pandas.io.pytables.HDFStore ... FAIL
Doctest: pandas.io.pytables.get_store ... FAIL
Doctest: pandas.io.sql.sequence2dict ... ok
Failure: ImportError (No module named util) ... ERROR
Failure: ImportError (No module named rpy2.robjects.packages) ... ERROR
Failure: ImportError (No module named util) ... ERROR
Failure: ImportError (cannot import name WLS) ... ERROR
Doctest: pandas.tools.merge.merge ... FAIL
Doctest: pandas.tools.merge.ordered_merge ... FAIL
Doctest: pandas.tools.pivot.crosstab ... FAIL
Doctest: pandas.tools.pivot.pivot_table ... FAIL
Doctest: pandas.tools.plotting.boxplot_frame_groupby ... FAIL
Doctest: pandas.tools.plotting.parallel_coordinates ... FAIL
Doctest: pandas.tools.plotting.scatter_matrix ... FAIL
Doctest: pandas.tools.tile.cut ... FAIL
Doctest: pandas.tseries.period.Period.strftime ... FAIL
Doctest: pandas.tseries.period.PeriodIndex ... FAIL
Doctest: pandas.util.compat._Counter ... ok
Doctest: pandas.util.compat._Counter.__add__ ... ok
Doctest: pandas.util.compat._Counter.__and__ ... ok
Doctest: pandas.util.compat._Counter.__init__ ... ok
Doctest: pandas.util.compat._Counter.__or__ ... ok
Doctest: pandas.util.compat._Counter.__sub__ ... ok
Doctest: pandas.util.compat._Counter.elements ... ok
Doctest: pandas.util.compat._Counter.most_common ... ok
Doctest: pandas.util.compat._Counter.update ... ok
Doctest: pandas.util.counter.Counter ... ok
Doctest: pandas.util.counter.Counter.__add__ ... ok
Doctest: pandas.util.counter.Counter.__and__ ... ok
Doctest: pandas.util.counter.Counter.__init__ ... ok
Doctest: pandas.util.counter.Counter.__or__ ... ok
Doctest: pandas.util.counter.Counter.__sub__ ... ok
Doctest: pandas.util.counter.Counter.elements ... ok
Doctest: pandas.util.counter.Counter.most_common ... ok
Doctest: pandas.util.counter.Counter.subtract ... ok
Doctest: pandas.util.counter.Counter.update ... ok
...
Ran 81 tests in 4.671s
FAILED (errors=6, failures=52)
```
From the Messages (not included in the code snippet above) one can see that most errors result from ImportErrors, so maybe the problem is only that the imports are not in the docstring.
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https://api.github.com/repos/pandas-dev/pandas/issues/3440 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3440/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3440/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3440/events | https://github.com/pandas-dev/pandas/issues/3440 | 13,558,621 | MDU6SXNzdWUxMzU1ODYyMQ== | 3,440 | Panel attribute naming conflict if item is named 'a' | {
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} | 3 | "2013-04-23T21:33:55Z" | "2013-08-05T01:44:08Z" | "2013-08-05T01:44:08Z" | CONTRIBUTOR | null | Including an item named 'a' breaks the expected attribute-retrieval behavior for Panels
```
import numpy as np
from pandas import DataFrame, Panel
df0 = DataFrame(np.zeros([3,4]))
df1 = DataFrame(np.ones([3,4]))
p = Panel({'a':df0, 'b':df1})
p.b
Out[6]:
0 1 2 3
0 1 1 1 1
1 1 1 1 1
2 1 1 1 1
p.a
Out[7]: 'minor_axis'
```
The expected behavior can be recovered by using anything other than 'a' itself:
```
p = Panel({'a_':df0, 'b':df1})
p.a_
Out[9]:
0 1 2 3
0 0 0 0 0
1 0 0 0 0
2 0 0 0 0
```
I'm guessing this is related to the fact that many Panel methods use `a` in iterating through the axes:
```
passed_axes = [kwargs.get(a) for a in self._AXIS_ORDERS]
```
Though it's not clear immediately which is the offending use, or if changing all of these is worth it for the recovery of the `a` namespace...
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https://api.github.com/repos/pandas-dev/pandas/issues/3441 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3441/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3441/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3441/events | https://github.com/pandas-dev/pandas/issues/3441 | 13,560,477 | MDU6SXNzdWUxMzU2MDQ3Nw== | 3,441 | Allow ExcelWriter() to add sheets to existing workbook | {
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} | 40 | "2013-04-23T22:22:18Z" | "2020-03-20T16:11:29Z" | "2018-06-19T01:03:15Z" | NONE | null | The ability of ExcelWriter to save different dataframes to different worksheets is great for sharing those dfs with the python-deficient. But this quickly leads to a need to _add_ worksheets to an existing workbook, not just creating one from scratch; something like:
```
df0=pd.DataFrame(np.arange(3))
df0.to_excel('foo.xlsx','Data 0')
df1=pd.DataFrame(np.arange(2))
df1.to_excel('foo.xlsx','Data 1')
```
The following little diff to io/parsers.py implements this behavior for *.xlsx files:
```
diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py
index 89f892d..7f010ee 100644
--- a/pandas/io/parsers.py
+++ b/pandas/io/parsers.py
@@ -2099,12 +2099,19 @@ class ExcelWriter(object):
self.fm_date = xlwt.easyxf(num_format_str='YYYY-MM-DD')
else:
from openpyxl.workbook import Workbook
- self.book = Workbook() # optimized_write=True)
- # open pyxl 1.6.1 adds a dummy sheet remove it
- if self.book.worksheets:
- self.book.remove_sheet(self.book.worksheets[0])
+ from openpyxl.reader.excel import load_workbook
+
+ try:
+ self.book=load_workbook(filename = path)
+ self.sheets={wks.title:wks for wks in self.book.worksheets}
+ except InvalidFileException:
+ self.book = Workbook() # optimized_write=True)
+ # open pyxl 1.6.1 adds a dummy sheet remove it
+ if self.book.worksheets:
+ self.book.remove_sheet(self.book.worksheets[0])
+ self.sheets = {}
+
self.path = path
- self.sheets = {}
self.cur_sheet = None
```
Doing this for *.xls files is a little harder.
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df = pd.DataFrame()
df["test"] = 7
df["test2"] = pd.Series({"a" : "b"})
df
```
Returns:
```
Empty DataFrame
Columns: [test, test2]
Index: []
```
Tested on pandas 0.10.1 and 0.11.0
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} | 30 | "2013-04-24T13:49:52Z" | "2016-10-05T13:05:50Z" | "2016-10-05T11:25:28Z" | NONE | null | With 0.11 out, Pandas supports more dtypes than before, which is very useful to us science folks. However, some data is intrinsically multi-dimensional, high enough dimensional so that using labels on columns is impractical (for instance, images).
I understand DataFrames or Panels are usually the recommended panacea for this problem. This works if the datatype doesn't have any annotation. For instance, for each frame of a video, I have electrophysiology traces, timestamps, and environmental variables measured.
I have a working solution where I explicitly separate out the non-scalar data from the scalar data. I use Pandas exclusively for the scalar data, and then a dictionary of multi-D arrays for the array data.
What is the work and overhead involved in supporting multi-D data types? I would love to keep my entire ecosystem in Pandas, as it's much faster and richer than just NumPy data wrangling.
See below for the code that I hope is possible to run, with fixes.
If you can point me to a place in the codebase where I can tinker, that would also be much appreciated.
```
import pandas as pd
mydtype=np.dtype('(3,3)f4')
pd.Series(np.zeros(3,), dtype=mydtype)
```
```
Exception: Data must be 1-dimensional
```
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} | 19 | "2013-04-24T15:54:46Z" | "2018-06-09T14:38:19Z" | "2015-03-05T08:58:07Z" | NONE | null | Original title: rplot.rst needs cleaning up
---
http://pandas.pydata.org/pandas-docs/dev/rplot.html
plot style, dimensions, grids, legend.
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} | 8 | "2013-04-24T17:01:42Z" | "2013-04-30T04:14:50Z" | "2013-04-25T14:25:43Z" | CONTRIBUTOR | null | Here is a case where I where the HDFStore would complain:
```
In [60]: store
Out[60]:
<class 'pandas.io.pytables.HDFStore'>
File path: water/expected2.h5
/spelling series (shape->[2])
In [61]: store.remove('spellllling')
```
Is there a reason to keep it silent?
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} | 1 | "2013-04-24T21:30:46Z" | "2013-04-30T04:14:54Z" | "2013-04-25T14:24:18Z" | CONTRIBUTOR | null | original mods made < 0.11 in #3136 / #2437
incorrect selections, depending on the start:end pos gettting weird values
also should be able to select a value that's bigger/smaller than the contained range and have it work (this is how ordered series works)
```
[21]: s = Series(randn(10),date_range('20130101',periods=10))
In [22]: x = s.index.values
In [23]: random.shuffle(x)
In [24]: s.index = x
In [25]: s
Out[25]:
2013-01-09 0.911882
2013-01-06 -0.310166
2013-01-10 1.090196
2013-01-07 -0.846249
2013-01-02 -0.205013
2013-01-08 -1.284152
2013-01-05 0.018700
2013-01-04 -0.503527
2013-01-01 0.073035
2013-01-03 0.695904
dtype: float64
In [26]: s['20130104':'20130105']
Out[26]: Series([], dtype: float64)
In [27]: s['20130104':'20130107']
Out[27]: Series([], dtype: float64)
In [28]: s['20130102':'20130107']
Out[28]: Series([], dtype: float64)
In [29]: s['20130102':'20130104']
Out[29]:
2013-01-02 -0.205013
2013-01-08 -1.284152
2013-01-05 0.018700
2013-01-04 -0.503527
dtype: float64
```
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} | 2 | "2013-04-24T22:07:11Z" | "2013-04-30T04:15:05Z" | "2013-04-25T01:56:02Z" | CONTRIBUTOR | null | I didn't expect the last line of code to pass.
```
import numpy as np
import pandas as pd
pd.__version__
#[Out]# '0.12.0.dev-5c3ccdb'
np.random.seed(33)
df = pd.DataFrame(np.random.random((3, 3)),
index=['a', 'b', 'c'],
columns=['e', 'f', 'g'])
# raise a KeyError?
df.loc[[1, 2], [1, 2]]
#[Out]# f g
#[Out]# b 0.870396 0.185040
#[Out]# c 0.953252 0.680451
```
Being strictly label based, shouldn't `loc` fail when it doesn't find 1 or 2 as a label?
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} | 0 | "2013-04-24T22:39:37Z" | "2014-07-16T08:06:17Z" | "2013-04-24T22:39:44Z" | NONE | null | fyi @hayd
#3419
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} | 0 | "2013-04-25T00:05:20Z" | "2014-06-22T03:25:32Z" | "2013-04-25T01:55:28Z" | CONTRIBUTOR | null | closes #3449
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} | 0 | "2013-04-25T01:10:17Z" | "2014-07-16T08:06:20Z" | "2013-04-25T12:29:36Z" | CONTRIBUTOR | null | closes #3448
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} | 4 | "2013-04-25T05:22:37Z" | "2013-06-02T23:27:57Z" | "2013-06-02T23:26:45Z" | NONE | null | There seems to be an issue with quotes containing the separator in read_csv
``` shell
>>> pd.read_csv(StringIO.StringIO(' a,b,c\r"a,b","e,d","f,f"'), header=None)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/john/app/venv/lib/python2.7/site-packages/pandas/io/parsers.py", line 399, in parser_f
return _read(filepath_or_buffer, kwds)
File "/home/john/app/venv/lib/python2.7/site-packages/pandas/io/parsers.py", line 215, in _read
return parser.read()
File "/home/john/app/venv/lib/python2.7/site-packages/pandas/io/parsers.py", line 631, in read
ret = self._engine.read(nrows)
File "/home/john/app/venv/lib/python2.7/site-packages/pandas/io/parsers.py", line 954, in read
data = self._reader.read(nrows)
File "parser.pyx", line 644, in pandas._parser.TextReader.read (pandas/src/parser.c:5925)
File "parser.pyx", line 666, in pandas._parser.TextReader._read_low_memory (pandas/src/parser.c:6145)
File "parser.pyx", line 719, in pandas._parser.TextReader._read_rows (pandas/src/parser.c:6750)
File "parser.pyx", line 706, in pandas._parser.TextReader._tokenize_rows (pandas/src/parser.c:6634)
File "parser.pyx", line 1572, in pandas._parser.raise_parser_error (pandas/src/parser.c:17055)
pandas._parser.CParserError: Error tokenizing data. C error: Expected 3 fields in line 2, saw 4
```
##### EXPECTED BEHAVIOR:
``` python
>>> pd.read_csv(StringIO.StringIO(' a,b,c\n"a,b","e,d","f,f"'), header=None)
0 1 2
0 a b c
1 a,b e,d f,f
```
This should have the same behavior as when the line ending is `\n`
---
Maybe this should be in a separate bug report, but a possibly related issue occurs when you don't say `header=None`
``` python
>>> pd.read_csv(StringIO.StringIO(' a,b,c\r"a,b","e,d","f,f"'))
a b c
"a b" e,d f,f
```
The above shows the first quoted-delimited item set as the `index_col`. The following shows what happens when we tell pandas to use `index_col=False`
``` python
>>> pd.read_csv(StringIO.StringIO(' a,b,c\r"a,b","e,d","f,f"'), index_col=False)
a b c
0 "a b" e,d
```
##### EXPECTED BEHAVIOR:
``` python
>>> pd.read_csv(StringIO.StringIO(' a,b,c\n"a,b","e,d","f,f"'))
a b c
0 a,b e,d f,f
```
and with index_col=False
``` python
>>> pd.read_csv(StringIO.StringIO(' a,b,c\n"a,b","e,d","f,f"'), index_col=False)
a b c
0 a,b e,d f,f
```
---
Here is my system information if that is necessary
``` python
>>> pd.__version__
'0.10.1'
>>> sys.version_info
sys.version_info(major=2, minor=7, micro=3, releaselevel='final', serial=0)
>>> sys.platform
'darwin'
>>> os.name
posix'
```
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} | 4 | "2013-04-25T06:40:46Z" | "2013-12-04T00:57:21Z" | "2013-04-25T16:11:11Z" | NONE | null | <b>pandas version:</b> 0.11
python version: 2.7.4
platforms: linux & windows
When using cols parameter of to_csv() method only the order of the header is changed, while actual values are not.
``` python
import pandas as pd
from cStringIO import StringIO
data = """\
date,time,X1,X2
2013-1-1,0030,0.1,0.4
2013-1-1,0100,0.2,0.3
2013-1-1,0130,0.3,0.2
2013-1-1,0200,0.4,0.1
"""
df = pd.read_csv(StringIO(data), parse_dates=[['date', 'time']], index_col=0)
df.to_csv('data.csv', index_label='date_time', cols=['X2', 'X1'])
```
<b>Expected output:</b>
date_time,X2,X1
2013-01-01 00:30:00,0.4,0.1
2013-01-01 01:00:00,0.3,0.2
2013-01-01 01:30:00,0.2,0.3
2013-01-01 02:00:00,0.1,0.4
<b>Actual output:</b>
date_time,X2,X1
2013-01-01 00:30:00,0.1,0.4
2013-01-01 01:00:00,0.2,0.3
2013-01-01 01:30:00,0.3,0.2
2013-01-01 02:00:00,0.4,0.1
``` python
df.to_csv('data.csv', index_label='date_time', cols=['X2'])
```
date_time,X2
2013-01-01 00:30:00,0.1
2013-01-01 01:00:00,0.2
2013-01-01 01:30:00,0.3
2013-01-01 02:00:00,0.4
``` python
print df['X1']
```
date_time
2013-01-01 00:30:00 0.1
2013-01-01 01:00:00 0.2
2013-01-01 01:30:00 0.3
2013-01-01 02:00:00 0.4
Name: X1, dtype: float64
``` python
print df['X2']
```
date_time
2013-01-01 00:30:00 0.4
2013-01-01 01:00:00 0.3
2013-01-01 01:30:00 0.2
2013-01-01 02:00:00 0.1
Name: X2, dtype: float64
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} | 3 | "2013-04-25T10:49:27Z" | "2013-05-03T17:15:39Z" | "2013-04-25T14:38:23Z" | NONE | null | ```
In [16]: from pandas.util.testing import makeCustomDataframe as mkdf
...: df= mkdf(10, 3)
...: df.columns = ['a','a','b']
...: cols = ['b','a']
...: df[['b','a']].columns
Out[16]: Index([a, a, b], dtype=object)
```
expected to see
```
['b','a','a']
```
Once fixed, can relax format.py:CSVFormatter raising NotImplementedError()
on cols selection with dupe columns, for the new engine only. see #3457 .
_edit:_ #3509 removed that limitation from CSVFormatter
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} | 5 | "2013-04-25T11:43:54Z" | "2016-04-19T20:40:50Z" | "2016-04-19T20:40:50Z" | NONE | null | use max_seq_len, after setting default to close #3391.
also duplicate behaviour of "..." in other situations, rather then
always printing 6 items
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https://api.github.com/repos/pandas-dev/pandas/issues/3457 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3457/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3457/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3457/events | https://github.com/pandas-dev/pandas/issues/3457 | 13,635,705 | MDU6SXNzdWUxMzYzNTcwNQ== | 3,457 | engine='python' df.to_csv() duplicate columns doesn't work | {
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} | 7 | "2013-04-25T12:29:18Z" | "2013-04-30T04:14:37Z" | "2013-04-25T15:25:49Z" | NONE | null | example from 0.10.1, and carried on to `to_csv(engine='python')` in 0.11
note the first columns duplicates the second columns, which shouldn't be.
Corrolary #3454
```
from pandas.util.testing import makeCustomDataframe as mkdf
N=10
df= mkdf(N, 3)
df.columns = ['a','a','b']
path = "/tmp/k.csv"
df.to_csv("/tmp/k.csv")
!cat "/tmp/k.csv"
R0,a,a,b
R_l0_g0,R0C1,R0C1,R0C2
R_l0_g1,R1C1,R1C1,R1C2
R_l0_g10,R2C1,R2C1,R2C2
R_l0_g2,R3C1,R3C1,R3C2
R_l0_g3,R4C1,R4C1,R4C2
R_l0_g4,R5C1,R5C1,R5C2
R_l0_g5,R6C1,R6C1,R6C2
R_l0_g6,R7C1,R7C1,R7C2
R_l0_g7,R8C1,R8C1,R8C2
R_l0_g8,R9C1,R9C1,R9C2
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3458 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3458/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3458/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3458/events | https://github.com/pandas-dev/pandas/pull/3458 | 13,636,560 | MDExOlB1bGxSZXF1ZXN0NTM2NjIwOQ== | 3,458 | BUG: to_csv handles cols= reordering,dupe cols GH3454 | {
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@jreback, comments?
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https://api.github.com/repos/pandas-dev/pandas/issues/3459 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3459/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3459/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3459/events | https://github.com/pandas-dev/pandas/pull/3459 | 13,640,626 | MDExOlB1bGxSZXF1ZXN0NTM2ODI3Mg== | 3,459 | BUG: GH3455 Duplicate indexes with getitem will return items in the correct order | {
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} | [] | closed | false | null | [] | null | 3 | "2013-04-25T14:17:46Z" | "2014-06-28T10:24:30Z" | "2013-04-25T14:38:06Z" | CONTRIBUTOR | null | BUG: GH3457 to_csv writing duplicate columns incorrectly
closes #3455, #3457
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} | 2 | "2013-04-25T15:36:06Z" | "2013-05-10T09:30:13Z" | "2013-05-10T09:30:13Z" | NONE | null | ```
_p=PeriodIndex([u'2000-01-03'],freq='B')
print unicode(_p)
PeriodIndex([u'2000-01-03'], dtype=int64)
```
can't recreate object with result from `str(self)`.
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} | 9 | "2013-04-25T15:48:23Z" | "2013-04-30T04:14:24Z" | "2013-04-25T21:57:05Z" | NONE | null | Issue: `frame.sort_index` uses `argsort` for a single sort column, but `_lexsort_indexer` for multi-columns
need to do a transform on datetimens[64] before passing to ``_lexsort_`indexer`
In [54]: a.columns
Out[54]: Index([ticker, disclosuredate, txnid], dtype=object)
In [55]: a.values
Out[55]:
array([[A, 2010-03-09 00:00:00, 11110508],
[A, 2010-03-12 00:00:00, 11121853],
[A, 2011-02-15 00:00:00, 12488915],
[A, 2011-03-08 00:00:00, 12563380],
[A, 2011-04-22 00:00:00, 12653015],
[A, 2013-01-28 00:00:00, 15244694]], dtype=object)
In [56]: a.sort(columns=['disclosuredate']).values
Out[56]:
array([[A, 2010-03-09 00:00:00, 11110508],
[A, 2010-03-12 00:00:00, 11121853],
[A, 2011-04-22 00:00:00, 12653015],
[A, 2013-01-28 00:00:00, 15244694],
[A, 2011-03-08 00:00:00, 12563380],
[A, 2011-02-15 00:00:00, 12488915]], dtype=object)
In [57]: pd.**version**
Out[57]: '0.11.0'
In [58]: import time
In [59]: a['epoch'] = a['disclosuredate'].map(lambda x: time.mktime(x.timetuple()))
In [60]: a.sort(['epoch']).values
Out[60]:
array([[A, 2010-03-09 00:00:00, 11110508, 1268110800.0],
[A, 2010-03-12 00:00:00, 11121853, 1268370000.0],
[A, 2011-02-15 00:00:00, 12488915, 1297746000.0],
[A, 2011-03-08 00:00:00, 12563380, 1299560400.0],
[A, 2011-04-22 00:00:00, 12653015, 1303444800.0],
[A, 2013-01-28 00:00:00, 15244694, 1359349200.0]], dtype=object)
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} | 1 | "2013-04-25T15:48:34Z" | "2015-10-09T23:28:35Z" | "2015-10-09T23:28:35Z" | NONE | null | ```
cols=period_range('2000',periods=2)
df=DataFrame([[1,2],[3,4]],columns=cols)
cols = map(unicode,cols)
df.loc[:,cols]
KeyError: "[[u'2000-01-01', u'2000-01-02']] are not in ALL in the [columns]"
```
@jreback , not sure whether to call this a bug.
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https://api.github.com/repos/pandas-dev/pandas/issues/3463 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3463/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3463/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3463/events | https://github.com/pandas-dev/pandas/pull/3463 | 13,655,080 | MDExOlB1bGxSZXF1ZXN0NTM3NDk5Nw== | 3,463 | CLN: series to now inherit from NDFrame | {
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} | [] | closed | false | null | [] | null | 1 | "2013-04-25T18:40:30Z" | "2014-07-10T19:18:54Z" | "2013-04-29T14:37:13Z" | CONTRIBUTOR | null | Major refactor primarily to make Series inherit from NDFrame
Preserves pickle compat
very few tests were changed (and only for compat on return objects)
a few performance enhancements, a couple of regressions (see bottom)
_obviously this is a large change in terms of the codebase, but it brings more consistency between series/frame/panel (not all of this is there yet, but future changes are much easier)_
_Series is now like Frame in that it has a BlockManager (called SingleBlockManager), which holds a block (of any type we support). This introduced some overhead in doing certain operations, which I spent a lot of time optimizing away, further optimizations will come from cythonizing the core/internals, which should be straightforward at this point_
Highlites below:
- Refactor of PandasObject to become new generic Pandas base class
- moved methods
- **str**,**bytes**,**repr**,save,load
- affects all NDFrame hierarchy, Index hierarchy, Period (Timestamp not included)
- Refactor of series.py/frame.py/panel.py to move common code to generic.py
- added _setup_axes to created generic NDFrame structures
- moved methods
- from_axes,_wrap_array,axes,ix,shape,empty,swapaxes,transpose,pop
- **iter**,keys,**contains**,**len**,**neg**,**invert**
- convert_objects,as_blocks,as_matrix,values
- **getstate**,**setstate** (though compat remains in frame/panel)
- **getattr**,**setattr**
- _indexed_same,reindex_like,reindex,align,where,mask
- filter (also added axis argument to selectively filter on a different axis)
- reindex,reindex_axis (which was the biggest change to make generic)
- truncate (moved to become part of NDFrame)
These are API changes which make Panel more consistent with DataFrame
- swapaxes on a Panel with the same axes specified now return a copy
- support attribute access for setting
- filter supports same api as original DataFrame filter
- Reindex called with no arguments will now return a copy of the input object
- Series now inherits from `NDFrame` rather than directly from `ndarray`.
There are several minor changes that affect the API.
- numpy functions that do not support the array interface will now
return `ndarrays` rather than series, e.g. `np.diff` and `np.where`
- `Series(0.5)` would previously return the scalar `0.5`, this is not
longer supported
- several methods from frame/series have moved to `NDFrame`
(convert_objects,where,mask)
- `TimeSeries` is now an alias for `Series`. the property `is_time_series`
can be used to distinguish (if desired)
- Refactor of Sparse objects to use BlockManager
- Created a new block type in internals, SparseBlock, which can hold multi-dtypes
and is non-consolidatable. SparseSeries and SparseDataFrame now inherit
more methods from there hierarchy (Series/DataFrame), and no longer inherit
from SparseArray (which instead is the object of the SparseBlock)
- Sparse suite now supports integration with non-sparse data. Non-float sparse
data is supportable (partially implemented)
- Operations on sparse structures within DataFrames should preserve sparseness,
merging type operations will convert to dense (and back to sparse), so might
be somewhat inefficient
- enable setitem on SparseSeries for boolean/integer/slices
- SparsePanels implementation is unchanged (e.g. not using BlockManager, needs work)
- added `ftypes` method to Series/DataFame, similar to `dtypes`, but indicates
if the underlying is sparse/dense (as well as the dtype)
Perf changed a bit primarily in groupby where a Series has to be reconstructed in order to be passed to the function (in some cases). the biggest regression is actually a special case and can trivially be changed to be MUCH faster (as this requires a Series for a sum operation), in any event the cython versions are quite fast.
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
frame_reindex_both_axes | 18.8640 | 36.5677 | 0.5159 |
frame_reindex_both_axes_ix | 18.7081 | 35.0070 | 0.5344 |
groupby_apply_dict_return | 31.2620 | 43.8453 | 0.7130 |
indexing_panel_subset | 0.4027 | 0.5000 | 0.8053 |
series_constructor_ndarray | 0.0173 | 0.0113 | 1.5352 |
frame_iteritems | 3.4393 | 1.8907 | 1.8191 |
frame_fancy_lookup | 3.4020 | 1.4997 | 2.2685 |
sparse_frame_constructor | 12.0993 | 4.8280 | 2.5061 |
groupby_multi_python | 204.1063 | 44.7550 | 4.5605 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Target [dfb119d] : PERF: made constructions of SparseFrame have less redundant steps
Base [67ad556] : Merge pull request #3459 from jreback/GH3455
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3464 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3464/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3464/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3464/events | https://github.com/pandas-dev/pandas/pull/3464 | 13,662,880 | MDExOlB1bGxSZXF1ZXN0NTM3OTE2OQ== | 3,464 | BUG: GH3461 Fix sorting in a frame with a list of columns which contains datetime64 | {
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} | 2 | "2013-04-26T18:11:36Z" | "2016-10-12T23:03:45Z" | "2013-04-26T18:52:57Z" | MEMBER | null | I love the idea of valid-python-code reprs, but this just seems plain wrong:
This throws an exception if you try to eval it.
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makes #3391 a very good idea.
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} | 1 | "2013-04-26T21:14:43Z" | "2014-07-16T08:06:35Z" | "2013-04-28T00:55:20Z" | CONTRIBUTOR | null | `to_latex` was adding an extra alignment to `column_format` when the
`index` argument was False
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https://api.github.com/repos/pandas-dev/pandas/issues/3468 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3468/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3468/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3468/events | https://github.com/pandas-dev/pandas/issues/3468 | 13,708,937 | MDU6SXNzdWUxMzcwODkzNw== | 3,468 | Pandas inconsistenly handles identically named columns in csv export and merging | {
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} | 9 | "2013-04-26T21:25:39Z" | "2013-07-10T13:53:19Z" | "2013-07-10T13:53:19Z" | NONE | null | Using pandas 0.10.1
Pandas allows creating a dataframe with two columns with the same name. (I disagree that it should be allowed, but it is allowed, so OK). However, it doesn't handle that correctly in several cases.
Pandas ought to either completely disallow duplicate named columns _or_ handle them everywhere. But it shouldn't handle them in some cases but not others.
Problem 1: Round-trip to a CSV
Dump the dataframe to a CSV and then read it back. Even though duplicate columns are supposed to be legal, Pandas won't allow that in the CSV import/export.
``` python
df = pandas.DataFrame([[1,2]], columns=['a','a'])
#Note df has two identically named columns now
df.to_csv('foo.csv')
df2 = pandas.DataFrame.from_csv('foo.csv')
#Note df2 does NOT have the same columns anymore. the "a" was renamed "a.1"
#At the least, you ought to get some warning that dumping to a CSV will
#lose column names.
```
Problem 2: Merging to a dataframe with dup columns does not work
``` python
df = pandas.DataFrame([[1,2,3]], columns=['a','a','b'])
df.merge(df, how='left', on='b')
#Result: Very misleading error message
# edit: reindexing error
```
I'd be ok with almost any solution (disallowing duplicate named columns, giving you a warning when you do it, handling it correctly in the merge and csv read, etc). But it should be consistently one way or the other.
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} | 17 | "2013-04-27T00:53:30Z" | "2014-06-14T08:26:30Z" | "2013-04-27T20:14:38Z" | CONTRIBUTOR | null | The St. Louis Fed's [FRED](http://research.stlouisfed.org/fred2/) uses '.' as an NaN marker. I don't think the user has any way to specify the na_value via DataReader, so this just hard codes '.' as an na_value in the fred data-fetching function.
Before:
``` python
In [4]: from pandas.io.data import DataReader
In [5]: df = DataReader('DFII5', data_source="fred")
In [6]: df.head()
Out[6]:
DFII5
DATE
2010-01-01 .
2010-01-04 0.52
2010-01-05 0.44
2010-01-06 0.44
2010-01-07 0.43
```
After
``` python
In [2]: from pandas.io.data import DataReader
In [3]: df = DataReader('DFII5', data_source="fred")
In [4]: df.head()
Out[4]:
DFII5
DATE
2010-01-01 NaN
2010-01-04 0.52
2010-01-05 0.44
2010-01-06 0.44
2010-01-07 0.43
In [5]: pd.__version__
Out[5]: '0.11.0rc1'
```
Hopefully I've done everything correctly with the pull request. Let me know if anything needs fixing.
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} | 5 | "2013-04-27T03:19:17Z" | "2014-07-16T08:06:39Z" | "2013-04-29T17:43:37Z" | CONTRIBUTOR | null | If second argument is Series or DataFrame, ensures alignment of indices.
If second argument is some type of ndarray, just fall back to np.dot on the values.
This mirrors the behavior in DataFrame.dot
Includes unittest
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https://api.github.com/repos/pandas-dev/pandas/issues/3471 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3471/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3471/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3471/events | https://github.com/pandas-dev/pandas/issues/3471 | 13,717,884 | MDU6SXNzdWUxMzcxNzg4NA== | 3,471 | module compiled against API version 8 but this version of numpy is 7 | {
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```
In [1]: import pandas
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
RuntimeError: module compiled against API version 8 but this version of numpy is 7
numpy.core.multiarray failed to import
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-1-d6ac987968b6> in <module>()
----> 1 import pandas
/usr/local/lib/python2.7/site-packages/pandas/__init__.py in <module>()
4
5 try:
----> 6 from . import hashtable, tslib, lib
7 except Exception: # pragma: no cover
8 import sys
ImportError: numpy.core.multiarray failed to import
```
I have numpy version 1.7.1, running OS X 10.8.3
Installing pandas on an empty virtualenv failed due to a numpy dependency which couldn't install automatically, however, after installing numpy explicitly, it did work. Is this a bug or is something just messed up with my install?
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} | 60 | "2013-04-27T21:06:11Z" | "2014-06-12T20:43:38Z" | "2013-10-17T12:50:56Z" | CONTRIBUTOR | null | closes #2215
This is a simple pull request that creates a shortcut for scatter plots from a df. This is in reference to #2215 (and #1527) and follows the same pattern as df.hist().
```
#where you would previously have had to do:
from pandas.tools.plotting import scatter_plot
scatter_plot(df, x = "x", y = 'y')
#you can now do
df.scatter(x="x",y="y")
#example
import pandas as pd
import numpy as np
df = pd.DataFrame( np.random.randn(100,4))
df.scatter(x=1,y=2)
```
I followed the same pattern as df.hist() even though it looks like you are intended make a class object for scatterplots that can be passed to the plot_frame() function as is the case for BarPlot and LinePlot. In any case, this works and may be useful.
best,
zach cp
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} | 18 | "2013-04-28T00:44:16Z" | "2013-08-23T13:08:18Z" | "2013-08-23T13:08:18Z" | NONE | null | following #3473:
```
import pandas as pd
import numpy as np
from pandas.tools.plotting import scatter_plot
df = pd.DataFrame( np.random.randn(100,4))
scatter_plot(df,x=1,y=2)
**Plot**
**Same Plot Again**
```
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```
In [11]: hospitalized['birth_date'] = pd.to_datetime(hospitalized.child_birth_date, errors='raise', format='%Y-%m-%d')
newborns['birth_date'] = pd.to_datetime(newborns.birth_date_newborn, errors='raise')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-11-b2cd6c80de7e> in <module>()
----> 1 hospitalized['birth_date'] = pd.to_datetime(hospitalized.child_birth_date, errors='raise', format='%Y-%m-%d')
2 newborns['birth_date'] = pd.to_datetime(newborns.birth_date_newborn, errors='raise')
/Library/Python/2.7/site-packages/pandas-0.11.0rc1_20130415-py2.7-macosx-10.8-intel.egg/pandas/tseries/tools.pyc in to_datetime(arg, errors, dayfirst, utc, box, format)
102 values = arg.values
103 if not com.is_datetime64_dtype(values):
--> 104 values = _convert_f(values)
105 return Series(values, index=arg.index, name=arg.name)
106 elif isinstance(arg, (np.ndarray, list)):
/Library/Python/2.7/site-packages/pandas-0.11.0rc1_20130415-py2.7-macosx-10.8-intel.egg/pandas/tseries/tools.pyc in _convert_f(arg)
93 return DatetimeIndex._simple_new(values, None, tz=tz)
94 except (ValueError, TypeError):
---> 95 raise e
96
97 if arg is None:
ValueError: day is out of range for month
```
However, if I iterate through the elements of the Series to see where the bad date is, I do not get the error:
```
In [10]: hosp_birth_new = np.empty(len(hospitalized.child_birth_date), dtype=object)
for i,b in enumerate(hospitalized.child_birth_date):
try:
hosp_birth_new[i] = pd.to_datetime(b)
except ValueError:
print b
hosp_birth_new
Out[10]: array([datetime.datetime(2010, 2, 2, 0, 0),
datetime.datetime(2010, 1, 29, 0, 0),
datetime.datetime(2009, 3, 28, 0, 0), ...,
datetime.datetime(2012, 9, 19, 0, 0),
datetime.datetime(2013, 2, 28, 0, 0),
datetime.datetime(2012, 7, 2, 0, 0)], dtype=object)
```
Running pandas-0.11.0rc1_20130415 on Python 2.7.2 (OS X 10.8.3).
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} | [] | closed | false | null | [] | null | 3 | "2013-04-28T15:27:23Z" | "2013-10-07T14:44:58Z" | "2013-04-29T01:04:52Z" | MEMBER | null | It might be nice to add support for coverage info via [coveralls](http://coveralls.io). [Here](https://coveralls.io/info/features) are some of the features. Looks like [this Python interface](https://github.com/coagulant/coveralls-python) is slightly more actively developed and would be the way to go. Not sure if this is worth the pain of setup, but I always find coverage info helpful after running the full test suite and it can be useful for prioritizing areas of the code base. I know pandas's travis config doesn't run the whole ./test.sh but I believe coverage.py can be told what dirs to cover. Down/(up?) side: this will add to travis's running time, but only if tests are successful.
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} | 58 | "2013-04-28T18:01:06Z" | "2014-06-16T01:32:21Z" | "2013-05-03T16:54:13Z" | MEMBER | null | This PR adds new functionality for reading HTML tables from a URI, string, or file-like object into a DataFrame.
#3369
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} | 6 | "2013-04-29T05:50:47Z" | "2013-09-14T20:34:53Z" | "2013-09-14T20:34:53Z" | CONTRIBUTOR | null | The `DataFrame` constructor supports numpy's ndarray of primitive type, recarray (or structured array) or a masked array of simple type. It breaks when given a masked recarray:
``` python
>>> a = np.ma.mrecords.fromrecords([(1,2)], names=['a', 'b'], mask=False)
>>> pandas.DataFrame(a)
...
TypeError: cannot perform reduce with flexible type
```
Tested at latest public release and HEAD.
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https://api.github.com/repos/pandas-dev/pandas/issues/3479 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3479/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3479/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3479/events | https://github.com/pandas-dev/pandas/pull/3479 | 13,747,127 | MDExOlB1bGxSZXF1ZXN0NTQxNzg5MA== | 3,479 | Support mrecarrays in DataFrame constructor | {
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} | 9 | "2013-04-29T06:00:17Z" | "2014-07-17T09:17:17Z" | "2013-09-13T21:39:53Z" | CONTRIBUTOR | null | This is intended as a fix for #3478. ~~Sorry I do not have the time to write rigorous tests.~~
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https://api.github.com/repos/pandas-dev/pandas/issues/3480 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3480/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3480/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3480/events | https://github.com/pandas-dev/pandas/issues/3480 | 13,755,393 | MDU6SXNzdWUxMzc1NTM5Mw== | 3,480 | An apply on a DataFrame along axis=1 breaks aggregations on groupby's of that DataFrame | {
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} | 4 | "2013-04-29T11:18:59Z" | "2013-05-01T14:27:20Z" | "2013-05-01T14:27:20Z" | NONE | null | This seems to be a regression from 0.10.1 to 0.11.0
This works:
``` python
from pandas import DataFrame
df = DataFrame({'foo1' : ['one', 'two', 'two', 'three', 'one', 'two'],
'foo2' : np.random.randn(6)})
grouped = df.groupby('foo1')
grouped.agg(['mean'])
```
This does not (notice the apply):
``` python
from pandas import DataFrame
df = DataFrame({'foo1' : ['one', 'two', 'two', 'three', 'one', 'two'],
'foo2' : np.random.randn(6)})
df = df.apply(lambda x: x, axis=1)
grouped = df.groupby('foo1')
grouped.agg(['mean'])
```
The error raised is:
``` python
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-47-4df234603be2> in <module>()
----> 1 grouped.agg(['mean'])
/Users/gkoller/.virtualenvs/abo/lib/python2.6/site-packages/pandas/core/groupby.pyc in agg(self, func, *args, **kwargs)
336 @Appender(_agg_doc)
337 def agg(self, func, *args, **kwargs):
--> 338 return self.aggregate(func, *args, **kwargs)
339
340 def _iterate_slices(self):
/Users/gkoller/.virtualenvs/abo/lib/python2.6/site-packages/pandas/core/groupby.pyc in aggregate(self, arg, *args, **kwargs)
1688 result = DataFrame(result)
1689 elif isinstance(arg, list):
-> 1690 return self._aggregate_multiple_funcs(arg)
1691 else:
1692 cyfunc = _intercept_cython(arg)
/Users/gkoller/.virtualenvs/abo/lib/python2.6/site-packages/pandas/core/groupby.pyc in _aggregate_multiple_funcs(self, arg)
1736 except SpecificationError:
1737 raise
-> 1738 result = concat(results, keys=keys, axis=1)
1739
1740 return result
/Users/gkoller/.virtualenvs/abo/lib/python2.6/site-packages/pandas/tools/merge.pyc in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity)
870 ignore_index=ignore_index, join=join,
871 keys=keys, levels=levels, names=names,
--> 872 verify_integrity=verify_integrity)
873 return op.get_result()
874
/Users/gkoller/.virtualenvs/abo/lib/python2.6/site-packages/pandas/tools/merge.pyc in __init__(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity)
911
912 if len(objs) == 0:
--> 913 raise Exception('All objects passed were None')
914
915 # consolidate data
Exception: All objects passed were None
```
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} | 3 | "2013-04-29T12:38:49Z" | "2013-05-10T10:43:31Z" | "2013-05-10T10:43:31Z" | NONE | null | concat is supposed to be called with objs = a list or dict. Not multiple objects as as separate args. RIGHT: concat([df1,df2]) WRONG: concat(df1,df2)
The exception thrown in the latter case is seriously cryptic.
---
df1 = pd.DataFrame(randn(5,3), columns=['a','b','c'])
df2 = pd.DataFrame(randn(5,3), columns=['a','b','c'])
pd.concat(df1,df2)
... File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/frame.py", line 587, in **nonzero**
raise ValueError("Cannot call bool() on DataFrame.")
## ValueError: Cannot call bool() on DataFrame.
Version: 0.10.1
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https://api.github.com/repos/pandas-dev/pandas/issues/3482 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3482/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3482/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3482/events | https://github.com/pandas-dev/pandas/pull/3482 | 13,763,019 | MDExOlB1bGxSZXF1ZXN0NTQyNTU2OQ== | 3,482 | CLN: series to now inherit from NDFrame | {
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} | 70 | "2013-04-29T14:37:35Z" | "2014-06-12T18:13:47Z" | "2013-08-16T19:27:02Z" | CONTRIBUTOR | null | Major refactor primarily to make Series inherit from NDFrame
affects #4080, #3862, #816, #3217, #3386, #4463, #4204, #4118 , #4555
Preserves pickle compat
very few tests were changed (and only for compat on return objects)
a few performance enhancements, a couple of regressions (see bottom)
_obviously this is a large change in terms of the codebase, but it brings more consistency between series/frame/panel (not all of this is there yet, but future changes are much easier)_
_Series is now like Frame in that it has a BlockManager (called SingleBlockManager), which holds a block (of any type we support). This introduced some overhead in doing certain operations, which I spent a lot of time optimizing away, further optimizations will come from cythonizing the core/internals, which should be straightforward at this point_
Highlites below:
In 0.13.0 there is a major refactor primarily to subclass `Series` from `NDFrame`,
which is the base class currently for `DataFrame` and `Panel`, to unify methods
and behaviors. Series formerly subclassed directly from `ndarray`.
- Refactor of series.py/frame.py/panel.py to move common code to generic.py
- added `_setup_axes` to created generic NDFrame structures
- moved methods
- `from_axes,_wrap_array,axes,ix,shape,empty,swapaxes,transpose,pop`
- `__iter__,keys,__contains__,__len__,__neg__,__invert__`
- `convert_objects,as_blocks,as_matrix,values`
- `__getstate__,__setstate__` (though compat remains in frame/panel)
- `__getattr__,__setattr__`
- `_indexed_same,reindex_like,align,where,mask,replace`
- `filter` (also added axis argument to selectively filter on a different axis)
- `reindex,reindex_axis` (which was the biggest change to make generic)
- `truncate` (moved to become part of `NDFrame`)
- These are API changes which make `Panel` more consistent with `DataFrame`
- swapaxes on a Panel with the same axes specified now return a copy
- support attribute access for setting
- filter supports same api as original `DataFrame` filter
- Reindex called with no arguments will now return a copy of the input object
- Series now inherits from `NDFrame` rather than directly from `ndarray`.
There are several minor changes that affect the API.
- numpy functions that do not support the array interface will now
return `ndarrays` rather than series, e.g. `np.diff` and `np.where`
- `Series(0.5)` would previously return the scalar `0.5`, this is no
longer supported
- several methods from frame/series have moved to `NDFrame`
(convert_objects,where,mask)
- `TimeSeries` is now an alias for `Series`. the property `is_time_series`
can be used to distinguish (if desired)
- Refactor of Sparse objects to use BlockManager
- Created a new block type in internals, `SparseBlock`, which can hold multi-dtypes
and is non-consolidatable. `SparseSeries` and `SparseDataFrame` now inherit
more methods from there hierarchy (Series/DataFrame), and no longer inherit
from `SparseArray` (which instead is the object of the `SparseBlock`)
- Sparse suite now supports integration with non-sparse data. Non-float sparse
data is supportable (partially implemented)
- Operations on sparse structures within DataFrames should preserve sparseness,
merging type operations will convert to dense (and back to sparse), so might
be somewhat inefficient
- enable setitem on `SparseSeries` for boolean/integer/slices
- `SparsePanels` implementation is unchanged (e.g. not using BlockManager, needs work)
- added `ftypes` method to Series/DataFame, similar to `dtypes`, but indicates
if the underlying is sparse/dense (as well as the dtype)
- All `NDFrame` objects now have a `_prop_attributes`, which can be used to indcated various
values to propogate to a new object from an existing (e.g. name in `Series` will follow
more automatically now)
Perf changed a bit primarily in groupby where a Series has to be reconstructed in order to be passed to the function (in some cases). I basically pass a Series-like class to the grouped function to see if it doesn't raise, if its ok, then it is used rather than a full Series in order to reduce overhead of the Series creation for each group.
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
groupby_multi_python | 109.3636 | 78.3370 | 1.3961 |
frame_iteritems | 3.4664 | 2.0154 | 1.7200 |
frame_fancy_lookup | 3.3991 | 1.6137 | 2.1064 |
sparse_frame_constructor | 11.7100 | 5.3363 | 2.1944 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Target [c5d9495] : BUG: fix ujson handling of new series object
Base [1b91f4f] : BUG: Fixed non-unique indexing memory allocation issue with .ix/.loc (GH4280)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3483 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3483/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3483/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3483/events | https://github.com/pandas-dev/pandas/pull/3483 | 13,767,644 | MDExOlB1bGxSZXF1ZXN0NTQyNzkwMQ== | 3,483 | BUG: GH3468 Fix assigning a new index to a duplicate index in a DataFrame would fail | {
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} | 15 | "2013-04-29T16:06:30Z" | "2014-06-12T15:57:23Z" | "2013-05-02T01:15:09Z" | CONTRIBUTOR | null | partially fixes #3468
This would previously raise (same dtype assignment to a non-multi dtype frame with dup indicies)
```
In [6]: df = DataFrame([[1,2]], columns=['a','a'])
In [7]: df.columns = ['a','a.1']
In [8]: df
Out[8]:
a a.1
0 1 2
```
construction of a multi-dtype frame with a dup index (#2194) is fixed
```
In [1]: DataFrame([[1,2,1.,2.,3.,'foo','bar']], columns=list('aaaaaaa'))
Out[1]:
a a a a a a a
0 1 2 3 1 2 foo bar
```
This was also previously would raise
```
In [2]: %cpaste
Pasting code; enter '--' alone on the line to stop or use Ctrl-D.
: df_float = DataFrame(np.random.randn(10, 3),dtype='float64')
: df_int = DataFrame(np.random.randn(10, 3),dtype='int64')
: df_bool = DataFrame(True,index=df_float.index,columns=df_float.columns)
: df_object = DataFrame('foo',index=df_float.index,columns=df_float.columns)
: df_dt = DataFrame(Timestamp('20010101'),index=df_float.index,columns=df_float.columns)
: df = pan.concat([ df_float, df_int, df_bool, df_object, df_dt ], axis=1)
:--
In [3]: df
Out[3]:
0 1 2 0 1 \
0 True True True 2001-01-01 00:00:00 2001-01-01 00:00:00
1 True True True 2001-01-01 00:00:00 2001-01-01 00:00:00
2 True True True 2001-01-01 00:00:00 2001-01-01 00:00:00
3 True True True 2001-01-01 00:00:00 2001-01-01 00:00:00
4 True True True 2001-01-01 00:00:00 2001-01-01 00:00:00
5 True True True 2001-01-01 00:00:00 2001-01-01 00:00:00
6 True True True 2001-01-01 00:00:00 2001-01-01 00:00:00
7 True True True 2001-01-01 00:00:00 2001-01-01 00:00:00
8 True True True 2001-01-01 00:00:00 2001-01-01 00:00:00
9 True True True 2001-01-01 00:00:00 2001-01-01 00:00:00
2 0 1 2 0 1 2 0 1 2
0 2001-01-01 00:00:00 foo foo foo 0 0 0 0.431857 -0.131747 -1.039563
1 2001-01-01 00:00:00 foo foo foo 0 0 -1 0.516910 -0.683163 0.736468
2 2001-01-01 00:00:00 foo foo foo 0 0 0 -0.147417 -0.305452 0.006213
3 2001-01-01 00:00:00 foo foo foo 0 -1 0 1.443031 0.082710 -0.335054
4 2001-01-01 00:00:00 foo foo foo 1 0 0 -1.349293 0.645316 0.305524
5 2001-01-01 00:00:00 foo foo foo -1 0 0 0.571095 0.756571 -0.773880
6 2001-01-01 00:00:00 foo foo foo 0 0 0 -0.285091 1.196018 0.882786
7 2001-01-01 00:00:00 foo foo foo 2 0 0 0.003610 0.549072 -0.823217
8 2001-01-01 00:00:00 foo foo foo -1 1 0 -0.348279 -0.728958 -0.397435
9 2001-01-01 00:00:00 foo foo foo -1 0 0 0.363489 2.154132 0.494673
```
For those of you interested.....here is the new ref_loc indexer for duplicate columns
its by necessity a block oriented indexer, returns the column map (by column number) to a tuple of the block and the index in the block, only created when needed (e.g. when trying to get a column via iget and the index is non-unique, and the results are cached), this is #3092
```
In [1]: df = pd.DataFrame(np.random.randn(8,4),columns=['a']*4)
In [2]: df._data.blocks
Out[2]: [FloatBlock: [a, a, a, a], 4 x 8, dtype float64]
In [3]: df._data.blocks[0]._ref_locs
In [4]: df._data.set_ref_locs()
Out[4]:
array([(FloatBlock: [a, a, a, a], 4 x 8, dtype float64, 0),
(FloatBlock: [a, a, a, a], 4 x 8, dtype float64, 1),
(FloatBlock: [a, a, a, a], 4 x 8, dtype float64, 2),
(FloatBlock: [a, a, a, a], 4 x 8, dtype float64, 3)], dtype=object)
```
Fixed the #2786, #3230 bug that caused applymap to not work (we temp worked around by raising a ValueError; removed that check)
```
n [3]: In [3]: df = pd.DataFrame(np.random.random((3,4)))
In [4]: In [4]: cols = pd.Index(['a','a','a','a'])
In [5]: In [5]: df.columns = cols
In [6]: In [6]: df.applymap(str)
Out[6]:
a a a a
0 0.494204195164 0.534601503195 0.471870025143 0.880092879641
1 0.860369768954 0.0472931994392 0.775532754792 0.822046777859
2 0.478775855962 0.623584943227 0.932012693593 0.739502590395
```
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] | closed | false | null | [] | null | 3 | "2013-04-29T16:25:38Z" | "2015-01-26T00:57:32Z" | "2015-01-26T00:56:17Z" | NONE | null | The trellis plot shown in the documentation (http://pandas.pydata.org/pandas-docs/stable/_images/rplot7_tips.png) that uses ScaleShape to encode the data column "size" by shape shows a bug:
The same shape is used for "Sun, 5" and "Sun, 6" although they should have different shapes. Also "Thur, 4" and "Thur, 5" have the same shape (not visible in linked image).
The image also shows that gridlines are not correcly drawn on all panels.
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} | 11 | "2013-04-29T16:48:27Z" | "2020-05-03T19:48:28Z" | null | NONE | null | Using pandas 0.10.1. I think this is probably "working correctly as designed", but the design seems non-obvious and there's not much documentation on this.
The Series object has a vectorized .str.startswith() method which works when you give it a constant string. But if you give it a series instead of a string, it "works" (in the sense of returning data) but that data is not what you'd expect. It appears the function was simply not designed to handle being passed a series, which is OK, but passing a series seems like a logicial thing to do, so can we at least make the function return an error rather than misleading data, so people don't get confused? I had to spent a half hour debugging to figure out why this wasn't working as I expected.
# I want to select the rows where column 'two' starts with the string in column 'one'
d = pandas.DataFrame([['hi,hi 2'],['bye','bye 2']], columns=['one','two'])
d.two.str.startswith(d.one) #Returns false for everything, but you'd expect it to be true
The startswith function doesn't handle Series. Ideally, we should change it so it does handle series. But if that's too much work, can we at least have it return "series is not supported, pass a constant string instead" error?
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} | 3 | "2013-04-29T17:38:53Z" | "2017-05-14T10:33:54Z" | "2017-05-11T16:07:28Z" | NONE | null | ```
import pylab
import pandas.util.testing as put
df = put.makeTimeDataFrame()
pylab.figure()
df[ ['A'] ].plot(color = ['r'])
pylab.savefig('C:/tmp.png')
```
will give an error
```
raise ValueError('to_rgba: Invalid rgba arg "%s"\n%s' % (str(arg), exc))
ValueError: to_rgba: Invalid rgba arg "['r']"
```
All you need to do is to de-list the color parameter
```
df[ ['A'] ].plot(color = 'r')
```
No problem if df is more than one columns.
```
df.plot(color = ['r','k'])
```
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} | 9 | "2013-04-29T18:43:51Z" | "2013-05-10T16:38:13Z" | "2013-05-10T16:38:13Z" | NONE | null | Possibly related to #3468
Using pandas 0.10.1.
It's legal for sql (at least Sql Server) to have queries that return two columns with the same name. And it's legal for pandas to have dataframes that have two columns with the same name. But pandas gives a very misleading error message when you try it with read_frame:
# Query that has 2 columns with the same name:
sql = "select 1 as whatever, 2 as name, 3 as name where 1=0"
pos = pandas.io.sql.read_frame(sql, dbconn)
# That gives very misleading error message
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} | 4 | "2013-04-30T03:01:04Z" | "2013-05-10T10:09:08Z" | "2013-05-10T10:09:08Z" | NONE | null | It looks like that some changes were made to the DataFrame.to_csv method, as mentioned in the what's new document.
Today I found two issues with the updated to_csv:
1) I have an int column, when I use floatformat parameter, the int are changed to float
2) cols parameter simply changes the name of columns, it does not reorder the data in a whole column.
Thanks for your hard working. Hope we can make pandas less buggy and more powerful!
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} | 3 | "2013-04-30T06:03:31Z" | "2014-07-01T23:59:35Z" | "2014-07-01T23:59:35Z" | NONE | null | ``` python
In [1]: N = 10000
In [2]: rng = pd.date_range('1/1/2000', periods=N, freq='min')
In [3]: ts = pd.Series(np.random.randn(N), index=rng)
In [4]: ax = ts.plot()
In [5]: ax.get_xlim()
Out[5]: (15778080.0, 15788079.0)
In [6]: ts.resample('D').plot(style='ro', ax=ax)
Out[6]: <matplotlib.axes.AxesSubplot at 0x4482c50>
In [7]: ax.get_xlim()
Out[7]: (15778080.0, 15788079.0)
In [8]: ts.resample('D').plot(style='ro', secondary_y=True, ax=ax)
Out[8]: <matplotlib.axes.AxesSubplot at 0x497e450>
In [9]: ax.get_xlim()
Out[9]: (10957.0, 10963.0)
```
As the xlim changes, the original data is not visible any more.
Could be related to #2185
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} | 1 | "2013-04-30T08:35:55Z" | "2014-06-25T20:51:29Z" | "2013-05-05T10:53:33Z" | CONTRIBUTOR | null | This patch preserves the timezone information when applying an offset to `DatetimeIndex` with a timezone applied. The `MonthEnd` Offset wasn't retaining the timezone information resulting in an error even when both indexes had timezones.
```
TypeError: can't compare offset-naive and offset-aware datetimes
```
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} | 8 | "2013-04-30T13:51:51Z" | "2013-05-27T20:34:28Z" | "2013-05-07T17:34:46Z" | CONTRIBUTOR | null | ```
from pandas import DataFrame
df=DataFrame({'a':{1:'aaa',2:'bbb',3:'ccc'},'b':{1:111,2:222,3:333}})
#this works, new column is created correctly
df['test']=df['a'].apply(lambda x: '_' if x=='aaa' else x)
#this does not work, ie column test is not changed
idx=df['test']=='_'
temp=df.ix[idx,'a'].apply(lambda x: '-----' if x=='aaa' else x)
df.ix[idx,'test']=temp
#this works, column b is changed
temp=df.ix[idx,'a'].apply(lambda x: 99999 if x=='aaa' else x)
df.ix[idx,'b']=temp
#this does not work
temp=df.ix[idx,'a'].apply(lambda x: 'zzzzzz' if x=='aaa' else x)
df.ix[idx,'a']=temp
```
code works correctly on 0.9, not on 0.11
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} | 4 | "2013-04-30T17:29:01Z" | "2013-05-01T16:31:25Z" | "2013-05-01T16:31:25Z" | CONTRIBUTOR | null | Trying to store DataFrames with unsigned integer dtypes in HDFStore fails due to a bug in the `get_atom_data` methods of the `DataCol` and `DataIndexableCol` classes in `pandas.io.pytables`. These methods use Python's `capitalize` method to map dtypes to PyTables column types, and fail because PyTables' unsigned int classes start with two capital letters (e.g., UInt32Col). This error occurs on 0.11 and the current master:
``` python
import pandas as pd
import numpy as np
uint8_series = pd.Series(np.random.random_integers(0,high=255,size=5), dtype=np.uint8)
udf = pd.DataFrame({'u08': uint8_series}, index=np.arange(5))
store = pd.HDFStore('uint.h5')
# this invocation will throw an error in pandas 0.11 and current master
store.append('uints', udf)
```
I have a commit + test on my fork that fixes this, just needed to submit the bug here first to have the right commit message on my fork, per contribution standards. First time committer.
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} | 3 | "2013-04-30T17:56:04Z" | "2014-07-02T10:29:10Z" | "2013-05-01T16:29:09Z" | CONTRIBUTOR | null | Fix for self-reported uint bug in HDFStore. #3493
Travis results (pass): https://travis-ci.org/jmellen/pandas/builds/6764554
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} | 1 | "2013-04-30T18:20:28Z" | "2013-05-02T14:54:00Z" | "2013-05-02T14:54:00Z" | CONTRIBUTOR | null | continuation of #3095
this will work after #3483
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} | 6 | "2013-04-30T20:26:07Z" | "2018-02-15T08:30:11Z" | "2014-02-21T04:39:15Z" | MEMBER | null | matplotlib's `imshow` is pretty unawesome for DataFrames. For example, to label the axes one must do all sorts of tricks with the formatting API. Pandas provides great indexing/labeling capabilities. I propose a function similar to `imshow` that uses the information in a DataFrame to correctly label the axes. The API could be passing `kind='image'` to `DataFrame.plot`.
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} | 6 | "2013-04-30T22:42:31Z" | "2016-07-04T12:41:34Z" | "2016-07-04T08:28:29Z" | CONTRIBUTOR | null | Write time-zones out in csv file
Datetimes are written to csv in readable UTC. Using `pd.read_csv('file.csv', parse_dates=['date'])` expects UTC. `astype('datetime64')` expects readable dates (with no timezone specified) to be local (which also makes sense). But it leads to the following:
``` python
import pandas as pd
from StringIO import StringIO
df = pd.DataFrame({'date' : [0]})
df['date'] = df.date.astype('datetime64[s]')
print df.date[0]
s = StringIO()
df.to_csv(s)
s = s.getvalue()
print 'csv:'
print s
with_parse_dates = pd.read_csv(StringIO(s), parse_dates=['date'])
print 'parse_date:', with_parse_dates.date[0]
with_astype = pd.read_csv(StringIO(s))
with_astype.date = with_astype.date.astype('datetime64')
print 'astype :', with_astype.date[0]
with_astypeZ = pd.read_csv(StringIO(s))
with_astypeZ.date[0] += 'Z'
with_astypeZ.date = with_astypeZ.date.astype('datetime64')
print 'astype w/Z:', with_astypeZ.date[0]
```
output (for me):
```
1970-01-01 00:00:00
csv:
,date
0,1970-01-01 00:00:00
parse_date: 1970-01-01 00:00:00
astype : 1970-01-01 08:00:00
astype w/Z: 1970-01-01 00:00:00
```
Would be easily fixed by indicating UTC in the csv output, possibly with just the `'Z'` suffix.
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} | 2 | "2013-05-01T00:33:19Z" | "2013-05-08T21:48:32Z" | "2013-05-08T21:48:32Z" | NONE | null | I have found what possibly is a bug. When storing data as HDF5 with `table=True` the read value of series.index.freq from HDF5 is set to None rather than its original values.
Please see: http://stackoverflow.com/questions/16311045/in-pandas-why-is-frequency-information-lost-when-storing-in-hdf5-as-a-table
Not sure if there is a limitation for some reason in PyTables or if this is a bug.
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https://api.github.com/repos/pandas-dev/pandas/issues/3500 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3500/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3500/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3500/events | https://github.com/pandas-dev/pandas/pull/3500 | 13,837,703 | MDExOlB1bGxSZXF1ZXN0NTQ2NTMwNA== | 3,500 | My first PR ever. Adding documentation to Cookbook. | {
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} | 4 | "2013-05-01T04:03:03Z" | "2013-05-10T18:02:59Z" | "2013-05-10T10:38:12Z" | NONE | null | Just adding some more description to Cookbook documentation per J. Reback's request. If I didn't mess up anything here, I will go through the rest of the items and add some description.
Please let me know if I messed anything up.
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