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DOC: pydata/pandas -> pandas-dev/pandas (pandas-dev#14409)
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‎asv_bench/benchmarks/attrs_caching.py

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@@ -20,4 +20,4 @@ def setup(self):
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self.cur_index = self.df.index
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def time_setattr_dataframe_index(self):
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self.df.index = self.cur_index
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self.df.index = self.cur_index

‎asv_bench/benchmarks/ctors.py

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self.s = Series(([Timestamp('20110101'), Timestamp('20120101'), Timestamp('20130101')] * 1000))
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def time_index_from_series_ctor(self):
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Index(self.s)
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Index(self.s)

‎asv_bench/benchmarks/frame_ctor.py

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@@ -1703,4 +1703,4 @@ def setup(self):
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self.dict_list = [dict(zip(self.columns, row)) for row in self.frame.values]
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def time_series_ctor_from_dict(self):
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Series(self.some_dict)
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Series(self.some_dict)

‎asv_bench/benchmarks/hdfstore_bench.py

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try:
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os.remove(self.f)
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except:
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pass
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pass

‎asv_bench/benchmarks/index_object.py

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self.mi = MultiIndex.from_product([self.level1, self.level2])
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def time_multiindex_with_datetime_level_sliced(self):
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self.mi[:10].values
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self.mi[:10].values

‎asv_bench/benchmarks/io_sql.py

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@@ -212,4 +212,4 @@ def setup(self):
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self.df = DataFrame({'float1': randn(10000), 'float2': randn(10000), 'string1': (['foo'] * 10000), 'bool1': ([True] * 10000), 'int1': np.random.randint(0, 100000, size=10000), }, index=self.index)
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def time_sql_write_sqlalchemy(self):
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self.df.to_sql('test1', self.engine, if_exists='replace')
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self.df.to_sql('test1', self.engine, if_exists='replace')

‎asv_bench/benchmarks/panel_ctor.py

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self.data_frames[x] = self.df
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def time_panel_from_dict_two_different_indexes(self):
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Panel.from_dict(self.data_frames)
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Panel.from_dict(self.data_frames)

‎asv_bench/benchmarks/panel_methods.py

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self.panel = Panel(np.random.randn(100, len(self.index), 1000))
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def time_panel_shift_minor(self):
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self.panel.shift(1, axis='minor')
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self.panel.shift(1, axis='minor')

‎asv_bench/benchmarks/replace.py

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self.ts = Series(np.random.randn(self.N), index=self.rng)
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def time_replace_replacena(self):
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self.ts.replace(np.nan, 0.0, inplace=True)
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self.ts.replace(np.nan, 0.0, inplace=True)

‎asv_bench/benchmarks/reshape.py

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break
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def time_unstack_sparse_keyspace(self):
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self.idf.unstack()
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self.idf.unstack()

‎asv_bench/benchmarks/stat_ops.py

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rolling_skew(self.arr, self.win)
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def time_rolling_kurt(self):
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rolling_kurt(self.arr, self.win)
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rolling_kurt(self.arr, self.win)

‎asv_bench/benchmarks/strings.py

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self.many.str.upper()
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def make_series(self, letters, strlen, size):
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return Series([str(x) for x in np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))])
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return Series([str(x) for x in np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))])

‎doc/README.rst

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@@ -155,9 +155,9 @@ Where to start?
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---------------
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There are a number of issues listed under `Docs
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<https://github.com/pydata/pandas/issues?labels=Docs&sort=updated&state=open>`_
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<https://github.com/pandas-dev/pandas/issues?labels=Docs&sort=updated&state=open>`_
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and `Good as first PR
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<https://github.com/pydata/pandas/issues?labels=Good+as+first+PR&sort=updated&state=open>`_
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<https://github.com/pandas-dev/pandas/issues?labels=Good+as+first+PR&sort=updated&state=open>`_
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where you could start out.
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Or maybe you have an idea of your own, by using pandas, looking for something

‎doc/_templates/autosummary/accessor_attribute.rst

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.. currentmodule:: {{ module.split('.')[0] }}
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.. autoaccessorattribute:: {{ [module.split('.')[1], objname]|join('.') }}
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.. autoaccessorattribute:: {{ [module.split('.')[1], objname]|join('.') }}

‎doc/_templates/autosummary/accessor_method.rst

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.. currentmodule:: {{ module.split('.')[0] }}
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.. autoaccessormethod:: {{ [module.split('.')[1], objname]|join('.') }}
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.. autoaccessormethod:: {{ [module.split('.')[1], objname]|join('.') }}

‎doc/source/categorical.rst

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print("TypeError: " + str(e))
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.. note::
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If such a function works, please file a bug at https://github.com/pydata/pandas!
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If such a function works, please file a bug at https://github.com/pandas-dev/pandas!
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dtype in apply
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~~~~~~~~~~~~~~

‎doc/source/comparison_with_sas.rst

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Like SAS, pandas provides utilities for reading in data from
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many formats. The ``tips`` dataset, found within the pandas
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tests (`csv <https://raw.github.com/pydata/pandas/master/pandas/tests/data/tips.csv>`_)
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tests (`csv <https://raw.github.com/pandas-dev/pandas/master/pandas/tests/data/tips.csv>`_)
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will be used in many of the following examples.
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SAS provides ``PROC IMPORT`` to read csv data into a data set.
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.. ipython:: python
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url = 'https://raw.github.com/pydata/pandas/master/pandas/tests/data/tips.csv'
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url = 'https://raw.github.com/pandas-dev/pandas/master/pandas/tests/data/tips.csv'
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tips = pd.read_csv(url)
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tips.head()
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‎doc/source/comparison_with_sql.rst

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.. ipython:: python
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url = 'https://raw.github.com/pydata/pandas/master/pandas/tests/data/tips.csv'
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url = 'https://raw.github.com/pandas-dev/pandas/master/pandas/tests/data/tips.csv'
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tips = pd.read_csv(url)
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tips.head()
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‎doc/source/conf.py

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autosummary_generate = glob.glob("*.rst")
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# extlinks alias
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extlinks = {'issue': ('https://github.com/pydata/pandas/issues/%s',
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extlinks = {'issue': ('https://github.com/pandas-dev/pandas/issues/%s',
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'GH'),
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'wiki': ('https://github.com/pydata/pandas/wiki/%s',
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'wiki': ('https://github.com/pandas-dev/pandas/wiki/%s',
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'wiki ')}
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ipython_exec_lines = [
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fn = os.path.relpath(fn, start=os.path.dirname(pandas.__file__))
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if '+' in pandas.__version__:
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return "http://github.com/pydata/pandas/blob/master/pandas/%s%s" % (
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return "http://github.com/pandas-dev/pandas/blob/master/pandas/%s%s" % (
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fn, linespec)
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else:
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return "http://github.com/pydata/pandas/blob/v%s/pandas/%s%s" % (
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return "http://github.com/pandas-dev/pandas/blob/v%s/pandas/%s%s" % (
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pandas.__version__, fn, linespec)
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‎doc/source/contributing.rst

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enhancements and ideas are welcome.
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If you are simply looking to start working with the *pandas* codebase, navigate to the
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`GitHub "issues" tab <https://github.com/pydata/pandas/issues>`_ and start looking through
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`GitHub "issues" tab <https://github.com/pandas-dev/pandas/issues>`_ and start looking through
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interesting issues. There are a number of issues listed under `Docs
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<https://github.com/pydata/pandas/issues?labels=Docs&sort=updated&state=open>`_
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<https://github.com/pandas-dev/pandas/issues?labels=Docs&sort=updated&state=open>`_
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and `Difficulty Novice
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<https://github.com/pydata/pandas/issues?q=is%3Aopen+is%3Aissue+label%3A%22Difficulty+Novice%22>`_
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<https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3A%22Difficulty+Novice%22>`_
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where you could start out.
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Or maybe through using *pandas* you have an idea of your own or are looking for something
@@ -27,7 +27,7 @@ about it!
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Feel free to ask questions on the `mailing list
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<https://groups.google.com/forum/?fromgroups#!forum/pydata>`_ or on `Gitter
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<https://gitter.im/pydata/pandas>`_.
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<https://gitter.im/pandas-dev/pandas>`_.
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Bug reports and enhancement requests
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====================================
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straightforward and mostly trouble free. As always, if you are having difficulties please
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feel free to ask for help.
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The code is hosted on `GitHub <https://www.github.com/pydata/pandas>`_. To
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The code is hosted on `GitHub <https://www.github.com/pandas-dev/pandas>`_. To
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contribute you will need to sign up for a `free GitHub account
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<https://github.com/signup/free>`_. We use `Git <http://git-scm.com/>`_ for
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version control to allow many people to work together on the project.
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-------
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You will need your own fork to work on the code. Go to the `pandas project
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page <https://github.com/pydata/pandas>`_ and hit the ``Fork`` button. You will
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page <https://github.com/pandas-dev/pandas>`_ and hit the ``Fork`` button. You will
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want to clone your fork to your machine::
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git clone git@github.com:your-user-name/pandas.git pandas-yourname
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cd pandas-yourname
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git remote add upstream git://github.com/pydata/pandas.git
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git remote add upstream git://github.com/pandas-dev/pandas.git
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This creates the directory `pandas-yourname` and connects your repository to
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Additional standards are outlined on the `code style wiki
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page <https://github.com/pandas-dev/pandas/wiki/Code-Style-and-Conventions>`_.
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existing code, so don't break it if at all possible. If you think breakage is required,
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This folder contains many current examples of tests, and we suggest looking to these for
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<https://github.com/pandas-dev/pandas/wiki/Testing>`_ of the wiki.
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The ``pandas.util.testing`` module has many special ``assert`` functions that
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make it easier to make statements about whether Series or DataFrame objects are
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Integration tests for ``pandas.io.gbq`` are skipped in pull requests because
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the credentials that are required for running Google BigQuery integration
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tests are `encrypted <https://docs.travis-ci.com/user/encrypting-files/>`__
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on Travis-CI and are only accessible from the pydata/pandas repository. The
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on Travis-CI and are only accessible from the pandas-dev/pandas repository. The
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credentials won't be available on forks of pandas. Here are the steps to run
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gbq integration tests on a forked repository:
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You can run specific benchmarks using the ``-r`` flag, which takes a regular expression.
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Documenting your code
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This will put the text *New in version 0.17.0* wherever you put the sphinx
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directive. This should also be put in the docstring when adding a new function
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or method (`example <https://github.com/pydata/pandas/blob/v0.16.2/pandas/core/generic.py#L1959>`__)
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or a new keyword argument (`example <https://github.com/pydata/pandas/blob/v0.16.2/pandas/core/frame.py#L1171>`__).
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or method (`example <https://github.com/pandas-dev/pandas/blob/v0.16.2/pandas/core/generic.py#L1959>`__)
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or a new keyword argument (`example <https://github.com/pandas-dev/pandas/blob/v0.16.2/pandas/core/frame.py#L1171>`__).
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Contributing your changes to *pandas*
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=====================================
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origin git@github.com:yourname/pandas.git (fetch)
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origin git@github.com:yourname/pandas.git (push)
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upstream git://github.com/pydata/pandas.git (fetch)
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upstream git://github.com/pydata/pandas.git (push)
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upstream git://github.com/pandas-dev/pandas.git (fetch)
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upstream git://github.com/pandas-dev/pandas.git (push)
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Now your code is on GitHub, but it is not yet a part of the *pandas* project. For that to
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‎doc/source/cookbook.rst

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df[(df.AAA <= 6) & (df.index.isin([0,2,4]))]
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`Use loc for label-oriented slicing and iloc positional slicing
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<https://github.com/pydata/pandas/issues/2904>`__
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<https://github.com/pandas-dev/pandas/issues/2904>`__
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.. ipython:: python
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`Partial Selection, the need for sortedness;
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<https://github.com/pydata/pandas/issues/2995>`__
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<https://github.com/pandas-dev/pandas/issues/2995>`__
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Levels
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******
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<http://stackoverflow.com/questions/14569223/timegrouper-pandas>`__
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`Using TimeGrouper and another grouping to create subgroups, then apply a custom function
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<https://github.com/pydata/pandas/issues/3791>`__
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<https://github.com/pandas-dev/pandas/issues/3791>`__
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`Resampling with custom periods
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`Self Join of a DataFrame
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<https://github.com/pydata/pandas/issues/2996>`__
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.. ipython:: python
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<http://stackoverflow.com/questions/15555005/get-inferred-dataframe-types-iteratively-using-chunksize>`__
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`Dealing with bad lines
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<http://github.com/pydata/pandas/issues/2886>`__
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<http://github.com/pandas-dev/pandas/issues/2886>`__
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`Dealing with bad lines II
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<http://nipunbatra.github.io/2013/06/reading-unclean-data-csv-using-pandas/>`__
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<http://stackoverflow.com/questions/13926089/selecting-columns-from-pandas-hdfstore-table>`__
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`Managing heterogeneous data using a linked multiple table hierarchy
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<http://github.com/pydata/pandas/issues/3032>`__
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<http://github.com/pandas-dev/pandas/issues/3032>`__
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`Merging on-disk tables with millions of rows
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<http://stackoverflow.com/questions/14614512/merging-two-tables-with-millions-of-rows-in-python/14617925#14617925>`__
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The :ref:`Timedeltas <timedeltas.timedeltas>` docs.
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`Using timedeltas
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<http://github.com/pydata/pandas/pull/2899>`__
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<http://github.com/pandas-dev/pandas/pull/2899>`__
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.. ipython:: python
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‎doc/source/ecosystem.rst

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API
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-----
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`pandas-datareader <https://github.com/pandas-dev/pandas-datareader>`__
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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``pandas-datareader`` is a remote data access library for pandas. ``pandas.io`` from pandas < 0.17.0 is now refactored/split-off to and importable from ``pandas_datareader`` (PyPI:``pandas-datareader``). Many/most of the supported APIs have at least a documentation paragraph in the `pandas-datareader docs <https://pandas-datareader.readthedocs.io/en/latest/>`_:
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‎doc/source/gotchas.rst

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changes accordingly. This can cause some issues when using ``numpy`` ``ufuncs``
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See the `this old issue <https://github.com/pydata/pandas/issues/2388>`__ for a more
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See the `this old issue <https://github.com/pandas-dev/pandas/issues/2388>`__ for a more
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Parsing Dates from Text Files

‎doc/source/install.rst

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`development version <http://github.com/pydata/pandas>`__ are also provided.
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`development version <http://github.com/pandas-dev/pandas>`__ are also provided.
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Python version support
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----------------------

‎doc/source/io.rst

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that having so many network-accessing functions slows down the documentation
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build. If you spot an error or an example that doesn't run, please do not
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hesitate to report it over on `pandas GitHub issues page
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<http://www.github.com/pydata/pandas/issues>`__.
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<http://www.github.com/pandas-dev/pandas/issues>`__.
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Read a URL and match a table that contains specific text

‎doc/source/overview.rst

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---------------
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The first stop for pandas issues and ideas is the `Github Issue Tracker
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<https://github.com/pydata/pandas/issues>`__. If you have a general question,
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<https://github.com/pandas-dev/pandas/issues>`__. If you have a general question,
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pandas community experts can answer through `Stack Overflow
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<http://stackoverflow.com/questions/tagged/pandas>`__.
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@@ -103,7 +103,7 @@ training, and consulting for pandas.
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pandas is only made possible by a group of people around the world like you
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who have contributed new code, bug reports, fixes, comments and ideas. A
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complete list can be found `on Github <http://www.github.com/pydata/pandas/contributors>`__.
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complete list can be found `on Github <http://www.github.com/pandas-dev/pandas/contributors>`__.
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Development Team
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----------------

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