{"action":"opened","number":20222,"pull_request":{"url":"https://api.github.com/repos/pandas-dev/pandas/pulls/20222","id":174185323,"html_url":"https://github.com/pandas-dev/pandas/pull/20222","diff_url":"https://github.com/pandas-dev/pandas/pull/20222.diff","patch_url":"https://github.com/pandas-dev/pandas/pull/20222.patch","issue_url":"https://api.github.com/repos/pandas-dev/pandas/issues/20222","number":20222,"state":"open","locked":false,"title":"DOC: Improve the docstring of DataFrame.describe()","user":{"login":"nehiljain","id":2761999,"avatar_url":"https://avatars1.githubusercontent.com/u/2761999?v=4","gravatar_id":"","url":"https://api.github.com/users/nehiljain","html_url":"https://github.com/nehiljain","followers_url":"https://api.github.com/users/nehiljain/followers","following_url":"https://api.github.com/users/nehiljain/following{/other_user}","gists_url":"https://api.github.com/users/nehiljain/gists{/gist_id}","starred_url":"https://api.github.com/users/nehiljain/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/nehiljain/subscriptions","organizations_url":"https://api.github.com/users/nehiljain/orgs","repos_url":"https://api.github.com/users/nehiljain/repos","events_url":"https://api.github.com/users/nehiljain/events{/privacy}","received_events_url":"https://api.github.com/users/nehiljain/received_events","type":"User","site_admin":false},"body":"Checklist for the pandas documentation sprint (ignore this if you are doing\r\nan unrelated PR):\r\n\r\n- [ ] PR title is \"DOC: update the <your-function-or-method> docstring\"\r\n- [ ] The validation script passes: `scripts/validate_docstrings.py <your-function-or-method>`\r\n- [ ] The PEP8 style check passes: `git diff upstream/master -u -- \"*.py\" | flake8 --diff`\r\n- [ ] The html version looks good: `python doc/make.py --single <your-function-or-method>`\r\n- [ ] It has been proofread on language by another sprint participant\r\n\r\nPlease include the output of the validation script below between the \"```\" ticks:\r\n\r\n```\r\n\r\n################################################################################\r\n#################### Docstring (pandas.DataFrame.describe)  ####################\r\n################################################################################\r\n\r\nGenerate descriptive statistics that summarize the central tendency,\r\ndispersion and shape of a dataset's distribution, excluding\r\n``NaN`` values.\r\n\r\nAnalyzes both numeric and object series, as well\r\nas ``DataFrame`` column sets of mixed data types. The output\r\nwill vary depending on what is provided. Refer to the notes\r\nbelow for more detail.\r\n\r\nParameters\r\n----------\r\npercentiles : list-like of numbers, optional\r\n    The percentiles to include in the output. All should\r\n    fall between 0 and 1. The default is\r\n    ``[.25, .5, .75]``, which returns the 25th, 50th, and\r\n    75th percentiles.\r\ninclude : 'all', list-like of dtypes or None (default), optional\r\n    A white list of data types to include in the result. Ignored\r\n    for ``Series``. Here are the options:\r\n\r\n    - 'all' : All columns of the input will be included in the output.\r\n    - A list-like of dtypes : Limits the results to the\r\n      provided data types.\r\n      To limit the result to numeric types submit\r\n      ``numpy.number``. To limit it instead to object columns submit\r\n      the ``numpy.object`` data type. Strings\r\n      can also be used in the style of\r\n      ``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To\r\n      select pandas categorical columns, use ``'category'``\r\n    - None (default) : The result will include all numeric columns.\r\nexclude : list-like of dtypes or None (default), optional,\r\n    A black list of data types to omit from the result. Ignored\r\n    for ``Series``. Here are the options:\r\n\r\n    - A list-like of dtypes : Excludes the provided data types\r\n      from the result. To exclude numeric types submit\r\n      ``numpy.number``. To exclude object columns submit the data\r\n      type ``numpy.object``. Strings can also be used in the style of\r\n      ``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To\r\n      exclude pandas categorical columns, use ``'category'``\r\n    - None (default) : The result will exclude nothing.\r\n\r\nReturns\r\n-------\r\nsummary:  Series/DataFrame of summary statistics\r\n\r\nNotes\r\n-----\r\nFor numeric data, the result's index will include ``count``,\r\n``mean``, ``std``, ``min``, ``max`` as well as lower, ``50`` and\r\nupper percentiles. By default the lower percentile is ``25`` and the\r\nupper percentile is ``75``. The ``50`` percentile is the\r\nsame as the median.\r\n\r\nFor object data (e.g. strings or timestamps), the result's index\r\nwill include ``count``, ``unique``, ``top``, and ``freq``. The ``top``\r\nis the most common value. The ``freq`` is the most common value's\r\nfrequency. Timestamps also include the ``first`` and ``last`` items.\r\n\r\nIf multiple object values have the highest count, then the\r\n``count`` and ``top`` results will be arbitrarily chosen from\r\namong those with the highest count.\r\n\r\nFor mixed data types provided via a ``DataFrame``, the default is to\r\nreturn only an analysis of numeric columns. If the dataframe consists\r\nonly of object and categorical data without any numeric columns, the\r\ndefault is to return an analysis of both the object and categorical\r\ncolumns. If ``include='all'`` is provided as an option, the result\r\nwill include a union of attributes of each type.\r\n\r\nThe `include` and `exclude` parameters can be used to limit\r\nwhich columns in a ``DataFrame`` are analyzed for the output.\r\nThe parameters are ignored when analyzing a ``Series``.\r\n\r\nExamples\r\n--------\r\nDescribing a numeric ``Series``.\r\n\r\n>>> s = pd.Series([1, 2, 3])\r\n>>> s.describe()\r\ncount    3.0\r\nmean     2.0\r\nstd      1.0\r\nmin      1.0\r\n25%      1.5\r\n50%      2.0\r\n75%      2.5\r\nmax      3.0\r\ndtype: float64\r\n\r\nDescribing a categorical ``Series``.\r\n\r\n>>> s = pd.Series(['a', 'a', 'b', 'c'])\r\n>>> s.describe()\r\ncount     4\r\nunique    3\r\ntop       a\r\nfreq      2\r\ndtype: object\r\n\r\nDescribing a timestamp ``Series``.\r\n\r\n>>> s = pd.Series([\r\n...   np.datetime64(\"2000-01-01\"),\r\n...   np.datetime64(\"2010-01-01\"),\r\n...   np.datetime64(\"2010-01-01\")\r\n... ])\r\n>>> s.describe()\r\ncount                       3\r\nunique                      2\r\ntop       2010-01-01 00:00:00\r\nfreq                        2\r\nfirst     2000-01-01 00:00:00\r\nlast      2010-01-01 00:00:00\r\ndtype: object\r\n\r\nDescribing a ``DataFrame``. By default only numeric fields\r\nare returned.\r\n\r\n>>> df = pd.DataFrame({ 'object': ['a', 'b', 'c'],\r\n...                     'numeric': [1, 2, 3],\r\n...                     'categorical': pd.Categorical(['d','e','f'])\r\n...                   })\r\n>>> df.describe()\r\n       numeric\r\ncount      3.0\r\nmean       2.0\r\nstd        1.0\r\nmin        1.0\r\n25%        1.5\r\n50%        2.0\r\n75%        2.5\r\nmax        3.0\r\n\r\nDescribing all columns of a ``DataFrame`` regardless of data type.\r\n\r\n>>> df.describe(include='all')\r\n       object  numeric categorical\r\ncount       3      3.0           3\r\nunique      3      NaN           3\r\ntop         c      NaN           f\r\nfreq        1      NaN           1\r\nmean      NaN      2.0         NaN\r\nstd       NaN      1.0         NaN\r\nmin       NaN      1.0         NaN\r\n25%       NaN      1.5         NaN\r\n50%       NaN      2.0         NaN\r\n75%       NaN      2.5         NaN\r\nmax       NaN      3.0         NaN\r\n\r\nDescribing a column from a ``DataFrame`` by accessing it as\r\nan attribute.\r\n\r\n>>> df.numeric.describe()\r\ncount    3.0\r\nmean     2.0\r\nstd      1.0\r\nmin      1.0\r\n25%      1.5\r\n50%      2.0\r\n75%      2.5\r\nmax      3.0\r\nName: numeric, dtype: float64\r\n\r\nIncluding only numeric columns in a ``DataFrame`` description.\r\n\r\n>>> df.describe(include=[np.number])\r\n       numeric\r\ncount      3.0\r\nmean       2.0\r\nstd        1.0\r\nmin        1.0\r\n25%        1.5\r\n50%        2.0\r\n75%        2.5\r\nmax        3.0\r\n\r\nIncluding only string columns in a ``DataFrame`` description.\r\n\r\n>>> df.describe(include=[np.object])\r\n       object\r\ncount       3\r\nunique      3\r\ntop         c\r\nfreq        1\r\n\r\nIncluding only categorical columns from a ``DataFrame`` description.\r\n\r\n>>> df.describe(include=['category'])\r\n       categorical\r\ncount            3\r\nunique           3\r\ntop              f\r\nfreq             1\r\n\r\nExcluding numeric columns from a ``DataFrame`` description.\r\n\r\n>>> df.describe(exclude=[np.number])\r\n       object categorical\r\ncount       3           3\r\nunique      3           3\r\ntop         c           f\r\nfreq        1           1\r\n\r\nExcluding object columns from a ``DataFrame`` description.\r\n\r\n>>> df.describe(exclude=[np.object])\r\n        numeric categorical\r\ncount       3.0           3\r\nunique      NaN           3\r\ntop         NaN           f\r\nfreq        NaN           1\r\nmean        2.0         NaN\r\nstd         1.0         NaN\r\nmin         1.0         NaN\r\n25%         1.5         NaN\r\n50%         2.0         NaN\r\n75%         2.5         NaN\r\nmax         3.0         NaN\r\n\r\nSee Also\r\n--------\r\nDataFrame.count : Count number of non-NA/null observations\r\nDataFrame.max : Maximum of the values in the object\r\nDataFrame.min : Minimum of the values in the object\r\nDataFrame.mean : Mean of the values\r\nDataFrame.std : Standard deviation of the obersvations\r\nDataFrame.select_dtypes : Subset of a DataFrame including/excluding columns based on their dtype\r\n\r\n################################################################################\r\n################################## Validation ##################################\r\n################################################################################\r\n\r\nDocstring for \"pandas.DataFrame.describe\" correct. :)\r\n```\r\n\r\nIf the validation script still gives errors, but you think there is a good reason\r\nto deviate in this case (and there are certainly such cases), please state this\r\nexplicitly.\r\n\r\n\r\n\r\n","created_at":"2018-03-10T19:23:53Z","updated_at":"2018-03-10T19:23:53Z","closed_at":null,"merged_at":null,"merge_commit_sha":null,"assignee":null,"assignees":[],"requested_reviewers":[],"requested_teams":[],"labels":[],"milestone":null,"commits_url":"https://api.github.com/repos/pandas-dev/pandas/pulls/20222/commits","review_comments_url":"https://api.github.com/repos/pandas-dev/pandas/pulls/20222/comments","review_comment_url":"https://api.github.com/repos/pandas-dev/pandas/pulls/comments{/number}","comments_url":"https://api.github.com/repos/pandas-dev/pandas/issues/20222/comments","statuses_url":"https://api.github.com/repos/pandas-dev/pandas/statuses/916624c4aedb9a20d063dca69800567ef7908e1c","head":{"label":"nehiljain:docstrings_dataframe_describe","ref":"docstrings_dataframe_describe","sha":"916624c4aedb9a20d063dca69800567ef7908e1c","user":{"login":"nehiljain","id":2761999,"avatar_url":"https://avatars1.githubusercontent.com/u/2761999?v=4","gravatar_id":"","url":"https://api.github.com/users/nehiljain","html_url":"https://github.com/nehiljain","followers_url":"https://api.github.com/users/nehiljain/followers","following_url":"https://api.github.com/users/nehiljain/following{/other_user}","gists_url":"https://api.github.com/users/nehiljain/gists{/gist_id}","starred_url":"https://api.github.com/users/nehiljain/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/nehiljain/subscriptions","organizations_url":"https://api.github.com/users/nehiljain/orgs","repos_url":"https://api.github.com/users/nehiljain/repos","events_url":"https://api.github.com/users/nehiljain/events{/privacy}","received_events_url":"https://api.github.com/users/nehiljain/received_events","type":"User","site_admin":false},"repo":{"id":124665479,"name":"pandas","full_name":"nehiljain/pandas","owner":{"login":"nehiljain","id":2761999,"avatar_url":"https://avatars1.githubusercontent.com/u/2761999?v=4","gravatar_id":"","url":"https://api.github.com/users/nehiljain","html_url":"https://github.com/nehiljain","followers_url":"https://api.github.com/users/nehiljain/followers","following_url":"https://api.github.com/users/nehiljain/following{/other_user}","gists_url":"https://api.github.com/users/nehiljain/gists{/gist_id}","starred_url":"https://api.github.com/users/nehiljain/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/nehiljain/subscriptions","organizations_url":"https://api.github.com/users/nehiljain/orgs","repos_url":"https://api.github.com/users/nehiljain/repos","events_url":"https://api.github.com/users/nehiljain/events{/privacy}","received_events_url":"https://api.github.com/users/nehiljain/received_events","type":"User","site_admin":false},"private":false,"html_url":"https://github.com/nehiljain/pandas","description":"Flexible 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