{"action":"created","comment":{"url":"https://api.github.com/repos/pandas-dev/pandas/pulls/comments/173943341","pull_request_review_id":103215627,"id":173943341,"diff_hunk":"@@ -26,69 +26,104 @@\n def cut(x, bins, right=True, labels=None, retbins=False, precision=3,\n         include_lowest=False):\n     \"\"\"\n-    Return indices of half-open bins to which each value of `x` belongs.\n+    Bin `x` and return data about the bin to which each `x` value belongs.\n+\n+    Splits `x` into the specified number of equal-width half-open bins.\n+    Based on the parameters specified and the input, returns data about\n+    the half-open bins to which each value of `x` belongs or the bins\n+    themselves.\n+    Use `cut` when you need to segment and sort data values into bins. This\n+    function is also useful for going from a continuous variable to a\n+    categorical variable. For example, `cut` could convert ages to groups\n+    of age ranges.\n \n     Parameters\n     ----------\n     x : array-like\n-        Input array to be binned. It has to be 1-dimensional.\n-    bins : int, sequence of scalars, or IntervalIndex\n-        If `bins` is an int, it defines the number of equal-width bins in the\n-        range of `x`. However, in this case, the range of `x` is extended\n-        by .1% on each side to include the min or max values of `x`. If\n-        `bins` is a sequence it defines the bin edges allowing for\n-        non-uniform bin width. No extension of the range of `x` is done in\n-        this case.\n-    right : bool, optional\n-        Indicates whether the bins include the rightmost edge or not. If\n-        right == True (the default), then the bins [1,2,3,4] indicate\n+        The input array to be binned. Must be 1-dimensional.\n+    bins : int, sequence of scalars, or pandas.IntervalIndex\n+        If int, defines the number of equal-width bins in the range of `x`.\n+        The range of `x` is extended by .1% on each side to include the min or\n+        max values of `x`.\n+        If a sequence, defines the bin edges allowing for non-uniform width.\n+        No extension of the range of `x` is done.\n+    right : bool, default 'True'\n+        Indicates whether the `bins` include the rightmost edge or not. If\n+        `right == True` (the default), then the `bins` [1,2,3,4] indicate\n         (1,2], (2,3], (3,4].\n-    labels : array or boolean, default None\n-        Used as labels for the resulting bins. Must be of the same length as\n-        the resulting bins. If False, return only integer indicators of the\n+    labels : array or bool, optional\n+        Specifies the labels for the returned bins. Must be the same length as\n+        the resulting bins. If False, returns only integer indicators of the\n         bins.\n-    retbins : bool, optional\n-        Whether to return the bins or not. Can be useful if bins is given\n+    retbins : bool, default 'False'\n+        Whether to return the bins or not. Useful when bins is provided\n         as a scalar.\n-    precision : int, optional\n-        The precision at which to store and display the bins labels\n-    include_lowest : bool, optional\n+    precision : int, default '3'\n+        The precision at which to store and display the bins labels.\n+    include_lowest : bool, default 'False'\n         Whether the first interval should be left-inclusive or not.\n \n     Returns\n     -------\n-    out : Categorical or Series or array of integers if labels is False\n-        The return type (Categorical or Series) depends on the input: a Series\n-        of type category if input is a Series else Categorical. Bins are\n-        represented as categories when categorical data is returned.\n-    bins : ndarray of floats\n-        Returned only if `retbins` is True.\n+    out : pandas.Categorical, Series, or ndarray\n+        An array-like object representing the respective bin for each value\n+        of `x`. The type depends on the value of `labels`.\n \n-    Notes\n-    -----\n-    The `cut` function can be useful for going from a continuous variable to\n-    a categorical variable. For example, `cut` could convert ages to groups\n-    of age ranges.\n+        * True : returns a Series for Series `x` or a pandas.Categorical for\n+        pandas.Categorial `x`.","path":"pandas/core/reshape/tile.py","position":79,"original_position":79,"commit_id":"544af0e57f6e1462b21fe0670a852184123f3f83","original_commit_id":"544af0e57f6e1462b21fe0670a852184123f3f83","user":{"login":"TomAugspurger","id":1312546,"avatar_url":"https://avatars3.githubusercontent.com/u/1312546?v=4","gravatar_id":"","url":"https://api.github.com/users/TomAugspurger","html_url":"https://github.com/TomAugspurger","followers_url":"https://api.github.com/users/TomAugspurger/followers","following_url":"https://api.github.com/users/TomAugspurger/following{/other_user}","gists_url":"https://api.github.com/users/TomAugspurger/gists{/gist_id}","starred_url":"https://api.github.com/users/TomAugspurger/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/TomAugspurger/subscriptions","organizations_url":"https://api.github.com/users/TomAugspurger/orgs","repos_url":"https://api.github.com/users/TomAugspurger/repos","events_url":"https://api.github.com/users/TomAugspurger/events{/privacy}","received_events_url":"https://api.github.com/users/TomAugspurger/received_events","type":"User","site_admin":false},"body":"For series input it's a Series with categorical dtype.","created_at":"2018-03-12T20:54:32Z","updated_at":"2018-03-12T20:54:32Z","html_url":"https://github.com/pandas-dev/pandas/pull/20104#discussion_r173943341","pull_request_url":"https://api.github.com/repos/pandas-dev/pandas/pulls/20104","author_association":"CONTRIBUTOR","_links":{"self":{"href":"https://api.github.com/repos/pandas-dev/pandas/pulls/comments/173943341"},"html":{"href":"https://github.com/pandas-dev/pandas/pull/20104#discussion_r173943341"},"pull_request":{"href":"https://api.github.com/repos/pandas-dev/pandas/pulls/20104"}},"in_reply_to_id":173937946},"pull_request":{"url":"https://api.github.com/repos/pandas-dev/pandas/pulls/20104","id":174163587,"html_url":"https://github.com/pandas-dev/pandas/pull/20104","diff_url":"https://github.com/pandas-dev/pandas/pull/20104.diff","patch_url":"https://github.com/pandas-dev/pandas/pull/20104.patch","issue_url":"https://api.github.com/repos/pandas-dev/pandas/issues/20104","number":20104,"state":"open","locked":false,"title":"DOC: Update pandas.cut docstring","user":{"login":"ikoevska","id":3539221,"avatar_url":"https://avatars2.githubusercontent.com/u/3539221?v=4","gravatar_id":"","url":"https://api.github.com/users/ikoevska","html_url":"https://github.com/ikoevska","followers_url":"https://api.github.com/users/ikoevska/followers","following_url":"https://api.github.com/users/ikoevska/following{/other_user}","gists_url":"https://api.github.com/users/ikoevska/gists{/gist_id}","starred_url":"https://api.github.com/users/ikoevska/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ikoevska/subscriptions","organizations_url":"https://api.github.com/users/ikoevska/orgs","repos_url":"https://api.github.com/users/ikoevska/repos","events_url":"https://api.github.com/users/ikoevska/events{/privacy}","received_events_url":"https://api.github.com/users/ikoevska/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- [X ] PR title is \"DOC: update the <your-function-or-method> docstring\"\r\n- [X ] The validation script passes: `scripts/validate_docstrings.py <your-function-or-method>`\r\n- [X ] The PEP8 style check passes: `git diff upstream/master -u -- \"*.py\" | flake8 --diff`\r\n- [X ] The html version looks good: `python doc/make.py --single <your-function-or-method>`\r\n- [X ] 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############################ Docstring (pandas.cut) ############################\r\n################################################################################\r\n\r\nBin `x` and return data about the bin to which each `x` value belongs.\r\n\r\nSplits `x` into the specified number of equal-width half-open bins.\r\nBased on the parameters specified and the input, returns data about\r\nthe half-open bins to which each value of `x` belongs or the bins\r\nthemselves.\r\nUse `cut` when you need to segment and sort data values into bins. This\r\nfunction is also useful for going from a continuous variable to a\r\ncategorical variable. For example, `cut` could convert ages to groups\r\nof age ranges.\r\n\r\nParameters\r\n----------\r\nx : array-like\r\n    The input array to be binned. Must be 1-dimensional.\r\nbins : int, sequence of scalars, or pandas.IntervalIndex\r\n    If int, defines the number of equal-width bins in the range of `x`.\r\n    The range of `x` is extended by .1% on each side to include the min or\r\n    max values of `x`.\r\n    If a sequence, defines the bin edges allowing for non-uniform width.\r\n    No extension of the range of `x` is done.\r\nright : bool, default 'True'\r\n    Indicates whether the `bins` include the rightmost edge or not. If\r\n    `right == True` (the default), then the `bins` [1,2,3,4] indicate\r\n    (1,2], (2,3], (3,4].\r\nlabels : array or bool, optional\r\n    Specifies the labels for the returned bins. Must be the same length as\r\n    the resulting bins. If False, returns only integer indicators of the\r\n    bins.\r\nretbins : bool, default 'False'\r\n    Whether to return the bins or not. Useful when bins is provided\r\n    as a scalar.\r\nprecision : int, default '3'\r\n    The precision at which to store and display the bins labels.\r\ninclude_lowest : bool, default 'False'\r\n    Whether the first interval should be left-inclusive or not.\r\n\r\nReturns\r\n-------\r\nout : pandas.Categorical, Series, or ndarray\r\n    An array-like object representing the respective bin for each value\r\n    of `x`. The type depends on the value of `labels`.\r\n\r\n    * True : returns a Series for Series `x` or a pandas.Categorical for\r\n    pandas.Categorial `x`.\r\n\r\n    * False : returns an ndarray of integers.\r\nbins : numpy.ndarray of floats\r\n    Returned when `retbins` is 'True'.\r\n\r\nSee Also\r\n--------\r\nqcut : Discretize variable into equal-sized buckets based on rank\r\n    or based on sample quantiles.\r\npandas.Categorical : Represents a categorical variable in\r\n    classic R / S-plus fashion.\r\nSeries : One-dimensional ndarray with axis labels (including time series).\r\npandas.IntervalIndex : Immutable Index implementing an ordered,\r\n    sliceable set. IntervalIndex represents an Index of intervals that\r\n    are all closed on the same side.\r\n\r\nNotes\r\n-----\r\nAny NA values will be NA in the result. Out of bounds values will be NA in\r\nthe resulting pandas.Categorical object.\r\n\r\nExamples\r\n--------\r\n>>> pd.cut(np.array([1,7,5,4,6,3]), 3)\r\n... # doctest: +ELLIPSIS\r\n[(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ...\r\nCategories (3, interval[float64]): [(0.994, 3.0] < (3.0, 5.0] ...\r\n\r\n>>> pd.cut(np.array([1,7,5,4,6,3]), 3, retbins=True)\r\n... # doctest: +ELLIPSIS\r\n([(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ...\r\nCategories (3, interval[float64]): [(0.994, 3.0] < (3.0, 5.0] ...\r\narray([0.994, 3.   , 5.   , 7.   ]))\r\n\r\n>>> pd.cut(np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1]),\r\n...        3, labels=[\"good\", \"medium\", \"bad\"])\r\n... # doctest: +SKIP\r\n[good, good, good, medium, bad, good]\r\nCategories (3, object): [good < medium < bad]\r\n\r\n>>> pd.cut(np.ones(5, dtype='int64'), 4, labels=False)\r\narray([1, 1, 1, 1, 1], dtype=int64)\r\n\r\n>>> s = pd.Series(np.array([2,4,6,8,10]), index=['a', 'b', 'c', 'd', 'e'])\r\n>>> pd.cut(s, 3)\r\n... # doctest: +ELLIPSIS\r\na    (1.992, 4.667]\r\nb    (1.992, 4.667]\r\nc    (4.667, 7.333]\r\nd     (7.333, 10.0]\r\ne     (7.333, 10.0]\r\ndtype: category\r\nCategories (3, interval[float64]): [(1.992, 4.667] < (4.667, ...\r\n\r\n################################################################################\r\n################################## Validation ##################################\r\n################################################################################\r\n\r\nDocstring for \"pandas.cut\" correct. :)\r\n```\r\n\r\nComment: Resubmitting https://github.com/pandas-dev/pandas/pull/20069 after a botched rebase. \r\n","created_at":"2018-03-10T10:08:23Z","updated_at":"2018-03-12T20:54:32Z","closed_at":null,"merged_at":null,"merge_commit_sha":"5ec9a05180b6764e81c8718ab3cdde0462778f93","assignee":null,"assignees":[],"requested_reviewers":[],"requested_teams":[],"labels":[{"id":134699,"url":"https://api.github.com/repos/pandas-dev/pandas/labels/Docs","name":"Docs","color":"3465A4","default":false},{"id":150096370,"url":"https://api.github.com/repos/pandas-dev/pandas/labels/Interval","name":"Interval","color":"009800","default":false},{"id":13098779,"url":"https://api.github.com/repos/pandas-dev/pandas/labels/Reshaping","name":"Reshaping","color":"02d7e1","default":false}],"milestone":null,"commits_url":"https://api.github.com/repos/pandas-dev/pandas/pulls/20104/commits","review_comments_url":"https://api.github.com/repos/pandas-dev/pandas/pulls/20104/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/20104/comments","statuses_url":"https://api.github.com/repos/pandas-dev/pandas/statuses/544af0e57f6e1462b21fe0670a852184123f3f83","head":{"label":"ikoevska:pandas-cut-patch-2","ref":"pandas-cut-patch-2","sha":"544af0e57f6e1462b21fe0670a852184123f3f83","user":{"login":"ikoevska","id":3539221,"avatar_url":"https://avatars2.githubusercontent.com/u/3539221?v=4","gravatar_id":"","url":"https://api.github.com/users/ikoevska","html_url":"https://github.com/ikoevska","followers_url":"https://api.github.com/users/ikoevska/followers","following_url":"https://api.github.com/users/ikoevska/following{/other_user}","gists_url":"https://api.github.com/users/ikoevska/gists{/gist_id}","starred_url":"https://api.github.com/users/ikoevska/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ikoevska/subscriptions","organizations_url":"https://api.github.com/users/ikoevska/orgs","repos_url":"https://api.github.com/users/ikoevska/repos","events_url":"https://api.github.com/users/ikoevska/events{/privacy}","received_events_url":"https://api.github.com/users/ikoevska/received_events","type":"User","site_admin":false},"repo":{"id":124294270,"name":"pandas","full_name":"ikoevska/pandas","owner":{"login":"ikoevska","id":3539221,"avatar_url":"https://avatars2.githubusercontent.com/u/3539221?v=4","gravatar_id":"","url":"https://api.github.com/users/ikoevska","html_url":"https://github.com/ikoevska","followers_url":"https://api.github.com/users/ikoevska/followers","following_url":"https://api.github.com/users/ikoevska/following{/other_user}","gists_url":"https://api.github.com/users/ikoevska/gists{/gist_id}","starred_url":"https://api.github.com/users/ikoevska/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ikoevska/subscriptions","organizations_url":"https://api.github.com/users/ikoevska/orgs","repos_url":"https://api.github.com/users/ikoevska/repos","events_url":"https://api.github.com/users/ikoevska/events{/privacy}","received_events_url":"https://api.github.com/users/ikoevska/received_events","type":"User","site_admin":false},"private":false,"html_url":"https://github.com/ikoevska/pandas","description":"Flexible 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