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Note that ``copy=False`` does not *ensure* that ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that a copy is made, even if not strictly necessary. Returns ------- numpy.ndarray See Also -------- Series.array : Get the actual data stored within. Index.array : Get the actual data stored within. DataFrame.to_numpy : Similar method for DataFrame. Notes ----- The returned array will be the same up to equality (values equal in `self` will be equal in the returned array; likewise for values that are not equal). When `self` contains an ExtensionArray, the dtype may be different. For example, for a category-dtype Series, ``to_numpy()`` will return a NumPy array and the categorical dtype will be lost. For NumPy dtypes, this will be a reference to the actual data stored in this Series or Index (assuming ``copy=False``). Modifying the result in place will modify the data stored in the Series or Index (not that we recommend doing that). For extension types, ``to_numpy()`` *may* require copying data and coercing the result to a NumPy type (possibly object), which may be expensive. When you need a no-copy reference to the underlying data, :attr:`Series.array` should be used instead. This table lays out the different dtypes and default return types of ``to_numpy()`` for various dtypes within pandas. ================== ================================ dtype array type ================== ================================ category[T] ndarray[T] (same dtype as input) period ndarray[object] (Periods) interval ndarray[object] (Intervals) IntegerNA ndarray[object] datetime64[ns] datetime64[ns] datetime64[ns, tz] ndarray[object] (Timestamps) ================== ================================ Examples -------- >>> ser = pd.Series(pd.Categorical(['a', 'b', 'a'])) >>> ser.to_numpy() array(['a', 'b', 'a'], dtype=object) Specify the `dtype` to control how datetime-aware data is represented. Use ``dtype=object`` to return an ndarray of pandas :class:`Timestamp` objects, each with the correct ``tz``. >>> ser = pd.Series(pd.date_range('2000', periods=2, tz="CET")) >>> ser.to_numpy(dtype=object) array([Timestamp('2000-01-01 00:00:00+0100', tz='CET', freq='D'), Timestamp('2000-01-02 00:00:00+0100', tz='CET', freq='D')], dtype=object) Or ``dtype='datetime64[ns]'`` to return an ndarray of native datetime64 values. The values are converted to UTC and the timezone info is dropped. >>> ser.to_numpy(dtype="datetime64[ns]") ... # doctest: +ELLIPSIS array(['1999-12-31T23:00:00.000000000', '2000-01-01T23:00:00...'], dtype='datetime64[ns]') R5RN(R RR8RftasarrayRR(R"RRR((s/lib/python2.7/site-packages/pandas/core/base.pytto_numpybs U cCst|r|jjS|jS(s The data as an ndarray, possibly losing information. The expectation is that this is cheap to compute, and is primarily used for interacting with our indexers. - categorical -> codes (RRRR(R"((s/lib/python2.7/site-packages/pandas/core/base.pyRs  cCs|j S(N(R(R"((s/lib/python2.7/site-packages/pandas/core/base.pytemptyscCs#tj|tj|jd|S(sv Return the maximum value of the Index. Parameters ---------- axis : int, optional For compatibility with NumPy. Only 0 or None are allowed. skipna : bool, default True Returns ------- scalar Maximum value. See Also -------- Index.min : Return the minimum value in an Index. Series.max : Return the maximum value in a Series. DataFrame.max : Return the maximum values in a DataFrame. Examples -------- >>> idx = pd.Index([3, 2, 1]) >>> idx.max() 3 >>> idx = pd.Index(['c', 'b', 'a']) >>> idx.max() 'c' For a MultiIndex, the maximum is determined lexicographically. >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)]) >>> idx.max() ('b', 2) tskipna(Rtvalidate_minmax_axistnanopsRR(R"RmR((s/lib/python2.7/site-packages/pandas/core/base.pyRVs% cCs#tj|tj|jd|S(s Return a ndarray of the maximum argument indexer. Parameters ---------- axis : {None} Dummy argument for consistency with Series skipna : bool, default True See Also -------- numpy.ndarray.argmax R(RRRt nanargmaxR(R"RmR((s/lib/python2.7/site-packages/pandas/core/base.pytargmaxs cCs#tj|tj|jd|S(sb Return the minimum value of the Index. Parameters ---------- axis : {None} Dummy argument for consistency with Series skipna : bool, default True Returns ------- scalar Minimum value. See Also -------- Index.max : Return the maximum value of the object. Series.min : Return the minimum value in a Series. DataFrame.min : Return the minimum values in a DataFrame. Examples -------- >>> idx = pd.Index([3, 2, 1]) >>> idx.min() 1 >>> idx = pd.Index(['c', 'b', 'a']) >>> idx.min() 'a' For a MultiIndex, the minimum is determined lexicographically. >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)]) >>> idx.min() ('a', 1) R(RRRRR(R"RmR((s/lib/python2.7/site-packages/pandas/core/base.pyRWs% cCs#tj|tj|jd|S(s Return a ndarray of the minimum argument indexer. Parameters ---------- axis : {None} Dummy argument for consistency with Series skipna : bool, default True See Also -------- numpy.ndarray.argmin R(RRRt nanargminR(R"RmR((s/lib/python2.7/site-packages/pandas/core/base.pytargmin7s cCs_t|jr2g|jD]}tj|^qSt|jrNt|jS|jjSdS(s Return a list of the values. These are each a scalar type, which is a Python scalar (for str, int, float) or a pandas scalar (for Timestamp/Timedelta/Interval/Period) See Also -------- numpy.ndarray.tolist N(R RRtmaybe_box_datetimelikeRRdttolist(R"R((s/lib/python2.7/site-packages/pandas/core/base.pyRHs # cCsat|jr"ttj|jSt|jr>t|jSt|jjt|jj SdS(s Return an iterator of the values. These are each a scalar type, which is a Python scalar (for str, int, float) or a pandas scalar (for Timestamp/Timedelta/Interval/Period) N( R RRRRRtiterRRR(R"((s/lib/python2.7/site-packages/pandas/core/base.pyt__iter__]s  cCstt|jS(sK Return if I have any nans; enables various perf speedups. (tboolRRY(R"((s/lib/python2.7/site-packages/pandas/core/base.pythasnansmsic KsUt||d}|dkrEtdjd|jjd|n|d||S(s0 perform the reduction type operation if we can s){klass} cannot perform the operation {op}RtopRN(R7R8RROR3R/( R"RRRmRt numeric_onlyt filter_typetkwdsR((s/lib/python2.7/site-packages/pandas/core/base.pyt_reducets   csAt|trUt|dr6|fd}qUddlm}||}nt|trt|jr|j}n |j }|j j |}t j |j|}|St|jr|j}|d k rtnd}nB|jt}t|d|}|dkr%d}n tj}|||}|S( s An internal function that maps values using the input correspondence (which can be a dict, Series, or function). Parameters ---------- mapper : function, dict, or Series The input correspondence object na_action : {None, 'ignore'} If 'ignore', propagate NA values, without passing them to the mapping function Returns ------- applied : Union[Index, MultiIndex], inferred The output of the mapping function applied to the index. If the function returns a tuple with more than one element a MultiIndex will be returned. t __missing__cs|S(N((R(tdict_with_default(s/lib/python2.7/site-packages/pandas/core/base.pyRsi(RcSs |j|S(N(R(RR((s/lib/python2.7/site-packages/pandas/core/base.pyRsRtignorecSs%tj||t|jtjS(N(tlibtmap_infer_maskRtviewRftuint8(RR((s/lib/python2.7/site-packages/pandas/core/base.pytmap_fs N(RcRR?RRRRRRRRt get_indexerRttake_1dR8RtastypeR5R7R t map_infer(R"tmappert na_actionRRtindexert new_valuesR((R s/lib/python2.7/site-packages/pandas/core/base.pyt _map_values}s2         c Cs>ddlm}||d|d|d|d|d|}|S(s Return a Series containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default. Parameters ---------- normalize : boolean, default False If True then the object returned will contain the relative frequencies of the unique values. sort : boolean, default True Sort by values. ascending : boolean, default False Sort in ascending order. bins : integer, optional Rather than count values, group them into half-open bins, a convenience for ``pd.cut``, only works with numeric data. dropna : boolean, default True Don't include counts of NaN. Returns ------- counts : Series See Also -------- Series.count: Number of non-NA elements in a Series. DataFrame.count: Number of non-NA elements in a DataFrame. Examples -------- >>> index = pd.Index([3, 1, 2, 3, 4, np.nan]) >>> index.value_counts() 3.0 2 4.0 1 2.0 1 1.0 1 dtype: int64 With `normalize` set to `True`, returns the relative frequency by dividing all values by the sum of values. >>> s = pd.Series([3, 1, 2, 3, 4, np.nan]) >>> s.value_counts(normalize=True) 3.0 0.4 4.0 0.2 2.0 0.2 1.0 0.2 dtype: float64 **bins** Bins can be useful for going from a continuous variable to a categorical variable; instead of counting unique apparitions of values, divide the index in the specified number of half-open bins. >>> s.value_counts(bins=3) (2.0, 3.0] 2 (0.996, 2.0] 2 (3.0, 4.0] 1 dtype: int64 **dropna** With `dropna` set to `False` we can also see NaN index values. >>> s.value_counts(dropna=False) 3.0 2 NaN 1 4.0 1 2.0 1 1.0 1 dtype: int64 i(t value_countsRt ascendingt normalizetbinstdropna(tpandas.core.algorithmsR(R"RRRRRRR((s/lib/python2.7/site-packages/pandas/core/base.pyRsOcCsG|j}t|dr'|j}nddlm}||}|S(NRi(tunique1d(RR?RRR(R"RRR((s/lib/python2.7/site-packages/pandas/core/base.pyRs   cCsA|j}t|}|r=t|jr=|d8}n|S(s Return number of unique elements in the object. Excludes NA values by default. Parameters ---------- dropna : boolean, default True Don't include NaN in the count. Returns ------- nunique : int i(RRoRRY(R"Rtuniqstn((s/lib/python2.7/site-packages/pandas/core/base.pytnunique&s    cCs|jdtt|kS(s Return boolean if values in the object are unique. Returns ------- is_unique : boolean R(R"RKRo(R"((s/lib/python2.7/site-packages/pandas/core/base.pyt is_unique;s cCsddlm}||jS(s Return boolean if values in the object are monotonic_increasing. .. versionadded:: 0.19.0 Returns ------- is_monotonic : boolean i(tIndex(RR$t is_monotonic(R"R$((s/lib/python2.7/site-packages/pandas/core/base.pyR%Fs cCsddlm}||jS(s Return boolean if values in the object are monotonic_decreasing. .. versionadded:: 0.19.0 Returns ------- is_monotonic_decreasing : boolean i(R$(RR$tis_monotonic_decreasing(R"R$((s/lib/python2.7/site-packages/pandas/core/base.pyR&Ws cCsgt|jdr%|jjd|S|jj}|rct|rct rc|tj|j7}n|S(s Memory usage of the values Parameters ---------- deep : bool Introspect the data deeply, interrogate `object` dtypes for system-level memory consumption Returns ------- bytes used See Also -------- numpy.ndarray.nbytes Notes ----- Memory usage does not include memory consumed by elements that are not components of the array if deep=False or if used on PyPy R=R>(R?RR=RRRR tmemory_usage_of_objects(R"R>R((s/lib/python2.7/site-packages/pandas/core/base.pyR=fs  RRtordert size_hintRs sort : boolean, default False Sort `uniques` and shuffle `labels` to maintain the relationship. t factorizeicCstj|d|d|S(NRt na_sentinel(RR*(R"RR+((s/lib/python2.7/site-packages/pandas/core/base.pyR*s sl Find indices where elements should be inserted to maintain order. Find the indices into a sorted %(klass)s `self` such that, if the corresponding elements in `value` were inserted before the indices, the order of `self` would be preserved. Parameters ---------- value : array_like Values to insert into `self`. side : {'left', 'right'}, optional If 'left', the index of the first suitable location found is given. If 'right', return the last such index. If there is no suitable index, return either 0 or N (where N is the length of `self`). sorter : 1-D array_like, optional Optional array of integer indices that sort `self` into ascending order. They are typically the result of ``np.argsort``. Returns ------- int or array of int A scalar or array of insertion points with the same shape as `value`. .. versionchanged :: 0.24.0 If `value` is a scalar, an int is now always returned. Previously, scalar inputs returned an 1-item array for :class:`Series` and :class:`Categorical`. See Also -------- numpy.searchsorted Notes ----- Binary search is used to find the required insertion points. Examples -------- >>> x = pd.Series([1, 2, 3]) >>> x 0 1 1 2 2 3 dtype: int64 >>> x.searchsorted(4) 3 >>> x.searchsorted([0, 4]) array([0, 3]) >>> x.searchsorted([1, 3], side='left') array([0, 2]) >>> x.searchsorted([1, 3], side='right') array([1, 3]) >>> x = pd.Categorical(['apple', 'bread', 'bread', 'cheese', 'milk'], ordered=True) [apple, bread, bread, cheese, milk] Categories (4, object): [apple < bread < cheese < milk] >>> x.searchsorted('bread') 1 >>> x.searchsorted(['bread'], side='right') array([3]) t searchsortedRRtleftcCs|jj|d|d|S(Ntsidetsorter(RR,(R"RPR.R/((s/lib/python2.7/site-packages/pandas/core/base.pyR,stfirstcCstt|d}t|tr4|jr4|jSn|jd|}|tj|}|rl|j|S|SdS(NRtkeep( R RcRR#RR Rft logical_nott_update_inplace(R"R1RR R((s/lib/python2.7/site-packages/pandas/core/base.pytdrop_duplicatess   cCsddlm}t|trT|jrDtjt|dtjS||d|S|j ||d|d|j j |SdS(Ni(R RR1R( RR RcRR#RftzerosRoRR4Rt __finalize__(R"R1R ((s/lib/python2.7/site-packages/pandas/core/base.pyR s cKst|dS(N(R(R"RR((s/lib/python2.7/site-packages/pandas/core/base.pyR3sN(7R/R0R1t__array_priority__RRFtTRRRkRRRRRRRRRR8RKRRRR@RVRRWRRtto_listRR RRRRRR"R#R%tis_monotonic_increasingR&R=RttextwraptdedentRRt _shared_docsR*R,R4R R3(((s/lib/python2.7/site-packages/pandas/core/base.pyRsh       Pa((     I S    J    (@R1R;RtnumpyRftpandas._libs.libt_libsR t pandas.compatR$RRRRRtpandas.compat.numpyRRt pandas.errorsRtpandas.util._decoratorsRRR tpandas.util._validatorsR tpandas.core.dtypes.commonR R R RRRRRRtpandas.core.dtypes.genericRRRtpandas.core.dtypes.missingRt pandas.coreRRRtpandas.core.accessorRtpandas.core.nanopstcoreRRR=t_indexops_doc_kwargsR5R!R2RGt ExceptionRQRRRSRTR(((s/lib/python2.7/site-packages/pandas/core/base.pyts8   (@ 0.