o _~#gf@sdZddlmZddlmZddlmZmZmZm Z m Z ddl Z ddl Z ddl mZddlmZddlmZdd lmZmZmZdd lmZmZmZmZmZdd lmZdd lm Z dd l!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(ddl)m*Z*m+Z+m,Z,m-Z-ddl.m/Z/ddl0m1Z1m2Z2m3Z3m4Z4ddl5m6Z6ddl7m8m9Z:erddl;mm?Z?  dNdOddZ@eAhdZBe dPdQd&d'ZCe dPdRd*d'ZC +dSdRd,d'ZCd-d.ZDdTd1d2ZE  +dUd+d3dVd8d9ZFdWd>> pd.array(['a', 'b'], dtype=str) ['a', 'b'] Length: 2, dtype: str32 This would instead return the new ExtensionArray dedicated for string data. If you really need the new array to be backed by a NumPy array, specify that in the dtype. >>> pd.array(['a', 'b'], dtype=np.dtype(" ['a', 'b'] Length: 2, dtype: str32 Finally, Pandas has arrays that mostly overlap with NumPy * :class:`arrays.DatetimeArray` * :class:`arrays.TimedeltaArray` When data with a ``datetime64[ns]`` or ``timedelta64[ns]`` dtype is passed, pandas will always return a ``DatetimeArray`` or ``TimedeltaArray`` rather than a ``NumpyExtensionArray``. This is for symmetry with the case of timezone-aware data, which NumPy does not natively support. >>> pd.array(['2015', '2016'], dtype='datetime64[ns]') ['2015-01-01 00:00:00', '2016-01-01 00:00:00'] Length: 2, dtype: datetime64[ns] >>> pd.array(["1h", "2h"], dtype='timedelta64[ns]') ['0 days 01:00:00', '0 days 02:00:00'] Length: 2, dtype: timedelta64[ns] Examples -------- If a dtype is not specified, pandas will infer the best dtype from the values. See the description of `dtype` for the types pandas infers for. >>> pd.array([1, 2]) [1, 2] Length: 2, dtype: Int64 >>> pd.array([1, 2, np.nan]) [1, 2, ] Length: 3, dtype: Int64 >>> pd.array([1.1, 2.2]) [1.1, 2.2] Length: 2, dtype: Float64 >>> pd.array(["a", None, "c"]) ['a', , 'c'] Length: 3, dtype: string >>> with pd.option_context("string_storage", "pyarrow"): ... arr = pd.array(["a", None, "c"]) ... >>> arr ['a', , 'c'] Length: 3, dtype: string >>> pd.array([pd.Period('2000', freq="D"), pd.Period("2000", freq="D")]) ['2000-01-01', '2000-01-01'] Length: 2, dtype: period[D] You can use the string alias for `dtype` >>> pd.array(['a', 'b', 'a'], dtype='category') ['a', 'b', 'a'] Categories (2, object): ['a', 'b'] Or specify the actual dtype >>> pd.array(['a', 'b', 'a'], ... dtype=pd.CategoricalDtype(['a', 'b', 'c'], ordered=True)) ['a', 'b', 'a'] Categories (3, object): ['a' < 'b' < 'c'] If pandas does not infer a dedicated extension type a :class:`arrays.NumpyExtensionArray` is returned. >>> pd.array([1 + 1j, 3 + 2j]) [(1+1j), (3+2j)] Length: 2, dtype: complex128 As mentioned in the "Notes" section, new extension types may be added in the future (by pandas or 3rd party libraries), causing the return value to no longer be a :class:`arrays.NumpyExtensionArray`. Specify the `dtype` as a NumPy dtype if you need to ensure there's no future change in behavior. >>> pd.array([1, 2], dtype=np.dtype("int32")) [1, 2] Length: 2, dtype: int32 `data` must be 1-dimensional. A ValueError is raised when the input has the wrong dimensionality. >>> pd.array(1) Traceback (most recent call last): ... ValueError: Cannot pass scalar '1' to 'pandas.array'. r) BooleanArray DatetimeArrayr) FloatingArray IntegerArray IntervalArrayNumpyExtensionArray PeriodArrayTimedeltaArray StringDtypezCannot pass scalar 'z' to 'pandas.array'.z'Cannot pass DataFrame to 'pandas.array'NT) extract_numpyr,r.)skipnaperiodr.intervaldatetime timedeltastringintegeremptyr,)floatingzmixed-integer-floatbooleanMmmMzdatetime64 and timedelta64 dtype resolutions other than 's', 'ms', 'us', and 'ns' are deprecated. In future releases passing unsupported resolutions will raise an exception.) stacklevel).pandas.core.arraysr1r2r)r3r4r5r6r7r8pandas.core.arrays.string_r:r is_scalar ValueError isinstancer" TypeErrorr%r$r, extract_arrayr r.rconstruct_array_type_from_sequence infer_dtyperrrrr r startswithhasattrlengetattrnpfloat16 is_np_dtyperwarningswarn FutureWarningr)r*r,r.r1r2r)r3r4r5r6r7r8r:msgclsinferred_dtype period_datard}/var/www/static.ux5.de/https/Moving-Object-Detection-with-OpenCV/env/lib/python3.10/site-packages/pandas/core/construction.pyarrayJsv,S             rf> indexseries multiindex rangeindex periodindex datetimeindex intervalindextimedeltaindexcategoricalindex.objSeries | Indexr; extract_rangercCdSNrdrpr;rrrdrdrerRrRr T | ArrayLikecCrsrtrdrurdrdrerRrvFcCsHt|dd}|tvr|dkr|r|jS|S|jS|r"|dkr"|S|S)a Extract the ndarray or ExtensionArray from a Series or Index. For all other types, `obj` is just returned as is. Parameters ---------- obj : object For Series / Index, the underlying ExtensionArray is unboxed. extract_numpy : bool, default False Whether to extract the ndarray from a NumpyExtensionArray. extract_range : bool, default False If we have a RangeIndex, return range._values if True (which is a materialized integer ndarray), otherwise return unchanged. Returns ------- arr : object Examples -------- >>> extract_array(pd.Series(['a', 'b', 'c'], dtype='category')) ['a', 'b', 'c'] Categories (3, object): ['a', 'b', 'c'] Other objects like lists, arrays, and DataFrames are just passed through. >>> extract_array([1, 2, 3]) [1, 2, 3] For an ndarray-backed Series / Index the ndarray is returned. >>> extract_array(pd.Series([1, 2, 3])) array([1, 2, 3]) To extract all the way down to the ndarray, pass ``extract_numpy=True``. >>> extract_array(pd.Series([1, 2, 3]), extract_numpy=True) array([1, 2, 3]) _typNrj npy_extension)rY_typs_valuesto_numpy)rpr;rrtyprdrdrerRs - cCspt|tjr6|jjdkrddlm}t|j}|j||dS|jjdkr6ddlm }t|j}|j||dS|S)zS Wrap datetime64 and timedelta64 ndarrays in DatetimeArray/TimedeltaArray. rHr)r2r,rI)r8) rPrZndarrayr,kindrLr2r rTr8)arrr2r,r8rdrdreensure_wrapped_if_datetimelikes       rma.MaskedArray np.ndarraycCsdt|}|r,t|jtj\}}ttj|}t|j |dd}| |||<|S| }|S)z? Convert numpy MaskedArray to ensure mask is softened. Tr?) r getmaskarrayanyrr,rZnanrasarrayastype soften_maskr.)r*maskr, fill_valuerdrdresanitize_masked_arrays  rallow_2drg Index | NoneDtypeObj | Nonerc Cs |}t|tjr t|}t|tr|j}d}t|tr&|jtkr&|dur&d}t |ddd}t|t j rE|j dkrE|dur?|j}t |}n t|trPt|}d}t|s||dur\tdt|trrtrr|durrddlm}|d}t|t||}|St|tr|dur|j||d }n|r|}n|}nt|trt||} | j|||d }nt|t j r t|t j r|j!}|dur|}|jtkrt"|}|rtrt#|r|}n|jj$d krtrddlm}|dd }|j||d }||ur|r|}nct%|||}n\t&|dr)|st '|}nt j(||d }t)|||d|dSt|t*|}t|dkrF|durFt j(gt j+d }n |durRt%|||}nt,|}|jtkrft-t j |}t"|}t.|||||d}t|t j rt-t j|}t/||||}|S)a Sanitize input data to an ndarray or ExtensionArray, copy if specified, coerce to the dtype if specified. Parameters ---------- data : Any index : Index or None, default None dtype : np.dtype, ExtensionDtype, or None, default None copy : bool, default False allow_2d : bool, default False If False, raise if we have a 2D Arraylike. Returns ------- np.ndarray or ExtensionArray FNT)r;rrrz2index must be specified when data is not list-liker9 pyarrow_numpyr?r<U)storager~ __array__)rgr,r.rr)0rPr MaskedArrayrr! numpy_dtyper$r,objectrRrZrndimr item_from_zerodimrangerange_to_ndarrayrrOstrr rMr:rrXr#rr.r_sanitize_non_orderedrSrTmatrixArrr _try_castrWrrfsanitize_arraylistfloat64rr_sanitize_ndim_sanitize_str_dtypes) r*rgr,r.roriginal_dtype object_indexr:subarrrardrdrers                    rrngrc Csztj|j|j|jdd}W|StyY|jdkr |jdks.|jdkr,|jkrPnn"ztj|j|j|jdd}WY|StyOtt|}YY|Swtt|}Y|Sw)z) Cast a range object to ndarray. int64r~ruint64)rZarangestartstopstep OverflowErrorrr)rrrdrdrers 0 rNonecCs(t|ttfrtdt|jddS)z@ Raise only for unordered sets, e.g., not for dict_keys 'z' type is unorderedN)rPset frozensetrQtype__name__)r*rdrdrersrresultcCst|dddkr td|jdkrt||}|S|jdkrWt|tjr0|r'|Std|jdt|rPt|t rPt j |t dd}| }|j||d}|St j ||d}|S) z6 Ensure we have a 1-dimensional result array. rrz(result should be arraylike with ndim > 0z1Data must be 1-dimensional, got ndarray of shape z insteadrr~)rYrOr _maybe_repeatrPrZrshaperrcomasarray_tuplesafer,rSrT)rr*r,rgrrardrdrers&     rnp.dtype | NonecCs^t|jjtr-t|s-tt|stj ||d}|s%tj |t d}|Stj |t |d}|S)z= Ensure we have a dtype that is supported by pandas. r~r<) issubclassr,rrr rNrZallr&rrrf)rr*r,r.rdrdrers rrcCs<|durdt|krt|krn|S|t|}|S)zx If we have a length-1 array and an index describing how long we expect the result to be, repeat the array. Nr)rXrepeat)rrgrdrdrers rlist | np.ndarraynp.dtypecCst|tj}|tkr|st|}|St|j||dS|jdkrE|r5ttj|}|j }|j dkr4| }nt |f}t j|d|d|S|jdvrOt||S|jdvr[t||}|S|sftj||d}|Stj|||d }|S) aL Convert input to numpy ndarray and optionally cast to a given dtype. Parameters ---------- arr : ndarray or list Excludes: ExtensionArray, Series, Index. dtype : np.dtype copy : bool If False, don't copy the data if not needed. Returns ------- np.ndarray or ExtensionArray r?rrF)convert_na_valuer.rJiur~r<)rPrZrrrrrrrrrravelrXr ensure_string_arrayreshaperrrrf)rr,r. is_ndarrayrrrdrdrers6         r)NT)r*r+r,r-r.r/r0r))..)rprqr;r/rrr/r0r)rprr;r/rrr/r0rw)FF)r*rr0r)NF) rgrr,rr.r/rr/r0r)rrr0r)r0r) rrr,rrgrrr/r0r)rrr,rr.r/r0r)rrrgrr0r)rrr,rr.r/r0r)M__doc__ __future__rcollections.abcrtypingrrrrrr]numpyrZr pandas._configr pandas._libsr pandas._libs.tslibsr r rpandas._typingrrrrrpandas.util._exceptionsrpandas.core.dtypes.baserpandas.core.dtypes.castrrrrrrrpandas.core.dtypes.commonrrrr pandas.core.dtypes.dtypesr!pandas.core.dtypes.genericr"r#r$r%pandas.core.dtypes.missingr&pandas.core.commoncorecommonrpandasr'r(pandas.core.arrays.baser)rfrrzrRrrrrrrrrrrdrdrdresj       $    ; A    '