o NrfI@sUddlmZddlZddlZddlmZmZmZddlm Z m Z m Z ddl Z ddlZddlmZmZddlmZddlmZddlmZdd lmZgd Zd d d ifd dd ifd dd ifdifddd ifdZded<eGdddZeGdddZeGdddZ eGdddZ!dbddZ"de#d d fdcd-d.Z$gd/Z%ddded4d5Z&dfd6d7Z'dgd8d9Z(e)e"e#e$e*e'e+e'd:e'd;e'de$d?e$d@e$dAe"dBe"dCe"dDe"iZ,dEedF<GdGdHdHeZ-dIdJZ.dhdMdNZ/didSdTZ0erdUndVZ1dWdXddYdZe1dfd[d\Z2djdkd`daZ3dS)l) annotationsN)asdict dataclassfield)AnyCallablecast) PANDAS_GE_220 PANDAS_GE_300)is_object_string_dtype)tokenize)DataFrameIOFunction)random_state_data)make_timeseries with_spec ColumnSpecRangeIndexSpecDatetimeIndexSpec DatasetSpecZlamscalehigh)rg?)r)poissonnormaluniformZbinomialrandomz1dict[str, tuple[tuple[Any, ...], dict[str, Any]]]default_int_argsc@seZdZUdZdZded< dZded< dZded < dZd ed < e e d Z d ed< dZ d ed< dZ d ed< dZd ed< dZded< dZded< e ed Zded< e ed Zded<dS)raEncapsulates properties of a family of columns with the same dtype. 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NcCs(||_|p t||_||_||_dSN) index_dtyper(keys_columnsdtypesr0)selfrxr{r0columnsrrr8__init__ s zMakeDataframePart.__init__cCs|jSrw)rz)r|rrr8r}szMakeDataframePart.columnscCs$||jkr|St|j|j|j|dS)zUReturn a new MakeTimeseriesPart object with a sub-column projection. r})r}rvrxr{r0)r|r}rrr8project_columnss z!MakeDataframePart.project_columnscCs,|\}}t|j|d|d|j|j||jS)Nrr!)make_dataframe_partrxr{r}r0)r|part divisions state_datarrr8__call__ szMakeDataframePart.__call__rw) r1r2r3r4r~propertyr}rrrrrr8rvs   rvc Cstj|}tjj|rtj|||ddd}n!tjj |r4|d} tj ||| | d |}nt d|t |||||} | jd|krX| jdd} | jd|ksJ| S)Nr= timestamp)r;endr=name)r;stopr:zUnhandled index dtype: )nprZ RandomStaterqapitypesZis_datetime64_any_dtype date_rangerJZis_integer_dtype RangeIndexastype TypeErrormake_partitionindexZiloc) rxr;rr{r}rr0rRrr:dfrrr8r-s  rabcCs tjj||pt|ot|S)zOSame as pandas.api.types.is_dtype_equal, but also returns True for str / object)rqrrZis_dtype_equalr )rrrrr8 same_astypeAsrr}r(r{dict[str, type | str]c si}|D]$\}fdd|D}t|t||fi|}vr*||<qtj||dfdd|D} | rRtrEinddi}j| fi|S)Ncs6i|]\}}|dddkr|ddd|qS)rbr!r)rsplit)rakkv)krr8 Ks z"make_partition..)rr}cs.i|]\}}|vrt||js||qSr)rr rarr)r}rrr8rXs rKF)itemsrurprqZ DataFramer r) r}r{rr0rRrVdtkwsresultZ update_dtypesr)r}rrr8rHs"   rZMEMz 2000-01-01z 2000-12-31Z10s1c Ks<|dur ttttd}ttj|||d}t|d}|dur(tjj d|d} nt ||} g} t t|dD]} | || | d| | fq7||d<d } ttjd |dd \} }d d l m}|rmd dlm}i}nd dlm}dt|||||| i}|t| ||| ft| | ||t|| d ||ddd|S)aSCreate timeseries dataframe with random data Parameters ---------- start: datetime (or datetime-like string) Start of time series end: datetime (or datetime-like string) End of time series dtypes: dict (optional) Mapping of column names to types. Valid types include {float, int, str, 'category'} freq: string String like '2s' or '1H' or '12W' for the time series frequency partition_freq: string String like '1M' or '2Y' to divide the dataframe into partitions seed: int (optional) Randomstate seed kwargs: Keywords to pass down to individual column creation functions. Keywords should be prefixed by the column name and then an underscore. Examples -------- >>> import dask.dataframe as dd >>> df = dd.demo.make_timeseries('2000', '2010', ... {'value': float, 'name': str, 'id': int}, ... freq='2h', partition_freq='1D', seed=1) >>> df.head() # doctest: +SKIP id name value 2000-01-01 00:00:00 969 Jerry -0.309014 2000-01-01 02:00:00 1010 Ray -0.760675 2000-01-01 04:00:00 1016 Patricia -0.063261 2000-01-01 06:00:00 960 Charlie 0.788245 2000-01-01 08:00:00 1031 Kevin 0.466002 N)ridrnyr;rr=r!eAr^rDr=zdatetime64[ns]2000)r;r=Zperiodsr_dask_expr_enabledfrom_maptokenzmake-timeseriesFmetarlabelZenforce_metadata)r<r"floatr(rqrrprrrSrriappenddask.dataframer dask_exprrdask.dataframe.io.ior rvrry)r;rr{r=r>seedr0rr?rpartsirx meta_startmeta_endrrrrrr8rfsL,         rspecrr$c sLt|jdkr%tddddddtdtddtd d gd d td tdg|_g}i}t|jtrmt |jj }|jj }|jj }t |jj |j t|}ttj|||dd|krb|||t|}} n@t|jtr|jj}|j ||j}|j |d}ttjd||dd|dkr|dd|}} ntd|jd|i} |jD]~} | jr| j} n"t| jtrtdd| jd} nt| jdr| jj} n| jj} t| j D]M} | d}| ||vr|d}| ||vs|| j|<| !fddt"| #D| j$#D]\}}|| d|<q"qqtd}|durNt%tt&t'j(j)t*d|dnt+||fddt|D}ddl,m-}|rrdd l.m/}i}ndd l0m/}d!t1d|j |||i}|t2|jj|| |d"|ft3|jj|| ||d| d#d$d%|S)&aLGenerate a random dataset according to provided spec Parameters ---------- spec : DatasetSpec Specify all the parameters of the dataset seed: int (optional) Randomstate seed Notes ----- This API is still experimental, and will likely change in the future Examples -------- >>> from dask.dataframe.io.demo import ColumnSpec, DatasetSpec, with_spec >>> ddf = with_spec( ... DatasetSpec( ... npartitions=10, ... nrecords=10_000, ... column_specs=[ ... ColumnSpec(dtype=int, number=2, prefix="p"), ... ColumnSpec(dtype=int, number=2, prefix="n", method="normal"), ... ColumnSpec(dtype=float, number=2, prefix="f"), ... ColumnSpec(dtype=str, prefix="s", number=2, random=True, length=10), ... ColumnSpec(dtype="category", prefix="c", choices=["Y", "N"]), ... ], ... ), seed=42) >>> ddf.head(10) # doctest: +SKIP p1 p2 n1 n2 f1 f2 s1 s2 c1 0 1002 972 -811 20 0.640846 -0.176875 L#h98#}J`? _8C607/:6e N 1 985 982 -1663 -777 0.790257 0.792796 u:XI3,omoZ w~@ /d)'-@ N 2 947 970 799 -269 0.740869 -0.118413 O$dnwCuq\ !WtSe+(;#9 Y 3 1003 983 1133 521 -0.987459 0.278154 j+Qr_2{XG& &XV7cy$y1T Y 4 1017 1049 826 5 -0.875667 -0.744359 bJ3E-{:o {+jC).?vK+ Y 5 984 1017 -492 -399 0.748181 0.293761 ~zUNHNgD"! yuEkXeVot| Y 6 992 1027 -856 67 -0.125132 -0.234529 j.7z;o]Gc9 g|Fi5*}Y92 Y 7 1011 974 762 -1223 0.471696 0.937935 yT?j~N/-u] JhEB[W-}^$ N 8 984 974 856 74 0.109963 0.367864 _j"&@ i&;/ OYXQ)w{hoH N 9 1030 1001 -792 -262 0.435587 -0.647970 Pmrwl{{|.K 3UTqM$86Sg N rrrti@BT)rr r*rrf)rr rcrs)rrrd)rr r)s)rr rrr!)rr:zUnhandled index: r=z [^a-zA-Z0-9]rbrcs4i|]\}}|dvr|dgfvrd||qS)>r#rr0Nrbrr)col_namerr8r!s zwith_spec..Nrr^cs$g|]}||d|fqS)rDr)rar)rrrr8rd1s$zwith_spec..rrrrz make-randomFr)4rprBrrr<rTrArrq Timestampr;r=r>r@Z Timedeltar(rrrr:r?r ValueErrorrr resubrstriphasattrrr1rir#rLrrr0rrrrrSr"rrrrrrr rvr)rrr}r{r;r:r>rrrr0colrrZcol_nZkw_nameZkw_valr?rrrrr)rrrr8rs*                       r)F) rFr"rGrrr,r r9r-rIr/r.)rY)r+r"rZr[)NFN)NN)rr9rr9)r}r(r{rrw)rrrr$)4 __future__rrre dataclassesrrrtypingrrrnumpyrZpandasrqZdask.dataframe._compatr r Zdask.dataframe._pyarrowr Zdask.dataframe.corer Zdask.dataframe.io.utilsr Z dask.utilsr__all__rr5rrrrrHr"rXrkrjrlrrrr<objectrurvrrrZ_MErrrrrr8s        2   !     *    `