x\c@ s*dZddlmZddlmZddlZddlZddlm Z m Z m Z m Z ddljZddlmZmZmZmZmZddlmZddlmZmZmZdd lmZdd lmZm Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.dd l/m0Z0m1Z1m2Z2m3Z3m4Z4m5Z5dd l6m7Z7m8Z8m9Z9m:Z:dd l;m<Z<m=Z=m>Z>m?Z?m@Z@ddlAmBZBddlCmDZDmEZEddlFmGZGmHZHddlImJZJddlKjLjMZNddlOmPZPddlQmRZRmSZSmTZTmUZUmVZVddlWmXZXddlYjLjZj=Z[ddl\m]Z]ddl^m_Z_ddl`maZaddlbmcZcmdZdddlemfZfddlgmhZhddlimjZjddlkmlZlddlmjnjojpZqddlrmsZsddltjujvZwdgZxeydd d!dd"d#d$d%d&d'd(d)d*dd+d,d-d,d.d,d/d,d0d1 Zzd2Z{d3Z|de=j}e>j~fd4YZejd gd5d6d7d6d8id6d96d:id;d 6ejejeje@jee@jedS(<sG Data structure for 1-dimensional cross-sectional and time series data i(tdivision(tdedentN(tiNaTtindextlibttslibs(tPY36t OrderedDicttStringIOtutzip(tfunction(tAppendert Substitutiont deprecate(tvalidate_bool_kwarg(t_is_unorderable_exceptiontensure_platform_inttis_booltis_categorical_dtypetis_datetime64_dtypetis_datetimeliket is_dict_liketis_extension_array_dtypetis_extension_typet is_hashablet is_integert is_iteratort is_list_liket is_scalartis_string_liketis_timedelta64_dtype(t ABCDataFrametABCDatetimeArraytABCDatetimeIndext ABCSeriestABCSparseArraytABCSparseSeries(tisnatna_value_for_dtypetnotnatremove_na_arraylike(t algorithmstbasetgenerictnanopstops(tCachedAccessor(tExtensionArrayt SparseArray(t CategoricaltCategoricalAccessor(tSparseAccessor(t get_option(t Float64IndextIndextInvalidIndexErrort MultiIndext ensure_index(tCombinedDatetimelikeProperties(t DatetimeIndex(t PeriodIndex(tTimedeltaIndex(tcheck_bool_indexertmaybe_convert_indices(tSingleBlockManager(tsanitize_array(t StringMethods(t to_datetime(tget_terminal_sizetSeriestaxesRtklasstaxes_single_args{0 or 'index'}taxissPaxis : {0 or 'index'} Parameter needed for compatibility with DataFrame.tinplaces^inplace : boolean, default False If True, performs operation inplace and returns None.tuniques np.ndarrayt duplicatedt optional_byttoptional_mappertoptional_labelst optional_axistversionadded_to_excels .. versionadded:: 0.20.0 cC s tjdtddt|S(sw Remove null values from array like structure. .. deprecated:: 0.21.0 Use s[s.notnull()] instead. s>remove_na is deprecated and is a private function. 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Install the scalar coercion methods. c sBt|dkr#|jdStdjtdS(Niis cannot convert the series to {0}(tlentiloct TypeErrortformattstr(tself(t converter(s1lib/python2.7/site-packages/pandas/core/series.pytwrapperYs s __{name}__tname(R]t__name__(R`Ra((R`s1lib/python2.7/site-packages/pandas/core/series.pyt_coerce_methodTsc B s eZdZdgZddddhZejjedddd d d d gBZe e j j j d e j j jZ ddddeedZdddZedddeedZe dZe dZe dZdZedZdZdZe dZejdZe dZe dZe dZe dZ e dZ!e dZ"dZ#d Z$e d!Z%d"d#Z&d$Z'd%Z(d&Z)d'Z*dd(Z+dd)Z,dd*Z-dd+Z.e d,Z/e/jd-Z/e d.Z0e0jd/Z0e1e2Z3e1e4Z5e1e4Z6d0Z7e d1Z8d2d3Z9e d4Z:d2dd5Z;d6Z<d7Z=d8Z>d9Z?d:Z@d;ZAd<ZBd=ZCd>ZDdd?ZEed@ZFedAZGeFjeG_edBZHedCZIeHjeI_dededDZJdEZKddFdeLeLeeeddG ZMdHZNeNZOdIZPeQdJZRddKZSdLddMZTedNZUddOZVeLdPZWdQZXdRedSZYdRdTZZd2eLdUZ[d2eLdVZ\e]dWe[dXdYe^dZZ_e]d[e\dXdYe^d\Z`d2d]Zad^d_d`ZbdaddbZcddcZddddeZedddfZfdgZgdhZhdiZiejdjdkeke jldldmddnZmeedoZndddpZoddqZpdrZqdsZrd2eLedtdudvZsd2deLedtdueLdwZtd2dtddxZudydRdzZvdydRd{Zwd|d}eLd~ZxdZyd}ddZzddZ{ddZ|e^dZ}e^dZ~ejde}de~ddeekejldd2dZeZekejlded2dZeLddZd2eLdddZdZdZekejldedddeLdddd2dd ZddZejeekejjjddZdd2dddeddZejeekejjjdddedddZekejldeddededdZekejldedddd2ddZd2dZeLedZekejjjd2edZdZeLdZedeLdd2dedZekejjjdZekejldedZekejldedZekejldedZekejldedZd2edZedZddeLdZdeLdZedeZedeZedeZedejZedeZejZRS(s One-dimensional ndarray with axis labels (including time series). Labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. Statistical methods from ndarray have been overridden to automatically exclude missing data (currently represented as NaN). Operations between Series (+, -, /, *, **) align values based on their associated index values-- they need not be the same length. The result index will be the sorted union of the two indexes. Parameters ---------- data : array-like, Iterable, dict, or scalar value Contains data stored in Series. .. versionchanged :: 0.23.0 If data is a dict, argument order is maintained for Python 3.6 and later. index : array-like or Index (1d) Values must be hashable and have the same length as `data`. Non-unique index values are allowed. Will default to RangeIndex (0, 1, 2, ..., n) if not provided. If both a dict and index sequence are used, the index will override the keys found in the dict. dtype : str, numpy.dtype, or ExtensionDtype, optional dtype for the output Series. If not specified, this will be inferred from `data`. See the :ref:`user guide ` for more usages. copy : bool, default False Copy input data. RbtdttcatR^tsparsetasobjecttreshapet get_valuet set_valuetfrom_csvtvalidttolisttdoccC s^|r]t|ts-t||dt}n|rB|j}n|dkr"|j}q"n|dk rxt|}n|dkri}n|dk r|j|}nt|trt dn-t|t r\|dkr|j }n|dk r|j |}nE|j j}t|trS|jdk rS|j jdt}nt}nt|tjrqnt|ttfr|dkr|j }n|dkr|j}n|j|d|}|j}nt|tr|j|||\}}d}t}nt|trb|dkr7|j}q|jj| sP|rtdqnt|rqnt|ttfrtdj|j j!nRt|t"j#rt|t"j$ rt%|}nt|t&r|j'}n|dkr2t(|s|g}nt)j*t+|}nlt(|ryIt+|t+|krt,djdt+|d t+|nWqtk rqXnt|tr|dk r|j d |d d d|}q"|r"|j}q"n0t-||||d t}t||dt}t.j/j0||dt||_ |j1d|dtdS(Ntfastpaths8initializing a Series from a MultiIndex is not supportedtdeeptcopyslCannot pass both SingleBlockManager `data` argument and a different `index` argument. `copy` must be False.s{0!r} type is unordereds5Length of passed values is {val}, index implies {ind}tvaltindtdtypeterrorstignoretraise_cast_failurei(2t isinstanceRAtTrueRrtNoneRR:t_validate_dtypeR9tNotImplementedErrorR7Rbtastypet_valuesR"ttztFalsetnptndarrayR#R%treindext_datatdictt _init_dicttequalstAssertionErrorRtsett frozensetR\R]t __class__RctcompattIterabletSizedtlistR$tto_denseRtibaset default_indexRZt ValueErrorRBR,tNDFramet__init__t _set_axis(R_tdataRRuRbRrRp((s1lib/python2.7/site-packages/pandas/core/series.pyRs                        %     cC s|r0ttj|\}}t|}n.|dk rQt|}|}n gg}}t|d|d|}|r|dk r|j|dt}nDt rt |t  r|ry|j }Wqt k rqXn|j|jfS(s1 Derive the "_data" and "index" attributes of a new Series from a dictionary input. Parameters ---------- data : dict or dict-like Data used to populate the new Series index : Index or index-like, default None index for the new Series: if None, use dict keys dtype : dtype, default None dtype for the new Series: if None, infer from data Returns ------- _data : BlockManager for the new Series index : index for the new Series RRuRrN(R Rt iteritemsRR{R'RFRRRRyRt sort_indexR\RR(R_RRRutkeystvaluests((s1lib/python2.7/site-packages/pandas/core/series.pyRs      c C sftjdtddt|tr>ddlm}|}n||d|d|d|d |d |S( s Construct Series from array. .. deprecated :: 0.23.0 Use pd.Series(..) constructor instead. su'from_array' is deprecated and will be removed in a future version. Please use the pd.Series(..) constructor instead.RTii(t SparseSeriesRRbRuRrRp(RURVRWRyR$tpandas.core.sparse.seriesR(tclsRXRRbRuRrRpR((s1lib/python2.7/site-packages/pandas/core/series.pyt from_array>s   cC stS(N(RF(R_((s1lib/python2.7/site-packages/pandas/core/series.pyt _constructorRscC sddlm}|S(Ni(t DataFrame(tpandas.core.frameR(R_R((s1lib/python2.7/site-packages/pandas/core/series.pyt_constructor_expanddimVscC s |jjS(N(Rt _can_hold_na(R_((s1lib/python2.7/site-packages/pandas/core/series.pyR\scC s|st|}n|j}|rt|tttfsy,t|}|rg|jj||nWqtj t fk rqXqn|j |t j |d||s|jj||ndS(sA Override generic, we want to set the _typ here. t_indexN(R:t is_all_datesRyR<R=R>Rtset_axisRtOutOfBoundsDatetimeRt _set_subtyptobjectt __setattr__(R_RJtlabelsRpR((s1lib/python2.7/site-packages/pandas/core/series.pyRbs     cC s3|rtj|ddntj|dddS(Nt_subtypt time_seriestseries(RR(R_R((s1lib/python2.7/site-packages/pandas/core/series.pyR~scK stjj|||S(N(R,Rt_update_inplace(R_tresulttkwargs((s1lib/python2.7/site-packages/pandas/core/series.pyRscC s|jS(s, Return name of the Series. (t_name(R_((s1lib/python2.7/site-packages/pandas/core/series.pyRbscC s?|dk r(t| r(tdntj|d|dS(Ns#Series.name must be a hashable typeR(R{RR\RR(R_tvalue((s1lib/python2.7/site-packages/pandas/core/series.pyRbscC s |jjS(sA Return the dtype object of the underlying data. (RRu(R_((s1lib/python2.7/site-packages/pandas/core/series.pyRuscC s |jjS(sA Return the dtype object of the underlying data. (RRu(R_((s1lib/python2.7/site-packages/pandas/core/series.pytdtypesscC s |jjS(s5 Return if the data is sparse|dense. (Rtftype(R_((s1lib/python2.7/site-packages/pandas/core/series.pyRscC s |jjS(s5 Return if the data is sparse|dense. (RR(R_((s1lib/python2.7/site-packages/pandas/core/series.pytftypesscC s |jjS(s Return Series as ndarray or ndarray-like depending on the dtype. .. warning:: We recommend using :attr:`Series.array` or :meth:`Series.to_numpy`, depending on whether you need a reference to the underlying data or a NumPy array. Returns ------- arr : numpy.ndarray or ndarray-like See Also -------- Series.array : Reference to the underlying data. Series.to_numpy : A NumPy array representing the underlying data. Examples -------- >>> pd.Series([1, 2, 3]).values array([1, 2, 3]) >>> pd.Series(list('aabc')).values array(['a', 'a', 'b', 'c'], dtype=object) >>> pd.Series(list('aabc')).astype('category').values [a, a, b, c] Categories (3, object): [a, b, c] Timezone aware datetime data is converted to UTC: >>> pd.Series(pd.date_range('20130101', periods=3, ... tz='US/Eastern')).values array(['2013-01-01T05:00:00.000000000', '2013-01-02T05:00:00.000000000', '2013-01-03T05:00:00.000000000'], dtype='datetime64[ns]') (Rtexternal_values(R_((s1lib/python2.7/site-packages/pandas/core/series.pyRs(cC s |jjS(s8 Return the internal repr of this data. (Rtinternal_values(R_((s1lib/python2.7/site-packages/pandas/core/series.pyRscC s |jjS(ss Return the values that can be formatted (used by SeriesFormatter and DataFrameFormatter). (Rtformatting_values(R_((s1lib/python2.7/site-packages/pandas/core/series.pyt_formatting_valuesscC s |jjS(sQ Same as values (but handles sparseness conversions); is a view. (Rt get_values(R_((s1lib/python2.7/site-packages/pandas/core/series.pyRscC s&tjdtdd|jtjS(s Return object Series which contains boxed values. .. deprecated :: 0.23.0 Use ``astype(object)`` instead. *this is an internal non-public method* s6'asobject' is deprecated. Use 'astype(object)' insteadRTi(RURVRWR~RR(R_((s1lib/python2.7/site-packages/pandas/core/series.pyRhs  tCcC s|jjd|S(s Return the flattened underlying data as an ndarray. See Also -------- numpy.ndarray.ravel torder(Rtravel(R_R((s1lib/python2.7/site-packages/pandas/core/series.pyRscO s4d}tj|tddtj||||S(s Return selected slices of an array along given axis as a Series. .. deprecated:: 0.24.0 See Also -------- numpy.ndarray.compress svSeries.compress(condition) is deprecated. Use 'Series[condition]' or 'np.asarray(series).compress(condition)' instead.RTi(RURVRWtnvtvalidate_compress(R_t conditiontargsRtmsg((s1lib/python2.7/site-packages/pandas/core/series.pytcompress s cC s)d}tj|tdd|jjS(s Return the *integer* indices of the elements that are non-zero. .. deprecated:: 0.24.0 Please use .to_numpy().nonzero() as a replacement. This method is equivalent to calling `numpy.nonzero` on the series data. For compatibility with NumPy, the return value is the same (a tuple with an array of indices for each dimension), but it will always be a one-item tuple because series only have one dimension. See Also -------- numpy.nonzero Examples -------- >>> s = pd.Series([0, 3, 0, 4]) >>> s.nonzero() (array([1, 3]),) >>> s.iloc[s.nonzero()[0]] 1 3 3 4 dtype: int64 >>> s = pd.Series([0, 3, 0, 4], index=['a', 'b', 'c', 'd']) # same return although index of s is different >>> s.nonzero() (array([1, 3]),) >>> s.iloc[s.nonzero()[0]] b 3 d 4 dtype: int64 snSeries.nonzero() is deprecated and will be removed in a future version.Use Series.to_numpy().nonzero() insteadRTi(RURVRWRtnonzero(R_R((s1lib/python2.7/site-packages/pandas/core/series.pyRs$cO s|jj||dS(s Applies the `put` method to its `values` attribute if it has one. See Also -------- numpy.ndarray.put N(Rtput(R_RR((s1lib/python2.7/site-packages/pandas/core/series.pyREscC s t|jS(s2 Return the length of the Series. (RZR(R_((s1lib/python2.7/site-packages/pandas/core/series.pyt__len__OscC s+|j|jj|d|jj|S(s Create a new view of the Series. This function will return a new Series with a view of the same underlying values in memory, optionally reinterpreted with a new data type. The new data type must preserve the same size in bytes as to not cause index misalignment. Parameters ---------- dtype : data type Data type object or one of their string representations. Returns ------- Series A new Series object as a view of the same data in memory. See Also -------- numpy.ndarray.view : Equivalent numpy function to create a new view of the same data in memory. Notes ----- Series are instantiated with ``dtype=float64`` by default. While ``numpy.ndarray.view()`` will return a view with the same data type as the original array, ``Series.view()`` (without specified dtype) will try using ``float64`` and may fail if the original data type size in bytes is not the same. Examples -------- >>> s = pd.Series([-2, -1, 0, 1, 2], dtype='int8') >>> s 0 -2 1 -1 2 0 3 1 4 2 dtype: int8 The 8 bit signed integer representation of `-1` is `0b11111111`, but the same bytes represent 255 if read as an 8 bit unsigned integer: >>> us = s.view('uint8') >>> us 0 254 1 255 2 0 3 1 4 2 dtype: uint8 The views share the same underlying values: >>> us[0] = 128 >>> s 0 -128 1 -1 2 0 3 1 4 2 dtype: int8 R(RRtviewRt __finalize__(R_Ru((s1lib/python2.7/site-packages/pandas/core/series.pyRUsBcC sk|dkrXt|jtrXt|jddrXd}tj|tddd}nt j |j|S(s( Return the values as a NumPy array. Users should not call this directly. Rather, it is invoked by :func:`numpy.array` and :func:`numpy.asarray`. Parameters ---------- dtype : str or numpy.dtype, optional The dtype to use for the resulting NumPy array. By default, the dtype is inferred from the data. Returns ------- numpy.ndarray The values in the series converted to a :class:`numpy.ndarary` with the specified `dtype`. See Also -------- pandas.array : Create a new array from data. Series.array : Zero-copy view to the array backing the Series. Series.to_numpy : Series method for similar behavior. Examples -------- >>> ser = pd.Series([1, 2, 3]) >>> np.asarray(ser) array([1, 2, 3]) For timezone-aware data, the timezones may be retained with ``dtype='object'`` >>> tzser = pd.Series(pd.date_range('2000', periods=2, tz="CET")) >>> np.asarray(tzser, 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 the values may be localized to UTC and the tzinfo discared with ``dtype='datetime64[ns]'`` >>> np.asarray(tzser, dtype="datetime64[ns]") # doctest: +ELLIPSIS array(['1999-12-31T23:00:00.000000000', ...], dtype='datetime64[ns]') RsNConverting timezone-aware DatetimeArray to timezone-naive ndarray with 'datetime64[ns]' dtype. In the future, this will return an ndarray with 'object' dtype where each element is a 'pandas.Timestamp' with the correct 'tz'. To accept the future behavior, pass 'dtype=object'. To keep the old behavior, pass 'dtype="datetime64[ns]"'.RTisM8[ns]N( R{RytarrayR!tgetattrRuRURVRWRtasarray(R_RuR((s1lib/python2.7/site-packages/pandas/core/series.pyt __array__s / cC s%|j|d|jdtj|S(s, Gets called after a ufunc. RRr(RRRR(R_Rtcontext((s1lib/python2.7/site-packages/pandas/core/series.pyt__array_wrap__sc C s|dk rt|jtjtf s:t|jtr|dd}tdjdt |j dt |ddd|dj n|S(s/ Gets called prior to a ufunc. iis9{obj} with dtype {dtype} cannot perform the numpy op {op}tobjRutopN( R{RyRRRR0R2R\R]ttypeRcR(R_RRR((s1lib/python2.7/site-packages/pandas/core/series.pyt__array_prepare__s  cC s |jjS(s2 Return the real value of vector. (Rtreal(R_((s1lib/python2.7/site-packages/pandas/core/series.pyRscC s||j_dS(N(RR(R_tv((s1lib/python2.7/site-packages/pandas/core/series.pyRscC s |jjS(s. Return imag value of vector. (Rtimag(R_((s1lib/python2.7/site-packages/pandas/core/series.pyRscC s||j_dS(N(RR(R_R((s1lib/python2.7/site-packages/pandas/core/series.pyRscC st|tr;|d|_|d|_|jj|_nt|tr|\}}tj|dd|d}tjj |||dd}}t |dkr|d}nt ||dt |_||_||_ntd|dS( NRRbiRuiiRps&cannot unpickle legacy formats -> [%s](RyRRRbRttupleRtemptyRt __setstate__R{RZRARzRt Exception(R_tstatetnd_statet own_stateRRRb((s1lib/python2.7/site-packages/pandas/core/series.pyt_unpickle_series_compats      cC s |jgS(s7 Return a list of the row axis labels. (R(R_((s1lib/python2.7/site-packages/pandas/core/series.pyRG0sicC sy7|j}t|tjr.tj||S||SWntk rMntk rt|tr|j j |dd}|j |S|j |}t|t r|j |d|dtStj||SnXdS(s Return the i-th value or values in the Series by location. Parameters ---------- i : int, slice, or sequence of integers Returns ------- value : scalar (int) or Series (slice, sequence) tkindR[RJtconvertN(RRyRRtlibindext get_value_att IndexErrorRtsliceRt_convert_slice_indexert _get_valuesR7ttakeRz(R_tiRJRtindexertlabel((s1lib/python2.7/site-packages/pandas/core/series.pyt_ixs7s       cC stS(N(R(R_((s1lib/python2.7/site-packages/pandas/core/series.pyt_is_mixed_typeXscC s+|jj|d|pd}|j|S(NRtgetitem(RRR(R_tslobjRJR((s1lib/python2.7/site-packages/pandas/core/series.pyt_slice\scC stj||}y|jj||}t|st|rt|t rySt|jj|s|j |d|gt |d|j j |}nWqt k rqXqn|SWntk rnt tfk rst|trt|jtrq|tkr|Stj|r0q|jj|dd}t|t|krm|j|Sntk rnXt|rt|}ntj|rt|j|}n|j|S(NRRuRR(tcomtapply_if_callableRRjRRRyRFtget_locRRZRuRtKeyErrorR8RRR9tEllipsistis_bool_indexert_convert_scalar_indexerRt __getitem__RRRR?t _get_with(R_tkeyRtnew_key((s1lib/python2.7/site-packages/pandas/core/series.pyRasD    !     cC st|tr4|jj|dd}|j|St|trRtdnut|try|j|SWqt k rt |dkr|d}t|tr|j|SnqXnt|t t j ttfst |}nt|tr|j}ntj|dt}|dkri|jjsN|jjrY|j|S|j|Sn|dkr|j|Sy1t|t tfr|j|S|j|SWn4t k rt|dtr|j|SnXdS( NRRsWIndexing a Series with DataFrame is not supported, use the appropriate DataFrame columniitskipnatintegertboolean(RyRRRRR R\Rt_get_values_tupleRRZRRRRFR7t inferred_typeRt infer_dtypeRRt is_floatingtlocR(R_RRtkey_type((s1lib/python2.7/site-packages/pandas/core/series.pyRsB            cC sxtj|r|j|St|jts=tdn|jj|\}}|j|j |d|j |S(Ns&Can only tuple-index with a MultiIndexR( Rt _any_noneRRyRR9Rt get_loc_levelRRR(R_RRt new_index((s1lib/python2.7/site-packages/pandas/core/series.pyRs cC sLy,|j|jj|dtj|SWntk rG|j|SXdS(NRp(RRt get_sliceRzRRR(R_R((s1lib/python2.7/site-packages/pandas/core/series.pyRs  c sQtj|}fd}j}||||rMjndS(Nc syj||dSWnWtjk r1nAttfk rj}t|rzjjdk rz|||>> s = pd.Series(['a', 'b', 'c']) >>> s 0 a 1 b 2 c dtype: object >>> s.repeat(2) 0 a 0 a 1 b 1 b 2 c 2 c dtype: object >>> s.repeat([1, 2, 3]) 0 a 1 b 1 b 2 c 2 c 2 c dtype: object RJR( Rtvalidate_repeatRRRtrepeatRRR(R_trepeatsRJR t new_values((s1lib/python2.7/site-packages/pandas/core/series.pyRPs 4 cC s)tjdtdd|j|d|S(sV Quickly retrieve single value at passed index label. .. deprecated:: 0.21.0 Please use .at[] or .iat[] accessors. Parameters ---------- label : object takeable : interpret the index as indexers, default False Returns ------- value : scalar value smget_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors insteadRTittakeable(RURVRWt _get_value(R_RR!((s1lib/python2.7/site-packages/pandas/core/series.pyRjs cC s6|tkr tj|j|S|jj|j|S(N(RzRtmaybe_box_datetimelikeRRRj(R_RR!((s1lib/python2.7/site-packages/pandas/core/series.pyR"s cC s,tjdtdd|j||d|S(s Quickly set single value at passed label. .. deprecated:: 0.21.0 Please use .at[] or .iat[] accessors. If label is not contained, a new object is created with the label placed at the end of the result index. Parameters ---------- label : object Partial indexing with MultiIndex not allowed value : object Scalar value takeable : interpret the index as indexers, default False Returns ------- series : Series If label is contained, will be reference to calling Series, otherwise a new object smset_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors insteadRTiR!(RURVRWt _set_value(R_RRR!((s1lib/python2.7/site-packages/pandas/core/series.pyRks cC s[y6|r||j|>> s = pd.Series([1, 2, 3, 4], name='foo', ... index=pd.Index(['a', 'b', 'c', 'd'], name='idx')) Generate a DataFrame with default index. >>> s.reset_index() idx foo 0 a 1 1 b 2 2 c 3 3 d 4 To specify the name of the new column use `name`. >>> s.reset_index(name='values') idx values 0 a 1 1 b 2 2 c 3 3 d 4 To generate a new Series with the default set `drop` to True. >>> s.reset_index(drop=True) 0 1 1 2 2 3 3 4 Name: foo, dtype: int64 To update the Series in place, without generating a new one set `inplace` to True. Note that it also requires ``drop=True``. >>> s.reset_index(inplace=True, drop=True) >>> s 0 1 1 2 2 3 3 4 Name: foo, dtype: int64 The `level` parameter is interesting for Series with a multi-level index. >>> arrays = [np.array(['bar', 'bar', 'baz', 'baz']), ... np.array(['one', 'two', 'one', 'two'])] >>> s2 = pd.Series( ... range(4), name='foo', ... index=pd.MultiIndex.from_arrays(arrays, ... names=['a', 'b'])) To remove a specific level from the Index, use `level`. >>> s2.reset_index(level='a') a foo b one bar 0 two bar 1 one baz 2 two baz 3 If `level` is not set, all levels are removed from the Index. >>> s2.reset_index() a b foo 0 bar one 0 1 bar two 1 2 baz one 2 3 baz two 3 RKRs<Cannot reset_index inplace on a Series to create a DataFrametleveltdropN(RRRRZR{RyRRRt_get_level_numbertnlevelst droplevelRbRRRrRR\tto_framet reset_index(R_R%R&RbRKR tlevtdf((s1lib/python2.7/site-packages/pandas/core/series.pyR+s$l  % c C sttd}t\}}tddkr9|n td}td}|jd|d|jd|jd|d ||j}|S( s Return a string representation for a particular DataFrame. Invoked by unicode(df) in py2 only. Yields a Unicode String in both py2/py3. ROsdisplay.max_rowsisdisplay.show_dimensionstbufRbRutmax_rowstlength(RR RER5t to_stringRbRutgetvalue(R_R.twidththeightR/tshow_dimensionsR((s1lib/python2.7/site-packages/pandas/core/series.pyt __unicode__Ys  !  tNaNc C stj|d|d|d|d|d|d|d|d| } | j} t| tjs{td j| jj n|d kr| Sy|j | Wn6t k rt |d } | j | Wd QXnXd S( s Render a string representation of the Series. Parameters ---------- buf : StringIO-like, optional buffer to write to na_rep : string, optional string representation of NAN to use, default 'NaN' float_format : one-parameter function, optional formatter function to apply to columns' elements if they are floats default None header : boolean, default True Add the Series header (index name) index : bool, optional Add index (row) labels, default True length : boolean, default False Add the Series length dtype : boolean, default False Add the Series dtype name : boolean, default False Add the Series name if not None max_rows : int, optional Maximum number of rows to show before truncating. If None, show all. Returns ------- formatted : string (if not buffer passed) RbR0theaderRRutna_rept float_formatR/s7result must be of type unicode, type of result is {0!r}twN(tfmttSeriesFormatterR1RyRt text_typeRR]RRcR{twritetAttributeErrortopen( R_R.R9R:R8RR0RuRbR/t formatterRtf((s1lib/python2.7/site-packages/pandas/core/series.pyR1ls "       cC stt|jt|S(s< Lazily iterate over (index, value) tuples. (R titerR(R_((s1lib/python2.7/site-packages/pandas/core/series.pyRscC s|jS(s" Alias for index. (R(R_((s1lib/python2.7/site-packages/pandas/core/series.pyRscC s"tj|}|tj|S(s Convert Series to {label -> value} dict or dict-like object. Parameters ---------- into : class, default dict The collections.Mapping subclass to use as the return object. Can be the actual class or an empty instance of the mapping type you want. If you want a collections.defaultdict, you must pass it initialized. .. versionadded:: 0.21.0 Returns ------- value_dict : collections.Mapping Examples -------- >>> s = pd.Series([1, 2, 3, 4]) >>> s.to_dict() {0: 1, 1: 2, 2: 3, 3: 4} >>> from collections import OrderedDict, defaultdict >>> s.to_dict(OrderedDict) OrderedDict([(0, 1), (1, 2), (2, 3), (3, 4)]) >>> dd = defaultdict(list) >>> s.to_dict(dd) defaultdict(, {0: 1, 1: 2, 2: 3, 3: 4}) (Rtstandardize_mappingRR(R_tintotinto_c((s1lib/python2.7/site-packages/pandas/core/series.pytto_dictscC s8|dkr|j|}n|ji||6}|S(s Convert Series to DataFrame. Parameters ---------- name : object, default None The passed name should substitute for the series name (if it has one). Returns ------- data_frame : DataFrame N(R{R(R_RbR-((s1lib/python2.7/site-packages/pandas/core/series.pyR*s tblockcC sMddlm}t|d|d|}||d|jd|jj|S(s Convert Series to SparseSeries. Parameters ---------- kind : {'block', 'integer'} fill_value : float, defaults to NaN (missing) Returns ------- sp : SparseSeries i(RRt fill_valueRRb(RRR1RRbR(R_RRJRR((s1lib/python2.7/site-packages/pandas/core/series.pyt to_sparses cC s4t|d}|r|n |j}||_|S(s Set the Series name. Parameters ---------- name : str inplace : bool whether to modify `self` directly or return a copy RK(RRrRb(R_RbRKtser((s1lib/python2.7/site-packages/pandas/core/series.pyt _set_names  cC s|dkr%ttj|jSt|tjrL|jj |}n|jj |}t j |jj |dtdt}|dk}|jrt|||<}|j||j}n|t|j}t j|dt|pd}|j|d|ddj|S( sy Return number of non-NA/null observations in the Series. Parameters ---------- level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a smaller Series Returns ------- nobs : int or Series (if level specified) tsubokRrit minlengthRRutint64N(R{R(Rtvalues_from_objecttsumRyRt string_typesRR'tlevelsRRtcodesRRzRRZtinsertt _na_valueRtbincountRR(R_R%R,t level_codesRtcnttobstout((s1lib/python2.7/site-packages/pandas/core/series.pytcounts %  !cC stj|d|S(sZ Return the mode(s) of the dataset. Always returns Series even if only one value is returned. Parameters ---------- dropna : boolean, default True Don't consider counts of NaN/NaT. .. versionadded:: 0.24.0 Returns ------- modes : Series (sorted) tdropna(R*tmode(R_R^((s1lib/python2.7/site-packages/pandas/core/series.pyR_8scC stt|j}|S(s Return unique values of Series object. Uniques are returned in order of appearance. Hash table-based unique, therefore does NOT sort. Returns ------- ndarray or ExtensionArray The unique values returned as a NumPy array. In case of an extension-array backed Series, a new :class:`~api.extensions.ExtensionArray` of that type with just the unique values is returned. This includes * Categorical * Period * Datetime with Timezone * Interval * Sparse * IntegerNA See Also -------- unique : Top-level unique method for any 1-d array-like object. Index.unique : Return Index with unique values from an Index object. Examples -------- >>> pd.Series([2, 1, 3, 3], name='A').unique() array([2, 1, 3]) >>> pd.Series([pd.Timestamp('2016-01-01') for _ in range(3)]).unique() array(['2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]') >>> pd.Series([pd.Timestamp('2016-01-01', tz='US/Eastern') ... for _ in range(3)]).unique() ['2016-01-01 00:00:00-05:00'] Length: 1, dtype: datetime64[ns, US/Eastern] An unordered Categorical will return categories in the order of appearance. >>> pd.Series(pd.Categorical(list('baabc'))).unique() [b, a, c] Categories (3, object): [b, a, c] An ordered Categorical preserves the category ordering. >>> pd.Series(pd.Categorical(list('baabc'), categories=list('abc'), ... ordered=True)).unique() [b, a, c] Categories (3, object): [a < b < c] (tsuperRFRL(R_R((s1lib/python2.7/site-packages/pandas/core/series.pyRLLs7tfirstcC stt|jd|d|S(s Return Series with duplicate values removed. Parameters ---------- keep : {'first', 'last', ``False``}, default 'first' - 'first' : Drop duplicates except for the first occurrence. - 'last' : Drop duplicates except for the last occurrence. - ``False`` : Drop all duplicates. inplace : boolean, default ``False`` If ``True``, performs operation inplace and returns None. Returns ------- deduplicated : Series See Also -------- Index.drop_duplicates : Equivalent method on Index. DataFrame.drop_duplicates : Equivalent method on DataFrame. Series.duplicated : Related method on Series, indicating duplicate Series values. Examples -------- Generate an Series with duplicated entries. >>> s = pd.Series(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo'], ... name='animal') >>> s 0 lama 1 cow 2 lama 3 beetle 4 lama 5 hippo Name: animal, dtype: object With the 'keep' parameter, the selection behaviour of duplicated values can be changed. The value 'first' keeps the first occurrence for each set of duplicated entries. The default value of keep is 'first'. >>> s.drop_duplicates() 0 lama 1 cow 3 beetle 5 hippo Name: animal, dtype: object The value 'last' for parameter 'keep' keeps the last occurrence for each set of duplicated entries. >>> s.drop_duplicates(keep='last') 1 cow 3 beetle 4 lama 5 hippo Name: animal, dtype: object The value ``False`` for parameter 'keep' discards all sets of duplicated entries. Setting the value of 'inplace' to ``True`` performs the operation inplace and returns ``None``. >>> s.drop_duplicates(keep=False, inplace=True) >>> s 1 cow 3 beetle 5 hippo Name: animal, dtype: object tkeepRK(R`RFtdrop_duplicates(R_RbRK((s1lib/python2.7/site-packages/pandas/core/series.pyRcsGcC stt|jd|S(s Indicate duplicate Series values. Duplicated values are indicated as ``True`` values in the resulting Series. Either all duplicates, all except the first or all except the last occurrence of duplicates can be indicated. Parameters ---------- keep : {'first', 'last', False}, default 'first' - 'first' : Mark duplicates as ``True`` except for the first occurrence. - 'last' : Mark duplicates as ``True`` except for the last occurrence. - ``False`` : Mark all duplicates as ``True``. Returns ------- pandas.core.series.Series See Also -------- Index.duplicated : Equivalent method on pandas.Index. DataFrame.duplicated : Equivalent method on pandas.DataFrame. Series.drop_duplicates : Remove duplicate values from Series. Examples -------- By default, for each set of duplicated values, the first occurrence is set on False and all others on True: >>> animals = pd.Series(['lama', 'cow', 'lama', 'beetle', 'lama']) >>> animals.duplicated() 0 False 1 False 2 True 3 False 4 True dtype: bool which is equivalent to >>> animals.duplicated(keep='first') 0 False 1 False 2 True 3 False 4 True dtype: bool By using 'last', the last occurrence of each set of duplicated values is set on False and all others on True: >>> animals.duplicated(keep='last') 0 True 1 False 2 True 3 False 4 False dtype: bool By setting keep on ``False``, all duplicates are True: >>> animals.duplicated(keep=False) 0 True 1 False 2 True 3 False 4 True dtype: bool Rb(R`RFRM(R_Rb((s1lib/python2.7/site-packages/pandas/core/series.pyRMsHcO sQtj|||}tjtj|d|}|dkrFtjS|j|S(s Return the row label of the minimum value. If multiple values equal the minimum, the first row label with that value is returned. Parameters ---------- skipna : boolean, default True Exclude NA/null values. If the entire Series is NA, the result will be NA. axis : int, default 0 For compatibility with DataFrame.idxmin. Redundant for application on Series. *args, **kwargs Additional keywords have no effect but might be accepted for compatibility with NumPy. Returns ------- idxmin : Index of minimum of values. Raises ------ ValueError If the Series is empty. See Also -------- numpy.argmin : Return indices of the minimum values along the given axis. DataFrame.idxmin : Return index of first occurrence of minimum over requested axis. Series.idxmax : Return index *label* of the first occurrence of maximum of values. Notes ----- This method is the Series version of ``ndarray.argmin``. This method returns the label of the minimum, while ``ndarray.argmin`` returns the position. To get the position, use ``series.values.argmin()``. Examples -------- >>> s = pd.Series(data=[1, None, 4, 1], ... index=['A' ,'B' ,'C' ,'D']) >>> s A 1.0 B NaN C 4.0 D 1.0 dtype: float64 >>> s.idxmin() 'A' If `skipna` is False and there is an NA value in the data, the function returns ``nan``. >>> s.idxmin(skipna=False) nan Ri( Rtvalidate_argmin_with_skipnaR-t nanargminRRQRtnanR(R_RJRRRR((s1lib/python2.7/site-packages/pandas/core/series.pytidxmins ? cO sQtj|||}tjtj|d|}|dkrFtjS|j|S(s Return the row label of the maximum value. If multiple values equal the maximum, the first row label with that value is returned. Parameters ---------- skipna : boolean, default True Exclude NA/null values. If the entire Series is NA, the result will be NA. axis : int, default 0 For compatibility with DataFrame.idxmax. Redundant for application on Series. *args, **kwargs Additional keywords have no effect but might be accepted for compatibility with NumPy. Returns ------- idxmax : Index of maximum of values. Raises ------ ValueError If the Series is empty. See Also -------- numpy.argmax : Return indices of the maximum values along the given axis. DataFrame.idxmax : Return index of first occurrence of maximum over requested axis. Series.idxmin : Return index *label* of the first occurrence of minimum of values. Notes ----- This method is the Series version of ``ndarray.argmax``. This method returns the label of the maximum, while ``ndarray.argmax`` returns the position. To get the position, use ``series.values.argmax()``. Examples -------- >>> s = pd.Series(data=[1, None, 4, 3, 4], ... index=['A', 'B', 'C', 'D', 'E']) >>> s A 1.0 B NaN C 4.0 D 3.0 E 4.0 dtype: float64 >>> s.idxmax() 'C' If `skipna` is False and there is an NA value in the data, the function returns ``nan``. >>> s.idxmax(skipna=False) nan Ri( Rtvalidate_argmax_with_skipnaR-t nanargmaxRRQRRfR(R_RJRRRR((s1lib/python2.7/site-packages/pandas/core/series.pytidxmax^s @ targmins0.21.0RsF The current behaviour of 'Series.argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin' will be corrected to return the positional minimum in the future. For now, use 'series.values.argmin' or 'np.argmin(np.array(values))' to get the position of the minimum row.targmaxsF The current behaviour of 'Series.argmax' is deprecated, use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. For now, use 'series.values.argmax' or 'np.argmax(np.array(values))' to get the position of the maximum row.cO sMtj||tj|j|}|j|d|jj|}|S(s Round each value in a Series to the given number of decimals. Parameters ---------- decimals : int Number of decimal places to round to (default: 0). If decimals is negative, it specifies the number of positions to the left of the decimal point. Returns ------- Series object See Also -------- numpy.around DataFrame.round R(Rtvalidate_roundRRQtroundRRR(R_tdecimalsRRR((s1lib/python2.7/site-packages/pandas/core/series.pyRns!g?tlinearcC s|j||j}|jd|d|dt}|jdkrb|jdddf}nt|r|j|_|j|dt |d|jS|jdSdS( s2 Return value at the given quantile. Parameters ---------- q : float or array-like, default 0.5 (50% quantile) 0 <= q <= 1, the quantile(s) to compute interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} .. versionadded:: 0.18.0 This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points `i` and `j`: * linear: `i + (j - i) * fraction`, where `fraction` is the fractional part of the index surrounded by `i` and `j`. * lower: `i`. * higher: `j`. * nearest: `i` or `j` whichever is nearest. * midpoint: (`i` + `j`) / 2. Returns ------- quantile : float or Series if ``q`` is an array, a Series will be returned where the index is ``q`` and the values are the quantiles. See Also -------- core.window.Rolling.quantile numpy.percentile Examples -------- >>> s = pd.Series([1, 2, 3, 4]) >>> s.quantile(.5) 2.5 >>> s.quantile([.25, .5, .75]) 0.25 1.75 0.50 2.50 0.75 3.25 dtype: float64 tqt interpolationt numeric_onlyiNiRRb( t_check_percentileR*tquantileRtndimR[RRbRR6(R_RqRrR-R((s1lib/python2.7/site-packages/pandas/core/series.pyRus,        tpearsoncC s|j|dddt\}}t|dkr:tjS|d ksRt|rttj|j|jd|d |St d j d|d S( s Compute correlation with `other` Series, excluding missing values. Parameters ---------- other : Series method : {'pearson', 'kendall', 'spearman'} or callable * pearson : standard correlation coefficient * kendall : Kendall Tau correlation coefficient * spearman : Spearman rank correlation * callable: callable with input two 1d ndarray and returning a float .. versionadded:: 0.24.0 min_periods : int, optional Minimum number of observations needed to have a valid result Returns ------- correlation : float Examples -------- >>> histogram_intersection = lambda a, b: np.minimum(a, b ... ).sum().round(decimals=1) >>> s1 = pd.Series([.2, .0, .6, .2]) >>> s2 = pd.Series([.3, .6, .0, .1]) >>> s1.corr(s2, method=histogram_intersection) 0.3 tjointinnerRriRwtspearmantkendalltmethodt min_periodssRmethod must be either 'pearson', 'spearman', or 'kendall', '{method}' was suppliedN(RwRzR{( talignRRZRRftcallableR-tnancorrRRR](R_totherR|R}tthis((s1lib/python2.7/site-packages/pandas/core/series.pytcorrs! cC sV|j|dddt\}}t|dkr:tjStj|j|jd|S(sc Compute covariance with Series, excluding missing values. Parameters ---------- other : Series min_periods : int, optional Minimum number of observations needed to have a valid result Returns ------- covariance : float Normalized by N-1 (unbiased estimator). RxRyRriR}(R~RRZRRfR-tnancovR(R_RR}R((s1lib/python2.7/site-packages/pandas/core/series.pytcov?s !icC s:tjtj||}|j|d|jj|S(sI First discrete difference of element. Calculates the difference of a Series element compared with another element in the Series (default is element in previous row). Parameters ---------- periods : int, default 1 Periods to shift for calculating difference, accepts negative values. Returns ------- diffed : Series See Also -------- Series.pct_change: Percent change over given number of periods. Series.shift: Shift index by desired number of periods with an optional time freq. DataFrame.diff: First discrete difference of object. Examples -------- Difference with previous row >>> s = pd.Series([1, 1, 2, 3, 5, 8]) >>> s.diff() 0 NaN 1 0.0 2 1.0 3 1.0 4 2.0 5 3.0 dtype: float64 Difference with 3rd previous row >>> s.diff(periods=3) 0 NaN 1 NaN 2 NaN 3 2.0 4 4.0 5 6.0 dtype: float64 Difference with following row >>> s.diff(periods=-1) 0 0.0 1 -1.0 2 -1.0 3 -2.0 4 -3.0 5 NaN dtype: float64 R(R*tdiffRRQRRR(R_tperiodsR((s1lib/python2.7/site-packages/pandas/core/series.pyRUs<cC s|j|j|S(s Compute the lag-N autocorrelation. This method computes the Pearson correlation between the Series and its shifted self. Parameters ---------- lag : int, default 1 Number of lags to apply before performing autocorrelation. Returns ------- float The Pearson correlation between self and self.shift(lag). See Also -------- Series.corr : Compute the correlation between two Series. Series.shift : Shift index by desired number of periods. DataFrame.corr : Compute pairwise correlation of columns. DataFrame.corrwith : Compute pairwise correlation between rows or columns of two DataFrame objects. Notes ----- If the Pearson correlation is not well defined return 'NaN'. Examples -------- >>> s = pd.Series([0.25, 0.5, 0.2, -0.05]) >>> s.autocorr() # doctest: +ELLIPSIS 0.10355... >>> s.autocorr(lag=2) # doctest: +ELLIPSIS -0.99999... If the Pearson correlation is not well defined, then 'NaN' is returned. >>> s = pd.Series([1, 0, 0, 0]) >>> s.autocorr() nan (Rtshift(R_tlag((s1lib/python2.7/site-packages/pandas/core/series.pytautocorrs+cC sddlm}t|t|fr|jj|j}t|t|jkspt|t|jkrtdn|jd|dt }|jd|dt }|j }|j }nQ|j }t j |}|j d|j dkrtd|j |j fnt||rO|jt j||d|jj|St|trnt j||St|t jrt j||Stdt|d S( sC Compute the dot product between the Series and the columns of other. This method computes the dot product between the Series and another one, or the Series and each columns of a DataFrame, or the Series and each columns of an array. It can also be called using `self @ other` in Python >= 3.5. Parameters ---------- other : Series, DataFrame or array-like The other object to compute the dot product with its columns. Returns ------- scalar, Series or numpy.ndarray Return the dot product of the Series and other if other is a Series, the Series of the dot product of Series and each rows of other if other is a DataFrame or a numpy.ndarray between the Series and each columns of the numpy array. See Also -------- DataFrame.dot: Compute the matrix product with the DataFrame. Series.mul: Multiplication of series and other, element-wise. Notes ----- The Series and other has to share the same index if other is a Series or a DataFrame. Examples -------- >>> s = pd.Series([0, 1, 2, 3]) >>> other = pd.Series([-1, 2, -3, 4]) >>> s.dot(other) 8 >>> s @ other 8 >>> df = pd.DataFrame([[0 ,1], [-2, 3], [4, -5], [6, 7]]) >>> s.dot(df) 0 24 1 14 dtype: int64 >>> arr = np.array([[0, 1], [-2, 3], [4, -5], [6, 7]]) >>> s.dot(arr) array([24, 14]) i(Rsmatrices are not alignedRRris$Dot product shape mismatch, %s vs %ssunsupported type: %sN(RRRyRFRtunionRZRRRRRRtshapeRRtdottcolumnsRRR\R(R_RRtcommontlefttrighttlvalstrvals((s1lib/python2.7/site-packages/pandas/core/series.pyRs.2   cC s |j|S(sQ Matrix multiplication using binary `@` operator in Python>=3.5. (R(R_R((s1lib/python2.7/site-packages/pandas/core/series.pyt __matmul__ scC s|jtj|S(sQ Matrix multiplication using binary `@` operator in Python>=3.5. (RRt transpose(R_R((s1lib/python2.7/site-packages/pandas/core/series.pyt __rmatmul__ sRHRFt searchsortedRcC sZ|dk rt|}n|jjt|jd|d|}t|rV|dS|S(Ntsidetsorteri(R{RRRRFR(R_RRRR((s1lib/python2.7/site-packages/pandas/core/series.pyR s  cC sWddlm}t|ttfr5|g|}n ||g}||d|d|S(s Concatenate two or more Series. Parameters ---------- to_append : Series or list/tuple of Series ignore_index : boolean, default False If True, do not use the index labels. .. versionadded:: 0.19.0 verify_integrity : boolean, default False If True, raise Exception on creating index with duplicates Returns ------- appended : Series See Also -------- concat : General function to concatenate DataFrame, Series or Panel objects. Notes ----- Iteratively appending to a Series can be more computationally intensive than a single concatenate. A better solution is to append values to a list and then concatenate the list with the original Series all at once. Examples -------- >>> s1 = pd.Series([1, 2, 3]) >>> s2 = pd.Series([4, 5, 6]) >>> s3 = pd.Series([4, 5, 6], index=[3,4,5]) >>> s1.append(s2) 0 1 1 2 2 3 0 4 1 5 2 6 dtype: int64 >>> s1.append(s3) 0 1 1 2 2 3 3 4 4 5 5 6 dtype: int64 With `ignore_index` set to True: >>> s1.append(s2, ignore_index=True) 0 1 1 2 2 3 3 4 4 5 5 6 dtype: int64 With `verify_integrity` set to True: >>> s1.append(s2, verify_integrity=True) Traceback (most recent call last): ... ValueError: Indexes have overlapping values: [0, 1, 2] i(tconcatt ignore_indextverify_integrity(tpandas.core.reshape.concatRRyRR(R_t to_appendRRRt to_concat((s1lib/python2.7/site-packages/pandas/core/series.pytappend( s H c C st|tstdn|j}|}|jj|jsu|j|d|dddt\}}|j}ntj|j |j |\}}t j dd|||} WdQXtj ||} |j | d |d | } | j|} | dkrd| _n| S( s? Perform generic binary operation with optional fill value. Parameters ---------- other : Series func : binary operator fill_value : float or object Value to substitute for NA/null values. If both Series are NA in a location, the result will be NA regardless of the passed fill value level : int or level name, default None Broadcast across a level, matching Index values on the passed MultiIndex level Returns ------- combined : Series sOther operand must be SeriesR%RxtouterRrtallRwNRRb(RyRFRRRR~RR.t fill_binopRRterrstatetget_op_result_nameRRR{Rb( R_RtfuncR%RJR Rt this_valst other_valsRRb((s1lib/python2.7/site-packages/pandas/core/series.pyt_binopy s$    c C s~|dkr$t|jdt}nt|tr|jj|j}tj ||}g}x|D]Y}|j ||}|j ||} t j dd|j ||| WdQXqgWnP|j}t j dd*g|jD]}|||^q}WdQX|j}t|jr)n<t|jrey|jj|}Wqetk raqeXn|j|d|d|S(s Combine the Series with a Series or scalar according to `func`. Combine the Series and `other` using `func` to perform elementwise selection for combined Series. `fill_value` is assumed when value is missing at some index from one of the two objects being combined. Parameters ---------- other : Series or scalar The value(s) to be combined with the `Series`. func : function Function that takes two scalars as inputs and returns an element. fill_value : scalar, optional The value to assume when an index is missing from one Series or the other. The default specifies to use the appropriate NaN value for the underlying dtype of the Series. Returns ------- Series The result of combining the Series with the other object. See Also -------- Series.combine_first : Combine Series values, choosing the calling Series' values first. Examples -------- Consider 2 Datasets ``s1`` and ``s2`` containing highest clocked speeds of different birds. >>> s1 = pd.Series({'falcon': 330.0, 'eagle': 160.0}) >>> s1 falcon 330.0 eagle 160.0 dtype: float64 >>> s2 = pd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0}) >>> s2 falcon 345.0 eagle 200.0 duck 30.0 dtype: float64 Now, to combine the two datasets and view the highest speeds of the birds across the two datasets >>> s1.combine(s2, max) duck NaN eagle 200.0 falcon 345.0 dtype: float64 In the previous example, the resulting value for duck is missing, because the maximum of a NaN and a float is a NaN. So, in the example, we set ``fill_value=0``, so the maximum value returned will be the value from some dataset. >>> s1.combine(s2, max, fill_value=0) duck 30.0 eagle 200.0 falcon 345.0 dtype: float64 RRRwNRRb(R{R'RuRRyRFRRR.RtgetRRRRRbRRRt_from_sequenceRR( R_RRRJR tnew_nameR tidxtlvtrv((s1lib/python2.7/site-packages/pandas/core/series.pytcombine s.C  # +  cC s}|jj|j}|j|dt}|j|dt}t|rgt| rgt|}n|jt||S(s Combine Series values, choosing the calling Series's values first. Parameters ---------- other : Series The value(s) to be combined with the `Series`. Returns ------- Series The result of combining the Series with the other object. See Also -------- Series.combine : Perform elementwise operation on two Series using a given function. Notes ----- Result index will be the union of the two indexes. Examples -------- >>> s1 = pd.Series([1, np.nan]) >>> s2 = pd.Series([3, 4]) >>> s1.combine_first(s2) 0 1.0 1 4.0 dtype: float64 Rr(RRRRRRDtwhereR((R_RR R((s1lib/python2.7/site-packages/pandas/core/series.pyt combine_first s cC sM|j|}t|}|jjd|d|dt|_|jdS(s Modify Series in place using non-NA values from passed Series. Aligns on index. Parameters ---------- other : Series Examples -------- >>> s = pd.Series([1, 2, 3]) >>> s.update(pd.Series([4, 5, 6])) >>> s 0 4 1 5 2 6 dtype: int64 >>> s = pd.Series(['a', 'b', 'c']) >>> s.update(pd.Series(['d', 'e'], index=[0, 2])) >>> s 0 d 1 b 2 e dtype: object >>> s = pd.Series([1, 2, 3]) >>> s.update(pd.Series([4, 5, 6, 7, 8])) >>> s 0 4 1 5 2 6 dtype: int64 If ``other`` contains NaNs the corresponding values are not updated in the original Series. >>> s = pd.Series([1, 2, 3]) >>> s.update(pd.Series([4, np.nan, 6])) >>> s 0 4 1 2 2 6 dtype: int64 RtnewRKN(t reindex_likeR(RtputmaskRzR(R_RR((s1lib/python2.7/site-packages/pandas/core/series.pytupdate5 s. $t quicksorttlastc st|d}|j||r:|jr:tdnfd}|j}tjt|dtj}t |} | } t j t|} ||| } t |rt|dkrtdt|n|d}nt |stdn|s#| d d d } n|d kr^| j} | | | || *| | || )nP|d kr| j} | | | || )| | || *ntd j||j||d|j|}|r|j|n |j|Sd S(s Sort by the values. Sort a Series in ascending or descending order by some criterion. Parameters ---------- axis : {0 or 'index'}, default 0 Axis to direct sorting. The value 'index' is accepted for compatibility with DataFrame.sort_values. ascending : bool, default True If True, sort values in ascending order, otherwise descending. inplace : bool, default False If True, perform operation in-place. kind : {'quicksort', 'mergesort' or 'heapsort'}, default 'quicksort' Choice of sorting algorithm. See also :func:`numpy.sort` for more information. 'mergesort' is the only stable algorithm. na_position : {'first' or 'last'}, default 'last' Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at the end. Returns ------- Series Series ordered by values. See Also -------- Series.sort_index : Sort by the Series indices. DataFrame.sort_values : Sort DataFrame by the values along either axis. DataFrame.sort_index : Sort DataFrame by indices. Examples -------- >>> s = pd.Series([np.nan, 1, 3, 10, 5]) >>> s 0 NaN 1 1.0 2 3.0 3 10.0 4 5.0 dtype: float64 Sort values ascending order (default behaviour) >>> s.sort_values(ascending=True) 1 1.0 2 3.0 4 5.0 3 10.0 0 NaN dtype: float64 Sort values descending order >>> s.sort_values(ascending=False) 3 10.0 4 5.0 2 3.0 1 1.0 0 NaN dtype: float64 Sort values inplace >>> s.sort_values(ascending=False, inplace=True) >>> s 3 10.0 4 5.0 2 3.0 1 1.0 0 NaN dtype: float64 Sort values putting NAs first >>> s.sort_values(na_position='first') 0 NaN 1 1.0 2 3.0 4 5.0 3 10.0 dtype: float64 Sort a series of strings >>> s = pd.Series(['z', 'b', 'd', 'a', 'c']) >>> s 0 z 1 b 2 d 3 a 4 c dtype: object >>> s.sort_values() 3 a 1 b 4 c 2 d 0 z dtype: object RKsRThis Series is a view of some other array, to sort in-place you must create a copyc s9y|jdSWntk r4|jddSXdS(NRR(targsortR\(RX(R(s1lib/python2.7/site-packages/pandas/core/series.pyt_try_kind_sort s Ruis-Length of ascending (%d) must be 1 for Seriesisascending must be booleanNiRRasinvalid na_position: {!r}R(Rt_get_axis_numbert _is_cachedRRRRRZtint32R&RRRRRRR]RRRR(R_RJt ascendingRKRt na_positionRRXt sortedIdxtbadtgoodRt argsortedtnR((Rs1lib/python2.7/site-packages/pandas/core/series.pyt sort_valuesl sBj           cC szt|d}|j||j}|d k rU|j|d|d|\} } nt|trddlm} |j } | | j d|d|} ndddlm } |r|j s| r|j r|rd S|jSn| |d |d|d|} t| } |j| } | j } |jj| }|j|d | }|ri|j|n |j|Sd S( s Sort Series by index labels. Returns a new Series sorted by label if `inplace` argument is ``False``, otherwise updates the original series and returns None. Parameters ---------- axis : int, default 0 Axis to direct sorting. This can only be 0 for Series. level : int, optional If not None, sort on values in specified index level(s). ascending : bool, default true Sort ascending vs. descending. inplace : bool, default False If True, perform operation in-place. kind : {'quicksort', 'mergesort', 'heapsort'}, default 'quicksort' Choice of sorting algorithm. See also :func:`numpy.sort` for more information. 'mergesort' is the only stable algorithm. For DataFrames, this option is only applied when sorting on a single column or label. na_position : {'first', 'last'}, default 'last' If 'first' puts NaNs at the beginning, 'last' puts NaNs at the end. Not implemented for MultiIndex. sort_remaining : bool, default True If true and sorting by level and index is multilevel, sort by other levels too (in order) after sorting by specified level. Returns ------- pandas.Series The original Series sorted by the labels See Also -------- DataFrame.sort_index: Sort DataFrame by the index. DataFrame.sort_values: Sort DataFrame by the value. Series.sort_values : Sort Series by the value. Examples -------- >>> s = pd.Series(['a', 'b', 'c', 'd'], index=[3, 2, 1, 4]) >>> s.sort_index() 1 c 2 b 3 a 4 d dtype: object Sort Descending >>> s.sort_index(ascending=False) 4 d 3 a 2 b 1 c dtype: object Sort Inplace >>> s.sort_index(inplace=True) >>> s 1 c 2 b 3 a 4 d dtype: object By default NaNs are put at the end, but use `na_position` to place them at the beginning >>> s = pd.Series(['a', 'b', 'c', 'd'], index=[3, 2, 1, np.nan]) >>> s.sort_index(na_position='first') NaN d 1.0 c 2.0 b 3.0 a dtype: object Specify index level to sort >>> arrays = [np.array(['qux', 'qux', 'foo', 'foo', ... 'baz', 'baz', 'bar', 'bar']), ... np.array(['two', 'one', 'two', 'one', ... 'two', 'one', 'two', 'one'])] >>> s = pd.Series([1, 2, 3, 4, 5, 6, 7, 8], index=arrays) >>> s.sort_index(level=1) bar one 8 baz one 6 foo one 4 qux one 2 bar two 7 baz two 5 foo two 3 qux two 1 dtype: int64 Does not sort by remaining levels when sorting by levels >>> s.sort_index(level=1, sort_remaining=False) qux one 2 foo one 4 baz one 6 bar one 8 qux two 1 foo two 3 baz two 5 bar two 7 dtype: int64 RKRtsort_remainingi(tlexsort_indexertordersR(tnargsortNRR(RRRR{t sortlevelRyR9tpandas.core.sortingRt_sort_levels_monotonict_get_codes_for_sortingRtis_monotonic_increasingtis_monotonic_decreasingRrRRRRRR(R_RJR%RRKRRRRR RRRRR R((s1lib/python2.7/site-packages/pandas/core/series.pyR s8r         cC s|j}t|}|jrtdd|jd|jdd}|}tj||d|||<|j|d|jj |S|jtj|d|d|jddj |SdS(s Overrides ndarray.argsort. Argsorts the value, omitting NA/null values, and places the result in the same locations as the non-NA values. Parameters ---------- axis : int Has no effect but is accepted for compatibility with numpy. kind : {'mergesort', 'quicksort', 'heapsort'}, default 'quicksort' Choice of sorting algorithm. See np.sort for more information. 'mergesort' is the only stable algorithm order : None Has no effect but is accepted for compatibility with numpy. Returns ------- argsorted : Series, with -1 indicated where nan values are present See Also -------- numpy.ndarray.argsort iRRbRuRPRN( RR&RRFRRbRRRR(R_RJRRRRRtnotmask((s1lib/python2.7/site-packages/pandas/core/series.pyR s     icC stj|d|d|jS(s Return the largest `n` elements. Parameters ---------- n : int, default 5 Return this many descending sorted values. keep : {'first', 'last', 'all'}, default 'first' When there are duplicate values that cannot all fit in a Series of `n` elements: - ``first`` : take the first occurrences based on the index order - ``last`` : take the last occurrences based on the index order - ``all`` : keep all occurrences. This can result in a Series of size larger than `n`. Returns ------- Series The `n` largest values in the Series, sorted in decreasing order. See Also -------- Series.nsmallest: Get the `n` smallest elements. Series.sort_values: Sort Series by values. Series.head: Return the first `n` rows. Notes ----- Faster than ``.sort_values(ascending=False).head(n)`` for small `n` relative to the size of the ``Series`` object. Examples -------- >>> countries_population = {"Italy": 59000000, "France": 65000000, ... "Malta": 434000, "Maldives": 434000, ... "Brunei": 434000, "Iceland": 337000, ... "Nauru": 11300, "Tuvalu": 11300, ... "Anguilla": 11300, "Monserat": 5200} >>> s = pd.Series(countries_population) >>> s Italy 59000000 France 65000000 Malta 434000 Maldives 434000 Brunei 434000 Iceland 337000 Nauru 11300 Tuvalu 11300 Anguilla 11300 Monserat 5200 dtype: int64 The `n` largest elements where ``n=5`` by default. >>> s.nlargest() France 65000000 Italy 59000000 Malta 434000 Maldives 434000 Brunei 434000 dtype: int64 The `n` largest elements where ``n=3``. Default `keep` value is 'first' so Malta will be kept. >>> s.nlargest(3) France 65000000 Italy 59000000 Malta 434000 dtype: int64 The `n` largest elements where ``n=3`` and keeping the last duplicates. Brunei will be kept since it is the last with value 434000 based on the index order. >>> s.nlargest(3, keep='last') France 65000000 Italy 59000000 Brunei 434000 dtype: int64 The `n` largest elements where ``n=3`` with all duplicates kept. Note that the returned Series has five elements due to the three duplicates. >>> s.nlargest(3, keep='all') France 65000000 Italy 59000000 Malta 434000 Maldives 434000 Brunei 434000 dtype: int64 RRb(R*t SelectNSeriestnlargest(R_RRb((s1lib/python2.7/site-packages/pandas/core/series.pyR s^cC stj|d|d|jS(s Return the smallest `n` elements. Parameters ---------- n : int, default 5 Return this many ascending sorted values. keep : {'first', 'last', 'all'}, default 'first' When there are duplicate values that cannot all fit in a Series of `n` elements: - ``first`` : take the first occurrences based on the index order - ``last`` : take the last occurrences based on the index order - ``all`` : keep all occurrences. This can result in a Series of size larger than `n`. Returns ------- Series The `n` smallest values in the Series, sorted in increasing order. See Also -------- Series.nlargest: Get the `n` largest elements. Series.sort_values: Sort Series by values. Series.head: Return the first `n` rows. Notes ----- Faster than ``.sort_values().head(n)`` for small `n` relative to the size of the ``Series`` object. Examples -------- >>> countries_population = {"Italy": 59000000, "France": 65000000, ... "Brunei": 434000, "Malta": 434000, ... "Maldives": 434000, "Iceland": 337000, ... "Nauru": 11300, "Tuvalu": 11300, ... "Anguilla": 11300, "Monserat": 5200} >>> s = pd.Series(countries_population) >>> s Italy 59000000 France 65000000 Brunei 434000 Malta 434000 Maldives 434000 Iceland 337000 Nauru 11300 Tuvalu 11300 Anguilla 11300 Monserat 5200 dtype: int64 The `n` largest elements where ``n=5`` by default. >>> s.nsmallest() Monserat 5200 Nauru 11300 Tuvalu 11300 Anguilla 11300 Iceland 337000 dtype: int64 The `n` smallest elements where ``n=3``. Default `keep` value is 'first' so Nauru and Tuvalu will be kept. >>> s.nsmallest(3) Monserat 5200 Nauru 11300 Tuvalu 11300 dtype: int64 The `n` smallest elements where ``n=3`` and keeping the last duplicates. Anguilla and Tuvalu will be kept since they are the last with value 11300 based on the index order. >>> s.nsmallest(3, keep='last') Monserat 5200 Anguilla 11300 Tuvalu 11300 dtype: int64 The `n` smallest elements where ``n=3`` with all duplicates kept. Note that the returned Series has four elements due to the three duplicates. >>> s.nsmallest(3, keep='all') Monserat 5200 Nauru 11300 Tuvalu 11300 Anguilla 11300 dtype: int64 RRb(R*Rt nsmallest(R_RRb((s1lib/python2.7/site-packages/pandas/core/series.pyR2 s]iicC s:|jj||}|j|jd|d|j|S(s Swap levels i and j in a MultiIndex. Parameters ---------- i, j : int, string (can be mixed) Level of index to be swapped. Can pass level name as string. Returns ------- swapped : Series .. versionchanged:: 0.18.1 The indexes ``i`` and ``j`` are now optional, and default to the two innermost levels of the index. RRr(Rt swaplevelRRR(R_RtjRrR ((s1lib/python2.7/site-packages/pandas/core/series.pyR scC sFt|jts!tdn|j}|jj||_|S(s= Rearrange index levels using input order. May not drop or duplicate levels. Parameters ---------- order : list of int representing new level order (reference level by number or key) Returns ------- type of caller (new object) s/Can only reorder levels on a hierarchical axis.(RyRR9RRrtreorder_levels(R_RR((s1lib/python2.7/site-packages/pandas/core/series.pyR s  cC s ddlm}||||S(s Unstack, a.k.a. pivot, Series with MultiIndex to produce DataFrame. The level involved will automatically get sorted. Parameters ---------- level : int, string, or list of these, default last level Level(s) to unstack, can pass level name fill_value : replace NaN with this value if the unstack produces missing values .. versionadded:: 0.18.0 Returns ------- unstacked : DataFrame Examples -------- >>> s = pd.Series([1, 2, 3, 4], ... index=pd.MultiIndex.from_product([['one', 'two'], ['a', 'b']])) >>> s one a 1 b 2 two a 3 b 4 dtype: int64 >>> s.unstack(level=-1) a b one 1 2 two 3 4 >>> s.unstack(level=0) one two a 1 3 b 2 4 i(tunstack(tpandas.core.reshape.reshapeR(R_R%RJR((s1lib/python2.7/site-packages/pandas/core/series.pyR s'cC s=tt|j|d|}|j|d|jj|S(s Map values of Series according to input correspondence. Used for substituting each value in a Series with another value, that may be derived from a function, a ``dict`` or a :class:`Series`. Parameters ---------- arg : function, dict, or Series Mapping correspondence. na_action : {None, 'ignore'}, default None If 'ignore', propagate NaN values, without passing them to the mapping correspondence. Returns ------- Series Same index as caller. See Also -------- Series.apply : For applying more complex functions on a Series. DataFrame.apply : Apply a function row-/column-wise. DataFrame.applymap : Apply a function elementwise on a whole DataFrame. Notes ----- When ``arg`` is a dictionary, values in Series that are not in the dictionary (as keys) are converted to ``NaN``. However, if the dictionary is a ``dict`` subclass that defines ``__missing__`` (i.e. provides a method for default values), then this default is used rather than ``NaN``. Examples -------- >>> s = pd.Series(['cat', 'dog', np.nan, 'rabbit']) >>> s 0 cat 1 dog 2 NaN 3 rabbit dtype: object ``map`` accepts a ``dict`` or a ``Series``. Values that are not found in the ``dict`` are converted to ``NaN``, unless the dict has a default value (e.g. ``defaultdict``): >>> s.map({'cat': 'kitten', 'dog': 'puppy'}) 0 kitten 1 puppy 2 NaN 3 NaN dtype: object It also accepts a function: >>> s.map('I am a {}'.format) 0 I am a cat 1 I am a dog 2 I am a nan 3 I am a rabbit dtype: object To avoid applying the function to missing values (and keep them as ``NaN``) ``na_action='ignore'`` can be used: >>> s.map('I am a {}'.format, na_action='ignore') 0 I am a cat 1 I am a dog 2 NaN 3 I am a rabbit dtype: object t na_actionR(R`RFt _map_valuesRRR(R_targRR ((s1lib/python2.7/site-packages/pandas/core/series.pytmap sK cC s|S(s  Sub-classes to define. Return a sliced object. Parameters ---------- key : string / list of selections ndim : 1,2 requested ndim of result subset : object, default None subset to act on ((R_RRvtsubset((s1lib/python2.7/site-packages/pandas/core/series.pyt_gotitem: s s See Also -------- Series.apply : Invoke function on a Series. Series.transform : Transform function producing a Series with like indexes. s Examples -------- >>> s = pd.Series([1, 2, 3, 4]) >>> s 0 1 1 2 2 3 3 4 dtype: int64 >>> s.agg('min') 1 >>> s.agg(['min', 'max']) min 1 max 4 dtype: int64 tsee_alsotexamplest versionaddeds.. versionadded:: 0.20.0t aggregatecO s|j||j|||\}}|dkr|jdd|jddy|j|||}Wqtttfk r||||}qXn|S(Nt_axist_level(Rt _aggregateR{tpoptapplyRR@R\(R_RRJRRRthow((s1lib/python2.7/site-packages/pandas/core/series.pyRc s   t transformcO s)|j|tt|j|||S(N(RR`RFR(R_RRJRR((s1lib/python2.7/site-packages/pandas/core/series.pyR s c st|dkr7|jd|jd|jj|Stttfr_|jStt j r|j Ssrtt j  rfd}n}t jddot|t j r||St|jr|jj|}n*|jtj}tj||d|}WdQXt|rt|dtrd d lm}||jd|jS|j|d|jj|SdS( s Invoke function on values of Series. Can be ufunc (a NumPy function that applies to the entire Series) or a Python function that only works on single values. Parameters ---------- func : function Python function or NumPy ufunc to apply. convert_dtype : bool, default True Try to find better dtype for elementwise function results. If False, leave as dtype=object. args : tuple Positional arguments passed to func after the series value. **kwds Additional keyword arguments passed to func. Returns ------- Series or DataFrame If func returns a Series object the result will be a DataFrame. See Also -------- Series.map: For element-wise operations. Series.agg: Only perform aggregating type operations. Series.transform: Only perform transforming type operations. Examples -------- Create a series with typical summer temperatures for each city. >>> s = pd.Series([20, 21, 12], ... index=['London', 'New York', 'Helsinki']) >>> s London 20 New York 21 Helsinki 12 dtype: int64 Square the values by defining a function and passing it as an argument to ``apply()``. >>> def square(x): ... return x ** 2 >>> s.apply(square) London 400 New York 441 Helsinki 144 dtype: int64 Square the values by passing an anonymous function as an argument to ``apply()``. >>> s.apply(lambda x: x ** 2) London 400 New York 441 Helsinki 144 dtype: int64 Define a custom function that needs additional positional arguments and pass these additional arguments using the ``args`` keyword. >>> def subtract_custom_value(x, custom_value): ... return x - custom_value >>> s.apply(subtract_custom_value, args=(5,)) London 15 New York 16 Helsinki 7 dtype: int64 Define a custom function that takes keyword arguments and pass these arguments to ``apply``. >>> def add_custom_values(x, **kwargs): ... for month in kwargs: ... x += kwargs[month] ... return x >>> s.apply(add_custom_values, june=30, july=20, august=25) London 95 New York 96 Helsinki 87 dtype: int64 Use a function from the Numpy library. >>> s.apply(np.log) London 2.995732 New York 3.044522 Helsinki 2.484907 dtype: float64 iRuRc s|S(N((tx(RRtkwds(s1lib/python2.7/site-packages/pandas/core/series.pyRC sRRwRNi(R(RZRRuRRRyRRRRRSt_try_aggregate_string_functionRtufuncRRRRR~RRRt map_inferRFRRRn( R_Rt convert_dtypeRRRCtmappedRR((RRRs1lib/python2.7/site-packages/pandas/core/series.pyR s,a  c K s|j}|dk r%|j|nt|trJ|j|d||St|tro|j|d||St|rt|}n_t|t j r|rt dj |nt j dd||d||SWdQXn|jd|d|d |d|d|d ||S( s Perform a reduction operation. If we have an ndarray as a value, then simply perform the operation, otherwise delegate to the object. RsRs+Series.{0} does not implement numeric_only.RRwNRRbRJt filter_type(RR{RRyR2t_reduceR0RR<RRR}R]R( R_RRbRJRRsRRtdelegate((s1lib/python2.7/site-packages/pandas/core/series.pyRs$    !cC sT|dkr |r|jS|Stj|j|dtdd}|j|d|S(Nt allow_fillRJR(R{RrR*ttake_1dRRzR(R_R RRrR ((s1lib/python2.7/site-packages/pandas/core/series.pyt_reindex_indexer7s  cC stS(sc Check if we do need a multi reindex; this is for compat with higher dims. (R(R_RGR|R%((s1lib/python2.7/site-packages/pandas/core/series.pyt_needs_reindex_multiAsR~Rc C sLtt|j|d|d|d|d|d|d|d|d| d |  S( NRxRJR%RrRJR|tlimitt fill_axistbroadcast_axis(R`RFR~( R_RRxRJR%RrRJR|RRR((s1lib/python2.7/site-packages/pandas/core/series.pyR~Hs !   cK st|jdtd|d` for more. Parameters ---------- index : scalar, hashable sequence, dict-like or function, optional dict-like or functions are transformations to apply to the index. Scalar or hashable sequence-like will alter the ``Series.name`` attribute. copy : bool, default True Also copy underlying data inplace : bool, default False Whether to return a new Series. If True then value of copy is ignored. level : int or level name, default None In case of a MultiIndex, only rename labels in the specified level. Returns ------- renamed : Series (new object) See Also -------- Series.rename_axis Examples -------- >>> s = pd.Series([1, 2, 3]) >>> s 0 1 1 2 2 3 dtype: int64 >>> s.rename("my_name") # scalar, changes Series.name 0 1 1 2 2 3 Name: my_name, dtype: int64 >>> s.rename(lambda x: x ** 2) # function, changes labels 0 1 1 2 4 3 dtype: int64 >>> s.rename({1: 3, 2: 5}) # mapping, changes labels 0 1 3 2 5 3 dtype: int64 RKR( RRRRRRRMR`RFtrename(R_RRt non_mapping((s1lib/python2.7/site-packages/pandas/core/series.pyRRs<  cK stt|jd||S(NR(R`RFR(R_RR((s1lib/python2.7/site-packages/pandas/core/series.pyRstraisecC s=tt|jd|d|d|d|d|d|d|S(s Return Series with specified index labels removed. Remove elements of a Series based on specifying the index labels. When using a multi-index, labels on different levels can be removed by specifying the level. Parameters ---------- labels : single label or list-like Index labels to drop. axis : 0, default 0 Redundant for application on Series. index, columns : None Redundant for application on Series, but index can be used instead of labels. .. versionadded:: 0.21.0 level : int or level name, optional For MultiIndex, level for which the labels will be removed. inplace : bool, default False If True, do operation inplace and return None. errors : {'ignore', 'raise'}, default 'raise' If 'ignore', suppress error and only existing labels are dropped. Returns ------- dropped : pandas.Series Raises ------ KeyError If none of the labels are found in the index. See Also -------- Series.reindex : Return only specified index labels of Series. Series.dropna : Return series without null values. Series.drop_duplicates : Return Series with duplicate values removed. DataFrame.drop : Drop specified labels from rows or columns. Examples -------- >>> s = pd.Series(data=np.arange(3), index=['A','B','C']) >>> s A 0 B 1 C 2 dtype: int64 Drop labels B en C >>> s.drop(labels=['B','C']) A 0 dtype: int64 Drop 2nd level label in MultiIndex Series >>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'], ... ['speed', 'weight', 'length']], ... codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], ... [0, 1, 2, 0, 1, 2, 0, 1, 2]]) >>> s = pd.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3], ... index=midx) >>> s lama speed 45.0 weight 200.0 length 1.2 cow speed 30.0 weight 250.0 length 1.5 falcon speed 320.0 weight 1.0 length 0.3 dtype: float64 >>> s.drop(labels='weight', level=1) lama speed 45.0 length 1.2 cow speed 30.0 length 1.5 falcon speed 320.0 length 0.3 dtype: float64 RRJRRR%RKRv(R`RFR&(R_RRJRRR%RKRv((s1lib/python2.7/site-packages/pandas/core/series.pyR&sW$ cK s:tt|jd|d|d|d|d|d||S(NRR|RJRKRtdowncast(R`RFtfillna(R_RR|RJRKRRR((s1lib/python2.7/site-packages/pandas/core/series.pyRs  treplacetpadc C s7tt|jd|d|d|d|d|d|S(Nt to_replaceRRKRtregexR|(R`RFR(R_RRRKRRR|((s1lib/python2.7/site-packages/pandas/core/series.pyRs Rc C s+tt|jd|d|d|d|S(NRtfreqRJRJ(R`RFR(R_RRRJRJ((s1lib/python2.7/site-packages/pandas/core/series.pyRs$cK sJ|dkrtdnd}tj|tdd|jd||S(s Conform Series to new index with optional filling logic. .. deprecated:: 0.21.0 Use ``Series.reindex`` instead. is'cannot reindex series on non-zero axis!s^'.reindex_axis' is deprecated and will be removed in a future version. Use '.reindex' instead.RTiR(RRURVRWR(R_RRJRR((s1lib/python2.7/site-packages/pandas/core/series.pyt reindex_axis s  cC sAtt|jd|}|r=||jjd|7}n|S(sO Return the memory usage of the Series. The memory usage can optionally include the contribution of the index and of elements of `object` dtype. Parameters ---------- index : bool, default True Specifies whether to include the memory usage of the Series index. deep : bool, default False If True, introspect the data deeply by interrogating `object` dtypes for system-level memory consumption, and include it in the returned value. Returns ------- int Bytes of memory consumed. See Also -------- numpy.ndarray.nbytes : Total bytes consumed by the elements of the array. DataFrame.memory_usage : Bytes consumed by a DataFrame. Examples -------- >>> s = pd.Series(range(3)) >>> s.memory_usage() 104 Not including the index gives the size of the rest of the data, which is necessarily smaller: >>> s.memory_usage(index=False) 24 The memory footprint of `object` values is ignored by default: >>> s = pd.Series(["a", "b"]) >>> s.values array(['a', 'b'], dtype=object) >>> s.memory_usage() 96 >>> s.memory_usage(deep=True) 212 Rq(R`RFt memory_usageR(R_RRqR((s1lib/python2.7/site-packages/pandas/core/series.pyRs1cC st|}|jj|}t|rXt|t|j|}itd6}ni}|jj||}|j |d|dt j |}|r|j|j |j|s|j |qn|S(NRRRp(RRRRR@RZt _get_axisRRRRzRRt _set_is_copy(R_tindicesRJtis_copyR RR R((s1lib/python2.7/site-packages/pandas/core/series.pyt_takeRs  !cC s1tj||}|j|d|jj|S(s Check whether `values` are contained in Series. Return a boolean Series showing whether each element in the Series matches an element in the passed sequence of `values` exactly. Parameters ---------- values : set or list-like The sequence of values to test. Passing in a single string will raise a ``TypeError``. Instead, turn a single string into a list of one element. .. versionadded:: 0.18.1 Support for values as a set. Returns ------- isin : Series (bool dtype) Raises ------ TypeError * If `values` is a string See Also -------- DataFrame.isin : Equivalent method on DataFrame. Examples -------- >>> s = pd.Series(['lama', 'cow', 'lama', 'beetle', 'lama', ... 'hippo'], name='animal') >>> s.isin(['cow', 'lama']) 0 True 1 True 2 True 3 False 4 True 5 False Name: animal, dtype: bool Passing a single string as ``s.isin('lama')`` will raise an error. Use a list of one element instead: >>> s.isin(['lama']) 0 True 1 False 2 True 3 False 4 True 5 False Name: animal, dtype: bool R(R*tisinRRR(R_RR((s1lib/python2.7/site-packages/pandas/core/series.pyR ks8cC sA|r!||k}||k}n||k}||k}||@S(s9 Return boolean Series equivalent to left <= series <= right. This function returns a boolean vector containing `True` wherever the corresponding Series element is between the boundary values `left` and `right`. NA values are treated as `False`. Parameters ---------- left : scalar Left boundary. right : scalar Right boundary. inclusive : bool, default True Include boundaries. Returns ------- Series Each element will be a boolean. See Also -------- Series.gt : Greater than of series and other. Series.lt : Less than of series and other. Notes ----- This function is equivalent to ``(left <= ser) & (ser <= right)`` Examples -------- >>> s = pd.Series([2, 0, 4, 8, np.nan]) Boundary values are included by default: >>> s.between(1, 4) 0 True 1 False 2 True 3 False 4 False dtype: bool With `inclusive` set to ``False`` boundary values are excluded: >>> s.between(1, 4, inclusive=False) 0 True 1 False 2 False 3 False 4 False dtype: bool `left` and `right` can be any scalar value: >>> s = pd.Series(['Alice', 'Bob', 'Carol', 'Eve']) >>> s.between('Anna', 'Daniel') 0 False 1 True 2 True 3 False dtype: bool ((R_RRt inclusivetlmasktrmask((s1lib/python2.7/site-packages/pandas/core/series.pytbetweens A   t,c C sddlm}|j|d|d|d|d|d|d|} | jd d d f} |d kr~d | j_| _n| S( s Read CSV file. .. deprecated:: 0.21.0 Use :func:`pandas.read_csv` instead. It is preferable to use the more powerful :func:`pandas.read_csv` for most general purposes, but ``from_csv`` makes for an easy roundtrip to and from a file (the exact counterpart of ``to_csv``), especially with a time Series. This method only differs from :func:`pandas.read_csv` in some defaults: - `index_col` is ``0`` instead of ``None`` (take first column as index by default) - `header` is ``None`` instead of ``0`` (the first row is not used as the column names) - `parse_dates` is ``True`` instead of ``False`` (try parsing the index as datetime by default) With :func:`pandas.read_csv`, the option ``squeeze=True`` can be used to return a Series like ``from_csv``. Parameters ---------- path : string file path or file handle / StringIO sep : string, default ',' Field delimiter parse_dates : boolean, default True Parse dates. Different default from read_table header : int, default None Row to use as header (skip prior rows) index_col : int or sequence, default 0 Column to use for index. If a sequence is given, a MultiIndex is used. Different default from read_table encoding : string, optional a string representing the encoding to use if the contents are non-ascii, for python versions prior to 3 infer_datetime_format : boolean, default False If True and `parse_dates` is True for a column, try to infer the datetime format based on the first datetime string. If the format can be inferred, there often will be a large parsing speed-up. Returns ------- y : Series See Also -------- read_csv i(RR8t index_coltsept parse_datestencodingtinfer_datetime_formatNi(RRRlR[R{RRb( RtpathRRR8RRRRR-R((s1lib/python2.7/site-packages/pandas/core/series.pyRls9   cO sddddddddd d d d d dddddddg}dddddddd d d ddg }d|krtjdtdd|jd|d                R&cC stt|jS(N(R`RFR&(R_((s1lib/python2.7/site-packages/pandas/core/series.pyR&mscC stt|jS(N(R`RFtisnull(R_((s1lib/python2.7/site-packages/pandas/core/series.pyR)qsR(cC stt|jS(N(R`RFR((R_((s1lib/python2.7/site-packages/pandas/core/series.pyR(uscC stt|jS(N(R`RFtnotnull(R_((s1lib/python2.7/site-packages/pandas/core/series.pyR*yscK st|d}|jdd|rMtdjt|jdn|j|p\d|jrt |}|r|j |q|Sn|rn |j SdS(s Return a new Series with missing values removed. See the :ref:`User Guide ` for more on which values are considered missing, and how to work with missing data. Parameters ---------- axis : {0 or 'index'}, default 0 There is only one axis to drop values from. inplace : bool, default False If True, do operation inplace and return None. **kwargs Not in use. Returns ------- Series Series with NA entries dropped from it. See Also -------- Series.isna: Indicate missing values. Series.notna : Indicate existing (non-missing) values. Series.fillna : Replace missing values. DataFrame.dropna : Drop rows or columns which contain NA values. Index.dropna : Drop missing indices. Examples -------- >>> ser = pd.Series([1., 2., np.nan]) >>> ser 0 1.0 1 2.0 2 NaN dtype: float64 Drop NA values from a Series. >>> ser.dropna() 0 1.0 1 2.0 dtype: float64 Keep the Series with valid entries in the same variable. >>> ser.dropna(inplace=True) >>> ser 0 1.0 1 2.0 dtype: float64 Empty strings are not considered NA values. ``None`` is considered an NA value. >>> ser = pd.Series([np.NaN, 2, pd.NaT, '', None, 'I stay']) >>> ser 0 NaN 1 2 2 NaT 3 4 None 5 I stay dtype: object >>> ser.dropna() 1 2 3 5 I stay dtype: object RKRs1dropna() got an unexpected keyword argument "{0}"iN( RRR{R\R]RRRRR)RRr(R_RJRKRR((s1lib/python2.7/site-packages/pandas/core/series.pyR^}sG   cK s)tjdtdd|jd||S(s Return Series without null values. .. deprecated:: 0.23.0 Use :meth:`Series.dropna` instead. sGMethod .valid will be removed in a future version. Use .dropna instead.RTiRK(RURVRWR^(R_RKR((s1lib/python2.7/site-packages/pandas/core/series.pyRms  tstartcC sU|j}|r|j}n|jjd|d|}|j|d|j|S(s Cast to datetimeindex of timestamps, at *beginning* of period. Parameters ---------- freq : string, default frequency of PeriodIndex Desired frequency how : {'s', 'e', 'start', 'end'} Convention for converting period to timestamp; start of period vs. end Returns ------- ts : Series with DatetimeIndex RRR(RRrRt to_timestampRR(R_RRRrR R ((s1lib/python2.7/site-packages/pandas/core/series.pyR,s   cC sO|j}|r|j}n|jjd|}|j|d|j|S(s Convert Series from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed). Parameters ---------- freq : string, default Returns ------- ts : Series with PeriodIndex RR(RRrRt to_periodRR(R_RRrR R ((s1lib/python2.7/site-packages/pandas/core/series.pyR-s  tplotN((Rct __module__t__doc__t _metadatat _accessorsR,Rt _deprecationsRtpropertyR+t IndexOpsMixinthasnansRR{RRRt classmethodRRRRRRRRRbtsetterRuRRRRRRRRhRRRRRRRRRRRRdtfloatt __float__tintt__long__t__int__RRGRRRRRRRRR RRRRRjR"RkR$R+R6RzR1RtitemsRRRHR*RKRMR]R_RLRcRMRgRjRRRkRlRnRuRRRRRRRR R t _shared_docsRRRRRRRRRRRRRRRRt_agg_see_also_doct_agg_examples_doct_shared_doc_kwargsRtaggRRRRRR~RRR&RRRRRR R RRlR$R&R)R(R*R^RmR,R-R/RCR^R;ReR3RftgfxtSeriesPlotMethodsR.R4Rgt hist_seriesthist(((s1lib/python2.7/site-packages/pandas/core/series.pyRFfst#    v/    *    *  H =       ! 0 1  = #  :         8   "   !  :I JEG     @+  ? - N    Q+ i ( 7  &`_ - P      &    E  Z    6 ; J B9    \ t info_axisit stat_axistaliasestrowstdocss&The index (axis labels) of the Series.(R0t __future__RttextwrapRRUtnumpyRt pandas._libsRRRRRt pandas.compatRRRRR R tpandas.compat.numpyR Rtpandas.util._decoratorsR R Rtpandas.util._validatorsRtpandas.core.dtypes.commonRRRRRRRRRRRRRRRRtpandas.core.dtypes.genericR R!R"R#R$R%tpandas.core.dtypes.missingR&R'R(R)t pandas.coreR*R+R,R-R.tpandas.core.accessorR/tpandas.core.arraysR0R1tpandas.core.arrays.categoricalR2R3tpandas.core.arrays.sparseR4tpandas.core.commontcoreRRtpandas.core.configR5tpandas.core.indexR6R7R8R9R:tpandas.core.indexes.accessorsR;tpandas.core.indexes.basetindexesRtpandas.core.indexes.datetimesR<tpandas.core.indexes.periodR=tpandas.core.indexes.timedeltasR>tpandas.core.indexingR?R@tpandas.core.internalsRAt"pandas.core.internals.constructionRBtpandas.core.stringsRCtpandas.core.tools.datetimesRDtpandas.io.formats.formattiotformatsR]R<tpandas.io.formats.terminalREtpandas.plotting._coretplottingt_coreRDt__all__RRBRYRdR5RRFt _setup_axest_add_numeric_operationst_add_series_only_operationst#_add_series_or_dataframe_operationstadd_flex_arithmetic_methodstadd_special_arithmetic_methods(((s1lib/python2.7/site-packages/pandas/core/series.pyts  "(j."((    (