def isin(comps, values): """ Compute the isin boolean array Parameters ---------- comps: array-like values: array-like Returns ------- boolean array same length as comps """ if not is_list_like(comps): raise TypeError("only list-like objects are allowed to be passed" " to isin(), you passed a " "[{0}]".format(type(comps).__name__)) comps = np.asarray(comps) if not is_list_like(values): raise TypeError("only list-like objects are allowed to be passed" " to isin(), you passed a " "[{0}]".format(type(values).__name__)) if not isinstance(values, np.ndarray): values = list(values) # GH11232 # work-around for numpy < 1.8 and comparisions on py3 # faster for larger cases to use np.in1d if (_np_version_under1p8 and compat.PY3) or len(comps) > 1000000: f = lambda x, y: np.in1d(x, np.asarray(list(y))) else: f = lambda x, y: lib.ismember_int64(x, set(y)) # may need i8 conversion for proper membership testing if is_datetime64_dtype(comps): from pandas.tseries.tools import to_datetime values = to_datetime(values)._values.view('i8') comps = comps.view('i8') elif is_timedelta64_dtype(comps): from pandas.tseries.timedeltas import to_timedelta values = to_timedelta(values)._values.view('i8') comps = comps.view('i8') elif is_int64_dtype(comps): pass else: f = lambda x, y: lib.ismember(x, set(values)) return f(comps, values)
def _validate_dtype(dtype): """ require dtype to be None or int64 """ if not (dtype is None or is_int64_dtype(dtype)): raise TypeError('Invalid to pass a non-int64 dtype to RangeIndex')
def isin(comps, values): """ Compute the isin boolean array Parameters ---------- comps: array-like values: array-like Returns ------- boolean array same length as comps """ if not is_list_like(comps): raise TypeError("only list-like objects are allowed to be passed" " to isin(), you passed a " "[{0}]".format(type(comps).__name__)) if not is_list_like(values): raise TypeError("only list-like objects are allowed to be passed" " to isin(), you passed a " "[{0}]".format(type(values).__name__)) from pandas import DatetimeIndex, PeriodIndex if not isinstance(values, (ABCIndex, ABCSeries, np.ndarray)): values = np.array(list(values), dtype='object') if needs_i8_conversion(comps): if is_period_dtype(values): comps = PeriodIndex(comps) values = PeriodIndex(values) else: comps = DatetimeIndex(comps) values = DatetimeIndex(values) values = values.asi8 comps = comps.asi8 elif is_bool_dtype(comps): try: comps = np.asarray(comps).view('uint8') values = np.asarray(values).view('uint8') except TypeError: # object array conversion will fail pass else: comps = np.asarray(comps) values = np.asarray(values) # GH11232 # work-around for numpy < 1.8 and comparisions on py3 # faster for larger cases to use np.in1d if (_np_version_under1p8 and compat.PY3) or len(comps) > 1000000: f = lambda x, y: np.in1d(x, np.asarray(list(y))) elif is_int64_dtype(comps): f = lambda x, y: lib.ismember_int64(x, set(y)) else: f = lambda x, y: lib.ismember(x, set(values)) return f(comps, values)