code: def construct_1d_arraylike_from_scalar(value, length, dtype):
      
      if is_datetime64tz_dtype(dtype):
          from pandas import DatetimeIndex
          subarr = DatetimeIndex([value] * length, dtype=dtype)
      elif is_categorical_dtype(dtype):
          from pandas import Categorical
          subarr = Categorical([value] * length, dtype=dtype)
      else:
          if not isinstance(dtype, (np.dtype, type(np.dtype))):
              dtype = dtype.dtype
  
          if length and is_integer_dtype(dtype) and isna(value):
              # coerce if we have nan for an integer dtype
              dtype = np.dtype('float64')
          elif isinstance(dtype, np.dtype) and dtype.kind in ("U", "S"):
              # we need to coerce to object dtype to avoid
              # to allow numpy to take our string as a scalar value
              dtype = object
              if not isna(value):
                  value = to_str(value)
  
          subarr = np.empty(length, dtype=dtype)
          subarr.fill(value)
  
      return subarr
docs: create a np.ndarray / pandas type of specified shape and dtype
      filled with values
  
      Parameters
      ----------
      value : scalar value
      length : int
      dtype : pandas_dtype / np.dtype
  
      Returns
      -------
      np.ndarray / pandas type of length, filled with value