text: r"""Locations of example data that are included in the package for convenience.
  
  Attributes
  ----------
  NEVO_PRODUCTS_LOCATION : `str`
      Location of a CSV file containing the fake cereal product data from :ref:`references:Nevo (2000)`. The file includes
      the same pre-computed excluded instruments used in the original paper.
  NEVO_AGENTS_LOCATION : `str`
      Location of a CSV file containing the fake cereal agent data. Included in the file are Monte Carlo weights and
      draws along with demographics, which are used by :ref:`references:Nevo (2000)` to solve the fake cereal problem.
  BLP_PRODUCTS_LOCATION : `str`
      Location of a CSV file containing the automobile product data extracted by
      :ref:`references:Andrews, Gentzkow, and Shapiro (2017)` from the original GAUSS code for
      :ref:`references:Berry, Levinsohn, and Pakes (1999)`, which is commonly assumed to be the same data used in
      :ref:`references:Berry, Levinsohn, and Pakes (1995)`.
  
      The file also includes a set of excluded instruments. First, "sums of characteristics" BLP instruments from the
      original paper were computed with :func:`~pyblp.build_blp_instruments`. As in the original paper, the ``mpd``
      variable was added to the set of excluded instruments for supply. Due to a collinearity problem with these original
      instruments, each set of excluded instruments was then interacted up to the second degree, standardized, replaced
      with the minimum set of principal components that explained at least 99% of the variance, and standardized again.
  
  BLP_AGENTS_LOCATION : `str`
      Location of a CSV file containing automobile agent data. Included in the file are 200 Monte Carlo weights and draws
      for each market, which, unlike in the fake cereal data, are not the same draws used in the original paper.
  
      Also included is an income demographic, which consists of draws from lognormal distributions. The log of income has
      a common standard deviation, ``1.72``, and the following market-varying means:
  
      ====  =======
      Year  Mean
      ====  =======
      1971  2.01156
      1972  2.06526
      1973  2.07843
      1974  2.05775
      1975  2.02915
      1976  2.05346
      1977  2.06745
      1978  2.09805
      1979  2.10404
      1980  2.07208
      1981  2.06019
      1982  2.06561
      1983  2.07672
      1984  2.10437
      1985  2.12608
      1986  2.16426
      1987  2.18071
      1988  2.18856
      1989  2.21250
      1990  2.18377
      ====  =======
  
      These numbers were extracted also extracted by :ref:`references:Andrews, Gentzkow, and Shapiro (2017)` from the
      original GAUSS code for :ref:`references:Berry, Levinsohn, and Pakes (1999)`.
  
  Examples
  --------
  .. toctree::
  
     /_notebooks/api/data.ipynb
  
  """
  
  from pathlib import Path
  
  
  _DATA_PATH = Path(__file__).resolve().parent
  NEVO_PRODUCTS_LOCATION = str(_DATA_PATH / 'nevo_products.csv')
  NEVO_AGENTS_LOCATION = str(_DATA_PATH / 'nevo_agents.csv')
  BLP_PRODUCTS_LOCATION = str(_DATA_PATH / 'blp_products.csv')
  BLP_AGENTS_LOCATION = str(_DATA_PATH / 'blp_agents.csv')
  