Xarray : Operations with cubes with different granularities / levels same hierarchy / Multiindex











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I am having trouble figuring out how to work with xarray DataArrays and DataSets and perform algebra operations; especially when the dimensions have different levels and my cubes have different granularities. I would be very grateful if someone could suggest me some documentation or give me some advice.



In the example below I am trying to compute the contribution of each child (SKU) under a parent (PFS). I found that to get the right values I need to convert the cube slice into a pandas dataframe. Otherwise, Xarray duplicates the dimension I am working with.



import pandas as pd
import numpy as np
import xarray as xr
from itertools import product

# Create hierachies

usage_type_entities = (('Regular',), ('Sample',),
('Tender',), ('Clinic Trial',))

usage_type_tree = pd.MultiIndex.from_tuples(
usage_type_entities, names=('Usage_Type',))

product_tree_hierarchy = (("PF1", "PFS1", "SKU1"),
("PF1", "PFS1", "SKU2"),
("PF1", "PFS2", "SKU3"),
("PF1", "PFS2", "SKU4"),
("PF2", "PFS3", "SKU5"))

product_tree_entities = ("PF", "PFS", "SKU")

product_tree = pd.MultiIndex.from_tuples(product_tree_hierarchy,
names=product_tree_entities)

market_tree_hierarchy = (("Group1", "Region1", "Market1"),
("Group1", "Region1", "Market2"),
("Group1", "Region2", "Market3"),
("Group1", "Region2", "Market4"),
("Group2", "Region3", "Market5"))

market_tree_entities = ("Groups", "Regions", "Markets")

market_tree = pd.MultiIndex.from_tuples(market_tree_hierarchy,
names=market_tree_entities)

time_tree_hierarchy = [(y, y+q) for y, q in product([str(2013+x) for x in range(6)],
["Q"+str(int(q)) for q in np.arange(1, 4.1, 1)])][0:22]

time_entities = ("Year", "Quarter")

time_tree = pd.MultiIndex.from_tuples(time_tree_hierarchy, names=time_entities)

# Create X-array Dataset

x1 = np.random.randint(100, size=(len(usage_type_tree), len(
product_tree), len(market_tree), len(time_tree)))

xda = xr.DataArray(x1, coords=(usage_type_tree, product_tree, market_tree, time_tree),
dims=("Usage", "Product", "Market", "Time"))

# Operations - I need to convert my slice into a pandas df to get
the right values. Converting to pandas df works ok.

market = "Market1"
ut = "Regular"

(xda.sel(Markets=market, Usage_Type=ut)[:, 0].to_pandas() /
xda.sel(Markets=market, Usage_Type=ut)[:, 0].to_pandas().groupby("PFS").sum(axis=0))


If I don't convert the slice to pandas df and I keep it as a xarray dataset, the dimension gets duplicated. For example, the line below produces a DatArray(Product: 5, Time: 22, PFS: 3), when it should be just (Product: 5, Time: 22)



(xda.sel(Markets=market, Usage_Type=ut)[:, 0] /
xda.sel(Markets=market, Usage_Type=ut)[:, 0].groupby("PFS").sum(axis=0))









share|improve this question


























    up vote
    1
    down vote

    favorite












    I am having trouble figuring out how to work with xarray DataArrays and DataSets and perform algebra operations; especially when the dimensions have different levels and my cubes have different granularities. I would be very grateful if someone could suggest me some documentation or give me some advice.



    In the example below I am trying to compute the contribution of each child (SKU) under a parent (PFS). I found that to get the right values I need to convert the cube slice into a pandas dataframe. Otherwise, Xarray duplicates the dimension I am working with.



    import pandas as pd
    import numpy as np
    import xarray as xr
    from itertools import product

    # Create hierachies

    usage_type_entities = (('Regular',), ('Sample',),
    ('Tender',), ('Clinic Trial',))

    usage_type_tree = pd.MultiIndex.from_tuples(
    usage_type_entities, names=('Usage_Type',))

    product_tree_hierarchy = (("PF1", "PFS1", "SKU1"),
    ("PF1", "PFS1", "SKU2"),
    ("PF1", "PFS2", "SKU3"),
    ("PF1", "PFS2", "SKU4"),
    ("PF2", "PFS3", "SKU5"))

    product_tree_entities = ("PF", "PFS", "SKU")

    product_tree = pd.MultiIndex.from_tuples(product_tree_hierarchy,
    names=product_tree_entities)

    market_tree_hierarchy = (("Group1", "Region1", "Market1"),
    ("Group1", "Region1", "Market2"),
    ("Group1", "Region2", "Market3"),
    ("Group1", "Region2", "Market4"),
    ("Group2", "Region3", "Market5"))

    market_tree_entities = ("Groups", "Regions", "Markets")

    market_tree = pd.MultiIndex.from_tuples(market_tree_hierarchy,
    names=market_tree_entities)

    time_tree_hierarchy = [(y, y+q) for y, q in product([str(2013+x) for x in range(6)],
    ["Q"+str(int(q)) for q in np.arange(1, 4.1, 1)])][0:22]

    time_entities = ("Year", "Quarter")

    time_tree = pd.MultiIndex.from_tuples(time_tree_hierarchy, names=time_entities)

    # Create X-array Dataset

    x1 = np.random.randint(100, size=(len(usage_type_tree), len(
    product_tree), len(market_tree), len(time_tree)))

    xda = xr.DataArray(x1, coords=(usage_type_tree, product_tree, market_tree, time_tree),
    dims=("Usage", "Product", "Market", "Time"))

    # Operations - I need to convert my slice into a pandas df to get
    the right values. Converting to pandas df works ok.

    market = "Market1"
    ut = "Regular"

    (xda.sel(Markets=market, Usage_Type=ut)[:, 0].to_pandas() /
    xda.sel(Markets=market, Usage_Type=ut)[:, 0].to_pandas().groupby("PFS").sum(axis=0))


    If I don't convert the slice to pandas df and I keep it as a xarray dataset, the dimension gets duplicated. For example, the line below produces a DatArray(Product: 5, Time: 22, PFS: 3), when it should be just (Product: 5, Time: 22)



    (xda.sel(Markets=market, Usage_Type=ut)[:, 0] /
    xda.sel(Markets=market, Usage_Type=ut)[:, 0].groupby("PFS").sum(axis=0))









    share|improve this question
























      up vote
      1
      down vote

      favorite









      up vote
      1
      down vote

      favorite











      I am having trouble figuring out how to work with xarray DataArrays and DataSets and perform algebra operations; especially when the dimensions have different levels and my cubes have different granularities. I would be very grateful if someone could suggest me some documentation or give me some advice.



      In the example below I am trying to compute the contribution of each child (SKU) under a parent (PFS). I found that to get the right values I need to convert the cube slice into a pandas dataframe. Otherwise, Xarray duplicates the dimension I am working with.



      import pandas as pd
      import numpy as np
      import xarray as xr
      from itertools import product

      # Create hierachies

      usage_type_entities = (('Regular',), ('Sample',),
      ('Tender',), ('Clinic Trial',))

      usage_type_tree = pd.MultiIndex.from_tuples(
      usage_type_entities, names=('Usage_Type',))

      product_tree_hierarchy = (("PF1", "PFS1", "SKU1"),
      ("PF1", "PFS1", "SKU2"),
      ("PF1", "PFS2", "SKU3"),
      ("PF1", "PFS2", "SKU4"),
      ("PF2", "PFS3", "SKU5"))

      product_tree_entities = ("PF", "PFS", "SKU")

      product_tree = pd.MultiIndex.from_tuples(product_tree_hierarchy,
      names=product_tree_entities)

      market_tree_hierarchy = (("Group1", "Region1", "Market1"),
      ("Group1", "Region1", "Market2"),
      ("Group1", "Region2", "Market3"),
      ("Group1", "Region2", "Market4"),
      ("Group2", "Region3", "Market5"))

      market_tree_entities = ("Groups", "Regions", "Markets")

      market_tree = pd.MultiIndex.from_tuples(market_tree_hierarchy,
      names=market_tree_entities)

      time_tree_hierarchy = [(y, y+q) for y, q in product([str(2013+x) for x in range(6)],
      ["Q"+str(int(q)) for q in np.arange(1, 4.1, 1)])][0:22]

      time_entities = ("Year", "Quarter")

      time_tree = pd.MultiIndex.from_tuples(time_tree_hierarchy, names=time_entities)

      # Create X-array Dataset

      x1 = np.random.randint(100, size=(len(usage_type_tree), len(
      product_tree), len(market_tree), len(time_tree)))

      xda = xr.DataArray(x1, coords=(usage_type_tree, product_tree, market_tree, time_tree),
      dims=("Usage", "Product", "Market", "Time"))

      # Operations - I need to convert my slice into a pandas df to get
      the right values. Converting to pandas df works ok.

      market = "Market1"
      ut = "Regular"

      (xda.sel(Markets=market, Usage_Type=ut)[:, 0].to_pandas() /
      xda.sel(Markets=market, Usage_Type=ut)[:, 0].to_pandas().groupby("PFS").sum(axis=0))


      If I don't convert the slice to pandas df and I keep it as a xarray dataset, the dimension gets duplicated. For example, the line below produces a DatArray(Product: 5, Time: 22, PFS: 3), when it should be just (Product: 5, Time: 22)



      (xda.sel(Markets=market, Usage_Type=ut)[:, 0] /
      xda.sel(Markets=market, Usage_Type=ut)[:, 0].groupby("PFS").sum(axis=0))









      share|improve this question













      I am having trouble figuring out how to work with xarray DataArrays and DataSets and perform algebra operations; especially when the dimensions have different levels and my cubes have different granularities. I would be very grateful if someone could suggest me some documentation or give me some advice.



      In the example below I am trying to compute the contribution of each child (SKU) under a parent (PFS). I found that to get the right values I need to convert the cube slice into a pandas dataframe. Otherwise, Xarray duplicates the dimension I am working with.



      import pandas as pd
      import numpy as np
      import xarray as xr
      from itertools import product

      # Create hierachies

      usage_type_entities = (('Regular',), ('Sample',),
      ('Tender',), ('Clinic Trial',))

      usage_type_tree = pd.MultiIndex.from_tuples(
      usage_type_entities, names=('Usage_Type',))

      product_tree_hierarchy = (("PF1", "PFS1", "SKU1"),
      ("PF1", "PFS1", "SKU2"),
      ("PF1", "PFS2", "SKU3"),
      ("PF1", "PFS2", "SKU4"),
      ("PF2", "PFS3", "SKU5"))

      product_tree_entities = ("PF", "PFS", "SKU")

      product_tree = pd.MultiIndex.from_tuples(product_tree_hierarchy,
      names=product_tree_entities)

      market_tree_hierarchy = (("Group1", "Region1", "Market1"),
      ("Group1", "Region1", "Market2"),
      ("Group1", "Region2", "Market3"),
      ("Group1", "Region2", "Market4"),
      ("Group2", "Region3", "Market5"))

      market_tree_entities = ("Groups", "Regions", "Markets")

      market_tree = pd.MultiIndex.from_tuples(market_tree_hierarchy,
      names=market_tree_entities)

      time_tree_hierarchy = [(y, y+q) for y, q in product([str(2013+x) for x in range(6)],
      ["Q"+str(int(q)) for q in np.arange(1, 4.1, 1)])][0:22]

      time_entities = ("Year", "Quarter")

      time_tree = pd.MultiIndex.from_tuples(time_tree_hierarchy, names=time_entities)

      # Create X-array Dataset

      x1 = np.random.randint(100, size=(len(usage_type_tree), len(
      product_tree), len(market_tree), len(time_tree)))

      xda = xr.DataArray(x1, coords=(usage_type_tree, product_tree, market_tree, time_tree),
      dims=("Usage", "Product", "Market", "Time"))

      # Operations - I need to convert my slice into a pandas df to get
      the right values. Converting to pandas df works ok.

      market = "Market1"
      ut = "Regular"

      (xda.sel(Markets=market, Usage_Type=ut)[:, 0].to_pandas() /
      xda.sel(Markets=market, Usage_Type=ut)[:, 0].to_pandas().groupby("PFS").sum(axis=0))


      If I don't convert the slice to pandas df and I keep it as a xarray dataset, the dimension gets duplicated. For example, the line below produces a DatArray(Product: 5, Time: 22, PFS: 3), when it should be just (Product: 5, Time: 22)



      (xda.sel(Markets=market, Usage_Type=ut)[:, 0] /
      xda.sel(Markets=market, Usage_Type=ut)[:, 0].groupby("PFS").sum(axis=0))






      python-xarray






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      share|improve this question











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      asked Nov 11 at 16:38









      Joan Ponsa-Cobas

      61




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