Pandas dataframe with hierarchical index of varying depths
Pandas has a nice and featureful mechanism for dataframes with hierarchical indexes: https://pandas.pydata.org/pandas-docs/stable/advanced.html, but it's mainly aimed at the case where the depth of the hierarchy is constant. Is there a standard, perhaps less featureful way to have a dataframe with a hierarchical index with varying depth?
I don't really need the partial indexing and grouping and stacking/unstacking features of a MultiIndex, a dataframe where the columns are indexed by raw tuples would suit my needs.
I'd like to do something like the following:
import pandas as pd
df = pd.DataFrame(
("a",): [1],
("b",): [2],
("x", 1): [3],
("x", 2): [4]
)
However, pandas's default MultiIndex is a bit overeager for my purposes in this situation. For example, it makes the "true" index of the first column be ("a", nan). Then df[("a",)] returns all columns whose index starts with ("a",) including, e.g. a column like ("a", 1) if it is present.
One option would be to do something like
import pandas as pd
df = pd.DataFrame(
"a": [1],
"b": [2],
("x", 1): [3],
("x", 2): [4]
)
which creates a dataframe indexed by a mix of strings and tuples, but this appears to be unsupported.
python pandas dataframe
asked Nov 15 '18 at 6:15
Anton MalyshevAnton Malyshev
1061
add a comment |
Pandas has a nice and featureful mechanism for dataframes with hierarchical indexes: https://pandas.pydata.org/pandas-docs/stable/advanced.html, but it's mainly aimed at the case where the depth of the hierarchy is constant. Is there a standard, perhaps less featureful way to have a dataframe with a hierarchical index with varying depth?
I don't really need the partial indexing and grouping and stacking/unstacking features of a MultiIndex, a dataframe where the columns are indexed by raw tuples would suit my needs.
I'd like to do something like the following:
import pandas as pd
df = pd.DataFrame(
("a",): [1],
("b",): [2],
("x", 1): [3],
("x", 2): [4]
)
However, pandas's default MultiIndex is a bit overeager for my purposes in this situation. For example, it makes the "true" index of the first column be ("a", nan). Then df[("a",)] returns all columns whose index starts with ("a",) including, e.g. a column like ("a", 1) if it is present.
One option would be to do something like
import pandas as pd
df = pd.DataFrame(
"a": [1],
"b": [2],
("x", 1): [3],
("x", 2): [4]
)
which creates a dataframe indexed by a mix of strings and tuples, but this appears to be unsupported.
python pandas dataframe
asked Nov 15 '18 at 6:15
Anton MalyshevAnton Malyshev
1061
which creates a dataframe indexed by a mix of strings and tuples, but this appears to be unsupported- what is not supported? Selecting? Creating df ? I think main problem is it is possible create, but pandas function are confused - obviously are buggy with this non statndard hybrid index.
– jezrael
Nov 15 '18 at 6:20
Creating the dataframe works. Selecting is sometimes broken: github.com/pandas-dev/pandas/issues/23610.
– Anton Malyshev
Nov 15 '18 at 17:49
add a comment |
Pandas has a nice and featureful mechanism for dataframes with hierarchical indexes: https://pandas.pydata.org/pandas-docs/stable/advanced.html, but it's mainly aimed at the case where the depth of the hierarchy is constant. Is there a standard, perhaps less featureful way to have a dataframe with a hierarchical index with varying depth?
I don't really need the partial indexing and grouping and stacking/unstacking features of a MultiIndex, a dataframe where the columns are indexed by raw tuples would suit my needs.
I'd like to do something like the following:
import pandas as pd
df = pd.DataFrame(
("a",): [1],
("b",): [2],
("x", 1): [3],
("x", 2): [4]
)
However, pandas's default MultiIndex is a bit overeager for my purposes in this situation. For example, it makes the "true" index of the first column be ("a", nan). Then df[("a",)] returns all columns whose index starts with ("a",) including, e.g. a column like ("a", 1) if it is present.
One option would be to do something like
import pandas as pd
df = pd.DataFrame(
"a": [1],
"b": [2],
("x", 1): [3],
("x", 2): [4]
)
which creates a dataframe indexed by a mix of strings and tuples, but this appears to be unsupported.
python pandas dataframe
asked Nov 15 '18 at 6:15
Anton MalyshevAnton Malyshev
1061
Pandas has a nice and featureful mechanism for dataframes with hierarchical indexes: https://pandas.pydata.org/pandas-docs/stable/advanced.html, but it's mainly aimed at the case where the depth of the hierarchy is constant. Is there a standard, perhaps less featureful way to have a dataframe with a hierarchical index with varying depth?
I don't really need the partial indexing and grouping and stacking/unstacking features of a MultiIndex, a dataframe where the columns are indexed by raw tuples would suit my needs.
I'd like to do something like the following:
import pandas as pd
df = pd.DataFrame(
("a",): [1],
("b",): [2],
("x", 1): [3],
("x", 2): [4]
)
However, pandas's default MultiIndex is a bit overeager for my purposes in this situation. For example, it makes the "true" index of the first column be ("a", nan). Then df[("a",)] returns all columns whose index starts with ("a",) including, e.g. a column like ("a", 1) if it is present.
One option would be to do something like
import pandas as pd
df = pd.DataFrame(
"a": [1],
"b": [2],
("x", 1): [3],
("x", 2): [4]
)
which creates a dataframe indexed by a mix of strings and tuples, but this appears to be unsupported.
python pandas dataframe
python pandas dataframe
asked Nov 15 '18 at 6:15
Anton MalyshevAnton Malyshev
1061
asked Nov 15 '18 at 6:15
Anton MalyshevAnton Malyshev
1061
asked Nov 15 '18 at 6:15
Anton MalyshevAnton Malyshev
1061
asked Nov 15 '18 at 6:15
Anton MalyshevAnton Malyshev
1061
asked Nov 15 '18 at 6:15
Anton MalyshevAnton Malyshev
1061
1061
which creates a dataframe indexed by a mix of strings and tuples, but this appears to be unsupported- what is not supported? Selecting? Creating df ? I think main problem is it is possible create, but pandas function are confused - obviously are buggy with this non statndard hybrid index.
– jezrael
Nov 15 '18 at 6:20
Creating the dataframe works. Selecting is sometimes broken: github.com/pandas-dev/pandas/issues/23610.
– Anton Malyshev
Nov 15 '18 at 17:49
add a comment |
which creates a dataframe indexed by a mix of strings and tuples, but this appears to be unsupported- what is not supported? Selecting? Creating df ? I think main problem is it is possible create, but pandas function are confused - obviously are buggy with this non statndard hybrid index.
– jezrael
Nov 15 '18 at 6:20
Creating the dataframe works. Selecting is sometimes broken: github.com/pandas-dev/pandas/issues/23610.
– Anton Malyshev
Nov 15 '18 at 17:49
which creates a dataframe indexed by a mix of strings and tuples, but this appears to be unsupported - what is not supported? Selecting? Creating df ? I think main problem is it is possible create, but pandas function are confused - obviously are buggy with this non statndard hybrid index.– jezrael
Nov 15 '18 at 6:20
which creates a dataframe indexed by a mix of strings and tuples, but this appears to be unsupported - what is not supported? Selecting? Creating df ? I think main problem is it is possible create, but pandas function are confused - obviously are buggy with this non statndard hybrid index.– jezrael
Nov 15 '18 at 6:20
Creating the dataframe works. Selecting is sometimes broken: github.com/pandas-dev/pandas/issues/23610.
– Anton Malyshev
Nov 15 '18 at 17:49
Creating the dataframe works. Selecting is sometimes broken: github.com/pandas-dev/pandas/issues/23610.
– Anton Malyshev
Nov 15 '18 at 17:49
add a comment |
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which creates a dataframe indexed by a mix of strings and tuples, but this appears to be unsupported- what is not supported? Selecting? Creating df ? I think main problem is it is possible create, but pandas function are confused - obviously are buggy with this non statndard hybrid index.– jezrael
Nov 15 '18 at 6:20
Creating the dataframe works. Selecting is sometimes broken: github.com/pandas-dev/pandas/issues/23610.
– Anton Malyshev
Nov 15 '18 at 17:49