How to convert a pandas MultiIndex DataFrame into a 3D array
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Suppose I have a MultiIndex DataFrame:
c o l u
major timestamp
ONE 2019-01-22 18:12:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:13:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:14:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:15:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:16:00 0.00008 0.00008 0.00008 0.00008
TWO 2019-01-22 18:12:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:13:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:14:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:15:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:16:00 0.00008 0.00008 0.00008 0.00008
I want to generate a NumPy array from this DataFrame with a 3-dimensional, given the dataframe has 15 categories in the major column, 4 columns and one time index of length 5. I would like to create a numpy array with a shape of (4,15,5) denoting (columns, categories, time_index) respectively.
should create an array:
array([[[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05],
[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05]],
[[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05],
[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05]],
[[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05],
[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05]],
[[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05],
[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05]]])
One used to be able to do this with pd.Panel:
panel = pd.Panel(items=[columns], major_axis=[categories], minor_axis=[time_index], dtype=np.float32)
...
How would I be able to most effectively accomplish this with a multi index dataframe?
Thanks
python arrays pandas numpy
asked Feb 10 at 11:25
BradBrad
331311
add a comment |
Suppose I have a MultiIndex DataFrame:
c o l u
major timestamp
ONE 2019-01-22 18:12:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:13:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:14:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:15:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:16:00 0.00008 0.00008 0.00008 0.00008
TWO 2019-01-22 18:12:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:13:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:14:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:15:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:16:00 0.00008 0.00008 0.00008 0.00008
I want to generate a NumPy array from this DataFrame with a 3-dimensional, given the dataframe has 15 categories in the major column, 4 columns and one time index of length 5. I would like to create a numpy array with a shape of (4,15,5) denoting (columns, categories, time_index) respectively.
should create an array:
array([[[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05],
[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05]],
[[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05],
[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05]],
[[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05],
[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05]],
[[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05],
[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05]]])
One used to be able to do this with pd.Panel:
panel = pd.Panel(items=[columns], major_axis=[categories], minor_axis=[time_index], dtype=np.float32)
...
How would I be able to most effectively accomplish this with a multi index dataframe?
Thanks
python arrays pandas numpy
asked Feb 10 at 11:25
BradBrad
331311
add a comment |
Suppose I have a MultiIndex DataFrame:
c o l u
major timestamp
ONE 2019-01-22 18:12:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:13:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:14:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:15:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:16:00 0.00008 0.00008 0.00008 0.00008
TWO 2019-01-22 18:12:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:13:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:14:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:15:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:16:00 0.00008 0.00008 0.00008 0.00008
I want to generate a NumPy array from this DataFrame with a 3-dimensional, given the dataframe has 15 categories in the major column, 4 columns and one time index of length 5. I would like to create a numpy array with a shape of (4,15,5) denoting (columns, categories, time_index) respectively.
should create an array:
array([[[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05],
[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05]],
[[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05],
[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05]],
[[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05],
[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05]],
[[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05],
[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05]]])
One used to be able to do this with pd.Panel:
panel = pd.Panel(items=[columns], major_axis=[categories], minor_axis=[time_index], dtype=np.float32)
...
How would I be able to most effectively accomplish this with a multi index dataframe?
Thanks
python arrays pandas numpy
asked Feb 10 at 11:25
BradBrad
331311
Suppose I have a MultiIndex DataFrame:
c o l u
major timestamp
ONE 2019-01-22 18:12:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:13:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:14:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:15:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:16:00 0.00008 0.00008 0.00008 0.00008
TWO 2019-01-22 18:12:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:13:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:14:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:15:00 0.00008 0.00008 0.00008 0.00008
2019-01-22 18:16:00 0.00008 0.00008 0.00008 0.00008
I want to generate a NumPy array from this DataFrame with a 3-dimensional, given the dataframe has 15 categories in the major column, 4 columns and one time index of length 5. I would like to create a numpy array with a shape of (4,15,5) denoting (columns, categories, time_index) respectively.
should create an array:
array([[[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05],
[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05]],
[[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05],
[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05]],
[[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05],
[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05]],
[[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05],
[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05]]])
One used to be able to do this with pd.Panel:
panel = pd.Panel(items=[columns], major_axis=[categories], minor_axis=[time_index], dtype=np.float32)
...
How would I be able to most effectively accomplish this with a multi index dataframe?
Thanks
python arrays pandas numpy
python arrays pandas numpy
asked Feb 10 at 11:25
BradBrad
331311
asked Feb 10 at 11:25
BradBrad
331311
edited Feb 10 at 11:34
Brad
asked Feb 10 at 11:25
BradBrad
331311
asked Feb 10 at 11:25
BradBrad
331311
asked Feb 10 at 11:25
BradBrad
331311
331311
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
How about using xarray?
res = df.to_xarray().to_array()
Result is an array of shape (4, 15, 5)
In fact the docs now recommend this as an alternative to pandas Panel. Note that you must have the xarray package installed.
answered Feb 10 at 11:40
Josh FriedlanderJosh Friedlander
3,1911933
add a comment |
Since df.values is a (15*100, 4)-shaped array, you can call reshape to make it a (15, 100, 4)-shaped array:
arr = df.values.reshape(15, 100, 4)
Then call transpose to rearrange the order of the axes:
arr = arr.transpose(2, 0, 1)
Now arr has shape (4, 15, 100).
Using reshape/transpose is ~960x faster than to_xarray().to_array():
In [21]: df = pd.DataFrame(np.random.randint(10, size=(15*100, 4)), index=pd.MultiIndex.from_product([range(15), range(100)], names=['A','B']), columns=list('colu'))
In [22]: %timeit arr = df.values.reshape(15, 100, 4).transpose(2, 0, 1)
3.31 µs ± 23.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
In [24]: %timeit df.to_xarray().to_array()
3.18 ms ± 24.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [25]: 3180/3.31
Out[25]: 960.7250755287009
answered Feb 10 at 11:32
unutbuunutbu
562k10612141268
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
How about using xarray?
res = df.to_xarray().to_array()
Result is an array of shape (4, 15, 5)
In fact the docs now recommend this as an alternative to pandas Panel. Note that you must have the xarray package installed.
answered Feb 10 at 11:40
Josh FriedlanderJosh Friedlander
3,1911933
add a comment |
How about using xarray?
res = df.to_xarray().to_array()
Result is an array of shape (4, 15, 5)
In fact the docs now recommend this as an alternative to pandas Panel. Note that you must have the xarray package installed.
answered Feb 10 at 11:40
Josh FriedlanderJosh Friedlander
3,1911933
add a comment |
How about using xarray?
res = df.to_xarray().to_array()
Result is an array of shape (4, 15, 5)
In fact the docs now recommend this as an alternative to pandas Panel. Note that you must have the xarray package installed.
answered Feb 10 at 11:40
Josh FriedlanderJosh Friedlander
3,1911933
How about using xarray?
res = df.to_xarray().to_array()
Result is an array of shape (4, 15, 5)
In fact the docs now recommend this as an alternative to pandas Panel. Note that you must have the xarray package installed.
answered Feb 10 at 11:40
Josh FriedlanderJosh Friedlander
3,1911933
answered Feb 10 at 11:40
Josh FriedlanderJosh Friedlander
3,1911933
answered Feb 10 at 11:40
Josh FriedlanderJosh Friedlander
3,1911933
answered Feb 10 at 11:40
Josh FriedlanderJosh Friedlander
3,1911933
3,1911933
add a comment |
add a comment |
Since df.values is a (15*100, 4)-shaped array, you can call reshape to make it a (15, 100, 4)-shaped array:
arr = df.values.reshape(15, 100, 4)
Then call transpose to rearrange the order of the axes:
arr = arr.transpose(2, 0, 1)
Now arr has shape (4, 15, 100).
Using reshape/transpose is ~960x faster than to_xarray().to_array():
In [21]: df = pd.DataFrame(np.random.randint(10, size=(15*100, 4)), index=pd.MultiIndex.from_product([range(15), range(100)], names=['A','B']), columns=list('colu'))
In [22]: %timeit arr = df.values.reshape(15, 100, 4).transpose(2, 0, 1)
3.31 µs ± 23.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
In [24]: %timeit df.to_xarray().to_array()
3.18 ms ± 24.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [25]: 3180/3.31
Out[25]: 960.7250755287009
answered Feb 10 at 11:32
unutbuunutbu
562k10612141268
add a comment |
Since df.values is a (15*100, 4)-shaped array, you can call reshape to make it a (15, 100, 4)-shaped array:
arr = df.values.reshape(15, 100, 4)
Then call transpose to rearrange the order of the axes:
arr = arr.transpose(2, 0, 1)
Now arr has shape (4, 15, 100).
Using reshape/transpose is ~960x faster than to_xarray().to_array():
In [21]: df = pd.DataFrame(np.random.randint(10, size=(15*100, 4)), index=pd.MultiIndex.from_product([range(15), range(100)], names=['A','B']), columns=list('colu'))
In [22]: %timeit arr = df.values.reshape(15, 100, 4).transpose(2, 0, 1)
3.31 µs ± 23.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
In [24]: %timeit df.to_xarray().to_array()
3.18 ms ± 24.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [25]: 3180/3.31
Out[25]: 960.7250755287009
answered Feb 10 at 11:32
unutbuunutbu
562k10612141268
add a comment |
Since df.values is a (15*100, 4)-shaped array, you can call reshape to make it a (15, 100, 4)-shaped array:
arr = df.values.reshape(15, 100, 4)
Then call transpose to rearrange the order of the axes:
arr = arr.transpose(2, 0, 1)
Now arr has shape (4, 15, 100).
Using reshape/transpose is ~960x faster than to_xarray().to_array():
In [21]: df = pd.DataFrame(np.random.randint(10, size=(15*100, 4)), index=pd.MultiIndex.from_product([range(15), range(100)], names=['A','B']), columns=list('colu'))
In [22]: %timeit arr = df.values.reshape(15, 100, 4).transpose(2, 0, 1)
3.31 µs ± 23.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
In [24]: %timeit df.to_xarray().to_array()
3.18 ms ± 24.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [25]: 3180/3.31
Out[25]: 960.7250755287009
answered Feb 10 at 11:32
unutbuunutbu
562k10612141268
Since df.values is a (15*100, 4)-shaped array, you can call reshape to make it a (15, 100, 4)-shaped array:
arr = df.values.reshape(15, 100, 4)
Then call transpose to rearrange the order of the axes:
arr = arr.transpose(2, 0, 1)
Now arr has shape (4, 15, 100).
Using reshape/transpose is ~960x faster than to_xarray().to_array():
In [21]: df = pd.DataFrame(np.random.randint(10, size=(15*100, 4)), index=pd.MultiIndex.from_product([range(15), range(100)], names=['A','B']), columns=list('colu'))
In [22]: %timeit arr = df.values.reshape(15, 100, 4).transpose(2, 0, 1)
3.31 µs ± 23.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
In [24]: %timeit df.to_xarray().to_array()
3.18 ms ± 24.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [25]: 3180/3.31
Out[25]: 960.7250755287009
answered Feb 10 at 11:32
unutbuunutbu
562k10612141268
edited Feb 10 at 11:52
answered Feb 10 at 11:32
unutbuunutbu
562k10612141268
answered Feb 10 at 11:32
unutbuunutbu
562k10612141268
answered Feb 10 at 11:32
unutbuunutbu
562k10612141268
562k10612141268
add a comment |
add a comment |
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