I have a dataframe df
that looks like (see Appendix for code to generate the dataframe):
fy 2018 2019 tag uom Assets USD 3.753190e+11 3.385160e+11 AssetsCurrent USD 1.286450e+11 1.628190e+11 AssetsNoncurrent USD 2.466740e+11 1.756970e+11 DeferredTaxAssetsDeferredCostSharing USD 6.670000e+08 NaN DeferredTaxAssetsDeferredIncome USD 1.521000e+09 1.141000e+09 DeferredTaxAssetsGoodwillAndIntangibleAssets USD NaN 1.143300e+10 DeferredTaxAssetsLiabilitiesNet USD 5.834000e+09 5.834000e+09 DeferredTaxAssetsNet USD 8.974000e+09 6.610000e+09 DeferredTaxAssetsOther USD 8.340000e+08 7.970000e+08 DeferredTaxAssetsPropertyPlantAndEquipment USD 1.230000e+09 1.370000e+08 DeferredTaxAssetsTaxDeferredExpenseCompensation... USD 7.030000e+08 5.130000e+08 DeferredTaxAssetsTaxDeferredExpenseReservesAndA... USD 4.019000e+09 3.151000e+09 DeferredTaxAssetsUnrealizedLossesOnAvailablefor... USD 0.000000e+00 8.710000e+08 DerivativeAssetsReductionforMasterNettingArrang... USD 1.400000e+09 2.100000e+09 IncreaseDecreaseInOtherOperatingAssets USD -1.055000e+09 5.318000e+09 NoncurrentAssets USD 3.378300e+10 4.130400e+10 OtherAssetsCurrent USD 1.208700e+10 1.208700e+10 OtherAssetsNoncurrent USD 2.228300e+10 2.228300e+10
Which is a MultiIndex pivot table with indices tag
and uom
. My goal is to filter rows by the tag
index using a regex and the filter function . For example:
df.filter(regex="^Assets$", axis="index")
Which ideally would filter out the row:
fy 2018 2019 tag uom Assets USD 3.753190e+11 3.385160e+11
However, when I do so it outputs an empty dataframe:
Empty DataFrame Columns: [2018, 2019] Index: []
I'm able to circumvent this problem by using:
df.index.get_level_values("tag").str.contains("^Assets$")
or as a function
search = lambda df, regex, index_name: df.loc[df.index.get_level_values(index_name).str.contains(regex)]
But this is way less satisfying to me. Am I missing something about the pandas filter function and how its regex input works? It does not behave as expected, and my guess is it's because I have 2 indices: tag
and uom
thus the regex is failing in the uom
index when I use "^Assets$"
as my regex. This is supported by using the regex "^Assets$|USD"
which returns the entire dataframe because all rows have uom=USD
, and it shows the filter function takes both indices into account. If this is the case, then how do I selectively choose index= tag
for the filter function on a MultiIndex dataframe?
Appendix:
import pandas as pd
import numpy as np
levels = ['Assets',
'AssetsCurrent',
'AssetsNoncurrent',
'DeferredTaxAssetsDeferredCostSharing',
'DeferredTaxAssetsDeferredIncome',
'DeferredTaxAssetsGoodwillAndIntangibleAssets',
'DeferredTaxAssetsLiabilitiesNet',
'DeferredTaxAssetsNet',
'DeferredTaxAssetsOther',
'DeferredTaxAssetsPropertyPlantAndEquipment',
'DeferredTaxAssetsTaxDeferredExpenseCompensationAndBenefitsShareBasedCompensationCost',
'DeferredTaxAssetsTaxDeferredExpenseReservesAndAccruals',
'DeferredTaxAssetsUnrealizedLossesOnAvailableforSaleSecuritiesGross',
'DerivativeAssetsReductionforMasterNettingArrangements',
'IncreaseDecreaseInOtherOperatingAssets',
'NoncurrentAssets',
'OtherAssetsCurrent',
'OtherAssetsNoncurrent']
codes = ['USD' for i in range(len(levels))]
index = pd.MultiIndex.from_arrays([levels, codes], names=['tag', 'uom'])
columns = pd.Int64Index([2018, 2019], dtype='int64', name='fy')
values = [[3.75319e+11, 3.38516e+11],
[1.28645e+11, 1.62819e+11],
[2.46674e+11, 1.75697e+11],
[6.67000e+08, np.NaN],
[1.52100e+09, 1.14100e+09],
[np.NaN, 1.14330e+10],
[5.83400e+09, 5.83400e+09],
[8.97400e+09, 6.61000e+09],
[8.34000e+08, 7.97000e+08],
[1.23000e+09, 1.37000e+08],
[7.03000e+08, 5.13000e+08],
[4.01900e+09, 3.15100e+09],
[0.00000e+00, 8.71000e+08],
[1.40000e+09, 2.10000e+09],
[-1.05500e+09, 5.31800e+09],
[3.37830e+10, 4.13040e+10],
[1.20870e+10, 1.20870e+10],
[2.22830e+10, 2.22830e+10]]
df = pd.DataFrame(values, columns=columns, index=index)
The implementation of the regex part of the filter function is short and easy to adapt for a multi-index scenario where you still want to only regex 1 part of the multi-index. I know this is not a direct answer to what you asked because you're right, as implemented the filter function does not handle multi-index.
I ended up here with the same problem and thought it might be a useful answer to others to post the code I have used, adapted from the pandas original:
import regex as re
def filter_multi(df, index_level_name, regex, axis=0):
def f(x):
return matcher.search(str(x)) is not None
matcher = re.compile(regex)
values = df.axes[axis].get_level_values(index_level_name).map(f)
return df.loc(axis=axis)[values]
Using the code in your Appendix:
print(df)
print(filter_multi(df, index_level_name='tag', regex='^Assets$', axis=0))
print(filter_multi(df, index_level_name='fy', regex='^2019$', axis=1))
If you want to filter a unique value from the first part of a multi-index, you can just use loc
:
df.loc[['Assets']]
which gives:
fy 2018 2019
tag uom
Assets USD 3.753190e+11 3.385160e+11
If for your real problem, filter must be used, you should reset the unused part of the index and set it back after filtering:
df.reset_index(level='uom').filter(regex='^Assets$', axis=0).set_index('uom', append=True)
Another option is to first remove uom
from your index, apply filter
(which then will be applied to the only index tag
) and add uom
back to your index, as in
df.reset_index('uom').filter(regex="^Assets$", axis=0).set_index('uom', append=True)
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