Repository: modin-project/modin

Issue:
MultiIndex .loc fails on modin and works on pandas
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### System information
- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**: macOS 10.14
- **Modin installed from (source or binary)**: binary
- **Modin version**: 0.4.0
- **Pandas version**: 0.24.1
- **Python version**: 3.7.0
- **Exact command to reproduce**:

```python
# import modin.pandas as pd
DF2 = pd.read_csv(
    'https://github.com/lemeb/Best_of_Times_study/raw/8c0c42296a6eb01e09817ad6be0ec827ee273808/blah.csv', 
    header=[0,1,2,3], index_col=0)

DF2.loc[1] # Will work
DF2.loc[1, 'Presidents'] # Will fail (see log 1)
DF2.loc[1, ('Presidents', 'Pure mentions', 'IND', 'all')] # Will fail (see log 2)
```


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You can obtain the Modin version with

python -c "import modin; print(modin.__version__)"
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### Describe the problem
<!-- Describe the problem clearly here. -->

I have a fairly large dataset, with a four-level MultiIndex. I tried to use modin to speed things up, but a fairly simple `.loc` command doesn't work, even though it works with a vanilla pandas implementation. 

_The code that I provided works under vanilla pandas, but doesn't under modin._

### Source code / logs
<!-- Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached. Try to provide a reproducible test case that is the bare minimum necessary to generate the problem. -->

#### Log 1

```
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-7-f7a945237ab9> in <module>()
      3     header=[0,1,2,3], index_col=0)
      4 DF2.loc[1]
----> 5 DF2.loc[1, 'Presidents']

~/anaconda3/lib/python3.7/site-packages/modin/pandas/indexing.py in __getitem__(self, key)
    232         row_lookup, col_lookup = self._compute_lookup(row_loc, col_loc)
    233         ndim = self._expand_dim(row_lookup, col_lookup, ndim)
--> 234         result = super(_LocIndexer, self).__getitem__(row_lookup, col_lookup, ndim)
    235         return result
    236 

~/anaconda3/lib/python3.7/site-packages/modin/pandas/indexing.py in __getitem__(self, row_lookup, col_lookup, ndim)
    163             single_axis = 1 if self.col_scaler else 0
    164             return SeriesView(
--> 165                 qc_view.squeeze(ndim=1, axis=single_axis),
    166                 self.df,
    167                 (row_lookup, col_lookup),

~/anaconda3/lib/python3.7/site-packages/modin/data_management/query_compiler/pandas_query_compiler.py in squeeze(self, ndim, axis)
   2781             if axis is None:
   2782                 axis = 0 if self.data.shape[1] > 1 else 1
-> 2783             squeezed = pandas.Series(to_squeeze.squeeze(axis))
   2784             scaler_axis = self.columns if axis else self.index
   2785             non_scaler_axis = self.index if axis else self.columns

~/anaconda3/lib/python3.7/site-packages/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
    260             else:
    261                 data = sanitize_array(data, index, dtype, copy,
--> 262                                       raise_cast_failure=True)
    263 
    264                 data = SingleBlockManager(data, index, fastpath=True)

~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/construction.py in sanitize_array(data, index, dtype, copy, raise_cast_failure)
    658             raise Exception('Data must be 1-dimensional')
    659         else:
--> 660             subarr = com.asarray_tuplesafe(data, dtype=dtype)
    661 
    662     # This is to prevent mixed-type Series getting all casted to

~/anaconda3/lib/python3.7/site-packages/pandas/core/common.py in asarray_tuplesafe(values, dtype)
    238         # Avoid building an array of arrays:
    239         # TODO: verify whether any path hits this except #18819 (invalid)
--> 240         values = [tuple(x) for x in values]
    241         result = construct_1d_object_array_from_listlike(values)
    242 

~/anaconda3/lib/python3.7/site-packages/pandas/core/common.py in <listcomp>(.0)
    238         # Avoid building an array of arrays:
    239         # TODO: verify whether any path hits this except #18819 (invalid)
--> 240         values = [tuple(x) for x in values]
    241         result = construct_1d_object_array_from_listlike(values)
    242 

TypeError: 'int' object is not iterable
```

#### Log 2

```
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-6-a1a1b4249d92> in <module>()
      3     header=[0,1,2,3], index_col=0)
      4 DF2.loc[1]
----> 5 DF2.loc[1, ('Presidents', 'Pure mentions', 'IND', 'all')]

~/anaconda3/lib/python3.7/site-packages/modin/pandas/indexing.py in __getitem__(self, key)
    231         self._handle_enlargement(row_loc, col_loc)
    232         row_lookup, col_lookup = self._compute_lookup(row_loc, col_loc)
--> 233         ndim = self._expand_dim(row_lookup, col_lookup, ndim)
    234         result = super(_LocIndexer, self).__getitem__(row_lookup, col_lookup, ndim)
    235         return result

~/anaconda3/lib/python3.7/site-packages/modin/pandas/indexing.py in _expand_dim(self, row_lookup, col_lookup, ndim)
    299         """
    300         many_rows = len(row_lookup) > 1
--> 301         many_cols = len(col_lookup) > 1
    302 
    303         if ndim == 0 and (many_rows or many_cols):

TypeError: object of type 'builtin_function_or_method' has no len()
```


Discussion:
Hi @lemeb, thanks for posting this!

I see what the issue is and I will get it fixed as soon as I can. We are still working through some things related to MultiIndex, and I appreciate the report. 

Resolving patch:
diff --git a/modin/data_management/query_compiler/pandas_query_compiler.py b/modin/data_management/query_compiler/pandas_query_compiler.py
--- a/modin/data_management/query_compiler/pandas_query_compiler.py
+++ b/modin/data_management/query_compiler/pandas_query_compiler.py
@@ -2775,16 +2775,20 @@ def view(self, index=None, columns=None):
         )
 
     def squeeze(self, ndim=0, axis=None):
-        to_squeeze = self.data.to_pandas()
+        to_squeeze = self.to_pandas()
         # This is the case for 1xN or Nx1 DF - Need to call squeeze
         if ndim == 1:
             if axis is None:
                 axis = 0 if self.data.shape[1] > 1 else 1
-            squeezed = pandas.Series(to_squeeze.squeeze(axis))
-            scaler_axis = self.columns if axis else self.index
-            non_scaler_axis = self.index if axis else self.columns
-            squeezed.name = scaler_axis[0]
-            squeezed.index = non_scaler_axis
+            squeezed = pandas.Series(to_squeeze.squeeze())
+            # In the case of `MultiIndex`, we already have the correct index and naming
+            # because we are going from pandas above. This step is to correct the
+            # `Series` to have the correct name and index.
+            if not isinstance(squeezed.index, pandas.MultiIndex):
+                scaler_axis = self.columns if axis else self.index
+                non_scaler_axis = self.index if axis else self.columns
+                squeezed.name = scaler_axis[0]
+                squeezed.index = non_scaler_axis
             return squeezed
         # This is the case for a 1x1 DF - We don't need to squeeze
         else:
diff --git a/modin/pandas/indexing.py b/modin/pandas/indexing.py
--- a/modin/pandas/indexing.py
+++ b/modin/pandas/indexing.py
@@ -230,8 +230,26 @@ def __getitem__(self, key):
         row_loc, col_loc, ndim, self.row_scaler, self.col_scaler = _parse_tuple(key)
         self._handle_enlargement(row_loc, col_loc)
         row_lookup, col_lookup = self._compute_lookup(row_loc, col_loc)
-        ndim = self._expand_dim(row_lookup, col_lookup, ndim)
+        ndim = (0 if len(row_lookup) == 1 else 1) + (0 if len(col_lookup) == 1 else 1)
         result = super(_LocIndexer, self).__getitem__(row_lookup, col_lookup, ndim)
+        # Pandas drops the levels that are in the `loc`, so we have to as well.
+        if hasattr(result, "index") and isinstance(result.index, pandas.MultiIndex):
+            if (
+                isinstance(result, pandas.Series)
+                and not isinstance(col_loc, slice)
+                and all(
+                    col_loc[i] in result.index.levels[i] for i in range(len(col_loc))
+                )
+            ):
+                result.index = result.index.droplevel(list(range(len(col_loc))))
+            elif all(row_loc[i] in result.index.levels[i] for i in range(len(row_loc))):
+                result.index = result.index.droplevel(list(range(len(row_loc))))
+        if (
+            hasattr(result, "columns")
+            and isinstance(result.columns, pandas.MultiIndex)
+            and all(col_loc[i] in result.columns.levels[i] for i in range(len(col_loc)))
+        ):
+            result.columns = result.columns.droplevel(list(range(len(col_loc))))
         return result
 
     def __setitem__(self, key, item):
@@ -293,28 +311,26 @@ def _compute_enlarge_labels(self, locator, base_index):
             )
         return nan_labels
 
-    def _expand_dim(self, row_lookup, col_lookup, ndim):
-        """Expand the dimension if necessary.
-        This method is for cases like duplicate labels.
-        """
-        many_rows = len(row_lookup) > 1
-        many_cols = len(col_lookup) > 1
-
-        if ndim == 0 and (many_rows or many_cols):
-            ndim = 1
-        if ndim == 1 and (many_rows and many_cols):
-            ndim = 2
-        return ndim
-
     def _compute_lookup(self, row_loc, col_loc) -> Tuple[pandas.Index, pandas.Index]:
-        if isinstance(row_loc, list) and len(row_loc) == 1:
+        if is_list_like(row_loc) and len(row_loc) == 1:
             if (
                 isinstance(self.qc.index.values[0], np.datetime64)
                 and type(row_loc[0]) != np.datetime64
             ):
                 row_loc = [pandas.to_datetime(row_loc[0])]
-        row_lookup = self.qc.index.to_series().loc[row_loc].index
-        col_lookup = self.qc.columns.to_series().loc[col_loc].index
+
+        if isinstance(row_loc, slice):
+            row_lookup = self.qc.index.to_series().loc[row_loc].values
+        elif isinstance(self.qc.index, pandas.MultiIndex):
+            row_lookup = self.qc.index[self.qc.index.get_locs(row_loc)]
+        else:
+            row_lookup = self.qc.index[self.qc.index.get_indexer_for(row_loc)]
+        if isinstance(col_loc, slice):
+            col_lookup = self.qc.columns.to_series().loc[col_loc].values
+        elif isinstance(self.qc.columns, pandas.MultiIndex):
+            col_lookup = self.qc.columns[self.qc.columns.get_locs(col_loc)]
+        else:
+            col_lookup = self.qc.columns[self.qc.columns.get_indexer_for(col_loc)]
         return row_lookup, col_lookup
 
 
diff --git a/modin/pandas/series.py b/modin/pandas/series.py
--- a/modin/pandas/series.py
+++ b/modin/pandas/series.py
@@ -41,6 +41,14 @@ def __init__(self, series, parent_df, loc):
         self.parent_df = parent_df
         self._loc = loc
 
+    def _get_index(self):
+        return self.series.index
+
+    def _set_index(self, index):
+        self.series.index = index
+
+    index = property(_get_index, _set_index)
+
     def __repr__(self):
         return repr(self.series)
 
@@ -176,6 +184,9 @@ def __getattribute__(self, item):
             "__arithmetic_op__",
             "__comparisons__",
             "__class__",
+            "index",
+            "_get_index",
+            "_set_index",
         ]
         if item not in default_behaviors:
             method = self.series.__getattribute__(item)


Test patch:
diff --git a/modin/pandas/test/data/blah.csv b/modin/pandas/test/data/blah.csv
new file mode 100644
--- /dev/null
+++ b/modin/pandas/test/data/blah.csv
@@ -0,0 +1,10 @@
+,Presidents,Presidents,Presidents,Presidents,Presidents,Presidents,Presidents,Presidents,Presidents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Subcontinents,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes,Themes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librarians_np,advertising_np,baseball_np,illegal aliens_np,media_np,crime and criminals_np,roads and traffic_np,automobiles_np,ethics_np,art_np,property taxes_np,speech_np,freedom of speech and expression_np,political advertising_np,reviews_np,sex crimes_np,prostitution_np,insurance_np,hurricanes and tropical storms_np,hurricane katrina_np,floods_np,election results_np,strikes_np,united states armament and defense_np,basketball_np,horse racing_np,united states international relations_np,international relations_np,firearms_np,health insurance and managed care_np,health insurance_np,discrimination_np,music_np,airlines and airplanes_np,drugs (pharmaceuticals)_np,diseases and conditions_np,banks and banking_np,college athletics_np,football_np,impeachment_np,frauds and swindling_np,new year_np,correction stories_np,trees and shrubs_np,home repairs_np,olympic games_np,apparel_np,home furnishings_np,earthquakes_np,home repairs and improvements_np,world trade center (nyc)_np,fish and other marine life_np,office buildings and commercial properties_np,noise_np,legislatures and parliaments_np,tuition_np,presidential election of 2004_np,mayors_np,soccer_np,restaurants_np,unemployment_np,biographical information_np,radio_np,"conventions, national (us)_np",computer and video games_np,presidential election of 2008_np,super bowl_np,demonstrations and riots_np,marriages_np,deaths (obituaries)_np,accidents and safety_np,standards and standardization_np,referendums_np,exercise_np,children and youth_np,murders and attempted murders_np,international trade and world market_np,wages and salaries_np,coaches and managers_np,archaeology and anthropology_np,palestinians_np,birth control and family planning_np,economic conditions and trends_np,united states economy_np,telephones and telecommunications_np,restoration and rehabilitation_np,dairy products_np,animals_np,sales_np,"prices (fares, fees and rates)_np",energy and power_np,atomic weapons_np,holidays and special occasions_np,medicaid_np,medicare_np,christmas_np,"war crimes, genocide and crimes against humanity_np",presidential election of 2000_np,fires and firefighters_np,fires and firemen_np,air pollution_np,robberies and thefts_np,conventions and conferences_np,food_np,diet and nutrition_np,stocks and bonds_np,electric light and power_np,light_np,blackouts and brownouts (electrical)_np,theater_np,hijacking_np,pentagon building_np,"suspensions, dismissals and resignations_np",scholarships and fellowships_np,newspapers_np,travel and vacations_np,building (construction)_np,games_np,torture_np,cellular telephones_np,sentences (criminal)_np,bridges and tunnels_np,affirmative action_np,credit_np,birds_np,space_np,postal service_np,pornography and obscenity_np,steroids_np,embargoes and economic sanctions_np,smoking and tobacco_np,social security (us)_np,child care_np,inventions and patents_np,vaccination and immunization_np,prisons and prisoners_np,retirement_np,currency_np,transit systems_np,subways_np,snow and snowstorms_np,housing_np,priests_np,company reports_np,corporations_np,layoffs and job reductions_np,magazines_np,aged_np,viruses_np,biological and chemical warfare_np,opera_np,parades_np,states (us)_np,constitutional amendments_np,cancer_np,pensions and retirement plans_np,child abuse and neglect_np,government employees_np,culture_np,blacks (in us)_np,radiation_np,documentary films and programs_np,retail stores and trade_np,spanish language_np,"mergers, acquisitions and divestitures_np",small business_np,poetry and poets_np,rock music_np,identification devices_np,space shuttle_np,atomic energy_np,interest rates_np,police brutality and misconduct_np,science and technology_np,running_np,marathon running_np,research_np,weight_np,homeless persons_np,cocaine and crack cocaine_np,suicides and suicide attempts_np,bicycles and bicycling_np,buses_np,pregnancy and obstetrics_np,contests and prizes_np,vetoes (us)_np,jewels and jewelry_np,academy awards (oscars)_np,parties (social)_np,festivals_np,"divorce, separations and annulments_np",gas (fuel)_np,photography_np,comedy and humor_np,world series_np,hotels and motels_np,serial murders_np,textiles_np,gambling_np,cooking and cookbooks_np,recipes_np,beverages_np,tennis_np,shoes and boots_np,dogs_np,"hockey, ice_np",extradition_np,boxing_np,"indians, american_np",violence_np,chemicals_np,sports of the times (times column)_np,arson_np,vietnam war_np,boycotts_np,toys_np,cruises_np,ships and shipping_np,trade shows and fairs_np,mental health and disorders_np,wines_np,alcoholic beverages_np,dancing_np,golf_np,auctions_np,mutual funds_np,swimming_np,historic buildings and sites_np,weddings and engagements_np,freedom and human rights_np,athletics and sports_np,draft and recruitment (sports)_np,hospitals_np,genetics and heredity_np,foreign aid_np,anthrax_np,acquired immune deficiency syndrome_np,insects_np,consumer protection_np,mines and mining_np,blood_np,doctors_np,nursing and nurses_np,airports_np,water_np,death and dying_np,dna (deoxyribonucleic acid)_np,third world and developing countries_np,food contamination and poisoning_np,agriculture_np,livestock_np,acquired immune deficiency syndrome (aids)_np,no index terms_np,regulation and deregulation of industry_np,taxicabs and taxicab drivers_np,meat_np,babies_np,shortages_np,nasdaq composite index_np,government bonds_np,security and warning systems_np,grain_np,transplants_np,freedom of the press_np,metals and minerals_np,computer security_np,bombs and explosives_np,population_np,mortgages_np,customs (tariff)_np,farmers_np,automobile racing_np,biology and biochemistry_np,anatomy and physiology_np,production_np,factories and industrial plants_np,track and field_np,summer games (olympics)_np,foreign investments_np,stadiums and arenas_np,foreign service_np,ncaa basketball tournament_np,waste materials and disposal_np,hunting and trapping_np,deportation_np,casinos_np,world cup (soccer)_np,reproduction (biological)_np,surgery and surgeons_np,kidnapping_np,heart_np,alcohol abuse_np,domestic violence_np,capital punishment_np,hostages_np,world war ii (1939-45)_np,war and revolution_np,civil war and guerrilla warfare_np,jury system_np,entertainment and amusements_np,child abuse_np,sexual harassment_np,war crimes and criminals_np,censorship_np,railroads_np,asylum (political)_np,legal profession_np,courts_np,political prisoners_np,prisoners of war_np,men_np,dow jones stock average_np,probation and parole_np,drunken and reckless driving_np,organized crime_np,futures and options trading_np,securities and commodities violations_np,assaults_np,physics_np,bribery_np,debating_np,recalls and bans of products_np,credit and money cards_np,drought_np,fines (penalties)_np,perjury_np,bars_np,trades (sports)_np,skiing_np,attacks on police_np,race_np,bankruptcies_np,bridge (card game)_np,renting and leasing_np,condominiums_np,commuting_np,stations and terminals (passenger)_np,shutdowns (institutional)_np,beaches_np,families and family life_np,brain_np,book trade_np,futures trading_np,federal taxes (us)_np,tax credits_np,assassinations and attempted assassinations_np,recycling of waste materials_np,automobile insurance and liability_np,delays (transportation)_np,transportation_np,flowers and plants_np,steel and iron_np,chemistry_np,anti-semitism_np,soft drinks_np,consumer behavior_np,parks and other recreation areas_np,leisure_np,gardens and gardening_np,figure skating_np,ice skating_np,"fishing, sport_np",executives and management_np,coups d'etat and attempted coups d'etat_np,supermarkets_np,rescues_np,accounting and accountants_np,rain_np,judges_np,water pollution_np,satellites_np,trucks and trucking_np,playoff games_np,"fishing, commercial_np",antitrust actions and laws_np,royal family_np,personal finances_np,parking_np,utility vehicles and other light trucks_np,racketeering and racketeers_np,interscholastic athletics_np,chess_np,jazz_np,breast_np,explosions_np,foster care_np,classical music_np,intelligence_np,navies_np,architecture_np,organized labor_np,bakeries and baked products_np,espionage_np,local government_np,interior design_np,guards_np,computer chips_np,boats and boating_np,forests and forestry_np,zoning_np,hiring and promotion_np,area planning and renewal_np,marketing and merchandising_np,embezzlement_np,boards of directors_np,smuggling_np,land use policies_np,monuments and memorials_np,endangered and extinct species_np,"age, chronological_np",welfare (us)_np,child custody and support_np,wiretapping and other eavesdropping devices and methods_np,ferries_np,history_np,collectors and collections_np,missiles and missile defense systems_np,arms control and limitation and disarmament_np,constitutions_np,shopping centers_np,tax evasion_np,design_np,free agents (sports)_np,genetic engineering_np,pilots_np,military aircraft_np,liability for products_np,real estate_np,veterans_np,antiques_np,missiles_np,furniture_np,drug traffic_np,discount selling_np,savings_np,fruit_np,deaths_np,united states open (tennis)_np,english language_np,records and achievements_np,united states foreign service_np,recording equipment_np,nightclubs and cabarets_np,beer_np,brokers and brokerage firms_np,buildings (structures)_np,"arbitration, conciliation and mediation_np",hazardous and toxic substances_np,defense contracts_np,arms sales abroad_np,military personnel_np,missing persons_np,concerts and recitals_np,drug addiction and abuse_np,geographic profiles_np,geography_np,fast food industry_np,labeling and labels_np,military bases and installations_np,vice presidents and vice presidency (us)_np,volunteers_np,layoffs (labor)_np,income_np,gifts_np,treaties_np,shootings_np,city councils_np,social conditions and trends_np,urban areas_np,shows (exhibits)_np,data processing (computers)_np,stock prices and trading volume_np,office buildings_np,immigration and emigration_np,kurds_np,income tax_np,software products_np,personal computers_np,summit conferences_np,prices_np,bombs and bomb plots_np,racial relations_np,search and seizure_np,museums_np,"health, personal_np",contracts_np,industry profiles_np,refugees and expatriates_np,trials_np,disclosure of information_np,unemployment and job market_np,suburbs_np,special sections_np,reform and reorganization_np,cooperatives_np,federal aid (us)_np,relocation of business_np,cable television_np,attorneys general_np,electronics_np,book reviews_np,"names, organizational_np","minorities (ethnic, racial, religious)_np","new models, design and products_np",terms not available_np,account changes_np,surveys and series_np,military action_np,whitewater case_np,company and organization profiles_np,savings and loan associations_np,art shows_np,independence movements_np,life styles_np,suits and claims against government_np,presidential election of 1996_np,forecasts_np,threats and threatening messages_np,persian gulf war_np
+1,blah,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+5,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/modin/pandas/test/test_dataframe.py b/modin/pandas/test/test_dataframe.py
--- a/modin/pandas/test/test_dataframe.py
+++ b/modin/pandas/test/test_dataframe.py
@@ -2831,6 +2831,60 @@ def test_loc(request, data):
         df_equals(modin_df_copy, pandas_df_copy)
 
 
+def test_loc_multi_index():
+    modin_df = pd.read_csv(
+        "modin/pandas/test/data/blah.csv", header=[0, 1, 2, 3], index_col=0
+    )
+    pandas_df = pandas.read_csv(
+        "modin/pandas/test/data/blah.csv", header=[0, 1, 2, 3], index_col=0
+    )
+
+    df_equals(modin_df.loc[1], pandas_df.loc[1])
+    assert modin_df.loc[1, "Presidents"].equals(pandas_df.loc[1, "Presidents"])
+    assert modin_df.loc[1, ("Presidents", "Pure mentions")].equals(
+        pandas_df.loc[1, ("Presidents", "Pure mentions")]
+    )
+    assert (
+        modin_df.loc[1, ("Presidents", "Pure mentions", "IND", "all")]
+        == pandas_df.loc[1, ("Presidents", "Pure mentions", "IND", "all")]
+    )
+    df_equals(modin_df.loc[(1, 2), "Presidents"], pandas_df.loc[(1, 2), "Presidents"])
+
+    tuples = [
+        ("bar", "one"),
+        ("bar", "two"),
+        ("bar", "three"),
+        ("bar", "four"),
+        ("baz", "one"),
+        ("baz", "two"),
+        ("baz", "three"),
+        ("baz", "four"),
+        ("foo", "one"),
+        ("foo", "two"),
+        ("foo", "three"),
+        ("foo", "four"),
+        ("qux", "one"),
+        ("qux", "two"),
+        ("qux", "three"),
+        ("qux", "four"),
+    ]
+
+    modin_index = pd.MultiIndex.from_tuples(tuples, names=["first", "second"])
+    pandas_index = pandas.MultiIndex.from_tuples(tuples, names=["first", "second"])
+    frame_data = np.random.randint(0, 100, size=(16, 100))
+    modin_df = pd.DataFrame(
+        frame_data, index=modin_index, columns=["col{}".format(i) for i in range(100)]
+    )
+    pandas_df = pandas.DataFrame(
+        frame_data, index=pandas_index, columns=["col{}".format(i) for i in range(100)]
+    )
+    assert modin_df.loc["bar", "col1"].equals(pandas_df.loc["bar", "col1"])
+    assert modin_df.loc[("bar", "one"), "col1"] == pandas_df.loc[("bar", "one"), "col1"]
+    df_equals(
+        modin_df.loc["bar", ("col1", "col2")], pandas_df.loc["bar", ("col1", "col2")]
+    )
+
+
 def test_lookup():
     data = test_data_values[0]
     with pytest.warns(UserWarning):