We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
AttributeError: module 'modin.pandas' has no attribute 'crosstab'

### Describe the problem
<!-- Describe the problem clearly here. -->
`crosstab` is not currently in the `modin.pandas` module. We can default to pandas for this.

### Source code / logs

```python
import modin.pandas as pd
pd.crosstab(args)
```
<!-- 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. -->


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `modin/pandas/reshape.py`
Content:
```
1 from __future__ import absolute_import
2 from __future__ import division
3 from __future__ import print_function
4 
5 import pandas
6 from pandas.core.dtypes.common import is_list_like
7 
8 from .dataframe import DataFrame
9 
10 
11 def get_dummies(
12     data,
13     prefix=None,
14     prefix_sep="_",
15     dummy_na=False,
16     columns=None,
17     sparse=False,
18     drop_first=False,
19 ):
20     """Convert categorical variable into indicator variables.
21 
22     Args:
23         data (array-like, Series, or DataFrame): data to encode.
24         prefix (string, [string]): Prefix to apply to each encoded column
25                                    label.
26         prefix_sep (string, [string]): Separator between prefix and value.
27         dummy_na (bool): Add a column to indicate NaNs.
28         columns: Which columns to encode.
29         sparse (bool): Not Implemented: If True, returns SparseDataFrame.
30         drop_first (bool): Whether to remove the first level of encoded data.
31 
32     Returns:
33         DataFrame or one-hot encoded data.
34     """
35     if sparse:
36         raise NotImplementedError(
37             "SparseDataFrame is not implemented. "
38             "To contribute to Modin, please visit "
39             "github.com/modin-project/modin."
40         )
41     if not isinstance(data, DataFrame):
42         return pandas.get_dummies(
43             data,
44             prefix=prefix,
45             prefix_sep=prefix_sep,
46             dummy_na=dummy_na,
47             columns=columns,
48             sparse=sparse,
49             drop_first=drop_first,
50         )
51     if isinstance(data, DataFrame):
52         df = data
53     elif is_list_like(data):
54         df = DataFrame(data)
55 
56     new_manager = df._query_compiler.get_dummies(
57         columns,
58         prefix=prefix,
59         prefix_sep=prefix_sep,
60         dummy_na=dummy_na,
61         drop_first=drop_first,
62     )
63     return DataFrame(query_compiler=new_manager)
64 
65 
66 def melt(
67     frame,
68     id_vars=None,
69     value_vars=None,
70     var_name=None,
71     value_name="value",
72     col_level=None,
73 ):
74     return frame.melt(
75         id_vars=id_vars,
76         value_vars=value_vars,
77         var_name=var_name,
78         value_name=value_name,
79         col_level=col_level,
80     )
81
```

Path: `modin/pandas/__init__.py`
Content:
```
1 from __future__ import absolute_import
2 from __future__ import division
3 from __future__ import print_function
4 
5 # TODO: In the future `set_option` or similar needs to run on every node
6 # in order to keep all pandas instances across nodes consistent
7 import pandas
8 from pandas import (
9     eval,
10     unique,
11     value_counts,
12     cut,
13     to_numeric,
14     factorize,
15     test,
16     qcut,
17     match,
18     Panel,
19     date_range,
20     period_range,
21     Index,
22     MultiIndex,
23     CategoricalIndex,
24     Series,
25     bdate_range,
26     DatetimeIndex,
27     Timedelta,
28     Timestamp,
29     to_timedelta,
30     set_eng_float_format,
31     set_option,
32     NaT,
33     PeriodIndex,
34     Categorical,
35 )
36 import threading
37 import os
38 import ray
39 
40 from .. import __version__
41 from .concat import concat
42 from .dataframe import DataFrame
43 from .datetimes import to_datetime
44 from .io import (
45     read_csv,
46     read_parquet,
47     read_json,
48     read_html,
49     read_clipboard,
50     read_excel,
51     read_hdf,
52     read_feather,
53     read_msgpack,
54     read_stata,
55     read_sas,
56     read_pickle,
57     read_sql,
58     read_gbq,
59     read_table,
60 )
61 from .reshape import get_dummies, melt
62 from .general import isna, merge, pivot_table
63 from .. import __execution_engine__ as execution_engine
64 
65 __pandas_version__ = "0.23.4"
66 
67 if pandas.__version__ != __pandas_version__:
68     raise ImportError(
69         "The pandas version installed does not match the required pandas "
70         "version in Modin. Please install pandas {} to use "
71         "Modin.".format(__pandas_version__)
72     )
73 
74 # Set this so that Pandas doesn't try to multithread by itself
75 os.environ["OMP_NUM_THREADS"] = "1"
76 num_cpus = 1
77 
78 if execution_engine == "Ray":
79     try:
80         if threading.current_thread().name == "MainThread":
81             ray.init(
82                 redirect_output=True,
83                 include_webui=False,
84                 redirect_worker_output=True,
85                 ignore_reinit_error=True,
86             )
87             num_cpus = ray.global_state.cluster_resources()["CPU"]
88     except AssertionError:
89         pass
90 elif execution_engine == "Dask":
91     from distributed.client import _get_global_client
92 
93     if threading.current_thread().name == "MainThread":
94         # initialize the dask client
95         client = _get_global_client()
96         if client is None:
97             from distributed import Client
98 
99             client = Client()
100         num_cpus = sum(client.ncores().values())
101 elif execution_engine != "Python":
102     raise ImportError("Unrecognized execution engine: {}.".format(execution_engine))
103 
104 DEFAULT_NPARTITIONS = max(4, int(num_cpus))
105 
106 __all__ = [
107     "DataFrame",
108     "Series",
109     "read_csv",
110     "read_parquet",
111     "read_json",
112     "read_html",
113     "read_clipboard",
114     "read_excel",
115     "read_hdf",
116     "read_feather",
117     "read_msgpack",
118     "read_stata",
119     "read_sas",
120     "read_pickle",
121     "read_sql",
122     "read_gbq",
123     "read_table",
124     "concat",
125     "eval",
126     "unique",
127     "value_counts",
128     "cut",
129     "to_numeric",
130     "factorize",
131     "test",
132     "qcut",
133     "match",
134     "to_datetime",
135     "get_dummies",
136     "isna",
137     "merge",
138     "pivot_table",
139     "Panel",
140     "date_range",
141     "Index",
142     "MultiIndex",
143     "Series",
144     "bdate_range",
145     "period_range",
146     "DatetimeIndex",
147     "to_timedelta",
148     "set_eng_float_format",
149     "set_option",
150     "CategoricalIndex",
151     "Timedelta",
152     "Timestamp",
153     "NaT",
154     "PeriodIndex",
155     "Categorical",
156     "__version__",
157     "melt",
158 ]
159 
160 del pandas
161
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/modin/pandas/__init__.py b/modin/pandas/__init__.py
--- a/modin/pandas/__init__.py
+++ b/modin/pandas/__init__.py
@@ -58,7 +58,7 @@
     read_gbq,
     read_table,
 )
-from .reshape import get_dummies, melt
+from .reshape import get_dummies, melt, crosstab
 from .general import isna, merge, pivot_table
 from .. import __execution_engine__ as execution_engine
 
@@ -155,6 +155,7 @@
     "Categorical",
     "__version__",
     "melt",
+    "crosstab",
 ]
 
 del pandas
diff --git a/modin/pandas/reshape.py b/modin/pandas/reshape.py
--- a/modin/pandas/reshape.py
+++ b/modin/pandas/reshape.py
@@ -6,6 +6,7 @@
 from pandas.core.dtypes.common import is_list_like
 
 from .dataframe import DataFrame
+from modin.error_message import ErrorMessage
 
 
 def get_dummies(
@@ -78,3 +79,31 @@
         value_name=value_name,
         col_level=col_level,
     )
+
+
+def crosstab(
+    index,
+    columns,
+    values=None,
+    rownames=None,
+    colnames=None,
+    aggfunc=None,
+    margins=False,
+    margins_name="All",
+    dropna=True,
+    normalize=False,
+):
+    ErrorMessage.default_to_pandas()
+    pandas_crosstab = pandas.crosstab(
+        index,
+        columns,
+        values,
+        rownames,
+        colnames,
+        aggfunc,
+        margins,
+        margins_name,
+        dropna,
+        normalize,
+    )
+    return DataFrame(pandas_crosstab)

{"golden_diff": "diff --git a/modin/pandas/__init__.py b/modin/pandas/__init__.py\n--- a/modin/pandas/__init__.py\n+++ b/modin/pandas/__init__.py\n@@ -58,7 +58,7 @@\n     read_gbq,\n     read_table,\n )\n-from .reshape import get_dummies, melt\n+from .reshape import get_dummies, melt, crosstab\n from .general import isna, merge, pivot_table\n from .. import __execution_engine__ as execution_engine\n \n@@ -155,6 +155,7 @@\n     \"Categorical\",\n     \"__version__\",\n     \"melt\",\n+    \"crosstab\",\n ]\n \n del pandas\ndiff --git a/modin/pandas/reshape.py b/modin/pandas/reshape.py\n--- a/modin/pandas/reshape.py\n+++ b/modin/pandas/reshape.py\n@@ -6,6 +6,7 @@\n from pandas.core.dtypes.common import is_list_like\n \n from .dataframe import DataFrame\n+from modin.error_message import ErrorMessage\n \n \n def get_dummies(\n@@ -78,3 +79,31 @@\n         value_name=value_name,\n         col_level=col_level,\n     )\n+\n+\n+def crosstab(\n+    index,\n+    columns,\n+    values=None,\n+    rownames=None,\n+    colnames=None,\n+    aggfunc=None,\n+    margins=False,\n+    margins_name=\"All\",\n+    dropna=True,\n+    normalize=False,\n+):\n+    ErrorMessage.default_to_pandas()\n+    pandas_crosstab = pandas.crosstab(\n+        index,\n+        columns,\n+        values,\n+        rownames,\n+        colnames,\n+        aggfunc,\n+        margins,\n+        margins_name,\n+        dropna,\n+        normalize,\n+    )\n+    return DataFrame(pandas_crosstab)\n", "issue": "AttributeError: module 'modin.pandas' has no attribute 'crosstab'\n\r\n### Describe the problem\r\n<!-- Describe the problem clearly here. -->\r\n`crosstab` is not currently in the `modin.pandas` module. We can default to pandas for this.\r\n\r\n### Source code / logs\r\n\r\n```python\r\nimport modin.pandas as pd\r\npd.crosstab(args)\r\n```\r\n<!-- 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. -->\r\n\n", "before_files": [{"content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport pandas\nfrom pandas.core.dtypes.common import is_list_like\n\nfrom .dataframe import DataFrame\n\n\ndef get_dummies(\n    data,\n    prefix=None,\n    prefix_sep=\"_\",\n    dummy_na=False,\n    columns=None,\n    sparse=False,\n    drop_first=False,\n):\n    \"\"\"Convert categorical variable into indicator variables.\n\n    Args:\n        data (array-like, Series, or DataFrame): data to encode.\n        prefix (string, [string]): Prefix to apply to each encoded column\n                                   label.\n        prefix_sep (string, [string]): Separator between prefix and value.\n        dummy_na (bool): Add a column to indicate NaNs.\n        columns: Which columns to encode.\n        sparse (bool): Not Implemented: If True, returns SparseDataFrame.\n        drop_first (bool): Whether to remove the first level of encoded data.\n\n    Returns:\n        DataFrame or one-hot encoded data.\n    \"\"\"\n    if sparse:\n        raise NotImplementedError(\n            \"SparseDataFrame is not implemented. \"\n            \"To contribute to Modin, please visit \"\n            \"github.com/modin-project/modin.\"\n        )\n    if not isinstance(data, DataFrame):\n        return pandas.get_dummies(\n            data,\n            prefix=prefix,\n            prefix_sep=prefix_sep,\n            dummy_na=dummy_na,\n            columns=columns,\n            sparse=sparse,\n            drop_first=drop_first,\n        )\n    if isinstance(data, DataFrame):\n        df = data\n    elif is_list_like(data):\n        df = DataFrame(data)\n\n    new_manager = df._query_compiler.get_dummies(\n        columns,\n        prefix=prefix,\n        prefix_sep=prefix_sep,\n        dummy_na=dummy_na,\n        drop_first=drop_first,\n    )\n    return DataFrame(query_compiler=new_manager)\n\n\ndef melt(\n    frame,\n    id_vars=None,\n    value_vars=None,\n    var_name=None,\n    value_name=\"value\",\n    col_level=None,\n):\n    return frame.melt(\n        id_vars=id_vars,\n        value_vars=value_vars,\n        var_name=var_name,\n        value_name=value_name,\n        col_level=col_level,\n    )\n", "path": "modin/pandas/reshape.py"}, {"content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\n# TODO: In the future `set_option` or similar needs to run on every node\n# in order to keep all pandas instances across nodes consistent\nimport pandas\nfrom pandas import (\n    eval,\n    unique,\n    value_counts,\n    cut,\n    to_numeric,\n    factorize,\n    test,\n    qcut,\n    match,\n    Panel,\n    date_range,\n    period_range,\n    Index,\n    MultiIndex,\n    CategoricalIndex,\n    Series,\n    bdate_range,\n    DatetimeIndex,\n    Timedelta,\n    Timestamp,\n    to_timedelta,\n    set_eng_float_format,\n    set_option,\n    NaT,\n    PeriodIndex,\n    Categorical,\n)\nimport threading\nimport os\nimport ray\n\nfrom .. import __version__\nfrom .concat import concat\nfrom .dataframe import DataFrame\nfrom .datetimes import to_datetime\nfrom .io import (\n    read_csv,\n    read_parquet,\n    read_json,\n    read_html,\n    read_clipboard,\n    read_excel,\n    read_hdf,\n    read_feather,\n    read_msgpack,\n    read_stata,\n    read_sas,\n    read_pickle,\n    read_sql,\n    read_gbq,\n    read_table,\n)\nfrom .reshape import get_dummies, melt\nfrom .general import isna, merge, pivot_table\nfrom .. import __execution_engine__ as execution_engine\n\n__pandas_version__ = \"0.23.4\"\n\nif pandas.__version__ != __pandas_version__:\n    raise ImportError(\n        \"The pandas version installed does not match the required pandas \"\n        \"version in Modin. Please install pandas {} to use \"\n        \"Modin.\".format(__pandas_version__)\n    )\n\n# Set this so that Pandas doesn't try to multithread by itself\nos.environ[\"OMP_NUM_THREADS\"] = \"1\"\nnum_cpus = 1\n\nif execution_engine == \"Ray\":\n    try:\n        if threading.current_thread().name == \"MainThread\":\n            ray.init(\n                redirect_output=True,\n                include_webui=False,\n                redirect_worker_output=True,\n                ignore_reinit_error=True,\n            )\n            num_cpus = ray.global_state.cluster_resources()[\"CPU\"]\n    except AssertionError:\n        pass\nelif execution_engine == \"Dask\":\n    from distributed.client import _get_global_client\n\n    if threading.current_thread().name == \"MainThread\":\n        # initialize the dask client\n        client = _get_global_client()\n        if client is None:\n            from distributed import Client\n\n            client = Client()\n        num_cpus = sum(client.ncores().values())\nelif execution_engine != \"Python\":\n    raise ImportError(\"Unrecognized execution engine: {}.\".format(execution_engine))\n\nDEFAULT_NPARTITIONS = max(4, int(num_cpus))\n\n__all__ = [\n    \"DataFrame\",\n    \"Series\",\n    \"read_csv\",\n    \"read_parquet\",\n    \"read_json\",\n    \"read_html\",\n    \"read_clipboard\",\n    \"read_excel\",\n    \"read_hdf\",\n    \"read_feather\",\n    \"read_msgpack\",\n    \"read_stata\",\n    \"read_sas\",\n    \"read_pickle\",\n    \"read_sql\",\n    \"read_gbq\",\n    \"read_table\",\n    \"concat\",\n    \"eval\",\n    \"unique\",\n    \"value_counts\",\n    \"cut\",\n    \"to_numeric\",\n    \"factorize\",\n    \"test\",\n    \"qcut\",\n    \"match\",\n    \"to_datetime\",\n    \"get_dummies\",\n    \"isna\",\n    \"merge\",\n    \"pivot_table\",\n    \"Panel\",\n    \"date_range\",\n    \"Index\",\n    \"MultiIndex\",\n    \"Series\",\n    \"bdate_range\",\n    \"period_range\",\n    \"DatetimeIndex\",\n    \"to_timedelta\",\n    \"set_eng_float_format\",\n    \"set_option\",\n    \"CategoricalIndex\",\n    \"Timedelta\",\n    \"Timestamp\",\n    \"NaT\",\n    \"PeriodIndex\",\n    \"Categorical\",\n    \"__version__\",\n    \"melt\",\n]\n\ndel pandas\n", "path": "modin/pandas/__init__.py"}], "after_files": [{"content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport pandas\nfrom pandas.core.dtypes.common import is_list_like\n\nfrom .dataframe import DataFrame\nfrom modin.error_message import ErrorMessage\n\n\ndef get_dummies(\n    data,\n    prefix=None,\n    prefix_sep=\"_\",\n    dummy_na=False,\n    columns=None,\n    sparse=False,\n    drop_first=False,\n):\n    \"\"\"Convert categorical variable into indicator variables.\n\n    Args:\n        data (array-like, Series, or DataFrame): data to encode.\n        prefix (string, [string]): Prefix to apply to each encoded column\n                                   label.\n        prefix_sep (string, [string]): Separator between prefix and value.\n        dummy_na (bool): Add a column to indicate NaNs.\n        columns: Which columns to encode.\n        sparse (bool): Not Implemented: If True, returns SparseDataFrame.\n        drop_first (bool): Whether to remove the first level of encoded data.\n\n    Returns:\n        DataFrame or one-hot encoded data.\n    \"\"\"\n    if sparse:\n        raise NotImplementedError(\n            \"SparseDataFrame is not implemented. \"\n            \"To contribute to Modin, please visit \"\n            \"github.com/modin-project/modin.\"\n        )\n    if not isinstance(data, DataFrame):\n        return pandas.get_dummies(\n            data,\n            prefix=prefix,\n            prefix_sep=prefix_sep,\n            dummy_na=dummy_na,\n            columns=columns,\n            sparse=sparse,\n            drop_first=drop_first,\n        )\n    if isinstance(data, DataFrame):\n        df = data\n    elif is_list_like(data):\n        df = DataFrame(data)\n\n    new_manager = df._query_compiler.get_dummies(\n        columns,\n        prefix=prefix,\n        prefix_sep=prefix_sep,\n        dummy_na=dummy_na,\n        drop_first=drop_first,\n    )\n    return DataFrame(query_compiler=new_manager)\n\n\ndef melt(\n    frame,\n    id_vars=None,\n    value_vars=None,\n    var_name=None,\n    value_name=\"value\",\n    col_level=None,\n):\n    return frame.melt(\n        id_vars=id_vars,\n        value_vars=value_vars,\n        var_name=var_name,\n        value_name=value_name,\n        col_level=col_level,\n    )\n\n\ndef crosstab(\n    index,\n    columns,\n    values=None,\n    rownames=None,\n    colnames=None,\n    aggfunc=None,\n    margins=False,\n    margins_name=\"All\",\n    dropna=True,\n    normalize=False,\n):\n    ErrorMessage.default_to_pandas()\n    pandas_crosstab = pandas.crosstab(\n        index,\n        columns,\n        values,\n        rownames,\n        colnames,\n        aggfunc,\n        margins,\n        margins_name,\n        dropna,\n        normalize,\n    )\n    return DataFrame(pandas_crosstab)\n", "path": "modin/pandas/reshape.py"}, {"content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\n# TODO: In the future `set_option` or similar needs to run on every node\n# in order to keep all pandas instances across nodes consistent\nimport pandas\nfrom pandas import (\n    eval,\n    unique,\n    value_counts,\n    cut,\n    to_numeric,\n    factorize,\n    test,\n    qcut,\n    match,\n    Panel,\n    date_range,\n    period_range,\n    Index,\n    MultiIndex,\n    CategoricalIndex,\n    Series,\n    bdate_range,\n    DatetimeIndex,\n    Timedelta,\n    Timestamp,\n    to_timedelta,\n    set_eng_float_format,\n    set_option,\n    NaT,\n    PeriodIndex,\n    Categorical,\n)\nimport threading\nimport os\nimport ray\n\nfrom .. import __version__\nfrom .concat import concat\nfrom .dataframe import DataFrame\nfrom .datetimes import to_datetime\nfrom .io import (\n    read_csv,\n    read_parquet,\n    read_json,\n    read_html,\n    read_clipboard,\n    read_excel,\n    read_hdf,\n    read_feather,\n    read_msgpack,\n    read_stata,\n    read_sas,\n    read_pickle,\n    read_sql,\n    read_gbq,\n    read_table,\n)\nfrom .reshape import get_dummies, melt, crosstab\nfrom .general import isna, merge, pivot_table\nfrom .. import __execution_engine__ as execution_engine\n\n__pandas_version__ = \"0.23.4\"\n\nif pandas.__version__ != __pandas_version__:\n    raise ImportError(\n        \"The pandas version installed does not match the required pandas \"\n        \"version in Modin. Please install pandas {} to use \"\n        \"Modin.\".format(__pandas_version__)\n    )\n\n# Set this so that Pandas doesn't try to multithread by itself\nos.environ[\"OMP_NUM_THREADS\"] = \"1\"\nnum_cpus = 1\n\nif execution_engine == \"Ray\":\n    try:\n        if threading.current_thread().name == \"MainThread\":\n            ray.init(\n                redirect_output=True,\n                include_webui=False,\n                redirect_worker_output=True,\n                ignore_reinit_error=True,\n            )\n            num_cpus = ray.global_state.cluster_resources()[\"CPU\"]\n    except AssertionError:\n        pass\nelif execution_engine == \"Dask\":\n    from distributed.client import _get_global_client\n\n    if threading.current_thread().name == \"MainThread\":\n        # initialize the dask client\n        client = _get_global_client()\n        if client is None:\n            from distributed import Client\n\n            client = Client()\n        num_cpus = sum(client.ncores().values())\nelif execution_engine != \"Python\":\n    raise ImportError(\"Unrecognized execution engine: {}.\".format(execution_engine))\n\nDEFAULT_NPARTITIONS = max(4, int(num_cpus))\n\n__all__ = [\n    \"DataFrame\",\n    \"Series\",\n    \"read_csv\",\n    \"read_parquet\",\n    \"read_json\",\n    \"read_html\",\n    \"read_clipboard\",\n    \"read_excel\",\n    \"read_hdf\",\n    \"read_feather\",\n    \"read_msgpack\",\n    \"read_stata\",\n    \"read_sas\",\n    \"read_pickle\",\n    \"read_sql\",\n    \"read_gbq\",\n    \"read_table\",\n    \"concat\",\n    \"eval\",\n    \"unique\",\n    \"value_counts\",\n    \"cut\",\n    \"to_numeric\",\n    \"factorize\",\n    \"test\",\n    \"qcut\",\n    \"match\",\n    \"to_datetime\",\n    \"get_dummies\",\n    \"isna\",\n    \"merge\",\n    \"pivot_table\",\n    \"Panel\",\n    \"date_range\",\n    \"Index\",\n    \"MultiIndex\",\n    \"Series\",\n    \"bdate_range\",\n    \"period_range\",\n    \"DatetimeIndex\",\n    \"to_timedelta\",\n    \"set_eng_float_format\",\n    \"set_option\",\n    \"CategoricalIndex\",\n    \"Timedelta\",\n    \"Timestamp\",\n    \"NaT\",\n    \"PeriodIndex\",\n    \"Categorical\",\n    \"__version__\",\n    \"melt\",\n    \"crosstab\",\n]\n\ndel pandas\n", "path": "modin/pandas/__init__.py"}]}