prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Some modules are missing from the top-level import
  Issue referenced during PR here: https://github.com/pyjanitor-devs/pyjanitor/pull/977#discussion_r781732964
  
  For example, now running:
  
  ```python
  import janitor as jn
  jn.io.read_csvs("")  # throws AttributeError: module 'janitor' has no attribute 'io'
  ```
  
  Similarly for other modules like biology or timeseries.
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `janitor/__init__.py`
  Content:
  ```
  1 """Top-level janitor API lives here."""
  2 try:
  3     import janitor.xarray  # noqa: F401
  4 except ImportError:
  5     pass
  6 
  7 from .functions import *  # noqa: F403, F401
  8 from .math import *  # noqa: F403, F401
  9 from .ml import get_features_targets as _get_features_targets
  10 from .utils import refactored_function
  11 from .accessors import *  # noqa: F403, F401
  12 
  13 
  14 @refactored_function(
  15     "get_features_targets() has moved. Please use ml.get_features_targets()."
  16 )
  17 def get_features_targets(*args, **kwargs):
  18     """Wrapper for get_features_targets."""
  19     return _get_features_targets(*args, **kwargs)
  20 
  21 
  22 __version__ = "0.22.0"
  23
  ```
  
  Path: `janitor/io.py`
  Content:
  ```
  1 import os
  2 import subprocess
  3 from glob import glob
  4 from io import StringIO
  5 from typing import Iterable, Union
  6 
  7 import pandas as pd
  8 
  9 from .errors import JanitorError
  10 from .utils import deprecated_alias, check
  11 
  12 
  13 @deprecated_alias(seperate_df="separate_df", filespath="files_path")
  14 def read_csvs(
  15     files_path: Union[str, Iterable[str]], separate_df: bool = False, **kwargs
  16 ) -> Union[pd.DataFrame, dict]:
  17     """
  18     Read multiple CSV files and return a dictionary of DataFrames, or
  19     one concatenated DataFrame.
  20 
  21     :param files_path: The filepath pattern matching the CSV files.
  22         Accepts regular expressions, with or without `.csv` extension.
  23         Also accepts iterable of file paths.
  24     :param separate_df: If `False` (default), returns a single Dataframe
  25         with the concatenation of the csv files.
  26         If `True`, returns a dictionary of separate DataFrames
  27         for each CSV file.
  28     :param kwargs: Keyword arguments to pass into the
  29         original pandas `read_csv`.
  30     :returns: DataFrame of concatenated DataFrames or dictionary of DataFrames.
  31     :raises JanitorError: if `None` provided for `files_path`.
  32     :raises JanitorError: if length of `files_path` is `0`.
  33     :raises ValueError: if no CSV files exist in `files_path`.
  34     :raises ValueError: if columns in input CSV files do not match.
  35     """
  36     # Sanitize input
  37     if files_path is None:
  38         raise JanitorError("`None` provided for `files_path`")
  39     if len(files_path) == 0:
  40         raise JanitorError("0 length `files_path` provided")
  41 
  42     # Read the csv files
  43     # String to file/folder or file pattern provided
  44     if isinstance(files_path, str):
  45         dfs_dict = {
  46             os.path.basename(f): pd.read_csv(f, **kwargs)
  47             for f in glob(files_path)
  48         }
  49     # Iterable of file paths provided
  50     else:
  51         dfs_dict = {
  52             os.path.basename(f): pd.read_csv(f, **kwargs) for f in files_path
  53         }
  54     # Check if dataframes have been read
  55     if len(dfs_dict) == 0:
  56         raise ValueError("No CSV files to read with the given `files_path`")
  57     # Concatenate the dataframes if requested (default)
  58     col_names = list(dfs_dict.values())[0].columns  # noqa: PD011
  59     if not separate_df:
  60         # If columns do not match raise an error
  61         for df in dfs_dict.values():  # noqa: PD011
  62             if not all(df.columns == col_names):
  63                 raise ValueError(
  64                     "Columns in input CSV files do not match."
  65                     "Files cannot be concatenated"
  66                 )
  67         return pd.concat(
  68             list(dfs_dict.values()),
  69             ignore_index=True,
  70             sort=False,  # noqa: PD011
  71         )
  72     else:
  73         return dfs_dict
  74 
  75 
  76 def read_commandline(cmd: str, **kwargs) -> pd.DataFrame:
  77     """
  78     Read a CSV file based on a command-line command.
  79 
  80     For example, you may wish to run the following command on `sep-quarter.csv`
  81     before reading it into a pandas DataFrame:
  82 
  83     ```bash
  84     cat sep-quarter.csv | grep .SEA1AA
  85     ```
  86 
  87     In this case, you can use the following Python code to load the dataframe:
  88 
  89     ```python
  90     import janitor as jn
  91     df = jn.io.read_commandline("cat data.csv | grep .SEA1AA")
  92 
  93     This function assumes that your command line command will return
  94     an output that is parsable using pandas.read_csv and StringIO.
  95     We default to using pd.read_csv underneath the hood.
  96     Keyword arguments are passed through to read_csv.
  97     ```
  98 
  99     :param cmd: Shell command to preprocess a file on disk.
  100     :param kwargs: Keyword arguments that are passed through to pd.read_csv().
  101     :raises JanitorError: If commandline command is malformed or invalid.
  102     :returns: A pandas DataFrame parsed from the stdout of the underlying
  103         shell.
  104     """
  105 
  106     check("cmd", cmd, [str])
  107     outcome = subprocess.run(cmd, shell=True, capture_output=True, text=True)
  108     if outcome.returncode != 0:
  109         raise JanitorError(outcome.stderr)
  110     else:
  111         outcome = outcome.stdout
  112     return pd.read_csv(StringIO(outcome), **kwargs)
  113
  ```
  --- 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()
  
  ```
  
golden_diff: diff --git a/janitor/__init__.py b/janitor/__init__.py
  --- a/janitor/__init__.py
  +++ b/janitor/__init__.py
  @@ -5,6 +5,7 @@
       pass
   
   from .functions import *  # noqa: F403, F401
  +from .io import *  # noqa: F403, F401
   from .math import *  # noqa: F403, F401
   from .ml import get_features_targets as _get_features_targets
   from .utils import refactored_function
  diff --git a/janitor/io.py b/janitor/io.py
  --- a/janitor/io.py
  +++ b/janitor/io.py
  @@ -88,7 +88,7 @@
   
       ```python
       import janitor as jn
  -    df = jn.io.read_commandline("cat data.csv | grep .SEA1AA")
  +    df = jn.read_commandline("cat data.csv | grep .SEA1AA")
   
       This function assumes that your command line command will return
       an output that is parsable using pandas.read_csv and StringIO.
  
verification_info: {"golden_diff": "diff --git a/janitor/__init__.py b/janitor/__init__.py\n--- a/janitor/__init__.py\n+++ b/janitor/__init__.py\n@@ -5,6 +5,7 @@\n     pass\n \n from .functions import *  # noqa: F403, F401\n+from .io import *  # noqa: F403, F401\n from .math import *  # noqa: F403, F401\n from .ml import get_features_targets as _get_features_targets\n from .utils import refactored_function\ndiff --git a/janitor/io.py b/janitor/io.py\n--- a/janitor/io.py\n+++ b/janitor/io.py\n@@ -88,7 +88,7 @@\n \n     ```python\n     import janitor as jn\n-    df = jn.io.read_commandline(\"cat data.csv | grep .SEA1AA\")\n+    df = jn.read_commandline(\"cat data.csv | grep .SEA1AA\")\n \n     This function assumes that your command line command will return\n     an output that is parsable using pandas.read_csv and StringIO.\n", "issue": "Some modules are missing from the top-level import\nIssue referenced during PR here: https://github.com/pyjanitor-devs/pyjanitor/pull/977#discussion_r781732964\r\n\r\nFor example, now running:\r\n\r\n```python\r\nimport janitor as jn\r\njn.io.read_csvs(\"\")  # throws AttributeError: module 'janitor' has no attribute 'io'\r\n```\r\n\r\nSimilarly for other modules like biology or timeseries.\n", "before_files": [{"content": "\"\"\"Top-level janitor API lives here.\"\"\"\ntry:\n    import janitor.xarray  # noqa: F401\nexcept ImportError:\n    pass\n\nfrom .functions import *  # noqa: F403, F401\nfrom .math import *  # noqa: F403, F401\nfrom .ml import get_features_targets as _get_features_targets\nfrom .utils import refactored_function\nfrom .accessors import *  # noqa: F403, F401\n\n\n@refactored_function(\n    \"get_features_targets() has moved. Please use ml.get_features_targets().\"\n)\ndef get_features_targets(*args, **kwargs):\n    \"\"\"Wrapper for get_features_targets.\"\"\"\n    return _get_features_targets(*args, **kwargs)\n\n\n__version__ = \"0.22.0\"\n", "path": "janitor/__init__.py"}, {"content": "import os\nimport subprocess\nfrom glob import glob\nfrom io import StringIO\nfrom typing import Iterable, Union\n\nimport pandas as pd\n\nfrom .errors import JanitorError\nfrom .utils import deprecated_alias, check\n\n\n@deprecated_alias(seperate_df=\"separate_df\", filespath=\"files_path\")\ndef read_csvs(\n    files_path: Union[str, Iterable[str]], separate_df: bool = False, **kwargs\n) -> Union[pd.DataFrame, dict]:\n    \"\"\"\n    Read multiple CSV files and return a dictionary of DataFrames, or\n    one concatenated DataFrame.\n\n    :param files_path: The filepath pattern matching the CSV files.\n        Accepts regular expressions, with or without `.csv` extension.\n        Also accepts iterable of file paths.\n    :param separate_df: If `False` (default), returns a single Dataframe\n        with the concatenation of the csv files.\n        If `True`, returns a dictionary of separate DataFrames\n        for each CSV file.\n    :param kwargs: Keyword arguments to pass into the\n        original pandas `read_csv`.\n    :returns: DataFrame of concatenated DataFrames or dictionary of DataFrames.\n    :raises JanitorError: if `None` provided for `files_path`.\n    :raises JanitorError: if length of `files_path` is `0`.\n    :raises ValueError: if no CSV files exist in `files_path`.\n    :raises ValueError: if columns in input CSV files do not match.\n    \"\"\"\n    # Sanitize input\n    if files_path is None:\n        raise JanitorError(\"`None` provided for `files_path`\")\n    if len(files_path) == 0:\n        raise JanitorError(\"0 length `files_path` provided\")\n\n    # Read the csv files\n    # String to file/folder or file pattern provided\n    if isinstance(files_path, str):\n        dfs_dict = {\n            os.path.basename(f): pd.read_csv(f, **kwargs)\n            for f in glob(files_path)\n        }\n    # Iterable of file paths provided\n    else:\n        dfs_dict = {\n            os.path.basename(f): pd.read_csv(f, **kwargs) for f in files_path\n        }\n    # Check if dataframes have been read\n    if len(dfs_dict) == 0:\n        raise ValueError(\"No CSV files to read with the given `files_path`\")\n    # Concatenate the dataframes if requested (default)\n    col_names = list(dfs_dict.values())[0].columns  # noqa: PD011\n    if not separate_df:\n        # If columns do not match raise an error\n        for df in dfs_dict.values():  # noqa: PD011\n            if not all(df.columns == col_names):\n                raise ValueError(\n                    \"Columns in input CSV files do not match.\"\n                    \"Files cannot be concatenated\"\n                )\n        return pd.concat(\n            list(dfs_dict.values()),\n            ignore_index=True,\n            sort=False,  # noqa: PD011\n        )\n    else:\n        return dfs_dict\n\n\ndef read_commandline(cmd: str, **kwargs) -> pd.DataFrame:\n    \"\"\"\n    Read a CSV file based on a command-line command.\n\n    For example, you may wish to run the following command on `sep-quarter.csv`\n    before reading it into a pandas DataFrame:\n\n    ```bash\n    cat sep-quarter.csv | grep .SEA1AA\n    ```\n\n    In this case, you can use the following Python code to load the dataframe:\n\n    ```python\n    import janitor as jn\n    df = jn.io.read_commandline(\"cat data.csv | grep .SEA1AA\")\n\n    This function assumes that your command line command will return\n    an output that is parsable using pandas.read_csv and StringIO.\n    We default to using pd.read_csv underneath the hood.\n    Keyword arguments are passed through to read_csv.\n    ```\n\n    :param cmd: Shell command to preprocess a file on disk.\n    :param kwargs: Keyword arguments that are passed through to pd.read_csv().\n    :raises JanitorError: If commandline command is malformed or invalid.\n    :returns: A pandas DataFrame parsed from the stdout of the underlying\n        shell.\n    \"\"\"\n\n    check(\"cmd\", cmd, [str])\n    outcome = subprocess.run(cmd, shell=True, capture_output=True, text=True)\n    if outcome.returncode != 0:\n        raise JanitorError(outcome.stderr)\n    else:\n        outcome = outcome.stdout\n    return pd.read_csv(StringIO(outcome), **kwargs)\n", "path": "janitor/io.py"}], "after_files": [{"content": "\"\"\"Top-level janitor API lives here.\"\"\"\ntry:\n    import janitor.xarray  # noqa: F401\nexcept ImportError:\n    pass\n\nfrom .functions import *  # noqa: F403, F401\nfrom .io import *  # noqa: F403, F401\nfrom .math import *  # noqa: F403, F401\nfrom .ml import get_features_targets as _get_features_targets\nfrom .utils import refactored_function\nfrom .accessors import *  # noqa: F403, F401\n\n\n@refactored_function(\n    \"get_features_targets() has moved. Please use ml.get_features_targets().\"\n)\ndef get_features_targets(*args, **kwargs):\n    \"\"\"Wrapper for get_features_targets.\"\"\"\n    return _get_features_targets(*args, **kwargs)\n\n\n__version__ = \"0.22.0\"\n", "path": "janitor/__init__.py"}, {"content": "import os\nimport subprocess\nfrom glob import glob\nfrom io import StringIO\nfrom typing import Iterable, Union\n\nimport pandas as pd\n\nfrom .errors import JanitorError\nfrom .utils import deprecated_alias, check\n\n\n@deprecated_alias(seperate_df=\"separate_df\", filespath=\"files_path\")\ndef read_csvs(\n    files_path: Union[str, Iterable[str]], separate_df: bool = False, **kwargs\n) -> Union[pd.DataFrame, dict]:\n    \"\"\"\n    Read multiple CSV files and return a dictionary of DataFrames, or\n    one concatenated DataFrame.\n\n    :param files_path: The filepath pattern matching the CSV files.\n        Accepts regular expressions, with or without `.csv` extension.\n        Also accepts iterable of file paths.\n    :param separate_df: If `False` (default), returns a single Dataframe\n        with the concatenation of the csv files.\n        If `True`, returns a dictionary of separate DataFrames\n        for each CSV file.\n    :param kwargs: Keyword arguments to pass into the\n        original pandas `read_csv`.\n    :returns: DataFrame of concatenated DataFrames or dictionary of DataFrames.\n    :raises JanitorError: if `None` provided for `files_path`.\n    :raises JanitorError: if length of `files_path` is `0`.\n    :raises ValueError: if no CSV files exist in `files_path`.\n    :raises ValueError: if columns in input CSV files do not match.\n    \"\"\"\n    # Sanitize input\n    if files_path is None:\n        raise JanitorError(\"`None` provided for `files_path`\")\n    if len(files_path) == 0:\n        raise JanitorError(\"0 length `files_path` provided\")\n\n    # Read the csv files\n    # String to file/folder or file pattern provided\n    if isinstance(files_path, str):\n        dfs_dict = {\n            os.path.basename(f): pd.read_csv(f, **kwargs)\n            for f in glob(files_path)\n        }\n    # Iterable of file paths provided\n    else:\n        dfs_dict = {\n            os.path.basename(f): pd.read_csv(f, **kwargs) for f in files_path\n        }\n    # Check if dataframes have been read\n    if len(dfs_dict) == 0:\n        raise ValueError(\"No CSV files to read with the given `files_path`\")\n    # Concatenate the dataframes if requested (default)\n    col_names = list(dfs_dict.values())[0].columns  # noqa: PD011\n    if not separate_df:\n        # If columns do not match raise an error\n        for df in dfs_dict.values():  # noqa: PD011\n            if not all(df.columns == col_names):\n                raise ValueError(\n                    \"Columns in input CSV files do not match.\"\n                    \"Files cannot be concatenated\"\n                )\n        return pd.concat(\n            list(dfs_dict.values()),\n            ignore_index=True,\n            sort=False,  # noqa: PD011\n        )\n    else:\n        return dfs_dict\n\n\ndef read_commandline(cmd: str, **kwargs) -> pd.DataFrame:\n    \"\"\"\n    Read a CSV file based on a command-line command.\n\n    For example, you may wish to run the following command on `sep-quarter.csv`\n    before reading it into a pandas DataFrame:\n\n    ```bash\n    cat sep-quarter.csv | grep .SEA1AA\n    ```\n\n    In this case, you can use the following Python code to load the dataframe:\n\n    ```python\n    import janitor as jn\n    df = jn.read_commandline(\"cat data.csv | grep .SEA1AA\")\n\n    This function assumes that your command line command will return\n    an output that is parsable using pandas.read_csv and StringIO.\n    We default to using pd.read_csv underneath the hood.\n    Keyword arguments are passed through to read_csv.\n    ```\n\n    :param cmd: Shell command to preprocess a file on disk.\n    :param kwargs: Keyword arguments that are passed through to pd.read_csv().\n    :raises JanitorError: If commandline command is malformed or invalid.\n    :returns: A pandas DataFrame parsed from the stdout of the underlying\n        shell.\n    \"\"\"\n\n    check(\"cmd\", cmd, [str])\n    outcome = subprocess.run(cmd, shell=True, capture_output=True, text=True)\n    if outcome.returncode != 0:\n        raise JanitorError(outcome.stderr)\n    else:\n        outcome = outcome.stdout\n    return pd.read_csv(StringIO(outcome), **kwargs)\n", "path": "janitor/io.py"}]}