Repository: pyjanitor-devs/pyjanitor

Issue:
[ENH] Naming conventions
I wanted to discuss naming conventions for the various functions and arguments for consistency. `expand_column` has a parameter `column` whereas `add_column` had `col_name`.

Also, is `_column` necessary in each function? Would it be ok to just have an `add()` or `transform()` method? In general I'm more on the side of more verbose function names, but just wanted to throw the idea out there.

Similarly, when following the format of `limit_column_characters` functions like `change_type` should probably be named `change_column_type`.

I'm not married to any of this (except the function arguments, those should be consistent) but wanted to get peoples' ideas.


Discussion:
Thanks for raising this issue, @szuckerman! I’m also in favor of consistent naming schemes. 

Breaking this down a bit:

### Columns

I vote in favor of “col_name”. It is more explicit than “column”.

### Verbose vs Concise Functions

Zen of Python states that explicit is better than implicit, with no qualifiers afterwards. For that reason, I vote in favor of verbose names. 
Labelling this a "good first issue" for newcomers, as it basically involves a find-and-replace.
I'd be interested in taking on this issue if no one has done so yet. Would this need some support to warn users that names have changed, and that previous names will no longer work in a future version?
@HectorM14 yes, that would be great! Please do go ahead.

> Would this need some support to warn users that names have changed, and that previous names will no longer work in a future version?

Yes agreed. Since we're at version 0.16.x right now, the responsible way to evolve the API is to keep it through the next two minor versions, and the deprecate it at the next major or 3rd minor version. Perhaps raising a warning if and only if certain functions are called/keyword arguments are used.

Personally, I would prefer one function at a time, which makes it easier to review, but at the same time, I think we should trust your judgment - especially if there's things that are logically groupable, feel free to PR in them together. Looking forward to what you've got; also, don't hesitate to ping in here!
Great, I'll use my best judgement for PR scope. Is there a standard way to deprecate/discourage old argument names? I was thinking of using decorators to keep the method APIs clear about their usage going forward.
Thanks @HectorM14!

> Is there a standard way to deprecate/discourage old argument names? 

To the best of my knowledge, the standard way to do this should be to insert a conditional checking to see if that old argument name was called or not, and then inside the conditional:

1. Raise a warning.
2. Assign the value of the old argument to a new variable that has the same name as the new argument.

For example:

```python
from warning import warning

def deprecation_warning(old_arg, new_arg):
    """generic warning function."""
    warning(f"{argname.__name__} is deprecated, and will be replaced with {new_arg.__name__}")

def func(df, old_arg1, arg2, arg3, new_arg):
    """docstring here"""
    if old_arg1 is not None:
        deprecation_warning(old_arg1, new_arg)
        new_arg = old_arg1
    # continue code
```

> I was thinking of using decorators to keep the method APIs clear about their usage going forward.

That might actually be better than what I had up there! I'd like to see the pattern in the first PR, it'd be educational for myself.
[Relevant PyCon talk on refactoring and adding `warnings` for legacy code](https://youtu.be/D_6ybDcU5gc?t=787). The actual code for warnings comes in around 17:00.
Oh cool, thanks for sharing that, @szuckerman! 
Following advice in that talk, we could update `expand_column()` as follows:

```python
def expand_column(df, sep, column_name=None, concat=True, **kwargs):
    """
    Expand a categorical column with multiple labels into dummy-coded columns.

    Super sugary syntax that wraps :py:meth:`pandas.Series.str.get_dummies`.

    Functional usage example:

    .. code-block:: python

        df = expand_column(df, column_name='col_name',
                           sep=', ')  # note space in sep

    Method chaining example:

    .. code-block:: python

        import pandas as pd
        import janitor
        df = pd.DataFrame(...).expand_column(df, column_name='col_name', sep=', ')

    :param df: A pandas DataFrame.
    :param column_name: A `str` indicating which column to expand.
    :param sep: The delimiter. Example delimiters include `|`, `, `, `,` etc.
    :param bool concat: Whether to return the expanded column concatenated to
        the original dataframe (`concat=True`), or to return it standalone
        (`concat=False`).
    """
    if kwargs and column_name is not None:
        raise TypeError('Mixed usage of column and column_name')
    if column_name is None:
        warnings.warn('Use of column is deprecated. You should use column_name.')
        column_name = kwargs['column']
    expanded = df[column_name].str.get_dummies(sep=sep)
    if concat:
        df = df.join(expanded)
        return df
    else:
        return expanded
```
One notable consequence of this, is having to set column_name as None, which requires us to move its position in the list of arguments (during the deprecation period). 

The way it's written also assumes that additional arguments received would only relate to deprecated usage, and include `column`.

Another issue is to decide what is the proper variable form of "column". Even when the variable names are explicit, `*_col_*` and `*_column_*` are both used throughout the code.
@HectorM14 those are great points you've raised :smile:. 

> Another issue is to decide what is the proper variable form of "column". Even when the variable names are explicit, `*_col_*` and `*_column_*` are both used throughout the code.

I am guilty of being inconsistent here, as I sometimes use `col` and sometimes use `column` and sometimes use `colname`. Yes, being consistent is a great thing. I would personally vote for `column_name`, in adherence to "explicit is better than implicit", but if there are better reasons (brevity, perhaps?) for other conventions, not a problem! I have no strong opinions on this one, as long as we can make it towards consistency in the end!
I like column_name too. I can aim for being explicit with these names unless they become too unwieldy. I'll start making individual PRs for each updated function. Updating variables relating columns is easy, but if anyone finds other variables that should be updated, they can list them here.
Updating names is becoming a bit more complicated. For example we currently have:
```python
def coalesce(df, columns, new_column_name):
```
Following the current style we've been using, we would update this to:
```python
def coalesce(df, new_column_name, columns=None, **kwargs):
```
But quick, intuitive usage of this function from `example.py` is:
```python
df = (
    df
    .coalesce(["certification", "certification_1"], "certification")
```
With the proposed changes, this would become (due to rearranging of argument order):
```python
df = (
    df
    .coalesce(column_names=["certification", "certification_1"], new_column_name="certification")
)
```
Which is excessively more complicated. So our fix will have to be more complex/clever to keep it clean for the user.
@HectorM14 I wouldn't worry too much about the kwargs. In Python, explicit is better than implicit, generally speaking. I find myself writing out kwarg names even though it is sometimes easier to not do so, because I recognize that it makes the code more readable, actually. I would encourage you to keep going with the PRs :smile:. 
Thanks @ericmjl , explicit is good!
I actually came across this example on [SO](https://stackoverflow.com/questions/49802412/how-to-implement-deprecation-in-python-with-argument-alias):

```python
import functools
import warnings

def _deprecated_alias(**aliases):
    def deco(f):
        @functools.wraps(f)
        def wrapper(*args, **kwargs):
            _rename_kwargs(f.__name__, kwargs, aliases)
            return f(*args, **kwargs)
        return wrapper
    return deco

def _rename_kwargs(func_name, kwargs, aliases):
    for alias, new in aliases.items():
        if alias in kwargs:
            if new in kwargs:
                raise TypeError('{} received both {} and {}'.format(
                    func_name, alias, new))
            warnings.warn('{} is deprecated; use {}'.format(alias, new),
                          DeprecationWarning)
            kwargs[new] = kwargs.pop(alias)
```
using this as a decorator we would be able to do:
```python
@_deprecated_alias(a='alpha', b='beta')
def simple_sum(alpha, beta):
    return alpha + beta

print(simple_sum(alpha=6, beta=9))
print(simple_sum(a=6, b=9))
```
With this method, updating the argument names would be much faster, and when the deprecation period has expired, we only need to delete one/two lines instead of 6+ lines currently. If we used this approach, I would make the above functions more human-readable.

What do you think?
This is a really great find! I'm all for reducing the maintenance burden :smile:. 

Perhaps this function can be inserted into `utils.py`? Be sure to acknowledge the source by linking to SO!

Also, does this mean you'd redo the name changes already PR'd? I don't want to put too much pressure on you, but if you'd do so, that'd be superb for maintenance purposes!
Cool! `utils.py` sounds like a good place. 

I'll link to the SO post in the `_deprecated_alias` docstring. 
I'll start with a PR that includes these new functions, and include some tests to make sure it works/fails/warns as expected. After that, I'm fine updating the previously changed functions and moving forward. I actually was considering revisiting those functions anyway after learning that there was an actual `DeprecationWarning` subclass.
I have been merging from `upstream` before committing, should I be rebasing instead?
Rebasing works as well! It's a suggestion from @zbarry, which, frankly speaking, I rarely do, because it's just easier to remember `git fetch upstream; git merge upstream/dev`. :smile:

That said, good practices are good to use, so I'm going to try moving to rebasing now.
@HectorM14 - that decorator is a great find. Thanks for adding it!
Yes! It's a very clean method that I would have been happy to find in the Python standard library. I assume most projects go through name changes and deprecation at some point. 🤷‍♂️
> Yes agreed. Since we're at version 0.16.x right now, the responsible way to evolve the API is to keep it through the next two minor versions, and the deprecate it at the next major or 3rd minor version. Perhaps raising a warning if and only if certain functions are called/keyword arguments are used.

I didn't think of this previously, but should I add some tag comment to the decorators to indicate under which release the names were deprecated? After the decided major/minor release, it'll be easy to search for `#0.16x` etc and remove those lines. In a similar vein, in the unlikely event a function has multiple rounds of deprecation at different releases, a second decorator could be added for the new deprecation (unless that's just too complicated).

Resolving patch:
diff --git a/janitor/io.py b/janitor/io.py
--- a/janitor/io.py
+++ b/janitor/io.py
@@ -1,34 +1,49 @@
 import os
 from glob import glob
+from typing import Iterable, Union
 
 import pandas as pd
 
+from .utils import deprecated_alias
 
-def read_csvs(filespath: str, seperate_df: bool = False, **kwargs):
+
+@deprecated_alias(seperate_df="separate_df", filespath="files_path")
+def read_csvs(
+    files_path: Union[str, Iterable[str]], separate_df: bool = False, **kwargs
+) -> Union[pd.DataFrame, dict]:
     """
-    :param filespath: The filepath pattern matching the CSVs files.
+    :param files_path: The filepath pattern matching the CSVs files.
         Accepts regular expressions, with or without csv extension.
-    :param seperate_df: If False (default) returns a single Dataframe
+        Also accepts iterable of file paths.
+    :param separate_df: If False (default) returns a single Dataframe
         with the concatenation of the csv files.
-        If True, returns a dictionary of seperate dataframes
+        If True, returns a dictionary of separate dataframes
         for each CSV file.
     :param kwargs: Keyword arguments to pass into the
         original pandas `read_csv`.
     """
     # Sanitize input
-    assert filespath is not None
-    assert len(filespath) != 0
+    assert files_path is not None
+    assert len(files_path) != 0
 
     # Read the csv files
-    dfs = {
-        os.path.basename(f): pd.read_csv(f, **kwargs) for f in glob(filespath)
-    }
+    # String to file/folder or file pattern provided
+    if isinstance(files_path, str):
+        dfs = {
+            os.path.basename(f): pd.read_csv(f, **kwargs)
+            for f in glob(files_path)
+        }
+    # Iterable of file paths provided
+    else:
+        dfs = {
+            os.path.basename(f): pd.read_csv(f, **kwargs) for f in files_path
+        }
     # Check if dataframes have been read
     if len(dfs) == 0:
-        raise ValueError("No CSV files to read with the given filespath")
+        raise ValueError("No CSV files to read with the given `files_path`")
     # Concatenate the dataframes if requested (default)
     col_names = list(dfs.values())[0].columns
-    if not seperate_df:
+    if not separate_df:
         # If columns do not match raise an error
         for df in dfs.values():
             if not all(df.columns == col_names):


Test patch:
diff --git a/tests/functions/test_read_csvs.py b/tests/io/test_read_csvs.py
similarity index 62%
rename from tests/functions/test_read_csvs.py
rename to tests/io/test_read_csvs.py
--- a/tests/functions/test_read_csvs.py
+++ b/tests/io/test_read_csvs.py
@@ -12,13 +12,12 @@
 def create_csv_file(number_of_files, col_names=None):
     for i in range(number_of_files):
         filename = CSV_FILE_PATH.format(i)
-        df = pd.DataFrame([[1, 2, 3], [1, 2, 3], [1, 2, 3]])
+        df = pd.DataFrame([[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]])
         df.to_csv(filename, index=False)
 
 
 def remove_csv_files():
-    # Get a list of all the file paths that ends with .txt
-    # from in specified directory
+    # Get a list of all the file paths matching pattern in specified directory
     fileList = glob.glob(CSV_FILE_PATH.format("*"))
 
     # Iterate over the list of filepaths & remove each file.
@@ -29,16 +28,17 @@ def remove_csv_files():
 @pytest.mark.functions
 def test_read_csvs_one_csv_path():
     # Setup
-    # When a CSV file with 3 cols and 3 rows is on disk
-    create_csv_file(1)
+    # When a CSV file with 3 cols and 4 rows is on disk
+    number_of_files = 1
+    create_csv_file(number_of_files)
 
     # If the csv file is read into DataFrame
     dfs = io.read_csvs(CSV_FILE_PATH.format("*"))
 
-    # Then the dataframe has 3 cols and 3 rows
+    # Then the dataframe has 3 cols and 4 rows
     try:
         assert len(dfs.columns) == 3
-        assert len(dfs) == 3
+        assert len(dfs) == 4
     finally:
         # Cleanup
         remove_csv_files()
@@ -62,16 +62,17 @@ def test_read_csvs_zero_csv_path():
 @pytest.mark.functions
 def test_read_csvs_three_csv_path():
     # Setup
-    # When a CSV file with 3 cols and 3 rows is on disk
-    create_csv_file(3)
+    # When a CSV file with 3 cols and 4 rows is on disk
+    number_of_files = 3
+    create_csv_file(number_of_files)
 
     # If the csv file is read into DataFrame
     dfs = io.read_csvs(CSV_FILE_PATH.format("*"))
 
-    # Then the dataframe has 3 cols and 9 rows
+    # Then the dataframe has 3 cols and 12 rows
     try:
         assert len(dfs.columns) == 3
-        assert len(dfs) == 9
+        assert len(dfs) == 4 * number_of_files
     finally:
         # Cleanup
         remove_csv_files()
@@ -80,17 +81,18 @@ def test_read_csvs_three_csv_path():
 @pytest.mark.functions
 def test_read_csvs_three_separated_csv_path():
     # Setup
-    # When a CSV file with 3 cols and 3 rows is on disk
-    create_csv_file(3)
+    # When a CSV file with 3 cols and 4 rows is on disk
+    number_of_files = 3
+    create_csv_file(number_of_files)
 
     # If the csv file is read into DataFrame
-    dfs = io.read_csvs(CSV_FILE_PATH.format("*"), seperate_df=True)
+    dfs = io.read_csvs(CSV_FILE_PATH.format("*"), separate_df=True)
 
     # Then the dataframe list has 3 dataframes
     try:
-        assert len(dfs) == 3
+        assert len(dfs) == number_of_files
         for df in dfs.values():
-            assert len(df) == 3
+            assert len(df) == 4
             assert len(df.columns) == 3
     finally:
         # Cleanup
@@ -99,7 +101,7 @@ def test_read_csvs_three_separated_csv_path():
 
 @pytest.mark.functions
 def test_read_csvs_two_unmatching_csv_files():
-    # Setupmy
+    # Setup
     # When two csv files do not have same column names
     df = pd.DataFrame(
         [[1, 2, 3], [1, 2, 3], [1, 2, 3]], columns=["a", "b", "c"]
@@ -120,3 +122,25 @@ def test_read_csvs_two_unmatching_csv_files():
         pass
     finally:
         remove_csv_files()
+
+
+@pytest.mark.functions
+def test_read_csvs_lists():
+    # Setup
+    # When a CSV file with 3 cols and 4 rows is on disk
+    number_of_files = 3
+    create_csv_file(number_of_files)
+    csvs_list = [CSV_FILE_PATH.format(i) for i in range(number_of_files)]
+
+    # If the list of csv files is read into DataFrame
+    dfs = io.read_csvs(files_path=csvs_list, separate_df=True)
+
+    # Then the dataframe list has 3 dataframes
+    try:
+        assert len(dfs) == number_of_files
+        for df in dfs.values():
+            assert len(df) == 4
+            assert len(df.columns) == 3
+    finally:
+        # Cleanup
+        remove_csv_files()