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.

Resolving patch:
diff --git a/janitor/functions.py b/janitor/functions.py
--- a/janitor/functions.py
+++ b/janitor/functions.py
@@ -313,23 +313,27 @@ def label_encode(df, column_names):
 
 
 @pf.register_dataframe_method
-def get_features_targets(df, target_columns, feature_columns=None):
+@deprecated_alias(
+    target_columns="target_column_names",
+    feature_columns="feature_column_names",
+)
+def get_features_targets(df, target_column_names, feature_column_names=None):
     """
     Get the features and targets as separate DataFrames/Series.
 
     The behaviour is as such:
 
-    - `target_columns` is mandatory.
-    - If `feature_columns` is present, then we will respect the column names
+    - `target_column_names` is mandatory.
+    - If `feature_column_names` is present, then we will respect the column names
     inside there.
-    - If `feature_columns` is not passed in, then we will assume that the
+    - If `feature_column_names` is not passed in, then we will assume that the
     rest of the columns are feature columns, and return them.
 
     Functional usage example:
 
     .. code-block:: python
 
-        X, y = get_features_targets(df, target_columns="measurement")
+        X, y = get_features_targets(df, target_column_names="measurement")
 
     Method chaining example:
 
@@ -339,34 +343,35 @@ def get_features_targets(df, target_columns, feature_columns=None):
         import janitor
         df = pd.DataFrame(...)
         target_cols = ['output1', 'output2']
-        X, y = df.get_features_targets(target_columns=target_cols)  # noqa: E501
+        X, y = df.get_features_targets(target_column_names=target_cols)  # noqa: E501
 
     :param df: The pandas DataFrame object.
-    :param str/iterable target_columns: Either a column name or an iterable\
+    :param str/iterable target_column_names: Either a column name or an iterable\
         (list or tuple) of column names that are the target(s) to be predicted.
-    :param str/iterable feature_columns: (optional) The column name or \
+    :param str/iterable feature_column_names: (optional) The column name or \
         iterable of column names that are the features (a.k.a. predictors) \
         used to predict the targets.
     :returns: (X, Y) the feature matrix (X) and the target matrix (Y). Both \
         are pandas DataFrames.
     """
-    Y = df[target_columns]
+    Y = df[target_column_names]
 
-    if feature_columns:
-        X = df[feature_columns]
+    if feature_column_names:
+        X = df[feature_column_names]
     else:
-        if isinstance(target_columns, str):
-            xcols = [c for c in df.columns if target_columns != c]
-        elif isinstance(target_columns, list) or isinstance(
-            target_columns, tuple
+        if isinstance(target_column_names, str):
+            xcols = [c for c in df.columns if target_column_names != c]
+        elif isinstance(target_column_names, list) or isinstance(
+            target_column_names, tuple
         ):  # noqa: W503
-            xcols = [c for c in df.columns if c not in target_columns]
+            xcols = [c for c in df.columns if c not in target_column_names]
         X = df[xcols]
     return X, Y
 
 
 @pf.register_dataframe_method
-def rename_column(df, old, new):
+@deprecated_alias(old="old_column_name", new="new_column_name")
+def rename_column(df, old_column_name, new_column_name):
     """
     Rename a column in place.
 
@@ -388,13 +393,15 @@ def rename_column(df, old, new):
     at a time. If you are convinced that there are multiple columns in need of
     changing, then use the :py:meth:`pandas.DataFrame.rename` method.
 
-    :param str old: The old column name.
-    :param str new: The new column name.
+    :param str old_column_name: The old column name.
+    :param str new_column_name: The new column name.
     :returns: A pandas DataFrame with renamed columns.
     """
-    if old not in df.columns:
-        raise ValueError(f"{old} not present in dataframe columns!")
-    return df.rename(columns={old: new})
+    if old_column_name not in df.columns:
+        raise ValueError(
+            f"{old_column_name} not present in dataframe columns!"
+        )
+    return df.rename(columns={old_column_name: new_column_name})
 
 
 @pf.register_dataframe_method
@@ -455,7 +462,8 @@ def reorder_columns(
 
 
 @pf.register_dataframe_method
-def coalesce(df, columns, new_column_name):
+@deprecated_alias(columns="column_names")
+def coalesce(df, column_names, new_column_name):
     """
 
     Coalesces two or more columns of data in order of column names provided.
@@ -464,7 +472,7 @@ def coalesce(df, columns, new_column_name):
 
     .. code-block:: python
 
-        df = coalesce(df, columns=['col1', 'col2'])
+        df = coalesce(df, column_names=['col1', 'col2'])
 
     Method chaining example:
 
@@ -483,16 +491,16 @@ def coalesce(df, columns, new_column_name):
     method (which we're just using internally anyways).
 
     :param df: A pandas DataFrame.
-    :param columns: A list of column names.
+    :param column_names: A list of column names.
     :param str new_column_name: The new column name after combining.
     :returns: A pandas DataFrame with coalesced columns.
     """
-    series = [df[c] for c in columns]
+    series = [df[c] for c in column_names]
 
     def _coalesce(series1, series2):
         return series1.combine_first(series2)
 
-    df = df.drop(columns=columns)
+    df = df.drop(columns=column_names)
     df[new_column_name] = reduce(_coalesce, series)  # noqa: F821
     return df
 
@@ -607,7 +615,8 @@ def _conv(value):
 
 
 @pf.register_dataframe_method
-def fill_empty(df, columns, value):
+@deprecated_alias(columns="column_names")
+def fill_empty(df, column_names, value):
     """
     Fill `NaN` values in specified columns with a given value.
 
@@ -617,7 +626,7 @@ def fill_empty(df, columns, value):
 
     .. code-block:: python
 
-        df = fill_empty(df, columns=['col1', 'col2'], value=0)
+        df = fill_empty(df, column_names=['col1', 'col2'], value=0)
 
     Method chaining example:
 
@@ -625,26 +634,27 @@ def fill_empty(df, columns, value):
 
         import pandas as pd
         import janitor
-        df = pd.DataFrame(...).fill_empty(df, columns='col1', value=0)
+        df = pd.DataFrame(...).fill_empty(df, column_names='col1', value=0)
 
     :param df: A pandas DataFrame.
-    :param columns: Either a `str` or `list` or `tuple`. If a string is passed
-        in, then only that column will be filled; if a list or tuple of strings
-        are passed in, then they will all be filled with the same value.
+    :param column_names: Either a `str` or `list` or `tuple`. If a string
+        is passed in, then only that column will be filled; if a list or tuple
+        of strings are passed in, then they will all be filled with the same
+        value.
     :param value: The value that replaces the `NaN` values.
     :returns: A pandas DataFrame with `Nan` values filled.
     """
-    if isinstance(columns, list) or isinstance(columns, tuple):
-        for col in columns:
+    if isinstance(column_names, list) or isinstance(column_names, tuple):
+        for col in column_names:
             assert (
                 col in df.columns
             ), "{col} missing from dataframe columns!".format(col=col)
             df[col] = df[col].fillna(value)
     else:
         assert (
-            columns in df.columns
-        ), "{col} missing from dataframe columns!".format(col=columns)
-        df[columns] = df[columns].fillna(value)
+            column_names in df.columns
+        ), "{col} missing from dataframe columns!".format(col=column_names)
+        df[column_names] = df[column_names].fillna(value)
 
     return df
 


Test patch:
diff --git a/tests/functions/test_fill_empty.py b/tests/functions/test_fill_empty.py
--- a/tests/functions/test_fill_empty.py
+++ b/tests/functions/test_fill_empty.py
@@ -3,11 +3,11 @@
 
 @pytest.mark.functions
 def test_fill_empty(null_df):
-    df = null_df.fill_empty(columns=["2"], value=3)
+    df = null_df.fill_empty(column_names=["2"], value=3)
     assert set(df.loc[:, "2"]) == set([3])
 
 
 @pytest.mark.functions
 def test_fill_empty_column_string(null_df):
-    df = null_df.fill_empty(columns="2", value=3)
+    df = null_df.fill_empty(column_names="2", value=3)
     assert set(df.loc[:, "2"]) == set([3])
diff --git a/tests/functions/test_get_features_targets.py b/tests/functions/test_get_features_targets.py
--- a/tests/functions/test_get_features_targets.py
+++ b/tests/functions/test_get_features_targets.py
@@ -7,7 +7,9 @@
 @pytest.mark.functions
 @given(df=df_strategy())
 def test_get_features_targets(df):
-    X, y = df.clean_names().get_features_targets(target_columns="bell_chart")
+    X, y = df.clean_names().get_features_targets(
+        target_column_names="bell_chart"
+    )
     assert X.shape[1] == 4
     assert len(y.shape) == 1
 
@@ -16,7 +18,8 @@ def test_get_features_targets(df):
 @given(df=df_strategy())
 def test_get_features_targets_multi_features(df):
     X, y = df.clean_names().get_features_targets(
-        feature_columns=["animals@#$%^", "cities"], target_columns="bell_chart"
+        feature_column_names=["animals@#$%^", "cities"],
+        target_column_names="bell_chart",
     )
     assert X.shape[1] == 2
     assert len(y.shape) == 1
@@ -26,7 +29,7 @@ def test_get_features_targets_multi_features(df):
 @given(df=df_strategy())
 def test_get_features_target_multi_columns(df):
     X, y = df.clean_names().get_features_targets(
-        target_columns=["a", "bell_chart"]
+        target_column_names=["a", "bell_chart"]
     )
     assert X.shape[1] == 3
     assert y.shape[1] == 2