.뒤에 명령을 실행할 때 () 유무 차이

안녕하세요.

좋은질문을 해주셨네요.

소괄호가 붙고 안 붙고의 차이는 Attributes 와 Methods 의 차이입니다.

아래 판다스 DataFrame의 Attributes 와 Methods 목록을 가져왔는데요.

가장 큰 차이는 옵션이 있는지 없는지에 대한 차이가 될거 같아요.

예를 들어 shape는 반환값이 (행, 열) 튜플로 반환이 되는데 이건 옵션이 따로 있지 않아요.

head()는 메소드인데 n 값에 대한 옵션이 있어요.

아래는 DataFrame 의 API 목록인데 Series 를 사용하더라도 같은 방법으로 대부분 사용하실 수 있습니다.

아래 링크를 참고해 보세요.

[pandas.DataFrame — pandas 1.0.3 documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html)

Attributes

`T`	Transpose index and columns.
`at`	Access a single value for a row/column label pair.
`attrs`	Dictionary of global attributes on this object.
`axes`	Return a list representing the axes of the DataFrame.
`columns`	The column labels of the DataFrame.
`dtypes`	Return the dtypes in the DataFrame.
`empty`	Indicator whether DataFrame is empty.
`iat`	Access a single value for a row/column pair by integer position.
`iloc`	Purely integer-location based indexing for selection by position.
`index`	The index (row labels) of the DataFrame.
`loc`	Access a group of rows and columns by label(s) or a boolean array.
`ndim`	Return an int representing the number of axes / array dimensions.
`shape`	Return a tuple representing the dimensionality of the DataFrame.
`size`	Return an int representing the number of elements in this object.
`style`	Returns a Styler object.
`values`	Return a Numpy representation of the DataFrame.

Methods

`abs`(self)	Return a Series/DataFrame with absolute numeric value of each element.
`add`(self, other[, axis, level, fill_value])	Get Addition of dataframe and other, element-wise (binary operator add).
`add_prefix`(self, prefix)	Prefix labels with string prefix.
`add_suffix`(self, suffix)	Suffix labels with string suffix.
`agg`(self, func[, axis])	Aggregate using one or more operations over the specified axis.
`aggregate`(self, func[, axis])	Aggregate using one or more operations over the specified axis.
`align`(self, other[, join, axis, level, …])	Align two objects on their axes with the specified join method.
`all`(self[, axis, bool_only, skipna, level])	Return whether all elements are True, potentially over an axis.
`any`(self[, axis, bool_only, skipna, level])	Return whether any element is True, potentially over an axis.
`append`(self, other[, ignore_index, …])	Append rows of other to the end of caller, returning a new object.
`apply`(self, func[, axis, raw, result_type, args])	Apply a function along an axis of the DataFrame.
`applymap`(self, func)	Apply a function to a Dataframe elementwise.
`asfreq`(self, freq[, method, fill_value])	Convert TimeSeries to specified frequency.
`asof`(self, where[, subset])	Return the last row(s) without any NaNs before where.
`assign`(self, \\kwargs)	Assign new columns to a DataFrame.
`astype`(self, dtype, copy, errors)	Cast a pandas object to a specified dtype `dtype`.
`at_time`(self, time, asof[, axis])	Select values at particular time of day (e.g.
`between_time`(self, start_time, end_time, …)	Select values between particular times of the day (e.g., 9:00-9:30 AM).
`bfill`(self[, axis, limit, downcast])	Synonym for `DataFrame.fillna()` with `method='bfill'`.
`bool`(self)	Return the bool of a single element PandasObject.
`boxplot`(self[, column, by, ax, fontsize, …])	Make a box plot from DataFrame columns.
`clip`(self[, lower, upper, axis])	Trim values at input threshold(s).
`combine`(self, other, func[, fill_value, …])	Perform column-wise combine with another DataFrame.
`combine_first`(self, other)	Update null elements with value in the same location in other.
`convert_dtypes`(self, infer_objects, …)	Convert columns to best possible dtypes using dtypes supporting `pd.NA`.
`copy`(self, deep)	Make a copy of this object’s indices and data.
`corr`(self[, method, min_periods])	Compute pairwise correlation of columns, excluding NA/null values.
`corrwith`(self, other[, axis, drop, method])	Compute pairwise correlation.
`count`(self[, axis, level, numeric_only])	Count non-NA cells for each column or row.
`cov`(self[, min_periods])	Compute pairwise covariance of columns, excluding NA/null values.
`cummax`(self[, axis, skipna])	Return cumulative maximum over a DataFrame or Series axis.
`cummin`(self[, axis, skipna])	Return cumulative minimum over a DataFrame or Series axis.
`cumprod`(self[, axis, skipna])	Return cumulative product over a DataFrame or Series axis.
`cumsum`(self[, axis, skipna])	Return cumulative sum over a DataFrame or Series axis.
`describe`(self[, percentiles, include, exclude])	Generate descriptive statistics.
`diff`(self[, periods, axis])	First discrete difference of element.
`div`(self, other[, axis, level, fill_value])	Get Floating division of dataframe and other, element-wise (binary operator truediv).
`divide`(self, other[, axis, level, fill_value])	Get Floating division of dataframe and other, element-wise (binary operator truediv).
`dot`(self, other)	Compute the matrix multiplication between the DataFrame and other.
`drop`(self[, labels, axis, index, columns, …])	Drop specified labels from rows or columns.
`drop_duplicates`(self, subset, …)	Return DataFrame with duplicate rows removed.
`droplevel`(self, level[, axis])	Return DataFrame with requested index / column level(s) removed.
`dropna`(self[, axis, how, thresh, subset, …])	Remove missing values.
`duplicated`(self, subset, Sequence[Hashable], …)	Return boolean Series denoting duplicate rows.
`eq`(self, other[, axis, level])	Get Equal to of dataframe and other, element-wise (binary operator eq).
`equals`(self, other)	Test whether two objects contain the same elements.
`eval`(self, expr[, inplace])	Evaluate a string describing operations on DataFrame columns.
`ewm`(self[, com, span, halflife, alpha, …])	Provide exponential weighted functions.

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