[python / Pandas ] 데이터프레임(DataFrame)(1)

파이썬으로 데이터를 처리할 때 사용하는 판다스(pandas)가 데이터 구조를 표현하는 클래스 객체인 데이터프레임(Dataframe)에 관한 내용입니다.

주요한 어트리뷰트(Attribute) 및 메소드(Method) 중 자주쓰는 몇 가지만 정리해보겠습니다.

1. 어트리뷰트(Attributes)

속성명	설명
at	Access a single value for a row/column label pair.
attrs	Dictionary of global attributes of this dataset.
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 Series/DataFrame is empty.
flags	Get the properties associated with this pandas object.
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.

2. 메소드(Methods)

• append(other, [, ignore_index, ...])
• dropna([axis, how, thresh, subset, inplace])
• equals(other)
• from_records(data[, index, exclude, ...])
• groupby([by, axis, level, as_index, sort, ...])
• iteritems()
• iterrows()
• join(otehr[, on, how, lsuffix, rsuffix, sort])
• max([axis, skipna, level, numeric_only])
• mean([axis, skipna, level, numeric_only])
• median([axis, skipna, level, numeric_only])
• melt([id_vars, value_vars, var_name, ...])
• multiply(other[, axis, level, fill_value])
• rename_axis([mapper, index, columns, axis, ...])
• squeeze([axis])
• to_csv([path_or_buf, sep, na_rep, ...])
• to_excel(excel_writer[, sheet_name, na_rep, ...])
• to_pickle(path,[, compression, protocol, ...])

끝으로, 공식도큐먼트를 첨부합니다.

pandas > dataframe