ipython console에서 pd.read_csv? 입력하고, shift+Enter누르면 사용법이 자세히 나온다.
ipython기반으로 한 jupyter notebook에서도 마찬가지로 적용된다.
pd.read_csv(
filepath_or_buffer: Union[str, pathlib.Path, IO[~AnyStr]],
sep=',',
delimiter=None,
header='infer',
names=None,
index_col=None,
usecols=None,
squeeze=False,
prefix=None,
mangle_dupe_cols=True,
dtype=None,
engine=None,
converters=None,
true_values=None,
false_values=None,
skipinitialspace=False,
skiprows=None,
skipfooter=0,
nrows=None,
na_values=None,
keep_default_na=True,
na_filter=True,
verbose=False,
skip_blank_lines=True,
parse_dates=False,
infer_datetime_format=False,
keep_date_col=False,
date_parser=None,
dayfirst=False,
cache_dates=True,
iterator=False,
chunksize=None,
compression='infer',
thousands=None,
decimal: str = '.',
lineterminator=None,
quotechar='"',
quoting=0,
doublequote=True,
escapechar=None,
comment=None,
encoding=None,
dialect=None,
error_bad_lines=True,
warn_bad_lines=True,
delim_whitespace=False,
low_memory=True,
memory_map=False,
float_precision=None,
)
특히 아래 옵션들은 실제 코딩시 한번씩 사용해볼일이 있어, 별도로 정리했습니다.
sep : str, default ','
=> seperator의 약자로, 구분자가 어떤것인지 지정할 때 사용. 기본값은 콤마. comma seperated 파일이니 당연하겠죠?
Delimiter to use. If sep is None, the C engine cannot automatically detect
the separator, but the Python parsing engine can, meaning the latter will
be used and automatically detect the separator by Python's builtin sniffer
tool, ``csv.Sniffer``. In addition, separators longer than 1 character and
different from ``'\s+'`` will be interpreted as regular expressions and
will also force the use of the Python parsing engine. Note that regex
delimiters are prone to ignoring quoted data. Regex example: ``'\r\t'``.
delimiter : str, default ``None``
Alias for sep.
header : int, list of int, default 'infer'
Row number(s) to use as the column names, and the start of the
data. Default behavior is to infer the column names: if no names
are passed the behavior is identical to ``header=0`` and column
names are inferred from the first line of the file, if column
names are passed explicitly then the behavior is identical to
``header=None``. Explicitly pass ``header=0`` to be able to
replace existing names. The header can be a list of integers that
specify row locations for a multi-index on the columns
e.g. [0,1,3]. Intervening rows that are not specified will be
skipped (e.g. 2 in this example is skipped). Note that this
parameter ignores commented lines and empty lines if
``skip_blank_lines=True``, so ``header=0`` denotes the first line of
data rather than the first line of the file.
names : array-like, optional
=> 컬럼이름을 지정
List of column names to use. If the file contains a header row,
then you should explicitly pass ``header=0`` to override the column names.
Duplicates in this list are not allowed.
index_col : int, str, sequence of int / str, or False, default ``None``
=> 컬럼이름 중 인덱스열로 사용할 이름 지정
Column(s) to use as the row labels of the ``DataFrame``, either given as
string name or column index. If a sequence of int / str is given, a
MultiIndex is used.
Note: ``index_col=False`` can be used to force pandas to *not* use the first
column as the index, e.g. when you have a malformed file with delimiters at
the end of each line.
usecols : list-like or callable, optional
=> 원본데이터 중 몇 개 column만 추출해서 dataframe으로 사용하고자 할때 이용
Return a subset of the columns. If list-like, all elements must either
be positional (i.e. integer indices into the document columns) or strings
that correspond to column names provided either by the user in `names` or
inferred from the document header row(s). For example, a valid list-like
`usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``.
Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.
To instantiate a DataFrame from ``data`` with element order preserved use
``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns
in ``['foo', 'bar']`` order or
``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``
for ``['bar', 'foo']`` order.
If callable, the callable function will be evaluated against the column
names, returning names where the callable function evaluates to True. An
example of a valid callable argument would be ``lambda x: x.upper() in
['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster
parsing time and lower memory usage.
dtype : Type name or dict of column -> type, optional
=> 데이터 type을 지정하면서 데이터를 불러올때 사용
Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32,
'c': 'Int64'}
Use `str` or `object` together with suitable `na_values` settings
to preserve and not interpret dtype.
If converters are specified, they will be applied INSTEAD
of dtype conversion.
skiprows : list-like, int or callable, optional
=> csv파일 앞부분에 표형식이 아닌 데이터(ex. 설명)를 줄단위삭제할때 사용
Line numbers to skip (0-indexed) or number of lines to skip (int)
at the start of the file.
If callable, the callable function will be evaluated against the row
indices, returning True if the row should be skipped and False otherwise.
An example of a valid callable argument would be ``lambda x: x in [0, 2]``.
skipfooter : int, default 0
=> csv파일 끝부분에 표형식이 아닌 데이터(ex. 설명)를 삭제할때 사용
Number of lines at bottom of file to skip (Unsupported with engine='c').
parse_dates : bool or list of int or names or list of lists or dict, default False
=> datetime형으로 변환시 사용
The behavior is as follows:
* boolean. If True -> try parsing the index.
* list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
each as a separate date column.
* list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as
a single date column.
* dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call
result 'foo'
thousands : str, optional
=> 123,456처럼 1000단위구분기호가 들어있는 데이터를 불러올때 구분기호 삭제되어 굿~
Thousands separator.
encoding : str, optional
=> 아래 링크를 보면, 한글은 cp949로 읽고 쓸수 있음.
Encoding to use for UTF when reading/writing (ex. 'utf-8').
`List of Python standard encodings
docs.python.org/3/library/codecs.html#standard-encodings>