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Contenu du module « pandas »

Liste des classes du module pandas

Nom de la classe	Description
BooleanDtype
Categorical
CategoricalDtype
CategoricalIndex
DataFrame
DateOffset
DatetimeIndex
DatetimeTZDtype
ExcelFile
ExcelWriter
Flags
Float32Dtype
Float64Dtype
Float64Index
Grouper
HDFStore
Index
Int16Dtype
Int32Dtype
Int64Dtype
Int64Index
Int8Dtype
Interval
IntervalDtype
IntervalIndex
MultiIndex
NamedAgg	NamedAgg(column, aggfunc) _{[extrait de NamedAgg.__doc__]}
option_context
Period
PeriodDtype
PeriodIndex
RangeIndex
Series
SparseDtype
StringDtype
Timedelta
TimedeltaIndex
Timestamp
UInt16Dtype
UInt32Dtype
UInt64Dtype
UInt64Index
UInt8Dtype

Liste des fonctions du module pandas

Signature de la fonction	Description
array(data: 'Union[Sequence[object], AnyArrayLike]', dtype: 'Optional[Dtype]' = None, copy: 'bool' = True) -> 'ExtensionArray'
bdate_range(start=None, end=None, periods=None, freq='B', tz=None, normalize=True, name=None, weekmask=None, holidays=None, closed=None, **kwargs) -> pandas.core.indexes.datetimes.DatetimeIndex
concat(objs: Union[Iterable[ForwardRef('NDFrame')], Mapping[Optional[Hashable], ForwardRef('NDFrame')]], axis=0, join='outer', ignore_index: bool = False, keys=None, levels=None, names=None, verify_integrity: bool = False, sort: bool = False, copy: bool = True) -> Union[ForwardRef('DataFrame'), ForwardRef('Series')]
crosstab(index, columns, values=None, rownames=None, colnames=None, aggfunc=None, margins=False, margins_name: str = 'All', dropna: bool = True, normalize=False) -> 'DataFrame'
cut(x, bins, right: bool = True, labels=None, retbins: bool = False, precision: int = 3, include_lowest: bool = False, duplicates: str = 'raise', ordered: bool = True)
date_range(start=None, end=None, periods=None, freq=None, tz=None, normalize=False, name=None, closed=None, **kwargs) -> pandas.core.indexes.datetimes.DatetimeIndex
describe_option(args, *kwds)
eval(expr, parser='pandas', engine: Optional[str] = None, truediv=<object object at 0x7f5051439e10>, local_dict=None, global_dict=None, resolvers=(), level=0, target=None, inplace=False)
factorize(values, sort: 'bool' = False, na_sentinel: 'Optional[int]' = -1, size_hint: 'Optional[int]' = None) -> "Tuple[np.ndarray, Union[np.ndarray, 'Index']]"
get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False, dtype=None) -> 'DataFrame'
get_option(args, *kwds)
infer_freq(index, warn: bool = True) -> Optional[str]
interval_range(start=None, end=None, periods=None, freq=None, name=None, closed='right')
isna(obj)
lreshape(data: 'DataFrame', groups, dropna: bool = True, label=None) -> 'DataFrame'
melt(frame: 'DataFrame', id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None, ignore_index: bool = True) -> 'DataFrame'
merge(left, right, how: str = 'inner', on=None, left_on=None, right_on=None, left_index: bool = False, right_index: bool = False, sort: bool = False, suffixes=('_x', '_y'), copy: bool = True, indicator: bool = False, validate=None) -> 'DataFrame'
merge_asof(left, right, on=None, left_on=None, right_on=None, left_index: bool = False, right_index: bool = False, by=None, left_by=None, right_by=None, suffixes=('_x', '_y'), tolerance=None, allow_exact_matches: bool = True, direction: str = 'backward') -> 'DataFrame'
merge_ordered(left, right, on=None, left_on=None, right_on=None, left_by=None, right_by=None, fill_method=None, suffixes=('_x', '_y'), how: str = 'outer') -> 'DataFrame'
notna(obj)
period_range(start=None, end=None, periods=None, freq=None, name=None) -> pandas.core.indexes.period.PeriodIndex
pivot(data: 'DataFrame', index: Union[Hashable, NoneType, Sequence[Optional[Hashable]]] = None, columns: Union[Hashable, NoneType, Sequence[Optional[Hashable]]] = None, values: Union[Hashable, NoneType, Sequence[Optional[Hashable]]] = None) -> 'DataFrame'
pivot_table(data, values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False) -> 'DataFrame'
qcut(x, q, labels=None, retbins: bool = False, precision: int = 3, duplicates: str = 'raise')
read_clipboard(sep='\\s+', **kwargs)
read_csv(filepath_or_buffer: Union[ForwardRef('PathLike[str]'), str, IO[~T], io.RawIOBase, io.BufferedIOBase, io.TextIOBase, _io.TextIOWrapper, mmap.mmap], sep=<object object at 0x7f5051439e10>, 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, storage_options: Optional[Dict[str, Any]] = None)
read_excel(io, sheet_name=0, header=0, names=None, index_col=None, usecols=None, squeeze=False, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skiprows=None, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, parse_dates=False, date_parser=None, thousands=None, comment=None, skipfooter=0, convert_float=True, mangle_dupe_cols=True, storage_options: Optional[Dict[str, Any]] = None)
read_feather(path, columns=None, use_threads: bool = True, storage_options: Optional[Dict[str, Any]] = None)
read_fwf(filepath_or_buffer: Union[ForwardRef('PathLike[str]'), str, IO[~T], io.RawIOBase, io.BufferedIOBase, io.TextIOBase, _io.TextIOWrapper, mmap.mmap], colspecs='infer', widths=None, infer_nrows=100, **kwds)
read_gbq(query: str, project_id: Optional[str] = None, index_col: Optional[str] = None, col_order: Optional[List[str]] = None, reauth: bool = False, auth_local_webserver: bool = False, dialect: Optional[str] = None, location: Optional[str] = None, configuration: Optional[Dict[str, Any]] = None, credentials=None, use_bqstorage_api: Optional[bool] = None, max_results: Optional[int] = None, progress_bar_type: Optional[str] = None) -> 'DataFrame'
read_hdf(path_or_buf, key=None, mode: str = 'r', errors: str = 'strict', where=None, start: Optional[int] = None, stop: Optional[int] = None, columns=None, iterator=False, chunksize: Optional[int] = None, **kwargs)
read_html(io: Union[ForwardRef('PathLike[str]'), str, IO[~T], io.RawIOBase, io.BufferedIOBase, io.TextIOBase, _io.TextIOWrapper, mmap.mmap], match: Union[str, Pattern] = '.+', flavor: Optional[str] = None, header: Union[int, Sequence[int], NoneType] = None, index_col: Union[int, Sequence[int], NoneType] = None, skiprows: Union[int, Sequence[int], slice, NoneType] = None, attrs: Optional[Dict[str, str]] = None, parse_dates: bool = False, thousands: Optional[str] = ',', encoding: Optional[str] = None, decimal: str = '.', converters: Optional[Dict] = None, na_values=None, keep_default_na: bool = True, displayed_only: bool = True) -> List[pandas.core.frame.DataFrame]
read_json(path_or_buf=None, orient=None, typ='frame', dtype=None, convert_axes=None, convert_dates=True, keep_default_dates: bool = True, numpy: bool = False, precise_float: bool = False, date_unit=None, encoding=None, lines: bool = False, chunksize: Optional[int] = None, compression: Union[str, Dict[str, Any], NoneType] = 'infer', nrows: Optional[int] = None, storage_options: Optional[Dict[str, Any]] = None)
read_orc(path: Union[ForwardRef('PathLike[str]'), str, IO[~T], io.RawIOBase, io.BufferedIOBase, io.TextIOBase, _io.TextIOWrapper, mmap.mmap], columns: Optional[List[str]] = None, **kwargs) -> 'DataFrame'
read_parquet(path, engine: str = 'auto', columns=None, use_nullable_dtypes: bool = False, **kwargs)
read_pickle(filepath_or_buffer: Union[ForwardRef('PathLike[str]'), str, IO[~T], io.RawIOBase, io.BufferedIOBase, io.TextIOBase, _io.TextIOWrapper, mmap.mmap], compression: Union[str, Dict[str, Any], NoneType] = 'infer', storage_options: Optional[Dict[str, Any]] = None)
read_sas(filepath_or_buffer: Union[ForwardRef('PathLike[str]'), str, IO[~T], io.RawIOBase, io.BufferedIOBase, io.TextIOBase, _io.TextIOWrapper, mmap.mmap], format: Optional[str] = None, index: Optional[Hashable] = None, encoding: Optional[str] = None, chunksize: Optional[int] = None, iterator: bool = False) -> Union[ForwardRef('DataFrame'), pandas.io.sas.sasreader.ReaderBase]
read_spss(path: Union[str, pathlib.Path], usecols: Optional[Sequence[str]] = None, convert_categoricals: bool = True) -> pandas.core.frame.DataFrame
read_sql(sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, columns=None, chunksize: Optional[int] = None) -> Union[pandas.core.frame.DataFrame, Iterator[pandas.core.frame.DataFrame]]
read_sql_query(sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, chunksize: Optional[int] = None) -> Union[pandas.core.frame.DataFrame, Iterator[pandas.core.frame.DataFrame]]
read_sql_table(table_name, con, schema=None, index_col=None, coerce_float=True, parse_dates=None, columns=None, chunksize: Optional[int] = None) -> Union[pandas.core.frame.DataFrame, Iterator[pandas.core.frame.DataFrame]]
read_stata(filepath_or_buffer: Union[ForwardRef('PathLike[str]'), str, IO[~T], io.RawIOBase, io.BufferedIOBase, io.TextIOBase, _io.TextIOWrapper, mmap.mmap], convert_dates: bool = True, convert_categoricals: bool = True, index_col: Optional[str] = None, convert_missing: bool = False, preserve_dtypes: bool = True, columns: Optional[Sequence[str]] = None, order_categoricals: bool = True, chunksize: Optional[int] = None, iterator: bool = False, storage_options: Optional[Dict[str, Any]] = None) -> Union[pandas.core.frame.DataFrame, pandas.io.stata.StataReader]
read_table(filepath_or_buffer: Union[ForwardRef('PathLike[str]'), str, IO[~T], io.RawIOBase, io.BufferedIOBase, io.TextIOBase, _io.TextIOWrapper, mmap.mmap], sep=<object object at 0x7f5051439e10>, 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)
reset_option(args, *kwds)
set_eng_float_format(accuracy: int = 3, use_eng_prefix: bool = False) -> None
set_option(args, *kwds)
show_versions(as_json: Union[str, bool] = False) -> None
test(extra_args=None)
timedelta_range(start=None, end=None, periods=None, freq=None, name=None, closed=None) -> pandas.core.indexes.timedeltas.TimedeltaIndex
to_datetime(arg: Union[~DatetimeScalar, List, Tuple, ~ArrayLike, ForwardRef('Series')], errors: str = 'raise', dayfirst: bool = False, yearfirst: bool = False, utc: Optional[bool] = None, format: Optional[str] = None, exact: bool = True, unit: Optional[str] = None, infer_datetime_format: bool = False, origin='unix', cache: bool = True) -> Union[pandas.core.indexes.datetimes.DatetimeIndex, ForwardRef('Series'), ~DatetimeScalar, ForwardRef('NaTType')]
to_numeric(arg, errors='raise', downcast=None)
to_pickle(obj: Any, filepath_or_buffer: Union[ForwardRef('PathLike[str]'), str, IO[~T], io.RawIOBase, io.BufferedIOBase, io.TextIOBase, _io.TextIOWrapper, mmap.mmap], compression: Union[str, Dict[str, Any], NoneType] = 'infer', protocol: int = 5, storage_options: Optional[Dict[str, Any]] = None)
to_timedelta(arg, unit=None, errors='raise')
unique(values)
value_counts(values, sort: 'bool' = True, ascending: 'bool' = False, normalize: 'bool' = False, bins=None, dropna: 'bool' = True) -> 'Series'
wide_to_long(df: 'DataFrame', stubnames, i, j, sep: str = '', suffix: str = '\\d+') -> 'DataFrame'

Liste des variables globales du module pandas

Nom de la variable globale	Valeur
IndexSlice	<pandas.core.indexing._IndexSlice object at 0x7f504b64c940>
NA	<NA>
NaT	NaT
options	<pandas._config.config.DictWrapper object at 0x7f504e55c5e0>

Liste des alias du module pandas

Nom de l'alias	Définition ciblée
isnull	isna
json_normalize	_json_normalize
notnull	notna