def __init__(
self,
df=None,
minimal=False,
config_file: Union[Path, str] = None,
lazy: bool = True,
**kwargs,
):
"""Generate a ProfileReport based on a pandas DataFrame
Args:
df: the pandas DataFrame
minimal: minimal mode is a default configuration with minimal computation
config_file: a config file (.yml), mutually exclusive with `minimal`
lazy: compute when needed
**kwargs: other arguments, for valid arguments, check the default configuration file.
"""
if config_file is not None and minimal:
raise ValueError(
"Arguments `config_file` and `minimal` are mutually exclusive."
)
if df is None and not lazy:
raise ValueError(
"Can init a not-lazy ProfileReport with no DataFrame")
if config_file:
config.set_file(config_file)
elif minimal:
config.set_file(get_config_minimal())
elif not config.is_default:
pass
# TODO: logging instead of warning
# warnings.warn(
# "Currently configuration is not the default, if you want to restore "
# "default configuration, please run 'pandas_profiling.clear_config()'"
# )
config.set_kwargs(kwargs)
self.df = None
self._df_hash = -1
self._description_set = None
self._title = None
self._report = None
self._html = None
self._widgets = None
self._json = None
if df is not None:
# preprocess df
self.df = self.preprocess(df)
if not lazy:
# Trigger building the report structure
_ = self.report
def __init__(self, df, minimal=False, config_file: Path = None, **kwargs):
if sys.version_info <= (3, 5):
warnings.warn(
"This is the last release to support Python 3.5, please upgrade.",
category=DeprecationWarning,
)
if config_file is not None and minimal:
raise ValueError(
"Arguments `config_file` and `minimal` are mutually exclusive."
)
if minimal:
config_file = get_config_minimal()
if config_file:
config.set_file(str(config_file))
config.set_kwargs(kwargs)
self.date_start = datetime.utcnow()
# Treat index as any other column
if (not pd.Index(np.arange(0, len(df))).equals(df.index)
or df.index.dtype != np.int64):
df = df.reset_index()
# Rename reserved column names
df = rename_index(df)
# Ensure that columns are strings
df.columns = df.columns.astype("str")
# Get dataset statistics
description_set = describe_df(df)
# Build report structure
self.sample = self.get_sample(df)
self.title = config["title"].get(str)
self.description_set = description_set
self.date_end = datetime.utcnow()
disable_progress_bar = not config["progress_bar"].get(bool)
with tqdm(total=1,
desc="build report structure",
disable=disable_progress_bar) as pbar:
self.report = get_report_structure(self.date_start, self.date_end,
self.sample, description_set)
pbar.update()
def __init__(
self,
df: Optional[pd.DataFrame] = None,
minimal: bool = False,
explorative: bool = False,
sensitive: bool = False,
dark_mode: bool = False,
orange_mode: bool = False,
sample: Optional[dict] = None,
config_file: Union[Path, str] = None,
lazy: bool = True,
**kwargs,
):
"""Generate a ProfileReport based on a pandas DataFrame
Args:
df: the pandas DataFrame
minimal: minimal mode is a default configuration with minimal computation
config_file: a config file (.yml), mutually exclusive with `minimal`
lazy: compute when needed
sample: optional dict(name="Sample title", caption="Caption", data=pd.DataFrame())
**kwargs: other arguments, for valid arguments, check the default configuration file.
"""
if config_file is not None and minimal:
raise ValueError(
"Arguments `config_file` and `minimal` are mutually exclusive."
)
if df is None and not lazy:
raise ValueError(
"Can init a not-lazy ProfileReport with no DataFrame")
if config_file:
config.set_file(config_file)
elif minimal:
config.set_file(get_config("config_minimal.yaml"))
elif not config.is_default:
pass
# warnings.warn(
# "Currently configuration is not the default, if you want to restore "
# "default configuration, please run 'pandas_profiling.clear_config()'"
# )
if explorative:
config.set_arg_group("explorative")
if sensitive:
config.set_arg_group("sensitive")
if dark_mode:
config.set_arg_group("dark_mode")
if orange_mode:
config.set_arg_group("orange_mode")
config.set_kwargs(kwargs)
self.df = None
self._df_hash = -1
self._description_set = None
self._sample = sample
self._title = None
self._report = None
self._html = None
self._widgets = None
self._json = None
self._typeset = None
self._summarizer = None
if df is not None:
# preprocess df
self.df = self.preprocess(df)
if not lazy:
# Trigger building the report structure
_ = self.report