def __init__(
self,
df=None,
minimal=False,
explorative=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("config_minimal.yaml"))
elif explorative:
config.set_file(get_config("config_explorative.yaml"))
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 test_double_config(console_data, test_output_dir):
report = test_output_dir / "test_double_config.html"
with pytest.raises(ValueError) as e:
console.main([
"-s",
"--config_file",
str(get_config("config_default.yaml")),
"--minimal",
str(console_data),
str(report),
])
assert (str(e.value) ==
"Arguments `config_file` and `minimal` are mutually exclusive.")
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
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,
typeset: Optional[VisionsTypeset] = None,
summarizer: Optional[BaseSummarizer] = None,
config: Optional[Settings] = None,
**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())
typeset: optional user typeset to use for type inference
summarizer: optional user summarizer to generate custom summary output
**kwargs: other arguments, for valid arguments, check the default configuration file.
"""
if df is None and not lazy:
raise ValueError(
"Can init a not-lazy ProfileReport with no DataFrame")
report_config: Settings = Settings() if config is None else config
if config_file is not None and minimal:
raise ValueError(
"Arguments `config_file` and `minimal` are mutually exclusive."
)
if config_file or minimal:
if not config_file:
config_file = get_config("config_minimal.yaml")
with open(config_file) as f:
data = yaml.safe_load(f)
report_config = report_config.parse_obj(data)
if explorative:
report_config = report_config.update(
Config.get_arg_groups("explorative"))
if sensitive:
report_config = report_config.update(
Config.get_arg_groups("sensitive"))
if dark_mode:
report_config = report_config.update(
Config.get_arg_groups("dark_mode"))
if orange_mode:
report_config = report_config.update(
Config.get_arg_groups("orange_mode"))
if len(kwargs) > 0:
report_config = report_config.update(Config.shorthands(kwargs))
self.df = None
self.config = report_config
self._df_hash = None
self._sample = sample
self._typeset = typeset
self._summarizer = summarizer
if df is not None:
# preprocess df
self.df = self.preprocess(df)
if not lazy:
# Trigger building the report structure
_ = self.report
