Esempi in Python per ProfileReport.to_widgets

Esempio n. 1

 def pandas_profiling_analyze(
     self,
     title: str = "Movie dataset",
     sort: Union[str, None] = None,
     explorative: bool = True,
     **kwargs: Any,
 ):
     """
     Generate pandas profiling report using pandas-profiling.
     https://pandas-profiling.github.io/
     :param title: The name of the report - str
     :param sort: Sort the variables asc(ending), desc(ending) or None (leaves original sorting). - Union[str, None]
     :param explorative: Whether use explorative config file or not. - bool
     :return: None
     """
     # Generate the Profiling Report
     profile = ProfileReport(
         self.data,
         title=title,
         html={"style": {
             "full_width": True
         }},
         sort=sort,
         explorative=explorative,
         **kwargs,
     )
     return profile.to_widgets()

Esempio n. 2

File: forecasting on airlines data.py Progetto: shashi-sharde/Machie-learning-assignments-in-python

air['log_pass'] = log_pass

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air

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#EDA
report = ProfileReport(air,
                       title="Profile Report of the Airlines data",
                       explorative=True)

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report.to_widgets()

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#REPORT SAYS THAT:
#1) THERE ARE NO MISSING VALUES
#2) NO DUPLICATE ROWS
#3) NO MULTICOLINEARITY

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#CHECKING THE VARIABLE  DISTRIBUTION THROUGHOUT ALL THE TIME PERIOD AVAILABLE IN THE DATA
df.Passengers.plot()

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Esempio n. 3

File: (Project) on Diabetes Data (2).py Progetto: Anas-coder/Diabetes-Predictor

data.describe()

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from pandas_profiling import ProfileReport

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profile = ProfileReport(data,
                        title='Pandas Profiling Report',
                        explorative=True)

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profile.to_widgets()

# # Data Manipulation

# Checking The correlation of Data

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corrmat = data.corr()

f, ax = plt.subplots(figsize=(15, 8))
sns.heatmap(corrmat, ax=ax, cmap="YlGnBu", linewidths=0.9)

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#Correlation with output variable

Esempio n. 4

File: Home Credit (2).py Progetto: omarelbassuoni/ckme136

# # EDA

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#Data frame at a glance
app_train.info()


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#profile report
trainprofile = ProfileReport(app_train, minimal=True)
trainprofile.to_widgets()


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#how many unique classes do we have for categotical features
app_train.select_dtypes('object').apply(pd.Series.nunique, axis = 0)


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#list coloumns with most missing values
def missing_values_table(df):
        # Total missing values

Esempio n. 5

File: test.py Progetto: renferpur/python-code

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Df.skew()

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#Fact Table Quick Summary using panda profiling
profile = ProfileReport(Df,
                        title='Pandas Profiling Report',
                        html={'style': {
                            'full_width': True
                        }})

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profile.to_widgets()

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display(profile)

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#Dimension Table Quick summary using Panda Profiler
DimProfile = ProfileReport(DimDf,
                           title='Pandas Dimension table Profile Report',
                           html={'style': {
                               'full_width': True
                           }})

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Esempio n. 6

File: explore.py Progetto: jivemachine/zillow_project

def do_a_pandas_profile(df, name):
    from pandas_profiling import ProfileReport
    profile = ProfileReport(df, title=name, html={'style':{'full_width':True}})
    profile.to_widgets(), profile.to_notebook_iframe()