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()
air['log_pass'] = log_pass
# In[7]:
air
# In[8]:
#EDA
report = ProfileReport(air,
title="Profile Report of the Airlines data",
explorative=True)
# In[29]:
report.to_widgets()
# In[ ]:
#REPORT SAYS THAT:
#1) THERE ARE NO MISSING VALUES
#2) NO DUPLICATE ROWS
#3) NO MULTICOLINEARITY
# In[9]:
#CHECKING THE VARIABLE DISTRIBUTION THROUGHOUT ALL THE TIME PERIOD AVAILABLE IN THE DATA
df.Passengers.plot()
# In[ ]:
data.describe()
# In[83]:
from pandas_profiling import ProfileReport
# In[84]:
profile = ProfileReport(data,
title='Pandas Profiling Report',
explorative=True)
# In[85]:
profile.to_widgets()
# # Data Manipulation
# Checking The correlation of Data
# In[4]:
corrmat = data.corr()
f, ax = plt.subplots(figsize=(15, 8))
sns.heatmap(corrmat, ax=ax, cmap="YlGnBu", linewidths=0.9)
# In[6]:
#Correlation with output variable
# # EDA
# In[253]:
#Data frame at a glance
app_train.info()
# In[254]:
#profile report
trainprofile = ProfileReport(app_train, minimal=True)
trainprofile.to_widgets()
# In[255]:
#how many unique classes do we have for categotical features
app_train.select_dtypes('object').apply(pd.Series.nunique, axis = 0)
# In[256]:
#list coloumns with most missing values
def missing_values_table(df):
# Total missing values
# In[ ]:
Df.skew()
# In[ ]:
#Fact Table Quick Summary using panda profiling
profile = ProfileReport(Df,
title='Pandas Profiling Report',
html={'style': {
'full_width': True
}})
# In[ ]:
profile.to_widgets()
# In[ ]:
display(profile)
# In[ ]:
#Dimension Table Quick summary using Panda Profiler
DimProfile = ProfileReport(DimDf,
title='Pandas Dimension table Profile Report',
html={'style': {
'full_width': True
}})
# In[ ]:
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()
