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Plot data with missing features#
plots the missing features and extracts insight with their correlation
# Author: L.Kouadio
# Licence: BSD-3-clause
Missing inherits from Data and use missingno. Install
the package missingno for taking advantage of many missing plot.
The parameter kind is passed to Missing for selecting the
kind of plot for visualisation:
Plot the missing in the data using the base visualization
from watex.datasets import fetch_data
from watex.base import Missing
data= fetch_data("bagoue original").get('data=df') # num flow
ms= Missing().fit (data)
ms.plot(figsize = (12, 4 ))
Missing(in_percent= False, kind= None, sample= None, ... , lwi= 0 32.023585
1 21.406531
2 0.0
3 28.709623
4 2.3
...
426 18.6
427 40.1
428 28.5
429 1.5
430 33.85
Name= lwi, Length= 431, dtype= object, geol= 0 VOLCANO-SEDIM. SCHISTS
1 GRANITES
2 GRANITES
3 GRANITES
4 GEOSYN. GRANITES
...
426 GEOSYN. GRANITES
427 GRANITES
428 GRANITES
429 GRANITES
430 GEOSYN. GRANITES
Name= geol, Length= 431, dtype= object, flow= 0 2.0
1 0.0
2 1.0
3 1.0
4 1.0
...
426 10.0
427 2.0
428 5.0
429 4.0
430 2.0
Name= flow, Length= 431, dtype= object)
Plot the same missing data using the correlation visualization
ms.kind='corr'
ms.plot(figsize = (12, 4 ))
Missing(in_percent= False, kind= corr, sample= None, ... , lwi= 0 32.023585
1 21.406531
2 0.0
3 28.709623
4 2.3
...
426 18.6
427 40.1
428 28.5
429 1.5
430 33.85
Name= lwi, Length= 431, dtype= object, geol= 0 VOLCANO-SEDIM. SCHISTS
1 GRANITES
2 GRANITES
3 GRANITES
4 GEOSYN. GRANITES
...
426 GEOSYN. GRANITES
427 GRANITES
428 GRANITES
429 GRANITES
430 GEOSYN. GRANITES
Name= geol, Length= 431, dtype= object, flow= 0 2.0
1 0.0
2 1.0
3 1.0
4 1.0
...
426 10.0
427 2.0
428 5.0
429 4.0
430 2.0
Name= flow, Length= 431, dtype= object)
Total running time of the script: ( 0 minutes 0.390 seconds)
