Merge pull request #179 from Minku-Koo/osskoo

minjk-bl · web-flow · commit f1260718c5a9 · 2022-09-19T12:00:28.000+09:00
to update python code for clean code
diff --git a/python/pandasCommand.py b/python/pandasCommand.py
@@ -36,15 +36,14 @@ def _vp_get_columns_list(df):
             cInfo['value'] = "'{}'".format(c)
         elif type(c).__name__ == 'Timestamp':
             cInfo['value'] = str(c)
+            
         # category - iopub data rate limit issue...
+        cInfo['category'] = []
         if str(df[c].dtype) == 'object':
             uniqValues = df[c].dropna().unique()
             if len(uniqValues) <= 20:
                 cInfo['category'] = [{ "value": "'{}'".format(u) if type(u) == str else u, "label": u } for u in uniqValues]
-            else:
-                cInfo['category'] = []
-        else:
-            cInfo['category'] = []
+            
         colList.append(cInfo)
     return colList
 
diff --git a/python/userCommand.py b/python/userCommand.py
@@ -10,6 +10,7 @@
 import fitz
 import nltk
 nltk.download('punkt')
+
 ######
 # Visual Python: Data Analysis > PDF
 ######
@@ -43,6 +44,7 @@ def vp_pdf_get_sentence(fname_lst):
         df = _vp_pd.concat([df,df_doc])
         
     return df.reset_index().drop('index', axis=1)
+
 ######
 # Visual Python: Data Analysis > Frame
 ######
@@ -63,6 +65,7 @@ def vp_drop_outlier(df, col, weight=1.5):
     df_res = df.drop(outlier_index).copy()
     
     return df_res
+
 ######
 # Visual Python: Machine Learning > Model Info
 ######
@@ -74,10 +77,12 @@ def vp_create_feature_importances(model, X_train=None, sort=False):
                         
     df_i = _vp_pd.DataFrame(model.feature_importances_, index=feature_names, columns=['Feature_importance'])
     df_i['Percentage'] = 100 * (df_i['Feature_importance'] / df_i['Feature_importance'].max())
-    if sort: df_i.sort_values(by='Feature_importance', ascending=False, inplace=True)
+    if sort: 
+        df_i.sort_values(by='Feature_importance', ascending=False, inplace=True)
     df_i = df_i.round(2)
                         
     return df_i
+
 ######
 # Visual Python: Machine Learning > Model Info
 ######
@@ -91,10 +96,13 @@ def vp_plot_feature_importances(model, X_train=None, sort=False, top_count=0):
             df_i['Percentage'].sort_values().plot(kind='barh')
     else: 
         df_i['Percentage'].plot(kind='barh')
+
     _vp_plt.xlabel('Feature importance Percentage')
     _vp_plt.ylabel('Features')
-                        
     _vp_plt.show()
+
+    return 
+
 ######
 # Visual Python: Visualization > Seaborn
 ######
@@ -134,4 +142,6 @@ def _single(ax):
         for idx, ax in _vp_np.ndenumerate(axs):
             _single(ax)
     else:
-        _single(axs)
+        _single(axs)
+    
+    return 
diff --git a/python/variableCommand.py b/python/variableCommand.py
@@ -1,3 +1,5 @@
+import numpy as _vp_np
+import random as _vp_rd
 """
 Search Variables
 """
@@ -77,8 +79,6 @@ def _vp_get_profiling_list():
 
     return result
 
-import numpy as _vp_np
-import random as _vp_rd
 def _vp_sample(data, sample_cnt):
     """
     Sampling data
@@ -94,16 +94,13 @@ def _vp_sample(data, sample_cnt):
         return data[_vp_np.random.choice(data.shape[0], sample_cnt, replace=False)]
     elif dataType == 'list':
         return _vp_rd.choices(data, k=sample_cnt)
+        
     return data
 
 def _vp_check_module_loaded(fname_list):
     """
     Check if this module is loaded
     """
-    result = []
-    for fname in fname_list:
-        if fname in globals():
-            result.append(True)
-        else:
-            result.append(False)
+    result = [True if fname in globals() else False for fname in fname_list]
+    
     return result
diff --git a/python/visualizationCommand.py b/python/visualizationCommand.py
@@ -7,12 +7,10 @@ def _vp_seaborn_show_values(axs, precision=1, space=0.01):
     pstr = '{:.' + str(precision) + 'f}'
     
     def _single(ax):
-        # check orient
+        # check orient / if 0
         orient = 'v'
-        if len(ax.patches) == 1:
-            # check if 0
-            if ax.patches[0].get_x() == 0:
-                orient = 'h'
+        if len(ax.patches) == 1 and ax.patches[0].get_x() == 0:
+            orient = 'h'
         else:
             # compare 0, 1 patches
             p0 = ax.patches[0]
@@ -36,7 +34,7 @@ def _single(ax):
                     ax.text(_x, _y, value, ha='left')
 
     if isinstance(axs, _vp_np.ndarray):
-        for idx, ax in _vp_np.ndenumerate(axs):
+        for _, ax in _vp_np.ndenumerate(axs):
             _single(ax)
     else:
         _single(axs)
