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to update python code for clean code#179

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Merged
minjk-bl merged 4 commits intovisualpython:oss22_libraryfromMinku-Koo:osskoo
Sep 19, 2022
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7 changes: 3 additions & 4 deletionspython/pandasCommand.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -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

Expand Down
16 changes: 13 additions & 3 deletionspython/userCommand.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -10,6 +10,7 @@
import fitz
import nltk
nltk.download('punkt')

######
# Visual Python: Data Analysis > PDF
######
Expand DownExpand Up@@ -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
######
Expand All@@ -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
######
Expand All@@ -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
######
Expand All@@ -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
######
Expand DownExpand Up@@ -134,4 +142,6 @@ def _single(ax):
for idx, ax in _vp_np.ndenumerate(axs):
_single(ax)
else:
_single(axs)
_single(axs)

return
13 changes: 5 additions & 8 deletionspython/variableCommand.py
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Original file line numberDiff line numberDiff line change
@@ -1,3 +1,5 @@
import numpy as _vp_np
import random as _vp_rd
"""
Search Variables
"""
Expand DownExpand Up@@ -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
Expand All@@ -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
10 changes: 4 additions & 6 deletionspython/visualizationCommand.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -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]
Expand All@@ -36,7 +34,7 @@ def _single(ax):
ax.text(_x, _y, value, ha='left')

if isinstance(axs, _vp_np.ndarray):
foridx, ax in _vp_np.ndenumerate(axs):
for_, ax in _vp_np.ndenumerate(axs):
_single(ax)
else:
_single(axs)
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