def __init__(self, parent=None):
self.parent = parent
Frame.__init__(self)
self.main = self.master
self.main.geometry('600x400+200+100')
self.main.title('Table app')
f = Frame(self.main)
f.pack(fill=BOTH, expand=1)
df = TableModel.getSampleData()
self.table = pt = Table(f,
dataframe=df,
showtoolbar=True,
showstatusbar=True)
pt.show()
return
def smmm(u):
#f.grid(row=15,column=0,sticky=W,ipadx=600,ipady=17)
df = TableModel.getSampleData()
pt = Table(leftFrame2,dataframe=u,showtoolbar=True, showstatusbar=True)
pt.grid(row=15,column=0,sticky=W,ipadx=345,ipady=17)
pt.show()
def __init__(self, parent=None):
window = Toplevel(chirag)
f = Frame(window)
f.pack(fill=BOTH,expand=1)
df = TableModel.getSampleData()
table = pt = Table(f, dataframe=df,showtoolbar=True, showstatusbar=True)
pt.importCSV(chirag.filename)
pt.show()
return
def __init__(self, passed_dataframe=TableModel.getSampleData(), parent=None):
self.is_showing = True
self.parent = parent
tk.Frame.__init__(self)
self.main = tk.Toplevel(parent)
self.main.geometry('1000x400+200+100')
self.main.title('My Trades Table')
self.main.protocol('WM_DELETE_WINDOW', self.on_closing)
f = tk.Frame(self.main)
f.pack(fill=tk.BOTH, expand=1)
df = passed_dataframe
self.table = pt = Table(f, dataframe=df, showtoolbar=True, showstatusbar=True)
pt.show()
return
def __init__(self, parent, controller):
from tkinter import ttk
tk.Frame.__init__(self, parent)
s=str("Month: "+m+" and Weather Condition: "+c+".")
from pandastable import Table, TableModel
lbl=tk.Label(self, text=s, font=("Arial Bold", 18))
lbl.grid(row=0, column=1)
f = Frame(self)
f.grid(row=2, column=0, columnspan=3)
df = TableModel.getSampleData()
print(type(df))
self.table = pt = Table(f, dataframe=df1,
showtoolbar=True, showstatusbar=True)
pt.show()
btn122=tk.Button(self, text="Back", command=lambda : controller.show_frame(pageone))
btn122.grid(row=0, column=0)
btn122=tk.Button(self, text='Go to Home', command=lambda : controller.show_frame(startpage))
btn122.grid(row=3, column=1)
label=tk.Label(self, text=txt)
label.grid(row=1, column=0, columnspan=4)
def test(self):
path = 'test_batch'
for i in range(20):
df = TableModel.getSampleData()
df.to_csv(os.path.join(path, 'test%s.csv' % str(i)))
return
from tkinter import Tk, Frame
from pandastable import Table, TableModel
data = {
'rec1': {
'col1': 99.88,
'col2': 108.79,
'label': 'rec1'
},
'rec2': {
'col1': 99.88,
'col2': 108.79,
'label': 'rec2'
}
}
root = Tk()
f = Frame(root)
f.pack(fill="both", expand=1)
model = TableModel.getSampleData()
table = Table(f, dataframe=model, showtoolbar=True, showstatusbar=True)
table.show()
table.showPlotViewer(layout='horizontal')
#table.showPlot()
root.mainloop()
def run():
print(dirname)
mypath = (dirname)
onlyfiles = [
os.path.join(mypath, f) for f in os.listdir(mypath)
if os.path.isfile(os.path.join(mypath, f))
]
def pdfextract(file):
text = extract_text(file)
return text
#function that does phrase matching and builds a candidate profile
def create_profile(file):
text = pdfextract(file)
text = str(text)
text = text.replace("\\n", "")
text = text.lower()
#below is the csv where we have all the keywords, you can customize your own
keyword_dict = pd.read_csv(
'https://raw.githubusercontent.com/darvinloganathan/resume-pharse/main/template.csv',
error_bad_lines=False)
stats_words = [
nlp(text) for text in keyword_dict['Statistics'].dropna(axis=0)
]
NLP_words = [nlp(text) for text in keyword_dict['NLP'].dropna(axis=0)]
ML_words = [
nlp(text)
for text in keyword_dict['Machine Learning'].dropna(axis=0)
]
DL_words = [
nlp(text) for text in keyword_dict['Deep Learning'].dropna(axis=0)
]
R_words = [
nlp(text) for text in keyword_dict['R Language'].dropna(axis=0)
]
python_words = [
nlp(text)
for text in keyword_dict['Python Language'].dropna(axis=0)
]
Data_Engineering_words = [
nlp(text)
for text in keyword_dict['Data Engineering'].dropna(axis=0)
]
matcher = PhraseMatcher(nlp.vocab)
matcher.add('Statstics', None, *stats_words)
matcher.add('NLP', None, *NLP_words)
matcher.add('MachineLearnig', None, *ML_words)
matcher.add('DeepLearning', None, *DL_words)
matcher.add('R', None, *R_words)
matcher.add('Python', None, *python_words)
matcher.add('DataEngineering', None, *Data_Engineering_words)
doc = nlp(text)
d = []
matches = matcher(doc)
for match_id, start, end in matches:
rule_id = nlp.vocab.strings[
match_id] # get the unicode ID, i.e. 'COLOR'
span = doc[start:end] # get the matched slice of the doc
d.append((rule_id, span.text))
keywords = "\n".join(f'{i[0]} {i[1]} ({j})'
for i, j in Counter(d).items())
## convertimg string of keywords to dataframe
df = pd.read_csv(StringIO(keywords), names=['Keywords_List'])
df1 = pd.DataFrame(df.Keywords_List.str.split(' ', 1).tolist(),
columns=['Subject', 'Keyword'])
df2 = pd.DataFrame(df1.Keyword.str.split('(', 1).tolist(),
columns=['Keyword', 'Count'])
df3 = pd.concat([df1['Subject'], df2['Keyword'], df2['Count']], axis=1)
df3['Count'] = df3['Count'].apply(lambda x: x.rstrip(")"))
base = os.path.basename(file)
filename = os.path.splitext(base)[0]
name = filename.split('_')
name2 = name[0]
name2 = name2.lower()
## converting str to dataframe
name3 = pd.read_csv(StringIO(name2), names=['Candidate Name'])
dataf = pd.concat([
name3['Candidate Name'], df3['Subject'], df3['Keyword'],
df3['Count']
],
axis=1)
dataf['Candidate Name'].fillna(dataf['Candidate Name'].iloc[0],
inplace=True)
return (dataf)
#function ends
#code to execute/call the above functions
final_database = pd.DataFrame()
i = 0
while i < len(onlyfiles):
file = onlyfiles[i]
dat = create_profile(file)
final_database = final_database.append(dat)
i += 1
print(final_database)
#code to count words under each category and visulaize it through Matplotlib
final_database2 = final_database['Keyword'].groupby(
[final_database['Candidate Name'],
final_database['Subject']]).count().unstack()
final_database2.reset_index(inplace=True)
final_database2.fillna(0, inplace=True)
new_data = final_database2.iloc[:, 1:]
new_data.index = final_database2['Candidate Name']
new_data['Score'] = new_data.sum(axis=1, skipna=True)
result = new_data
result.reset_index(level=0, inplace=True)
# base=list(result['Candidate Name'])
# result['resume'] = np.array(base)
total_exp = []
mail_id = []
mob_num = []
for i in onlyfiles:
data = ResumeParser(i).get_extracted_data()
a = data['total_experience']
b = data['email']
c = data['mobile_number']
total_exp.append(a)
mail_id.append(b)
mob_num.append(c)
te = []
for i in total_exp:
if i > 0:
te.append(i)
else:
te.append('fresher')
result.loc[:, 'total_experience'] = pd.Series(te)
result.loc[:, 'email'] = pd.Series(mail_id)
result.loc[:, 'mobile_number'] = pd.Series(mob_num)
result1 = result.sort_values(by='Score', ascending=False)
df = TableModel.getSampleData()
table = pt = Table(Top,
dataframe=result1,
showtoolbar=True,
showstatusbar=True)
pt.show()