import pandas as pd
from Week6.ggikko_helper import get_file_abs_path
mm1 = pd.read_csv(get_file_abs_path('202001_refined.csv')).drop_duplicates(
['사업장명'], keep='first')
mm2 = pd.read_csv(get_file_abs_path('202002_refined.csv')).drop_duplicates(
['사업장명'], keep='first')
mm3 = pd.read_csv(get_file_abs_path('202003_refined.csv')).drop_duplicates(
['사업장명'], keep='first')
mm4 = pd.read_csv(get_file_abs_path('202004_refined.csv')).drop_duplicates(
['사업장명'], keep='first')
mm5 = pd.read_csv(get_file_abs_path('202005_refined.csv')).drop_duplicates(
['사업장명'], keep='first')
mm6 = pd.read_csv(get_file_abs_path('202006_refined.csv')).drop_duplicates(
['사업장명'], keep='first')
mm7 = pd.read_csv(get_file_abs_path('202007_refined.csv')).drop_duplicates(
['사업장명'], keep='first')
mm8 = pd.read_csv(get_file_abs_path('202008_refined.csv')).drop_duplicates(
['사업장명'], keep='first')
print(mm8.shape)
concat_df = pd.concat([mm1, mm2, mm3, mm4, mm5, mm6, mm7, mm8], axis=0)
concat_df = concat_df[concat_df['사업장명'].notna()]
groupby_df = concat_df.groupby(['사업장명'])
new_df = pd.DataFrame({
'사업장명': groupby_df['사업장명'].first(),
'광역시코드': groupby_df['광역시코드'].first(),
'평균연봉': groupby_df['평균연봉'].mean(),
import pandas as pd
import numpy as np
from Week6.ggikko_helper import get_file_abs_path
df = pd.read_csv(get_file_abs_path('concat_salary.csv'))
print(df.head(10))
df['사업장명'] = df['사업장명'].str.strip()
df['사업장명'].replace('', np.nan, inplace=True)
df = df[df['사업장명'].notna()]
df.to_csv('concat_salary2.csv', index=False)
import pandas as pd
import numpy as np
from pycaret.classification import *
from sklearn.model_selection import train_test_split
from Week6.ggikko_helper import get_file_abs_path
# 사업장명,광역시코드,평균연봉,joinlossratio
sample = pd.read_csv(get_file_abs_path('train.csv'))
sample['joinlossratio'] = sample['가입대비상실']
print(sample.info())
print(sample.shape)
sample = sample.loc[:, ['사업장명', '광역시코드', '평균연봉', 'joinlossratio', 'status']]
sample.to_csv("train2.csv", index=False)
# 사업장명,광역시코드,평균연봉,joinlossratio
# 사업장명,가입자수,도로명주소,신규,상실,고지금액,평균월급,평균연봉,가입대비상실,status
# 베인앤드컴퍼니코리아인크,181,서울특별시 중구 퇴계로,10,14,74173220,4553297,54639572,7.73,1
# 브레이브모바일,61,경기도 성남시 분당구 황새울로200번길,6,4,17825180,3246845,38962142,6.56,1
# 카카오페이,544,경기도 성남시 분당구 판교역로,12,12,214293900,4376917,52523014,2.21,1
# train_features = sample['사업장명', '도로명주소']
# train_labels = train_df['status']
# test_features = test_df.loc[:100, :]
#
# x_train, x_valid, y_train, y_valid = train_test_split(sample[feature], sample[label], test_size=0.2, shuffle=True,
# random_state=30)
# refined_salary_data("202001.csv")
# refined_salary_data("202002.csv")
# refined_salary_data("202003.csv")
# refined_salary_data("202004.csv")
# refined_salary_data("202005.csv")
# refined_salary_data("202006.csv")
# refined_salary_data("202007.csv")
# refined_salary_data("202008.csv")
temp_list: list = []
# temp_list.append(pd.read_csv(get_file_abs_path('202001.csv')))
# temp_list.append(pd.read_csv(get_file_abs_path('202002.csv')))
temp_list.append(pd.read_csv(get_file_abs_path('202001_refined.csv')))
temp_list.append(pd.read_csv(get_file_abs_path('202002_refined.csv')))
# temp_list.append(pd.read_csv(get_file_abs_path('202003_refined.csv')))
# temp_list.append(pd.read_csv(get_file_abs_path('202004_refined.csv')))
# temp_list.append(pd.read_csv(get_file_abs_path('202005_refined.csv')))
# temp_list.append(pd.read_csv(get_file_abs_path('202006_refined.csv')))
# temp_list.append(pd.read_csv(get_file_abs_path('202007_refined.csv')))
# temp_list.append(pd.read_csv(get_file_abs_path('202008_refined.csv')))
# temp_list[0]['joinlossratio'] = temp_list[0]['joinlossratio'].add(temp_list[1]['joinlossratio'], fill_value=0)
# print(temp_list[0]['joinlossratio'])
root_df: DataFrame = temp_list[0]
second_df: DataFrame = temp_list[1]
# def hoho(company_name):