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get_df_functions.py
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get_df_functions.py
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'''
GET ALL GRAPHS AS SUBPLOTS
'''
''' IMPORTS '''
import pandas as pd
import numpy as np
from IPython.display import display
# from sklearn.preprocessing import LabelEncoder
from py_files.data_functions import read_df
''' GLOBAL VARIABLES '''
jobs_df = read_df("jobs")
unique_dates = sorted(list(set(jobs_df.date_applied)))
string_dates = [str(pd.to_datetime(x).strftime('%b %-d')) for x in unique_dates]
''' SHOWING GRAPHS '''
def get_slim_cats():
# get reformatted categories column to fit on graphs
cat_ser = jobs_df['job_cat']
for x in range(len(cat_ser)):
if 'Data Engineer' == cat_ser[x]:
cat_ser.iloc[x] = 'Data Engr.'
elif 'Data Analyst' == cat_ser[x]:
cat_ser.iloc[x] = 'Data An.'
# elif 'Python' in cat_ser[x]:
# cat_ser.iloc[x] = 'Software'
elif 'Technical Writer' == cat_ser[x]:
cat_ser.iloc[x] = 'Tech Writer'
elif 'Backend' in cat_ser[x]:
cat_ser.iloc[x] = 'Bknd Engr.'
elif 'Engineer' in cat_ser[x]:
cat_ser.iloc[x] = cat_ser[x].replace('Engineer', 'Engr.')
return cat_ser.sort_values()
def get_location_df():
X = jobs_df.loc[:, ['location']]
for idx, r in X.location.items():
if 'remote' in r.lower():
X.loc[idx, 'location'] = 'Remote'
elif 'Germany' in r:
X.loc[idx, 'location'] = 'Germany'
elif 'Austria' in r:
X.loc[idx, 'location'] = 'Austria'
elif 'Copenhagen' in r:
X.loc[idx, 'location'] = 'Copenhagen'
elif "Berlin" in r:
X.loc[idx, 'location'] = 'Berlin'
else:
X.loc[idx, 'location'] = 'Other'
return X
def get_init_responses():
slim_df = jobs_df.loc[:, ['date_applied', 'initial_response']]
outcomes_df = slim_df.sort_values('date_applied').drop(columns='date_applied')
return outcomes_df
def get_ohe_df():
X = jobs_df.loc[:, ['date_applied', 'initial_response']]
X = X.sort_values('date_applied', ignore_index=True)
gb_df = X.groupby('date_applied').value_counts().unstack(fill_value=0)
if 'Rejected' not in gb_df.columns:
gb_df['Rejected'] = 0
if 'No Response' not in gb_df.columns:
gb_df['No Response'] = 0
if 'Passed' not in gb_df.columns:
gb_df['Passed'] = 0
r_dict = {
'Rejected': list(gb_df['Rejected']),
'No Response': list(gb_df['No Response']),
'Passed': list(gb_df['Passed'])
}
r_df = pd.DataFrame(data=r_dict, index=string_dates).reset_index(drop=True)
return r_df
def get_outcomes():
slim_df = jobs_df.loc[:, ['date_applied', 'final_outcome']]
outcomes_df = slim_df.sort_values('date_applied')
return outcomes_df
def get_responses():
df = get_outcomes()
grouped_df = df.groupby('date_applied').value_counts().unstack(fill_value=0)
all_keys = ['No Response', 'Rejection', 'No Offer', 'In Interviews',
'Offer'
]
keys = list(grouped_df.keys())
nan_keys = list(set(all_keys) - set(keys))
yes_keys = list(set(all_keys) - set(nan_keys))
available_df = pd.DataFrame({x: list(grouped_df[x]) for x in yes_keys}, index=unique_dates)
if nan_keys:
nan_df = pd.DataFrame({x: len(available_df)*[0] for x in nan_keys}, index=unique_dates)
responses_df = pd.concat([available_df, nan_df], axis=1)
return responses_df
return available_df
def get_prep_df():
df = read_df('prep').drop(columns='submissions')
dates = sorted(list(set(df.date_completed)))
f_dates = [str(pd.to_datetime(x).strftime('%b %-d')) for x in dates]
grouped_df = df.groupby('date_completed').value_counts().unstack(fill_value=0)
cols = sorted(list(df.site.unique()))
keys = [x.replace("_", "").capitalize() for x in cols]
prep_df = pd.DataFrame(
{keys[x]: list(grouped_df[cols[x]]) for x in range(len(cols))},
index=f_dates
)
return prep_df
def get_slim_prep_df():
df = read_df('prep').drop(columns='site')
grouped_df = df.groupby('date_completed').count().reset_index()
dates = grouped_df.date_completed.values
prep_df = pd.DataFrame({"Coding Practice": list(grouped_df["submissions"])},
index=dates)
return prep_df
def get_work_col(column):
work_df = read_df('work')[column]
return work_df
def get_work_df():
df = read_df('work').drop(columns='category')
dates = sorted(list(set(df.date_completed)))
f_dates = [str(pd.to_datetime(x).strftime('%b %-d')) for x in dates]
grouped_df = df.groupby('date_completed').value_counts().unstack(fill_value=0)
cols = sorted(list(df.job.unique()))
keys = [x.capitalize() for x in cols]
work_df = pd.DataFrame(
{keys[x]: list(grouped_df[cols[x]]) for x in range(len(cols))},
index=f_dates
)
return work_df
def get_slim_work_df():
df = read_df('work').drop(columns='category')
grouped_df = df.groupby('date_completed').count().reset_index()
dates = grouped_df.date_completed.values
work_df = pd.DataFrame({"Work": list(grouped_df["job"])}, index=dates)
return work_df
def get_timeline_df():
rdf = get_responses()
pdf = get_slim_prep_df()
wdf = get_slim_work_df()
jdf = pd.merge(rdf, pdf, how='outer', left_index=True, right_index=True)
jdf2 = pd.merge(jdf, wdf, how='outer', left_index=True, right_index=True)
filled_df = jdf2.fillna(0).convert_dtypes().reset_index(names='Date')
tl_df = filled_df.set_index('Date')
return tl_df
def get_encoded_cols():
# slice and binary features
bi_columns = ['department', 'recruiter', 'referral']
X = jobs_df.loc[:, bi_columns].copy()
# custom encode binary features
X.loc[X['department'].isna(), 'department'] = 'Company'
X.loc[X['department'] != 'Company', 'department'] = 'Department'
for c in ['recruiter', 'referral']:
X.loc[X[c].isna(), c] = f"No {c}"
X.loc[X[c] != f"No {c}", c] = f"{c.capitalize()}"
# add named categorical features
X1 = jobs_df.loc[:, ['method']].copy()
for x in ['linkedin', 'stepstone', 'indeed']:
X1.loc[X1['method'] == x, 'method'] = 'Platform'
outcomes = get_outcomes().drop(columns='date_applied')
loc_df = get_location_df()
df = X.join(X1).join(loc_df).join(outcomes)
return df
'''
unused code for label encoder
'''
# le = LabelEncoder()
# for c in cat_columns:
# X.loc[:, c] = le.fit_transform(X.loc[:, c])
# # custom encode target (init. response)
# y = jobs_df['initial_response'].reset_index(drop=True)
# for idx, v in y.items():
# if v == 'Rejected':
# y[idx] = -1
# elif v == 'No Response':
# y[idx] = 0
# else:
# y[idx] = 1
'''
replaced with shorter code
'''
# def get_outcomes(jobs_df=read_df()):
# slim_df = jobs_df.loc[:, ['date_applied', 'company_name', 'initial_response', 'final_outcome']]
# # sort by date
# outcomes_df = slim_df.sort_values('date_applied')
# # re-label final outcome by init and final responses
# for idx in outcomes_df.index:
# row = outcomes_df.loc[idx]
# if row.final_outcome != 'Offer':
# pass
# elif row.final_outcome == 'Rejected' and row.initial_response == "Passed":
# outcomes_df.loc[idx, ['final_outcome']] = 'Rejected Post-Interview'
# elif row.initial_response == 'Rejected':
# outcomes_df.loc[idx, ['final_outcome']] = 'Immediate Rejection'
# elif row.initial_response == 'No Response':
# outcomes_df.loc[idx, ['final_outcome']] = 'No Response'
# elif row.initial_response == 'Passed':
# outcomes_df.loc[idx, ['final_outcome']] = 'In Interviews'
# else:
# print("error:", row.initial_response)
# outcomes_df.drop(columns=['initial_response'], inplace=True)
# return outcomes_df