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Copy pathloan_credits_per_day.py
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loan_credits_per_day.py
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"""
Exploring the loan credits per users.
Version 5/28: Normalization:only get users present bf and after
"""
import altair as alt
import numpy as np
import pandas as pd
import infra.constants
import infra.dask
import infra.pd
import infra.platform
def get_data(timeline):
transactions = infra.pd.read_parquet('./data/derived/untainted_transactions_INDEX_timestamp.parquet').reset_index()
transactions["user"] = transactions["user"].astype(object)
print("Total number of transactions: " + str(len(transactions.user)))
# find the admin to exclude from data for both before and after covid
df_admin = transactions[transactions["kind"] == "admin_topup"]
# excluded retailers
retailers = df_admin["dest_user"].unique()
excluded_retailers = transactions[~transactions['user'].isin(retailers)]
excluded_retailers = excluded_retailers[excluded_retailers['kind'] == 'user_transfer']
# #excluded self-transfer bug
excluded_self = excluded_retailers[excluded_retailers['user'] != excluded_retailers['dest_user']]
# exclude dirty money
start_tainted = "2020-05-24"
end_tainted = "2020-06-14"
query_start = f"(timestamp < '{start_tainted}') | (timestamp > '{end_tainted}')"
df_no_tainted = excluded_self.query(query_start)
# normalize by time: taking temperal average divide by months
# temperal average -- 238 days after (@12/16/2020) and before (@8/7/2019) exactly 34 weeks ---excluding tainted date.
start_date = "2019-08-07 00:00:00"
lockdown_date = "2020-04-01 00:00:00"
num_days = (pd.to_datetime(lockdown_date) - pd.to_datetime(start_date)).days
tainted_date_makeup = pd.DateOffset(7 * 3)
end_date = str(pd.to_datetime(lockdown_date) + pd.DateOffset(num_days) + tainted_date_makeup)
# assert(num_days == (pd.to_datetime(end_date) - pd.to_datetime(lockdown_date)).days)
# get only users from 238 days before
query_start = f"(timestamp >= '{start_date}') & (timestamp < '{end_date}')"
df_range = df_no_tainted.query(query_start)
df_range = df_range[df_range['user'].notnull()]
df_range = df_range[df_range['dest_user'].notnull()]
def get_users(df):
return pd.concat([df['user'], df['dest_user']]).rename('user').to_frame().drop_duplicates()
all_users = get_users(df_range)
all_users['key'] = 1
all_users['row_num'] = np.arange(len(all_users))
all_users['user_derive'] = 'user-' + all_users['row_num'].astype(str).map(lambda x:x.zfill(2))
x_users = pd.merge(all_users, all_users.rename(columns={'user': 'dest_user', 'user_derive': 'dest_user_derive'}), on='key')[['user', 'dest_user', 'user_derive', 'dest_user_derive']]
df_range = pd.merge(df_range, all_users, on='user')
# df_range.to_csv (r'/home/cwkt/Documents/ccn-traffic-analysis-2020/data/clean/' + 'full-nov-nb.csv', index = False, header=True)
# split the timeline before and after COVID
## March 25th, 2020 school closes April 1st, 2020 roads to capital closed to town
query_before = f"(timestamp >= '{start_date}') & (timestamp < '{lockdown_date}')"
# cut the last day
query_after = f"(timestamp >= '{lockdown_date}') & (timestamp < '{end_date}')"
df_before = df_range.query(query_before)
df_after = df_range.query(query_after)
# Normalization:only get users present bf and after
# do inner join
all_before_users = get_users(df_before)
all_after_users = get_users(df_after)
all_after_users_user = all_after_users['user'].unique()
intersection = all_before_users[all_before_users['user'].isin(all_after_users_user)]['user'].unique()
# both users and dest_users must be in the set of before and after dataset
intersect_before = df_before[df_before['user'].isin(intersection) & df_before['dest_user'].isin(intersection)]
intersect_after = df_after[df_after['user'].isin(intersection) & df_after['dest_user'].isin(intersection)]
# check if any of the users or dest_user never exists before/after
# print(~df_before['dest_user'].isin(intersection))
# print(~df_after['dest_user'].isin(intersection))
# export full dataset for tableau
intersect_before['timeline'] = 'before'
intersect_after['timeline'] = 'after'
full_table_same_set = pd.concat([intersect_before, intersect_after], ignore_index=True)
full_table_same_set.to_csv (r'/home/cwkt/Documents/ccn-traffic-analysis-2020/data/clean/' + 'full-table-same-users.csv', index = False, header=True)
if timeline == 'before':
data_cleaned = intersect_before
elif timeline == 'after':
data_cleaned = intersect_after
else:
raise ValueError("Timeline should be only 'before' or 'after' the pandemic lockdown.")
get_count(df_before, df_after, timeline)
data_cleaned = df_range.merge(x_users, on=['user', 'dest_user'], how='outer')
data_cleaned['user_derive'] = data_cleaned['user_derive_x']
data_cleaned['amount_idr'] = data_cleaned['amount_idr'].fillna(0)
# total loan for each user
total_loan = data_cleaned.groupby(["user_derive", "dest_user_derive"]).agg({"amount_idr" : 'sum'})
total_loan['amount_idr'] = total_loan['amount_idr'].div(num_days)
# reset index for plotting
df = pd.DataFrame(total_loan).reset_index()
# df.to_csv (r'/home/cwkt/Documents/ccn-traffic-analysis-2020/data/clean/' + timeline + '.csv', index = False, header=True)
return df
def make_plot(df, timeline):
count_origin = df['user_derive'].nunique()
count_dest =df['dest_user_derive'].nunique()
chrt = alt.Chart(
df,
# title="Loaning Between User-User " + timeline + " Covid"
title={"text":"Total Loans Between User-User", "fontSize":22},
width=2200,
height=2200
).mark_rect().encode(
alt.X('user_derive:N',
title= str(count_origin) +" Origin Users",
sort=alt.EncodingSortField(field='dest_user_derive', order='descending')
),
alt.Y('dest_user_derive:N',
title= str(count_dest) + " Destination Users",
sort=alt.EncodingSortField(field='dest_user_derive', order='descending')
),
alt.Color('amount_idr:Q',
title="Amount Transferred (Rupiah)",
legend={"titleFontSize": 18, "titleLimit": 400, "labelFontSize": 15},
scale=alt.Scale(type='symlog', nice=True),
condition={"test": "datum['amount_idr'] == 0", "value": "white"}
),
).save("./misc/charts/loan_credits_" + timeline +".html", scale_factor=2.0)
def get_count(df_before, def_after, timeline):
if timeline == 'before':
total_loan = df_before['amount_idr'].sum(skipna= True)
total_freq = len(df_before.index)
elif timeline == 'after':
total_loan = def_after['amount_idr'].sum(skipna= True)
total_freq = len(def_after.index)
else:
raise ValueError("Timeline should be only 'before' or 'after' the pandemic lockdown.")
loan_week = total_loan / 34.00
loan_day = total_loan / 238.00
freq_week = total_freq / 34
freq_day = total_freq / 238
print("Total loan all users for " + timeline + ": " + str(total_loan))
print(" Average loan per week for " + timeline + ": " + str(loan_week))
print(" Average loan per day for " + timeline + ": " + str(loan_day))
print("Total frequency all users for " + timeline + ": " + str(total_freq))
print(" Average frequency per week for " + timeline + ": " + str(freq_week))
print(" Average frequency per day for " + timeline + ": " + str(freq_day))
print(" ")
print(" ")
# loan_before = df_before.groupby(['user']).agg({"amount_idr" : 'sum'})
# loan_before['time'] = 'before'
# loan_after = def_after.groupby(['user']).agg({"amount_idr" : 'sum'})
# loan_after['time'] = 'after'
# loan = pd.concat([loan_before['user'], loan_after['user']]).to_frame()
# print(loan.head())
# alt.Chart(loan).mark_line().encode(
# x='user',
# y='amount_idr',
# color='time'
# ).save("./misc/charts/amount.html", scale_factor=2.0)
# return total_each_user
if __name__ == "__main__":
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_rows', None)
# df_before = get_data("before")
# df_after = get_data("after")
make_plot(get_data("before"), "before")
# make_plot(get_data("after"), "after")