Reimbursement Exploratory Data Analysis

This is a exploratory data analysis for global reimbursement amount.

  • The date used is "Sent for Payment Date"
  • Jun 2017 and July 2017 has been dropped in this dataset as that time we are under initial implementation status

Target:

  1. To generate a complete picture of global reimbursement spending over time
  2. To prepare for modeling cash forecast
  3. To help day-to-day management and decision making

Benefit:

  1. Generate cash forecast model to help prediction
  2. Get insights and intuitions into global reimbursement spendings
In [1]:
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import seaborn as sns
import time
from datetime import datetime

import warnings
warnings.filterwarnings('ignore')

def fxn():
    warnings.warn("deprecated", DeprecationWarning)

with warnings.catch_warnings():
    warnings.simplefilter("ignore")
    fxn()

%matplotlib inline
sns.set(font_scale = 1.2)

#plotly.offline doesn't push your charts to the clouds
import plotly.offline as pyo
#allows us to create the Data and Figure objects
from plotly.graph_objs import *
import plotly.graph_objs as go
#plotly.plotly pushes your charts to the cloud  
import plotly.plotly as py

#lets us see the charts in an iPython Notebook
pyo.offline.init_notebook_mode() # run at the start of every ipython