Jcunni/fin trend chart

.gitignore

@@ -1,2 +1,3 @@
 .env
-.idea
+.idea
+.streamlit

.streamlit/config.toml

@@ -0,0 +1,3 @@
+[theme]
+base="light"
+primaryColor="#66b1e2"

README.md

@@ -10,14 +10,14 @@ Once you are within you desired working python env (or conda, venv), run the fol
 ```pip install -r requirements.txt```
 
 ## Configuration
-Currently, this app uses a `.env` file to store secrets and sensitive information needed for runtime. Create a `.env`
+Currently, this app uses a `secrets.toml` file in the `.streamlit/` folder to store secrets and sensitive information needed for runtime. Create a `.env`
 file in the repo root folder and add the following key/value pairs to the file.
 ```
-SNOWFLAKE_USER = <username goes here>
-SNOWFLAKE_PASSWORD = <password goes here>
-SNOWFLAKE_ACCOUNT = <snowflake account url>
-SNOWFLAKE_WH = <name of desired warehouse to use>
-SNOWFLAKE_ROLE = <name of desired role to use>
+SNOWFLAKE_USER = "<username goes here>"
+SNOWFLAKE_PASSWORD = "<password goes here>"
+SNOWFLAKE_ACCOUNT = "<snowflake account url>"
+SNOWFLAKE_WH = "<name of desired warehouse to use>"
+SNOWFLAKE_ROLE = "<name of desired role to use>"
 ```
 
 ## App Start Up

components.py

@@ -5,7 +5,7 @@
 import util
 
 
-def financial_bans(summary_stats_data):
+def financial_bans(summary_stats_data, direction="horizontal"):
  # TODO: Add Year filter
  """Takes dataframe of financial summary data at the year level and displays BANs
  for med spend, pharm spend, member months and average pmpm. Can handle dataframe with
@@ -16,29 +16,22 @@ def financial_bans(summary_stats_data):
  summary_stats_data = summary_stats_data.loc[
  summary_stats_data["year"].isin(year_values)
  ]
- if len(year_values) == 1:
- year_string = year_values[0]
- else:
- year_string = "{} - {}".format(year_values[0], year_values[-1])
- st.markdown(
- f"""
- These financial summary charts offer a concise and comprehensive snapshot of your organization's financial
- performance, providing key metrics and insights at a glance.
-
- The top three metrics you need to know about your data at all times are medical paid amount, pharmacy
- paid amount and pmpm."""
- )
- st.markdown(f"### Spend Summary in {year_string}")
 
  med_spend = summary_stats_data["medical_paid_amount"].sum()
  pharm_spend = summary_stats_data["pharmacy_paid_amount"].sum()
  member_mon_count = summary_stats_data["member_month_count"].sum()
  avg_pmpm = med_spend / member_mon_count
- col1, col2, col3, col4 = st.columns(4)
- col1.metric("Medical Spend", util.human_format(med_spend))
- col2.metric("Pharmacy Spend", util.human_format(pharm_spend))
- col3.metric("Member Months", util.human_format(member_mon_count))
- col4.metric("Average PMPM", util.human_format(avg_pmpm))
+ if direction == "vertical":
+ st.metric("Medical Spend", util.human_format(med_spend))
+ st.metric("Pharmacy Spend", util.human_format(pharm_spend))
+ st.metric("Member Months", util.human_format(member_mon_count))
+ st.metric("Average PMPM", util.human_format(avg_pmpm))
+ else:
+ col1, col2, col3, col4 = st.columns(4)
+ col1.metric("Medical Spend", util.human_format(med_spend))
+ col2.metric("Pharmacy Spend", util.human_format(pharm_spend))
+ col3.metric("Member Months", util.human_format(member_mon_count))
+ col4.metric("Average PMPM", util.human_format(avg_pmpm))
 
 
 def year_slider(year_values):

pages/02_financial_summary.py

@@ -4,6 +4,8 @@
 import plost
 import util
 import components as comp
+from streamlit_echarts import st_echarts
+import time
 
 conn = util.connection(database="dev_lipsa")
 
@@ -156,28 +158,79 @@ def pmpm_by_service_category_1_2():
  return data
 
 
+def claim_type_line_chart(df, animated=True):
+ if animated:
+ t = st.session_state["iteration"]
+ month_list = sorted(list(set(pmpm_claim_type_data["year_month"])))
+ anim_data = df.loc[df["year_month"] <= month_list[t], :]
+ list_data = [anim_data.columns.to_list()] + anim_data.values.tolist()
+ else:
+ list_data = [df.columns.to_list()] + df.values.tolist()
+ series = list(set(df["claim_type"]))
+ datasetWithFilters = [
+ {
+ "id": f"dataset_{s}",
+ "fromDatasetId": "dataset_raw",
+ "transform": {
+ "type": "filter",
+ "config": {
+ "and": [
+ {"dimension": "claim_type", "=": s},
+ ]
+ },
+ },
+ }
+ for s in series
+ ]
+ seriesList = [
+ {
+ "type": "line",
+ "datasetId": f"dataset_{s}",
+ "showSymbol": False,
+ "name": s,
+ "labelLayout": {"moveOverlap": "shiftY"},
+ "emphasis": {"focus": "series"},
+ "encode": {
+ "x": "year_month",
+ "y": "paid_amount_pmpm",
+ "label": ["claim_type", "paid_amount_pmpm"],
+ "itemName": "year_month",
+ "tooltip": ["paid_amount_pmpm"],
+ },
+ }
+ for s in series
+ ]
+ option = {
+ "color": ["#06405C", "#FFCC05", "#66B1E2"],
+ "dataset": [{"id": "dataset_raw", "source": list_data}] + datasetWithFilters,
+ "title": {"text": "Paid Amount PMPM by Claim Type"},
+ "tooltip": {"order": "valueDesc", "trigger": "axis"},
+ "xAxis": {"type": "category", "nameLocation": "middle"},
+ "yAxis": {"name": "PMPM"},
+ "grid": {"right": 140},
+ "series": seriesList,
+ }
+ st_echarts(options=option, height="400px", key="chart")
+
+
 year_month_values = sorted(list(set(year_months()["year_month"])))
 year_values = sorted(list(set([x[:4] for x in year_month_values])))
-
 ## --------------------------------- ##
-## Header
+## --- --- ##
 ## --------------------------------- ##
-st.markdown("# Financial Overview")
-
-summary_stats_data = summary_stats()
-summary_stats_data = summary_stats_data.loc[
- summary_stats_data["year"] == year_values[-1]
-]
-
-comp.financial_bans(summary_stats_data)
-
-st.divider()
+pmpm_claim_type_data = pmpm_by_claim_type()
+pmpm_claim_type_data.sort_values(by="year_month", inplace=True)
+st.markdown("## Claim Type")
 st.markdown(
  """
-Use the following time slider to cut the following charts by the year range of your interest.
+Explore the per member per month costs across different claim types to gain insights into healthcare expenditure patterns. Inpatient spend will tend to be much higher than professional spend. Dig deeper to find out what is hidden in these costs.
 """
 )
 
+## --------------------------------- ##
+## Header
+## --------------------------------- ##
+st.markdown("# Financial Overview")
 start_year, end_year = st.select_slider(
  label="Select a range of years",
  options=year_values,
@@ -187,36 +240,51 @@ def pmpm_by_service_category_1_2():
 selected_range = year_values[
  year_values.index(start_year) : year_values.index(end_year) + 1
 ]
+if len(year_values) == 1:
+ year_string = year_values[0]
+else:
+ year_string = "{} - {}".format(year_values[0], year_values[-1])
+st.markdown(
+ f"""
+ These financial summary charts offer a concise and comprehensive snapshot of your organization's financial
+ performance, providing key metrics and insights at a glance.
 
-## --------------------------------- ##
-## --- --- ##
-## --------------------------------- ##
-pmpm_claim_type_data = pmpm_by_claim_type()
-pmpm_claim_type_data = pmpm_claim_type_data.loc[
- pmpm_claim_type_data["year_month"].str[:4].isin(selected_range)
-]
-pmpm_claim_type_data = (
- pmpm_claim_type_data.groupby("claim_type", as_index=False)[
- ["paid_amount_sum", "member_month_count"]
- ]
- .sum()
- .assign(paid_amount_pmpm=lambda x: x["paid_amount_sum"] / x["member_month_count"])
+ The top three metrics you need to know about your data at all times are medical paid amount, pharmacy
+ paid amount and pmpm."""
 )
+st.markdown(f"### Spend Summary in {year_string}")
+summary_stats_data = summary_stats()
+summary_stats_data = summary_stats_data.loc[
+ summary_stats_data["year"] == year_values[-1]
+]
 
-st.markdown("## Claim Type")
+if "iteration" not in st.session_state:
+ st.session_state["iteration"] = 0
+
+col1, col2 = st.columns([1, 3])
+with col1:
+ comp.financial_bans(summary_stats_data, direction="vertical")
+with col2:
+ animate = False
+ month_list = sorted(list(set(pmpm_claim_type_data["year_month"])))
+ if animate:
+ while st.session_state["iteration"] < len(month_list):
+ claim_type_line_chart(pmpm_claim_type_data, True)
+ time.sleep(0.05)
+ st.session_state["iteration"] += 1
+
+ if st.session_state["iteration"] < len(month_list) and animate:
+ st.experimental_rerun()
+ else:
+ claim_type_line_chart(pmpm_claim_type_data, False)
+
+
+st.divider()
 st.markdown(
  """
-Explore the per member per month costs across different claim types to gain insights into healthcare expenditure patterns. Inpatient spend will tend to be much higher than professional spend. Dig deeper to find out what is hidden in these costs.
+Use the following time slider to cut the following charts by the year range of your interest.
 """
 )
-plost.bar_chart(
- data=pmpm_claim_type_data,
- bar="claim_type",
- value="paid_amount_pmpm",
- direction="horizontal",
- legend="top",
- height=200,
-)
 
 
 ## --------------------------------- ##

requirements.txt

@@ -5,6 +5,7 @@ streamlit
 plost
 snowflake-connector-python
 boto3
-python-dotenv
+tomli
 pre-commit
 black
+streamlit-echarts

util.py

@@ -1,18 +1,18 @@
 import snowflake.connector as sn
 import pandas as pd
-from dotenv import load_dotenv
-import os
+import tomli
 
-load_dotenv()
+with open(".streamlit/secrets.toml", mode="rb") as fp:
+ config = tomli.load(fp)
 
 
 def connection(database=None):
  return sn.connect(
- user=os.getenv("SNOWFLAKE_USER"),
- password=os.getenv("SNOWFLAKE_PASSWORD"),
- account=os.getenv("SNOWFLAKE_ACCOUNT"),
- warehouse=os.getenv("SNOWFLAKE_WH"),
- role=os.getenv("SNOWFLAKE_ROLE"),
+ user=config["SNOWFLAKE_USER"],
+ password=config["SNOWFLAKE_PASSWORD"],
+ account=config["SNOWFLAKE_ACCOUNT"],
+ warehouse=config["SNOWFLAKE_WH"],
+ role=config["SNOWFLAKE_ROLE"],
  database=database or "tuva_project_demo",
  )