Merge pull request #8 from jcunninghame/jcunni/pre-commit

Jcunni/pre commit

.pre-commit-config.yaml

@@ -0,0 +1,12 @@
+repos:
+- repo: https://github.com/pre-commit/pre-commit-hooks
+ rev: v2.3.0
+ hooks:
+ - id: check-yaml
+ - id: end-of-file-fixer
+ - id: trailing-whitespace
+- repo: https://github.com/psf/black
+ rev: stable
+ hooks:
+ - id: black
+ language_version: python3.10

components.py

@@ -6,47 +6,56 @@
 
 
 def financial_bans(summary_stats_data):
+ # 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
- multiple years or a single year. Dataframe should be pre-filtered to the time frame desired."""
+ multiple years or a single year. Dataframe should be pre-filtered to the time frame desired.
+ """
  year_values = sorted(list(set(summary_stats_data["year"])))
  summary_stats_data = summary_stats_data.copy(deep=True)
- summary_stats_data = summary_stats_data.loc[summary_stats_data["year"].isin(year_values)]
+ 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"""
+ 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.
- """)
+ 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
+ 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))
+ col4.metric("Average PMPM", util.human_format(avg_pmpm))
 
 
 def year_slider(year_values):
  st.divider()
- st.markdown("""
+ st.markdown(
+ """
  Use the following time slider to cut the following charts by the year range of your interest.
- """)
+ """
+ )
 
  start_year, end_year = st.select_slider(
  label="Select a range of years",
  options=year_values,
  value=(year_values[0], year_values[-1]),
- label_visibility='collapsed'
+ label_visibility="collapsed",
  )
- selected_range = year_values[year_values.index(start_year): year_values.index(end_year)+1]
+ selected_range = year_values[
+ year_values.index(start_year) : year_values.index(end_year) + 1
+ ]
  return selected_range

main_page.py

@@ -12,6 +12,7 @@
 # Connect and fetch data
 conn = util.connection()
 
+
 @st.cache_data
 def summary_stats():
  query = """
@@ -43,37 +44,42 @@ def summary_stats():
  data = util.safe_to_pandas(conn, query)
  return data
 
+
 @st.cache_data
 def pmpm_data():
  query = """SELECT PT.*, PB.MEMBER_COUNT, PHARMACY_SPEND FROM TUVA_PROJECT_DEMO.PMPM.PMPM_TRENDS PT
- LEFT JOIN (SELECT CONCAT(LEFT(YEAR_MONTH, 4), '-', RIGHT(YEAR_MONTH, 2)) AS YEAR_MONTH, 
+ LEFT JOIN (SELECT CONCAT(LEFT(YEAR_MONTH, 4), '-', RIGHT(YEAR_MONTH, 2)) AS YEAR_MONTH,
  COUNT(*) AS MEMBER_COUNT,
  SUM(PHARMACY_PAID) AS PHARMACY_SPEND
  FROM TUVA_PROJECT_DEMO.PMPM.PMPM_BUILDER
  GROUP BY YEAR_MONTH) AS PB
  ON PT.YEAR_MONTH = PB.YEAR_MONTH;"""
 
  data = util.safe_to_pandas(conn, query)
- data['year_month'] = pd.to_datetime(data['year_month'], format='%Y-%m').dt.date
- data['year'] = pd.to_datetime(data['year_month'], format='%Y-%m').dt.year
- data['pharmacy_spend'] = data['pharmacy_spend'].astype(float)
+ data["year_month"] = pd.to_datetime(data["year_month"], format="%Y-%m").dt.date
+ data["year"] = pd.to_datetime(data["year_month"], format="%Y-%m").dt.year
+ data["pharmacy_spend"] = data["pharmacy_spend"].astype(float)
+
  return data
 
+
 @st.cache_data
 def gender_data():
  query = """SELECT GENDER, COUNT(*) AS COUNT FROM TUVA_PROJECT_DEMO.CORE.PATIENT GROUP BY 1;"""
  data = util.safe_to_pandas(conn, query)
  return data
 
+
 @st.cache_data
 def race_data():
  query = """SELECT RACE, COUNT(*) AS COUNT FROM TUVA_PROJECT_DEMO.CORE.PATIENT GROUP BY 1;"""
  data = util.safe_to_pandas(conn, query)
  return data
 
+
 @st.cache_data
 def age_data():
- query = """SELECT CASE 
+ query = """SELECT CASE
  WHEN div0(current_date() - BIRTH_DATE, 365) < 49 THEN '34-48'
  WHEN div0(current_date() - BIRTH_DATE, 365) >= 49 AND div0(current_date() - BIRTH_DATE, 365) < 65 THEN '49-64'
  WHEN div0(current_date() - BIRTH_DATE, 365) >= 65 AND div0(current_date() - BIRTH_DATE, 365) < 79 THEN '65-78'
@@ -95,43 +101,74 @@ def age_data():
 demo_age = age_data()
 
 st.markdown("# Summary of Claims")
-start_year, end_year = st.select_slider("Select date range for claims summary",
- options=sorted(list(set(data['year']))),
- value=(data['year'].min(), data['year'].max()))
-filtered_cost_data = cost_data.loc[(cost_data['year'] >= str(start_year)) & (cost_data['year'] <= str(end_year)), :]
-filtered_pmpm_data = data.loc[(data['year'] >= start_year) & (data['year'] <= end_year), :]
+start_year, end_year = st.select_slider(
+ "Select date range for claims summary",
+ options=sorted(list(set(data["year"]))),
+ value=(data["year"].min(), data["year"].max()),
+)
+filtered_cost_data = cost_data.loc[
+ (cost_data["year"] >= str(start_year)) & (cost_data["year"] <= str(end_year)), :
+]
+filtered_pmpm_data = data.loc[
+ (data["year"] >= start_year) & (data["year"] <= end_year), :
+]
+
 
 st.markdown("### High Level Summary")
-st.markdown("""At a glance, see the total medical spend and PMPM for the chosen time period. As well as a trend
-graph for other important financial metrics""")
+st.markdown(
+ """At a glance, see the total medical spend and PMPM for the chosen time period. As well as a trend
+graph for other important financial metrics"""
+)
 st.sidebar.markdown("# Claims Summary")
 comp.financial_bans(filtered_cost_data)
 
 st.divider()
-y_axis = st.selectbox('Select Metric for Trend Line', [x for x in data.columns if 'year' not in x])
+y_axis = st.selectbox(
+ "Select Metric for Trend Line", [x for x in data.columns if "year" not in x]
+)
 
 if y_axis:
- st.line_chart(filtered_pmpm_data,  x='year_month', y=y_axis)
+ st.line_chart(filtered_pmpm_data, x="year_month", y=y_axis)
 
 # Patient Demographic Section
 st.divider()
-st.markdown('### Patient Demographics')
-st.markdown("""The patient population during this claims period was mostly `female`, `white` and largely 
-over the age of 65, with nearly half of patients falling into the `65-78` age group""")
-st.write(' Please note that patient data is static, and not filtered by claims date sliders currently')
+st.markdown("### Patient Demographics")
+st.markdown(
+ """The patient population during this claims period was mostly `female`, `white` and largely
+over the age of 65, with nearly half of patients falling into the `65-78` age group"""
+)
+st.write(
+ " Please note that patient data is static, and not filtered by claims date sliders currently"
+)
 
 demo_col1, demo_col2 = st.columns([1, 2])
 with demo_col1:
- plost.donut_chart(demo_gender, theta='count',
- color=dict(field='gender', scale=dict(range=['#F8B7CD', '#67A3D9'])), legend='left',
- title='Gender Breakdown')
+ plost.donut_chart(
+ demo_gender,
+ theta="count",
+ color=dict(field="gender", scale=dict(range=["#F8B7CD", "#67A3D9"])),
+ legend="left",
+ title="Gender Breakdown",
+ )
 with demo_col2:
  plost.bar_chart(
- demo_age, bar='age_group', value='count', legend=None, use_container_width=True,
- title='Counts by Age Group', direction='horizontal', height=300
+ demo_age,
+ bar="age_group",
+ value="count",
+ legend=None,
+ use_container_width=True,
+ title="Counts by Age Group",
+ direction="horizontal",
+ height=300,
  )
 
 plost.bar_chart(
- demo_race, bar='race', value='count', color='race', legend='bottom', use_container_width=True,
- title='Counts by Race', height=400
+ demo_race,
+ bar="race",
+ value="count",
+ color="race",
+ legend="bottom",
+ use_container_width=True,
+ title="Counts by Race",
+ height=400,
 )