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Neues Dashboard: Kostenobjekte

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knedlik
2026-02-23 08:11:38 +01:00
parent 07854cc0ad
commit 3f7e405824
10 changed files with 495 additions and 14 deletions
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app/pages/management_first_level.py Normal file
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import streamlit as st
import pandas as pd
from data.scriptloader import get_sql
from data.db import get_conn
from auth_runtime import require_login
from ui.sidebar import build_sidebar, hide_sidebar_if_logged_out
from auth import get_fullname_for_user
from sqlalchemy import text
import duckdb
import altair as alt
from tools.helpers import display_value, calc_variance_pct
# hide_sidebar_if_logged_out()
st.set_page_config(page_title="Co-App Home", page_icon="🏠", layout="wide")
authenticator = require_login()
st.session_state["authenticator"] = authenticator
DISPLAY_UNIT = "Mio. €"
def load_data():
sql = get_sql("ergebnis_kpi")
engine = get_conn("co_dw")
with engine.connect() as conn:
df = pd.read_sql(text(sql), con=conn, params={"jahr": 2025, "monat": 12})
return df
# def calc_variance_pct(actual, plan):
# variance = actual - plan
# if plan == 0:
# return None
# if actual * plan < 0:
# return None
# return variance / abs(plan)
def build_dashboard():
df = load_data()
# st.dataframe(df)
be_ist_kum = df["akt_jahr"].sum()
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Kalkulation KPI Betriebsergebnis
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
operating_result_actual_ytd = df["akt_jahr_bis_monat"].sum()
operating_result_actual_ytd_str = f"{int(operating_result_actual_ytd)/ANZ_EINHEIT:,.2f}".replace(",", "X").replace(".", ",").replace("X", ".") + " Mio. €"
operating_result_plan_ytd = df["plan_akt_jahr_bis_monat"].sum()
operating_result_actual_ytd_py = df["vor_jahr_bis_monat"].sum()
operating_result_variance = operating_result_actual_ytd - operating_result_plan_ytd
operating_result_vaiance_pct = calc_variance_pct(operating_result_actual_ytd, operating_result_plan_ytd)
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Dashboard - Ebene 1 Betriebsergebnis
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
col_1, col_2 = st.columns(2, border=True,vertical_alignment="center")
with col_1:
st.metric(label="Betriebsergebnis", value=operating_result_actual_ytd)
operation_result_monthly = df["akt_jahr_monat"].sum()
be_ist_vorjahr_kum = df["vor_jahr"].sum()
be_ist_monat = df["akt_jahr_monat"].sum()
be_ist_vorjahr_monat = df["vor_jahr_monat"].sum()
be_ist_kum_anz = f"{int(be_ist_kum)/ANZ_EINHEIT:,.2f}".replace(",", "X").replace(".", ",").replace("X", ".") + " Mio. €"
be_ist_monat_anz = f"{int(be_ist_monat)/ANZ_EINHEIT:,.2f}".replace(",", "X").replace(".", ",").replace("X", ".") + " Mio. €"
be_ist_vorjahr_kum_anz = f"{int(be_ist_vorjahr_kum)/ANZ_EINHEIT:,.2f}".replace(",", "X").replace(".", ",").replace("X", ".") + " Mio. €"
be_ist_monat_vorjahr_anz = f"{int(be_ist_vorjahr_monat)/ANZ_EINHEIT:,.2f}".replace(",", "X").replace(".", ",").replace("X", ".") + " Mio. €"
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Ebene 1 - Ergebnis-KPIs
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Beispiel-Daten (ersetze das durch deine echten Monatswerte)
months = pd.date_range(end=pd.Timestamp.today().normalize(), periods=12, freq="MS")
values = pd.Series([120, 80, -30, 50, 40, 10, -20, 60, 70, 30, 20, -10], index=months)
df = pd.DataFrame({"month": months, "BE": values}).set_index("month")
current = df["BE"].iloc[-1]
prev = df["BE"].iloc[-2]
delta = current - prev
col1, col2, col3 = st.columns(3)
with col1:
st.metric(
"Betriebsergebnis (intern)",
f"{current:,.0f} €",
f"{delta:,.0f} €"
)
with st.expander("Verlauf letzte 12 Monate", expanded=False):
# st.bar_chart(df["BE"])
# fig, ax = plt.subplots()
# ax.plot(df.index, df["BE"], marker="o")
# ax.axhline(0, linewidth=1) # Nulllinie für negative Werte
# ax.set_xlabel("")
# ax.set_ylabel("€")
# ax.tick_params(axis="x", rotation=45)
# st.pyplot(fig, clear_figure=True)
df_reset = df.reset_index()
df_reset["Monat"] = df_reset["month"].dt.strftime("%Y-%m")
chart = (
alt.Chart(df_reset)
.mark_bar(size=28) # Balkendicke
.encode(
x=alt.X("Monat:N", sort=None, title=""),
y=alt.Y("BE:Q", title="€"),
color=alt.condition(
alt.datum.BE < 0,
alt.value("#d62728"), # rot
alt.value("#2ca02c"), # grün
)
)
)
labels = (
alt.Chart(df_reset)
.mark_text(dy=-8)
.encode(
x="Monat:N",
y="BE:Q",
text=alt.Text("BE:Q", format=",.0f")
)
)
spark = (
alt.Chart(df_reset)
.mark_line(point=True)
.encode(
x=alt.X("Monat:N", axis=None),
y=alt.Y("BE:Q", axis=None)
)
.properties(height=60)
)
st.altair_chart(spark, use_container_width=True)
# st.altair_chart(chart + labels, use_container_width=True)
# col_operating_result, col_contribution_margin = st.columns(2,border=True)
# col_ue_n, col_ae_f, col_ab_f = st.columns(3,border=True,)
# with col_ue_n:
# with st.expander(label="Umsatz",):
# st.metric(label="BE-Ist kumuliert (monat)",value=f"{be_ist_kum_anz} ({be_ist_monat_anz})", delta="-5", border=True)
# st.text("Umsatz")
# with col_ae_f:
# st.text("Auftragseingang fest")
# with col_ab_f:
# st.text("Auftragsbestand fest")
# with col_operating_result:
# internal_operating_result = f"BE = {be_ist_kum_anz} Mio. €"
# st.metric(label="BE-Ist kumuliert (monat)",value=f"{internal_operating_result} ({be_ist_monat_anz})", delta="-5", border=True)
# with st.expander(label=st.markdown(f"# {internal_operating_result}")):
# st.text("Verlauf...")
# # st.text("ERGTAB")
# st.metric(label="BE-Ist kumuliert (monat)",value=f"{be_ist_kum_anz} ({be_ist_monat_anz})", delta="-5", border=True)
# erg = duckdb.sql("""
# select
# 'Umsatz' as Kostenart,
# sum(case when co_koa_grp = 'CO1000' then akt_jahr else 0 end) as Aktuell,
# sum(case when co_koa_grp = 'CO1000' then plan_akt_jahr else 0 end) as Plan,
# sum(case when co_koa_grp = 'CO1000' then vor_jahr else 0 end) as Vorjahr
# from
# df
# """).fetchdf()
# st.metric(label="BE-Ist kumuliert (monat)",value=f"{be_ist_kum_anz} ({be_ist_monat_anz})", delta="-5", border=True)
# st.dataframe(erg, hide_index=True)
# col_erg_ist = st.columns(1)
# with col_erg_ist:
# st.metric(label="BE-Ist (monat)",value=be_ist_monat_anz, delta="-5", border=True)
# col1, col2 = st.columns(2)
# with col1:
# # st.metric(label="BE-Ist (kumuliert)",value=be_ist_kum_anz, delta="-5", border=True)
# with st.success("Ergebnis"):
# st.button(f"{be_ist_kum_anz}")
# # st.success("plus 10% zu Vorjahr")
# # with col2:
# # st.error("-10% zu Plan")
# # with col_erg_plan:
# # st.info("minus 5%")
# # st.warning("minus 10%")
# # st.success("plus 10%")
# # st.error("minus 20%")
# # with col_erg_vorjahr:
# # st.metric(label="BE-Vorjahr (kumuliert)",value=be_ist_vorjahr_kum_anz, delta="-5", border=True)
# # st.metric(label="BE-Vorjahr (monat)",value=be_ist_monat_anz, delta="-5", border=True)
# # st.dataframe(load_data())
if __name__ == "__main__":
df = build_dashboard()