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app.py.txt

+# @Email:  contact@pythonandvba.com
+# @Website:  https://pythonandvba.com
+# @YouTube:  https://youtube.com/c/CodingIsFun
+# @Project:  Sales Dashboard w/ Streamlit
+
+
+
+import pandas as pd  # pip install pandas openpyxl
+import plotly.express as px  # pip install plotly-express
+import streamlit as st  # pip install streamlit
+
+# emojis: https://www.webfx.com/tools/emoji-cheat-sheet/
+st.set_page_config(page_title="crime report statistics dashboard", page_icon=":bar_chart:", layout="wide")
+
+# ---- READ EXCEL ----
+@st.cache
+def get_data_from_excel():
+    df = pd.read_excel(
+        io="crime_dataset.xlsx",
+        engine="openpyxl",
+        sheet_name="Sales",
+        skiprows=3,
+        usecols="B:R",
+        nrows=1000,
+    )
+    # Add 'hour' column to dataframe
+    df["hour"] = pd.to_datetime(df["Time"], format="%H:%M:%S").dt.hour
+    return df
+
+df = get_data_from_excel()
+
+# ---- SIDEBAR ----
+st.sidebar.header("Please Filter Here:")
+city = st.sidebar.multiselect(
+    "Select the City:",
+    options=df["City"].unique(),
+    default=df["City"].unique()
+)
+
+jailed_or_not = st.sidebar.multiselect(
+    "Select the criminal Type:",
+    options=df["jailed_or_not"].unique(),
+    default=df["jailed_or_not"].unique(),
+)
+
+gender = st.sidebar.multiselect(
+    "Select the Gender:",
+    options=df["Gender"].unique(),
+    default=df["Gender"].unique()
+)
+
+df_selection = df.query(
+    "City == @city & jailed_or_not ==@jailed_or_not & Gender == @gender"
+)
+
+# ---- MAINPAGE ----
+st.title(":bar_chart: crime report statistics dashboard")
+st.markdown("##")
+
+# # TOP KPI's
+# total_sales = int(df_selection["Total"].sum())
+# average_rating = round(df_selection["Rating"].mean(), 1)
+# star_rating = ":star:" * int(round(average_rating, 0))
+# average_sale_by_transaction = round(df_selection["Total"].mean(), 2)
+#
+# left_column, middle_column, right_column = st.columns(3)
+# with left_column:
+#     st.subheader("Total Sales:")
+#     st.subheader(f"US $ {total_sales:,}")
+# with middle_column:
+#     st.subheader("Average Rating:")
+#     st.subheader(f"{average_rating} {star_rating}")
+# with right_column:
+#     st.subheader("Average Sales Per Transaction:")
+#     st.subheader(f"US $ {average_sale_by_transaction}")
+
+st.markdown("""---""")
+
+# SALES BY PRODUCT LINE [BAR CHART]
+sales_by_product_line = (
+    df_selection.groupby(by=["crime_catog"]).sum()[["Total"]].sort_values(by="Total")
+)
+fig_product_sales = px.bar(
+    sales_by_product_line,
+    x="Total",
+    y=sales_by_product_line.index,
+    orientation="h",
+    title="<b>crimes by category</b>",
+    color_discrete_sequence=["#0083B8"] * len(sales_by_product_line),
+    template="plotly_white",
+)
+fig_product_sales.update_layout(
+    plot_bgcolor="rgba(0,0,0,0)",
+    xaxis=(dict(showgrid=False))
+)
+
+# SALES BY HOUR [BAR CHART]
+sales_by_hour = df_selection.groupby(by=["month"]).sum()[["Total"]]
+fig_hourly_sales = px.bar(
+    sales_by_hour,
+    x=sales_by_hour.index,
+    y="Total",
+    title="<b>crimes by month</b>",
+    color_discrete_sequence=["#0083B8"] * len(sales_by_hour),
+    template="plotly_white",
+)
+fig_hourly_sales.update_layout(
+    xaxis=dict(tickmode="linear"),
+    plot_bgcolor="rgba(0,0,0,0)",
+    yaxis=(dict(showgrid=False)),
+)
+
+
+left_column, right_column = st.columns(2)
+left_column.plotly_chart(fig_hourly_sales, use_container_width=True)
+right_column.plotly_chart(fig_product_sales, use_container_width=True)
+
+
+# ---- HIDE STREAMLIT STYLE ----
+hide_st_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            header {visibility: hidden;}
+            </style>
+            """
+st.markdown(hide_st_style, unsafe_allow_html=True)