Create app.py

app.py

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+import streamlit as st
+import plotly.express as px
+import pandas as pd
+import os
+import warnings
+warnings.filterwarnings('ignore')
+
+st.set_page_config(page_title="Superstore!!!", page_icon=":bar_chart:",layout="wide")
+
+st.title(" :bar_chart: Sample SuperStore EDA")
+st.markdown('<style>div.block-container{padding-top:1rem;}</style>',unsafe_allow_html=True)
+
+fl = st.file_uploader(":file_folder: Upload a file",type=(["csv","txt","xlsx","xls"]))
+if fl is not None:
+    filename = fl.name
+    st.write(filename)
+    df = pd.read_csv(filename, encoding = "ISO-8859-1")
+else:
+    os.chdir(r"C:\Users\AEPAC\Desktop\Streamlit")
+    df = pd.read_csv("Superstore.csv", encoding = "ISO-8859-1")
+
+col1, col2 = st.columns((2))
+df["Order Date"] = pd.to_datetime(df["Order Date"])
+
+# Getting the min and max date 
+startDate = pd.to_datetime(df["Order Date"]).min()
+endDate = pd.to_datetime(df["Order Date"]).max()
+
+with col1:
+    date1 = pd.to_datetime(st.date_input("Start Date", startDate))
+
+with col2:
+    date2 = pd.to_datetime(st.date_input("End Date", endDate))
+
+df = df[(df["Order Date"] >= date1) & (df["Order Date"] <= date2)].copy()
+
+st.sidebar.header("Choose your filter: ")
+# Create for Region
+region = st.sidebar.multiselect("Pick your Region", df["Region"].unique())
+if not region:
+    df2 = df.copy()
+else:
+    df2 = df[df["Region"].isin(region)]
+
+# Create for State
+state = st.sidebar.multiselect("Pick the State", df2["State"].unique())
+if not state:
+    df3 = df2.copy()
+else:
+    df3 = df2[df2["State"].isin(state)]
+
+# Create for City
+city = st.sidebar.multiselect("Pick the City",df3["City"].unique())
+
+# Filter the data based on Region, State and City
+
+if not region and not state and not city:
+    filtered_df = df
+elif not state and not city:
+    filtered_df = df[df["Region"].isin(region)]
+elif not region and not city:
+    filtered_df = df[df["State"].isin(state)]
+elif state and city:
+    filtered_df = df3[df["State"].isin(state) & df3["City"].isin(city)]
+elif region and city:
+    filtered_df = df3[df["Region"].isin(region) & df3["City"].isin(city)]
+elif region and state:
+    filtered_df = df3[df["Region"].isin(region) & df3["State"].isin(state)]
+elif city:
+    filtered_df = df3[df3["City"].isin(city)]
+else:
+    filtered_df = df3[df3["Region"].isin(region) & df3["State"].isin(state) & df3["City"].isin(city)]
+
+category_df = filtered_df.groupby(by = ["Category"], as_index = False)["Sales"].sum()
+
+with col1:
+    st.subheader("Category wise Sales")
+    fig = px.bar(category_df, x = "Category", y = "Sales", text = ['${:,.2f}'.format(x) for x in category_df["Sales"]],
+                 template = "seaborn")
+    st.plotly_chart(fig,use_container_width=True, height = 200)
+
+with col2:
+    st.subheader("Region wise Sales")
+    fig = px.pie(filtered_df, values = "Sales", names = "Region", hole = 0.5)
+    fig.update_traces(text = filtered_df["Region"], textposition = "outside")
+    st.plotly_chart(fig,use_container_width=True)
+
+cl1, cl2 = st.columns((2))
+with cl1:
+    with st.expander("Category_ViewData"):
+        st.write(category_df.style.background_gradient(cmap="Blues"))
+        csv = category_df.to_csv(index = False).encode('utf-8')
+        st.download_button("Download Data", data = csv, file_name = "Category.csv", mime = "text/csv",
+                            help = 'Click here to download the data as a CSV file')
+
+with cl2:
+    with st.expander("Region_ViewData"):
+        region = filtered_df.groupby(by = "Region", as_index = False)["Sales"].sum()
+        st.write(region.style.background_gradient(cmap="Oranges"))
+        csv = region.to_csv(index = False).encode('utf-8')
+        st.download_button("Download Data", data = csv, file_name = "Region.csv", mime = "text/csv",
+                        help = 'Click here to download the data as a CSV file')
+        
+filtered_df["month_year"] = filtered_df["Order Date"].dt.to_period("M")
+st.subheader('Time Series Analysis')
+
+linechart = pd.DataFrame(filtered_df.groupby(filtered_df["month_year"].dt.strftime("%Y : %b"))["Sales"].sum()).reset_index()
+fig2 = px.line(linechart, x = "month_year", y="Sales", labels = {"Sales": "Amount"},height=500, width = 1000,template="gridon")
+st.plotly_chart(fig2,use_container_width=True)
+
+with st.expander("View Data of TimeSeries:"):
+    st.write(linechart.T.style.background_gradient(cmap="Blues"))
+    csv = linechart.to_csv(index=False).encode("utf-8")
+    st.download_button('Download Data', data = csv, file_name = "TimeSeries.csv", mime ='text/csv')
+
+# Create a treem based on Region, category, sub-Category
+st.subheader("Hierarchical view of Sales using TreeMap")
+fig3 = px.treemap(filtered_df, path = ["Region","Category","Sub-Category"], values = "Sales",hover_data = ["Sales"],
+                  color = "Sub-Category")
+fig3.update_layout(width = 800, height = 650)
+st.plotly_chart(fig3, use_container_width=True)
+
+chart1, chart2 = st.columns((2))
+with chart1:
+    st.subheader('Segment wise Sales')
+    fig = px.pie(filtered_df, values = "Sales", names = "Segment", template = "plotly_dark")
+    fig.update_traces(text = filtered_df["Segment"], textposition = "inside")
+    st.plotly_chart(fig,use_container_width=True)
+
+with chart2:
+    st.subheader('Category wise Sales')
+    fig = px.pie(filtered_df, values = "Sales", names = "Category", template = "gridon")
+    fig.update_traces(text = filtered_df["Category"], textposition = "inside")
+    st.plotly_chart(fig,use_container_width=True)
+
+import plotly.figure_factory as ff
+st.subheader(":point_right: Month wise Sub-Category Sales Summary")
+with st.expander("Summary_Table"):
+    df_sample = df[0:5][["Region","State","City","Category","Sales","Profit","Quantity"]]
+    fig = ff.create_table(df_sample, colorscale = "Cividis")
+    st.plotly_chart(fig, use_container_width=True)
+
+    st.markdown("Month wise sub-Category Table")
+    filtered_df["month"] = filtered_df["Order Date"].dt.month_name()
+    sub_category_Year = pd.pivot_table(data = filtered_df, values = "Sales", index = ["Sub-Category"],columns = "month")
+    st.write(sub_category_Year.style.background_gradient(cmap="Blues"))
+
+# Create a scatter plot
+data1 = px.scatter(filtered_df, x = "Sales", y = "Profit", size = "Quantity")
+data1['layout'].update(title="Relationship between Sales and Profits using Scatter Plot.",
+                       titlefont = dict(size=20),xaxis = dict(title="Sales",titlefont=dict(size=19)),
+                       yaxis = dict(title = "Profit", titlefont = dict(size=19)))
+st.plotly_chart(data1,use_container_width=True)
+
+with st.expander("View Data"):
+    st.write(filtered_df.iloc[:500,1:20:2].style.background_gradient(cmap="Oranges"))
+
+# Download orginal DataSet
+csv = df.to_csv(index = False).encode('utf-8')
+st.download_button('Download Data', data = csv, file_name = "Data.csv",mime = "text/csv")