├── .gitignore
├── LICENSE
├── README.md
├── assets
└── img1.png
└── src
├── connection.json
├── my_snowpark_streamlit_app.py
└── new_snowpark_streamlit_app.py
/.gitignore:
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1 | .DS_Store
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3 |
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/LICENSE:
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/README.md:
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1 | # Getting Started with Snowpark for Python and Streamlit
2 |
3 | ### ➡️ For overview, prerequisites, and to learn more, complete this end-to-end tutorial on [quickstarts.snowflake.com.](https://quickstarts.snowflake.com/guide/getting_started_with_snowpark_for_python_streamlit/index.html?index=..%2F..index#0)
4 |
5 |
6 |
7 | ___
8 |
9 |
10 |
11 | 
12 |
13 |
14 |
15 | ___
16 |
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/assets/img1.png:
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https://raw.githubusercontent.com/Snowflake-Labs/sfguide-snowpark-for-python-streamlit/49ecb9eb34e5dfd479692c6bcad792bd1fab969f/assets/img1.png
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/src/connection.json:
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1 | {
2 | "account" :"",
3 | "user" :"",
4 | "password" :"",
5 | "role" :"",
6 | "warehouse" :"",
7 | "database" :"",
8 | "schema" :""
9 | }
10 |
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/src/my_snowpark_streamlit_app.py:
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1 | # Snowpark
2 | from snowflake.snowpark.session import Session
3 | from snowflake.snowpark.functions import avg, sum, col,lit
4 | import streamlit as st
5 | import pandas as pd
6 |
7 | st.set_page_config(
8 | page_title="Environment Data Atlas",
9 | page_icon="🧊",
10 | layout="wide",
11 | initial_sidebar_state="expanded",
12 | menu_items={
13 | 'Get Help': 'https://developers.snowflake.com',
14 | 'About': "This is an *extremely* cool app powered by Snowpark for Python, Streamlit, and Snowflake Data Marketplace"
15 | }
16 | )
17 |
18 | # Create Session object
19 | def create_session_object():
20 | connection_parameters = {
21 | "account" : "",
22 | "user" : "",
23 | "password" : "",
24 | "role" : "",
25 | "warehouse" : "",
26 | "database" : "KNOEMA_ENVIRONMENT_DATA_ATLAS",
27 | "schema" : "ENVIRONMENT"
28 | }
29 | session = Session.builder.configs(connection_parameters).create()
30 | return session
31 |
32 | # Add header and a subheader
33 | st.header("Knoema: Environment Data Atlas")
34 | st.subheader("Powered by Snowpark for Python and Snowflake Data Marketplace | Made with Streamlit")
35 |
36 | # Create Snowpark DataFrames that loads data from Knoema: Environmental Data Atlas
37 | def load_data(session):
38 | # CO2 Emissions by Country
39 | snow_df_co2 = session.table("ENVIRONMENT.EDGARED2019").filter(col('Indicator Name') == 'Fossil CO2 Emissions').filter(col('Type Name') == 'All Type')
40 | snow_df_co2 = snow_df_co2.group_by('Location Name').agg(sum('$16').alias("Total CO2 Emissions")).filter(col('Location Name') != 'World').sort('Location Name')
41 |
42 | # Forest Occupied Land Area by Country
43 | snow_df_land = session.table("ENVIRONMENT.\"WBWDI2019Jan\"").filter(col('Series Name') == 'Forest area (% of land area)')
44 | snow_df_land = snow_df_land.group_by('Country Name').agg(sum('$61').alias("Total Share of Forest Land")).sort('Country Name')
45 |
46 | # Total Municipal Waste by Country
47 | snow_df_waste = session.table("ENVIRONMENT.UNENVDB2018").filter(col('Variable Name') == 'Municipal waste collected')
48 | snow_df_waste = snow_df_waste.group_by('Location Name').agg(sum('$12').alias("Total Municipal Waste")).sort('Location Name')
49 |
50 | # Convert Snowpark DataFrames to Pandas DataFrames for Streamlit
51 | pd_df_co2 = snow_df_co2.to_pandas()
52 | pd_df_land = snow_df_land.to_pandas()
53 | pd_df_waste = snow_df_waste.to_pandas()
54 |
55 | # Use columns to display the three dataframes side-by-side along with their headers
56 | col1, col2, col3 = st.columns(3)
57 | with st.container():
58 | with col1:
59 | st.subheader('CO2 Emissions by Country')
60 | st.dataframe(pd_df_co2)
61 | with col2:
62 | st.subheader('Forest Occupied Land Area by Country')
63 | st.dataframe(pd_df_land)
64 | with col3:
65 | st.subheader('Total Municipal Waste by Country')
66 | st.dataframe(pd_df_waste)
67 |
68 | # Display an interactive chart to visualize CO2 Emissions by Top N Countries
69 | with st.container():
70 | st.subheader('CO2 Emissions by Top N Countries')
71 | with st.expander(""):
72 | emissions_threshold = st.slider(label='Emissions Threshold',min_value=5000, value=20000, step=5000)
73 | pd_df_co2_top_n = snow_df_co2.filter(col('Total CO2 Emissions') > emissions_threshold).to_pandas()
74 | st.bar_chart(data=pd_df_co2_top_n.set_index('Location Name'), width=850, height=500, use_container_width=True)
75 |
76 | if __name__ == "__main__":
77 | session = create_session_object()
78 | load_data(session)
79 |
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/src/new_snowpark_streamlit_app.py:
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1 | # Snowpark
2 | from snowflake.snowpark.session import Session
3 | from snowflake.snowpark.functions import avg, sum, col,lit,in_
4 | # Misc
5 | import pandas as pd
6 | import json
7 | import altair as alt
8 |
9 | #Streamlit
10 | import streamlit as st
11 | st.set_page_config(
12 | page_title="Environment Data Atlas",
13 | page_icon="🧊",
14 | layout="wide",
15 | initial_sidebar_state="expanded",
16 | menu_items={
17 | 'Get Help': 'https://developers.snowflake.com',
18 | 'About': "This is an *extremely* cool app powered by Snowpark for Python, Snowflake Data Marketplace and Streamlit"
19 | }
20 | )
21 |
22 | def create_session():
23 | if "snowpark_session" not in st.session_state:
24 | connection_parameters = json.load(open('connection.json'))
25 | session = Session.builder.configs(connection_parameters).create()
26 | st.session_state['snowpark_session'] = session
27 | else:
28 | session = st.session_state['snowpark_session']
29 |
30 | return session
31 |
32 | @st.experimental_memo()
33 | def load_data():
34 | # CO2 Emissions
35 | snow_df_co2 = session.table("EDGARED2019").filter(col('Indicator Name') == 'Fossil CO2 Emissions').filter(col('Type Name') == 'All Type').sort('"Date"')
36 |
37 | # Forest Occupied Land Area by Country
38 | snow_df_land = session.table("\"WBWDI2019Jan\"").filter(col('Series Name') == 'Forest area (% of land area)')
39 | snow_df_land = snow_df_land.group_by('Country Name').agg(sum('$61').alias("Total Share of Forest Land")).sort('Country Name')
40 |
41 | return snow_df_co2.to_pandas(), snow_df_land.to_pandas()
42 |
43 | # Add header and a sidebar
44 | st.markdown("