Snowflake Notebooks offer an interactive, cell-based programming environment for Python and SQL. With a Snowflake Notebook, you can perform exploratory data analysis, visualize your data, build data dashboards, experiment with feature engineering for machine learning, and perform other data science tasks within Snowflake.

You can write and execute code, visualize results, and tell the story of your analysis all in one place.

Notebook

In this tutorial, we will walk you through the different ways you can enrich your data narrative through engaging visuals in Snowflake Notebooks. We will demonstrate how you can develop visualizations, work with Markdown text, embed images, and build awesome data apps all within your notebook, alongside your code and data.

Prerequisites

What will you Learn?

Here is a summary of what you will be able to learn in each step by following this quickstart:

You can create a Snowflake Notebook directly from the Snowsight UI or upload an existing IPython Notebook to Snowflake.

In this example, we will upload an existing notebook from Snowflake Notebooks demo repo into a Snowflake account.

The notebook files are available for download as .ipynb files in the demo repository. To load the demo notebooks into your Snowflake Notebook, follow these steps:

  1. On Github, click into each folder containing the tutorial and the corresponding .ipynb file, such as this. Download the file by clicking on the Download raw file from the top right.
  2. Go to the Snowflake web interface, Snowsight, on your browser.
  3. Navigate to Project > Notebooks from the left menu bar.
  4. Import the .ipynb file you've download into your Snowflake Notebook by using the Import from .ipynb button located on the top right of the Notebooks page.

Import

  1. Select the file from your local directory and press Open.
  2. A Create Notebook dialog will show up. Select a database, schema, and warehouse for the Notebook and click Create.

With Snowflake Notebook, you can use your favorite Python visualization library, including Altair, matplotlib and plotly, to develop your visualization.

First, let's generate some toy data for the Iris dataset.

# Sample data
species = ["setosa"] * 3 + ["versicolor"] * 3 + ["virginica"] * 3
measurements = ["sepal_length", "sepal_width", "petal_length"] * 3
values = [5.1, 3.5, 1.4, 6.2, 2.9, 4.3, 7.3, 3.0, 6.3]
df = pd.DataFrame({"species": species,"measurement": measurements,"value": values})
df

Plotting with Altair

Now let's plot a bar chart in Altair. You can learn more about Altair here.

import altair as alt
alt.Chart(df).mark_bar().encode(
    x= alt.X("measurement", axis = alt.Axis(labelAngle=0)),
    y="value",
    color="species"
).properties(
    width=700,
    height=500
)

altair

Plotting with Matplotlib

Let's do the same with matplotlib. Note how convenient it is to do df.plot with your dataframe with pandas. This uses matplotlib underneath the hood to generate the plots. You can learn more about pandas's pd.DataFrame.plot and about matplotlib here.

First, let's pivot our data so that our data is stacked.

pivot_df = pd.pivot_table(data=df, index=['measurement'], columns=['species'], values='value')

We build a quick Streamlit app to visualize the pivot operation. (Don't worry we will discuss what the st. Streamlit commands mean later in the tutorial!)

col1, col2 = st.columns(2)
with col1: 
    st.markdown("Old Dataframe")
    st.dataframe(df)    
with col2:
    st.markdown("Pivoted Dataframe")
    st.dataframe(pivot_df)

Now let's use matplotlib to plot the stacked bar chart.

import matplotlib.pyplot as plt
ax = pivot_df.plot.bar(stacked=True)
_ = ax.set_xticklabels(list(pivot_df.index), rotation=0)

matplotlib

Plotting with Plotly

Finally, let's do the same plot with plotly. Learn more about plotly here.

import plotly.express as px
px.bar(df, x='measurement', y='value', color='species')

plotly

With Snowflake Notebooks, you can leverage Markdown language to develop rich text displays with formatting.

You can change a cell type to render Markdown by clicking on the dropdown on the top left to select Markdown (or use m as the hotkey to convert a cell type to Markdown).

Markdown

Inline Text Formatting

Here is some italicized text and bolded text. Markdown: Here is some *italicized text* and **bolded text**.

Here is a link to the Snowflake website Markdown: Here is a link to the [Snowflake website](https://snowflake.com/)

From here on, you can double click onto each Markdown cell to take a look at the underlying Markdown content.

Bulleted List

Here is a bulleted list (with emojis 😊)

BulletedList

Formatting Code

Python:

import pandas as pd
df = pd.DataFrame([1,2,3])

SQL:

SELECT * FROM MYTABLE

Inline Code formatting:

My data is stored in DB.SCHEMA.

FormattingCode

Embedding Images and GIFs

You can use the Markdown Syntax to embed images and GIFs in your notebook.

The syntax looks like ![text](hyperlink). Here are some examples!

![](https://www.snowflake.com/wp-content/themes/snowflake/assets/img/brand-guidelines/logo-sno-blue-example.svg)![](https://upload.wikimedia.org/wikipedia/commons/2/2c/Rotating_earth_%28large%29.gif)

You can bring your data narrative alive in notebooks and make it even more interactive by using Streamlit.

Streamlit is an open-source framework for building interactive data apps in Python (not a single line of HTML or Javascript required!)

Unlike in other notebooks where you need to navigate to a separate terminal window to serve up your Streamlit app, you can test and develop your Streamlit app directly in your notebook.

We saw how you can embed images using Markdown. Here we show how you can embed images in your notebook using Streamlit which gives you more image customization options.

import streamlit as st
st.image("https://www.snowflake.com/wp-content/themes/snowflake/assets/img/brand-guidelines/logo-sno-blue-example.svg",width=500)

# Also works with a GIF animation!
st.image("https://upload.wikimedia.org/wikipedia/commons/2/2c/Rotating_earth_%28large%29.gif", caption="Rotating Earth!")

Let's say you have some images in your Snowflake stage, you can stream in the image file and display it with Streamlit.

LS @IMAGE_STAGE;

from snowflake.snowpark.context import get_active_session
session = get_active_session()
image=session.file.get_stream("@IMAGE_STAGE/snowflake-logo.png", decompress=False).read() 
st.image(image)

Think of each cell in your Snowflake Notebook as a mini Streamlit app. As you interact with your data app, the relevant cells will get re-executed and the results in your app updates.

st.markdown("""# Interactive Filtering with Streamlit! :balloon: 
Values will automatically cascade down the notebook cells""")
value = st.slider("Move the slider to change the filter value 👇", df.value.min(), df.value.max(), df.value.mean(), step = 0.3

interactive_filtering

# Filter the table from above using the Streamlit slider
df[df["value"]>value].sort_values("value")

alt.Chart(df).mark_bar().encode(
    x= alt.X("measurement", axis = alt.Axis(labelAngle=0)),
    y="value",
    color="species"
).properties(width=500,height=300)

altair_v2

Congratulations! You've successfully completed the Visual Data Stories with Snowflake Notebooks quickstart guide. Try out Notebooks yourself to build your own data narrative!

What You Learned

Related Resources

Here are some resources to learn more about Snowflake Notebooks: