Visualize data in Snowflake Notebooks¶

In Snowflake Notebooks, you can use your favorite Python visualization libraries, such as matplotlib and plotly, to develop your visualizations.

This topic shows how to visualize data in your notebooks using the following libraries:

Altair
Matplotlib
Plotly
Seaborn
Streamlit

The dataset¶

The examples in this topic use the following toy dataset that is based on the Palmer’s Penguin dataset.

species	measurement	value
adeli	bill_length	37.3
adeli	flipper_length	187.1
adeli	bill_depth	17.7
chinstrap	bill_length	46.6
chinstrap	flipper_length	191.7
chinstrap	bill_depth	17.6
gentoo	bill_length	45.5
gentoo	flipper_length	212.7
gentoo	bill_depth	14.2

You can create this dataset in your notebook with the following code:

species = ["adelie"] * 3 + ["chinstrap"] * 3 + ["gentoo"] * 3
measurements = ["bill_length", "flipper_length", "bill_depth"] * 3
values = [37.3, 187.1, 17.7, 46.6, 191.7, 17.6, 45.5, 212.7, 14.2]
df = pd.DataFrame({"species": species,"measurement": measurements,"value": values})
df

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Visualize results with Altair¶

Altair is imported by default on Snowflake Notebooks as part of Streamlit. Snowflake Notebooks currently support Altair version 4.0. For details on available visualization types when using Altair, see Vega-Altair: Declarative Visualization in Python.

The following code plots a stacked bar chart of all the measurements in a dataframe named df that contains the toy dataset:

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

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After you run the cell, the following visualization appears:

A stacked bar chart showing the stacked values of each of the measurements for each penguin type.

Visualize results with matplotlib¶

To use matplotlib, install the matplotlib library for your notebook:

From the notebook, select Packages.
Locate the matplotlib library and select the library to install it.

The following code plots the toy dataset, df, using matplotlib:

import matplotlib.pyplot as plt

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

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

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After you run the cell, the following visualization appears:

For more details on using the st.pyplot chart element, see st.pyplot.

Visualize results with plotly¶

To use plotly, install the plotly library for your notebook:

From the notebook, select Packages.
Locate the plotly library and select the library to install it.

The following code plots a bar chart of the penguin measurements from the toy dataset, df:

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

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After you run the cell, the following visualization appears:

Visualize results with seaborn¶

To use seaborn, you must install the seaborn library for your notebook:

From the notebook, select Packages.
Locate the seaborn library and select the library to install it.

The following code plots a bar chart of the penguin measurements from the toy dataset, df:

import seaborn as sns

sns.barplot(
    data=df,
    x="measurement", hue="species", y="value",
)

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After you run the cells, the following visualization appears:

Bar chart showing each of the measurement values for each penguin type.

For more examples of seaborn visualizations, see the seaborn Example gallery.

Visualize results using Streamlit¶

Streamlit is imported by default in Snowflake Notebooks. You can use chart elements supported by Streamlit version 1.26.0 to create a line chart, bar chart, area chart, or a map with points on it. See Chart elements .

Note

Some Streamlit chart elements are not supported in Snowflake or might be subject to additional terms. See Streamlit support in Notebooks.

To visualize the toy dataset, df, in a bar chart, you can use the following Python code:

import streamlit as st

st.bar_chart(df, x='measurement', y='value', color='species')

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After you run both cells, the following visualization appears:

Bar chart that stacks the penguin measurements for each penguin species.

To learn more about how you can build interactive data apps with Streamlit, see Streamlit in notebooks.