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:

The dataset

The examples in this topic use the following toy data set 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 by using 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:

  1. From the notebook, select Packages.

  2. 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:

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

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:

  1. From the notebook, select Packages.

  2. 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:

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

Visualize results with seaborn

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

  1. From the notebook, select Packages.

  2. 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 also imported by default in Snowflake Notebooks. You can use any chart elements supported by Streamlit version 1.22.0 to create a line chart, bar chart, area chart, scatter plot, or a map with points on it. See Chart elements in the API reference of the Streamlit library documentation.

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.

Next steps

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