Hello there 👋

import streamlit as st
import pandas as pd
import numpy as np

df = pd.DataFrame(
   np.random.randn(50, 20),
   columns=('col %d' % i for i in range(20)))

st.dataframe(df)  # Same as st.write(df)

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Built with Streamlit 🎈

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st.dataframe(df, 200, 100)

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You can also pass a Pandas Styler object to change the style of the rendered DataFrame:

import streamlit as st
import pandas as pd
import numpy as np

df = pd.DataFrame(
   np.random.randn(10, 20),
   columns=('col %d' % i for i in range(20)))

st.dataframe(df.style.highlight_max(axis=0))

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Built with Streamlit 🎈

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import streamlit as st
import pandas as pd
import numpy as np

df1 = pd.DataFrame(
   np.random.randn(50, 20),
   columns=('col %d' % i for i in range(20)))

my_table = st.table(df1)

df2 = pd.DataFrame(
   np.random.randn(50, 20),
   columns=('col %d' % i for i in range(20)))

my_table.add_rows(df2)
# Now the table shown in the Streamlit app contains the data for
# df1 followed by the data for df2.

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You can do the same thing with plots. For example, if you want to add more data to a line chart:

# Assuming df1 and df2 from the example above still exist...
my_chart = st.line_chart(df1)
my_chart.add_rows(df2)
# Now the chart shown in the Streamlit app contains the data for
# df1 followed by the data for df2.

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And for plots whose datasets are named, you can pass the data with a keyword argument where the key is the name:

my_chart = st.vega_lite_chart({
    'mark': 'line',
    'encoding': {'x': 'a', 'y': 'b'},
    'datasets': {
      'some_fancy_name': df1,  # <-- named dataset
     },
    'data': {'name': 'some_fancy_name'},
}),
my_chart.add_rows(some_fancy_name=df2)  # <-- name used as keyword

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