How to use the streamlit.dataframe function in streamlit

st.subheader("Unstyled")
st.dataframe(df)

st.subheader("Custom formatting")
st.dataframe(df.style.format("{:.2%}"))

st.subheader("Colors")
st.dataframe(
    df.style.applymap(color_negative_red).apply(
        highlight_max, color="darkorange", axis=0
    )
)

st.subheader("Add rows")
x = st.dataframe(
    df.style.set_properties(**{"background-color": "black", "color": "lawngreen"})
)
x.add_rows(
    pd.DataFrame(np.random.randn(3, 5)).style.set_properties(
        **{"background-color": "lawngreen", "color": "black"}
    )
)
x.add_rows(
    pd.DataFrame(np.random.randn(2, 5)).style.format(
        lambda value: "" if value > 0 else "*"
    )

"years."
    )
    st.sidebar.table(
        pd.DataFrame.from_dict(
            wins_turnover_corr, orient="index", columns=["correlation"]
        ).round(2)
    )

    # Data frame for the plot
    fig = get_turnover_vs_wins_plot(roster_turnover, year, teams_colorscale)
    st.plotly_chart(fig, width=1080, height=600)

    # Show the roster DataFrame
    st.header("Minutes Played Breakdown by Team")
    selected_team = st.selectbox("Select a team", teams)
    st.dataframe(
        roster_turnover_pivot(player_minutes, team=selected_team, year=year), width=1080
    )
    st.text("* The numbers in the table are minutes played")

"Entity labels", nlp.get_pipe("ner").labels, default_labels
    )
    html = displacy.render(doc, style="ent", options={"ents": labels})
    # Newlines seem to mess with the rendering
    html = html.replace("\n", " ")
    st.write(HTML_WRAPPER.format(html), unsafe_allow_html=True)
    attrs = ["text", "label_", "start", "end", "start_char", "end_char"]
    if "entity_linker" in nlp.pipe_names:
        attrs.append("kb_id_")
    data = [
        [str(getattr(ent, attr)) for attr in attrs]
        for ent in doc.ents
        if ent.label_ in labels
    ]
    df = pd.DataFrame(data, columns=attrs)
    st.dataframe(df)


if "textcat" in nlp.pipe_names:
    st.header("Text Classification")
    st.markdown(f"> {text}")
    df = pd.DataFrame(doc.cats.items(), columns=("Label", "Score"))
    st.dataframe(df)


vector_size = nlp.meta.get("vectors", {}).get("width", 0)
if vector_size:
    st.header("Vectors & Similarity")
    st.code(nlp.meta["vectors"])
    text1 = st.text_input("Text or word 1", "apple")
    text2 = st.text_input("Text or word 2", "orange")
    doc1 = process_text(spacy_model, text1)

"First name",
                "Last name",
                "Test1",
                "Test2",
                "Test3",
                "Test4",
                "Test Mean",
                "Relative diff from 1 to 4 (%)",
            ]
        ],
    )
# Show Soure
if show_source_data:
    st.subheader("Source Data")
    st.markdown(f"[{source_url}]({source_url})")
    st.dataframe(source_data)

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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

st.header("Empty list")
st.write([])

st.header("Empty dataframes")
st.write(np.array(0))
st.write(pd.DataFrame([]))
st.dataframe()
st.dataframe([])
st.dataframe(np.array(0))
st.dataframe(pd.DataFrame([]))

st.header("Empty one-column dataframes")
st.write(np.array([]))
st.dataframe(np.array([]))

st.header("Empty two-column dataframes (only shows 1)")
st.write(pd.DataFrame({"lat": [], "lon": []}))
st.dataframe(pd.DataFrame({"lat": [], "lon": []}))

st.header("Empty tables")
st.table()
st.table([])
st.table(np.array(0))
st.table(pd.DataFrame([]))

"years."
    )
    st.sidebar.table(
        pd.DataFrame.from_dict(
            wins_turnover_corr, orient="index", columns=["correlation"]
        ).round(2)
    )

    # Data frame for the plot
    fig = get_turnover_vs_wins_plot(roster_turnover, year, teams_colorscale)
    st.plotly_chart(fig, width=1080, height=600)

    # Show the roster DataFrame
    st.header("Minutes Played Breakdown by Team")
    selected_team = st.selectbox("Select a team", teams)
    st.dataframe(
        roster_turnover_pivot(player_minutes, team=selected_team, year=year), width=1080
    )
    st.text("* The numbers in the table are minutes played")

st.markdown('## Rendering')
    # Rendering (Second Wave)
    crawler = render_data(crawler)

    st.markdown('## Indexing')
    # Build the index
    indexer = index_data(crawler, i_type, title_boost)

    st.markdown('# Searching: {}'.format(search_query))

    if len(search_query):
        data = {'sim_weight':sim_weight, 'pr_weight':pr_weight, 'bert_weight':bert_weight}
        df, pre_results = indexer.search_index_st(search_query, **data)
        st.markdown('## Ranking Data')
        st.dataframe(pre_results.style.highlight_max(axis=0), width=800)
        st.markdown('## Search Results')
        st.markdown('#### \[Ad\] [{}]({}) \n {}  \n  {}'.format( 'LOCOMOTIVE® - Enterprise Technical SEO Agency', 'https://locomotive.agency/', 'https://locomotive.agency/', "LOCOMOTIVE® - 2019 U.S. Search Awards 'Best Small SEO Agency'. We are an agency team of enterprise technical, and on-page SEO specialists: Moving you forward."))
        for i, row in df.iterrows():
            desc = row['description'] if len(row['description']) < 280 else  row['description'][:280] + '...'
            st.markdown('#### [{}]({}) \n {}  \n  {}'.format(row['title'], row['url'], row['url'], desc))

    else:
        st.markdown('You need to enter a search term.')

# limitations under the License.

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

# Explicitly seed the RNG for deterministic results
np.random.seed(0)

data = np.random.randn(100, 100)

df = pd.DataFrame(data)
st.dataframe(df)
st.dataframe(df, 250, 150)
st.dataframe(df, width=250)
st.dataframe(df, height=150)