Copy sample files from streamlit/docs/python/api-examples-source/* (4e54057)

pages/charts.area_chart.py

@@ -1,12 +1,16 @@
-import streamlit as st
-import pandas as pd
 import numpy as np
+import pandas as pd
+import streamlit as st
+
 
-@st.experimental_memo
+@st.cache_data
 def load_data():
-    df = pd.DataFrame(np.random.randn(20, 3), columns=["a", "b", "c"])
+    df = pd.DataFrame(
+        np.random.randn(20, 3),
+        columns = ['a', 'b', 'c'])
     return df
 
+
 chart_data = load_data()
 
 st.area_chart(chart_data)

pages/charts.area_chart1.py

@@ -0,0 +1,23 @@
+import numpy as np
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    df = pd.DataFrame({
+        'col1' : np.random.randn(20),
+        'col2' : np.random.randn(20),
+        'col3' : np.random.choice(['A','B','C'], 20)
+    })
+    return df
+
+
+chart_data = load_data()
+
+st.area_chart(
+    chart_data,
+    x = 'col1',
+    y = 'col2',
+    color = 'col3'
+)

pages/charts.area_chart2.py

@@ -0,0 +1,21 @@
+import numpy as np
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    df = pd.DataFrame(
+        np.random.randn(20, 3),
+        columns = ['col1', 'col2', 'col3'])
+    return df
+
+
+chart_data = load_data()
+
+st.area_chart(
+    chart_data,
+    x = 'col1',
+    y = ['col2', 'col3'],
+    color = ['#FF0000', '#0000FF'] # Optional
+)

pages/charts.audio.py

@@ -1,8 +1,9 @@
+import numpy as np
 import requests
 import streamlit as st
 
 
-@st.experimental_memo
+@st.cache_data
 def read_file_from_url(url):
     headers = {
         "User-Agent": "StreamlitDocs/1.5.0 (https://docs.streamlit.io; hello@streamlit.io)"
@@ -32,3 +33,34 @@ st.write(
 
 """
 )
+
+st.code(
+    """
+import streamlit as st
+import numpy as np
+
+sample_rate = 44100  # 44100 samples per second
+seconds = 2  # Note duration of 2 seconds
+
+frequency_la = 440  # Our played note will be 440 Hz
+
+# Generate array with seconds*sample_rate steps, ranging between 0 and seconds
+t = np.linspace(0, seconds, seconds * sample_rate, False)
+
+# Generate a 440 Hz sine wave
+note_la = np.sin(frequency_la * t * 2 * np.pi)
+st.audio(note_la, sample_rate=sample_rate)
+"""
+)
+
+sample_rate = 44100  # 44100 samples per second
+seconds = 2  # Note duration of 2 seconds
+
+frequency_la = 440  # Our played note will be 440 Hz
+
+# Generate array with seconds*sample_rate steps, ranging between 0 and seconds
+t = np.linspace(0, seconds, seconds * sample_rate, False)
+
+# Generate a 440 Hz sine wave
+note_la = np.sin(frequency_la * t * 2 * np.pi)
+st.audio(note_la, sample_rate=sample_rate)

pages/charts.bar_chart.py

@@ -1,12 +1,16 @@
-import streamlit as st
-import pandas as pd
 import numpy as np
+import pandas as pd
+import streamlit as st
+
 
-@st.experimental_memo
+@st.cache_data
 def load_data():
-    df = pd.DataFrame(np.random.randn(50, 3), columns=["a", "b", "c"])
+    df = pd.DataFrame(
+        np.random.randn(50, 3), 
+        columns = ["a", "b", "c"])
     return df
 
+
 chart_data = load_data()
 
 st.bar_chart(chart_data)

pages/charts.bar_chart1.py

@@ -0,0 +1,23 @@
+import numpy as np
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    df = pd.DataFrame({
+        'col1' : list(range(20))*3,
+        'col2' : np.random.randn(60),
+        'col3' : ['A']*20 + ['B']*20 + ['C']*20
+    })
+    return df
+
+
+chart_data = load_data()
+
+st.bar_chart(
+    chart_data,
+    x = 'col1',
+    y = 'col2',
+    color = 'col3'
+)

pages/charts.bar_chart2.py

@@ -0,0 +1,23 @@
+import numpy as np
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    df = pd.DataFrame({
+        'col1' : list(range(20)),
+        'col2' : np.random.randn(20),
+        'col3' : np.random.randn(20)
+    })
+    return df
+
+
+chart_data = load_data()
+
+st.bar_chart(
+    chart_data,
+    x = 'col1',
+    y = ['col2', 'col3'],
+    color = ['#FF0000', '#0000FF'] # Optional
+)

pages/charts.graphviz_chart.py

@@ -1,7 +1,8 @@
-import streamlit as st
 import graphviz as graphviz
+import streamlit as st
 
-@st.experimental_memo
+
+@st.cache_data
 def load_graph():
     # Create a graphlib graph object
     graph = graphviz.Digraph()
@@ -20,6 +21,7 @@ def load_graph():
     graph.edge("sleep", "runmem")
     return graph
 
+
 graph = load_graph()
 
 st.graphviz_chart(graph)

pages/charts.line_chart.py

@@ -1,12 +1,16 @@
-import streamlit as st
-import pandas as pd
 import numpy as np
+import pandas as pd
+import streamlit as st
 
-@st.experimental_memo
+
+@st.cache_data
 def load_data():
-    df = pd.DataFrame(np.random.randn(20, 3), columns=["a", "b", "c"])
+    df = pd.DataFrame(
+        np.random.randn(20, 3),
+        columns = ['a', 'b', 'c'])
     return df
-    
+
+
 chart_data = load_data()
 
 st.line_chart(chart_data)

pages/charts.line_chart1.py

@@ -0,0 +1,23 @@
+import numpy as np
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    df = pd.DataFrame({
+        'col1' : np.random.randn(20),
+        'col2' : np.random.randn(20),
+        'col3' : np.random.choice(['A','B','C'], 20)
+    })
+    return df
+
+
+chart_data = load_data()
+
+st.line_chart(
+    chart_data,
+    x = 'col1',
+    y = 'col2',
+    color = 'col3'
+)

pages/charts.line_chart2.py

@@ -0,0 +1,21 @@
+import numpy as np
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    df = pd.DataFrame(
+        np.random.randn(20, 3),
+        columns = ['col1', 'col2', 'col3'])
+    return df
+
+
+chart_data = load_data()
+
+st.line_chart(
+    chart_data,
+    x = 'col1',
+    y = ['col2', 'col3'],
+    color = ['#FF0000', '#0000FF'] # Optional
+)

pages/charts.map.py

@@ -1,14 +1,16 @@
-import streamlit as st
-import pandas as pd
 import numpy as np
+import pandas as pd
+import streamlit as st
+
 
-@st.experimental_memo
+@st.cache_data
 def load_data():
     df = pd.DataFrame(
         np.random.randn(1000, 2) / [50, 50] + [37.76, -122.4], columns=["lat", "lon"]
     )
     return df
 
+
 df = load_data()
 
 st.map(df)

pages/charts.map_color.py

@@ -0,0 +1,20 @@
+import numpy as np
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    return pd.DataFrame(
+        {
+            "col1": np.random.randn(1000) / 50 + 37.76,
+            "col2": np.random.randn(1000) / 50 + -122.4,
+            "col3": np.random.randn(1000) * 100,
+            "col4": np.random.rand(1000, 4).tolist(),
+        }
+    )
+
+
+df = load_data()
+
+st.map(df, latitude="col1", longitude="col2", size="col3", color="col4")

pages/charts.plotly_chart.py

@@ -1,8 +1,9 @@
-import streamlit as st
-import plotly.figure_factory as ff
 import numpy as np
+import plotly.figure_factory as ff
+import streamlit as st
+
 
-@st.experimental_memo
+@st.cache_data
 def load_data():
     # Add histogram data
     x1 = np.random.randn(200) - 2
@@ -13,6 +14,7 @@ def load_data():
     hist_data = [x1, x2, x3]
     return hist_data
 
+
 hist_data = load_data()
 
 group_labels = ["Group 1", "Group 2", "Group 3"]

pages/charts.pydeck_chart.py

@@ -4,7 +4,7 @@ import pydeck as pdk
 import streamlit as st
 
 
-@st.experimental_memo
+@st.cache_data
 def load_data():
     return pd.DataFrame(
         np.random.randn(1000, 2) / [50, 50] + [37.76, -122.4], columns=["lat", "lon"]

pages/charts.pyplot.py

@@ -1,14 +1,16 @@
-import streamlit as st
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
+import streamlit as st
+
 
-@st.experimental_memo
+@st.cache_data
 def load_fig():
     arr = np.random.normal(1, 1, size=100)
     fig, ax = plt.subplots()
     ax.hist(arr, bins=20)
     return fig, ax
 
+
 fig, ax = load_fig()
 
 st.pyplot(fig)

pages/charts.vega_lite_chart.py

@@ -1,12 +1,14 @@
-import streamlit as st
-import pandas as pd
 import numpy as np
+import pandas as pd
+import streamlit as st
+
 
-@st.experimental_memo
+@st.cache_data
 def load_data():
     df = pd.DataFrame(np.random.randn(200, 3), columns=["a", "b", "c"])
     return df
 
+
 df = load_data()
 
 st.vega_lite_chart(

pages/chat.echo.py

@@ -0,0 +1,26 @@
+import streamlit as st
+
+st.title("Echo Bot")
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# React to user input
+if prompt := st.chat_input("What is up?"):
+    # Display user message in chat message container
+    st.chat_message("user").markdown(prompt)
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    response = f"Echo: {prompt}"
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        st.markdown(response)
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": response})

pages/chat.input.py

@@ -0,0 +1,5 @@
+import streamlit as st
+
+prompt = st.chat_input("Say something")
+if prompt:
+    st.write(f"User has sent the following prompt: {prompt}")

pages/chat.llm.py

@@ -0,0 +1,57 @@
+import openai
+import streamlit as st
+
+st.title("ChatGPT-like clone")
+with st.expander("ℹ️ Disclaimer"):
+    st.caption(
+        "We appreciate your engagement! Please note, this demo is designed to process a maximum of 10 interactions. Thank you for your understanding."
+    )
+
+openai.api_key = st.secrets["OPENAI_API_KEY"]
+
+if "openai_model" not in st.session_state:
+    st.session_state["openai_model"] = "gpt-3.5-turbo"
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Maximum allowed messages
+max_messages = (
+    20  # Counting both user and assistant messages, so 10 iterations of conversation
+)
+
+if len(st.session_state.messages) >= max_messages:
+    st.info(
+        """Notice: The maximum message limit for this demo version has been reached. We value your interest!
+        We encourage you to experience further interactions by building your own application with instructions
+        from Streamlit's [Build conversational apps](https://docs.streamlit.io/knowledge-base/tutorials/build-conversational-apps)
+        tutorial. Thank you for your understanding."""
+    )
+
+else:
+    if prompt := st.chat_input("What is up?"):
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        with st.chat_message("user"):
+            st.markdown(prompt)
+
+        with st.chat_message("assistant"):
+            message_placeholder = st.empty()
+            full_response = ""
+            for response in openai.ChatCompletion.create(
+                model=st.session_state["openai_model"],
+                messages=[
+                    {"role": m["role"], "content": m["content"]}
+                    for m in st.session_state.messages
+                ],
+                stream=True,
+            ):
+                full_response += response.choices[0].delta.get("content", "")
+                message_placeholder.markdown(full_response + "▌")
+            message_placeholder.markdown(full_response)
+        st.session_state.messages.append(
+            {"role": "assistant", "content": full_response}
+        )

pages/chat.message.py

@@ -0,0 +1,6 @@
+import numpy as np
+import streamlit as st
+
+with st.chat_message("user"):
+    st.write("Hello 👋")
+    st.line_chart(np.random.randn(30, 3))

pages/chat.message1.py

@@ -0,0 +1,6 @@
+import numpy as np
+import streamlit as st
+
+message = st.chat_message("assistant")
+message.write("Hello human")
+message.bar_chart(np.random.randn(30, 3))

pages/chat.simple.py

@@ -0,0 +1,44 @@
+import random
+import time
+
+import streamlit as st
+
+st.title("Simple chat")
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Accept user input
+if prompt := st.chat_input("What is up?"):
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    # Display user message in chat message container
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+        full_response = ""
+        assistant_response = random.choice(
+            [
+                "Hello there! How can I assist you today?",
+                "Hi, human! Is there anything I can help you with?",
+                "Do you need help?",
+            ]
+        )
+        # Simulate stream of response with milliseconds delay
+        for chunk in assistant_response.split():
+            full_response += chunk + " "
+            time.sleep(0.05)
+            # Add a blinking cursor to simulate typing
+            message_placeholder.markdown(full_response + "▌")
+        message_placeholder.markdown(full_response)
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": full_response})

pages/data.barchart_column.py

@@ -0,0 +1,32 @@
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    return pd.DataFrame(
+        {
+            "sales": [
+                [0, 4, 26, 80, 100, 40],
+                [80, 20, 80, 35, 40, 100],
+                [10, 20, 80, 80, 70, 0],
+                [10, 100, 20, 100, 30, 100],
+            ],
+        }
+    )
+
+
+data_df = load_data()
+
+st.data_editor(
+    data_df,
+    column_config={
+        "sales": st.column_config.BarChartColumn(
+            "Sales (last 6 months)",
+            help="The sales volume in the last 6 months",
+            y_min=0,
+            y_max=100,
+        ),
+    },
+    hide_index=True,
+)

pages/data.checkbox_column.py

@@ -0,0 +1,28 @@
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    return pd.DataFrame(
+        {
+            "widgets": ["st.selectbox", "st.number_input", "st.text_area", "st.button"],
+            "favorite": [True, False, False, True],
+        }
+    )
+
+
+data_df = load_data()
+
+st.data_editor(
+    data_df,
+    column_config={
+        "favorite": st.column_config.CheckboxColumn(
+            "Your favorite?",
+            help="Select your **favorite** widgets",
+            default=False,
+        )
+    },
+    disabled=["widgets"],
+    hide_index=True,
+)

pages/data.column.py

@@ -0,0 +1,28 @@
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    return pd.DataFrame(
+        {
+            "widgets": ["st.selectbox", "st.number_input", "st.text_area", "st.button"],
+        }
+    )
+
+
+data_df = load_data()
+
+st.data_editor(
+    data_df,
+    column_config={
+        "widgets": st.column_config.Column(
+            "Streamlit Widgets",
+            help="Streamlit **widget** commands 🎈",
+            width="medium",
+            required=True,
+        )
+    },
+    hide_index=True,
+    num_rows="dynamic",
+)

pages/data.column_config.empty.py

@@ -0,0 +1,15 @@
+import streamlit as st
+import pandas as pd
+
+df = pd.DataFrame(columns=['name','age','color'])
+colors = ['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet']
+config = {
+    'name' : st.column_config.TextColumn('Full Name (required)', width='large', required=True),
+    'age' : st.column_config.NumberColumn('Age (years)', min_value=0, max_value=122),
+    'color' : st.column_config.SelectboxColumn('Favorite Color', options=colors)
+}
+
+result = st.data_editor(df, column_config = config, num_rows='dynamic')
+
+if st.button('Get results'):
+    st.write(result)

pages/data.column_config.py

@@ -0,0 +1,103 @@
+import random
+from datetime import date
+
+import numpy as np
+import pandas as pd
+import streamlit as st
+
+st.set_page_config("Profiles", "👤")
+
+@st.cache_data
+def get_profile_dataset(number_of_items: int = 100, seed: int = 0) -> pd.DataFrame:
+    new_data = []
+
+    def calculate_age(born):
+        today = date.today()
+        return (
+            today.year - born.year - ((today.month, today.day) < (born.month, born.day))
+        )
+
+    from faker import Faker
+
+    fake = Faker()
+    random.seed(seed)
+    Faker.seed(seed)
+
+    for i in range(number_of_items):
+        profile = fake.profile()
+        new_data.append(
+            {
+                "avatar": f"https://picsum.photos/400/200?lock={i}",
+                "name": profile["name"],
+                "age": calculate_age(profile["birthdate"]),
+                "active": random.choice([True, False]),
+                "daily_activity": np.random.rand(25),
+                "homepage": profile["website"][0],
+                "email": profile["mail"],
+                "activity": np.random.randint(2, 90, size=25),
+                "gender": random.choice(["male", "female", "other", None]),
+                "birthdate": profile["birthdate"],
+                "status": round(random.uniform(0, 1), 2),
+            }
+        )
+
+    profile_df = pd.DataFrame(new_data)
+    profile_df["gender"] = profile_df["gender"].astype("category")
+    return profile_df
+
+
+column_configuration = {
+    "name": st.column_config.TextColumn(
+        "Name", help="The name of the user", max_chars=100
+    ),
+    "avatar": st.column_config.ImageColumn("Avatar", help="The user's avatar"),
+    "active": st.column_config.CheckboxColumn("Is Active?", help="Is the user active?"),
+    "homepage": st.column_config.LinkColumn(
+        "Homepage", help="The homepage of the user"
+    ),
+    "gender": st.column_config.SelectboxColumn(
+        "Gender", options=["male", "female", "other"]
+    ),
+    "age": st.column_config.NumberColumn(
+        "Age",
+        min_value=0,
+        max_value=120,
+        format="%d years",
+        help="The user's age",
+    ),
+    "activity": st.column_config.LineChartColumn(
+        "Activity (1 year)",
+        help="The user's activity over the last 1 year",
+        width="large",
+        y_min=0,
+        y_max=100,
+    ),
+    "daily_activity": st.column_config.BarChartColumn(
+        "Activity (daily)",
+        help="The user's activity in the last 25 days",
+        width="medium",
+        y_min=0,
+        y_max=1,
+    ),
+    "status": st.column_config.ProgressColumn(
+        "Status", min_value=0, max_value=1, format="%.2f"
+    ),
+    "birthdate": st.column_config.DateColumn(
+        "Birthdate",
+        help="The user's birthdate",
+        min_value=date(1920, 1, 1),
+    ),
+    "email": st.column_config.TextColumn(
+        "Email",
+        help="The user's email address",
+        validate="^[a-zA-Z0-9.!#$%&'*+/=?^_`{|}~-]+@[a-zA-Z0-9-]+(?:\.[a-zA-Z0-9-]+)*$",
+    ),
+}
+
+st.data_editor(
+    get_profile_dataset(),
+    column_config=column_configuration,
+    use_container_width=True,
+    hide_index=True,
+    num_rows="fixed",
+)

pages/data.data_editor.py

@@ -0,0 +1,20 @@
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    return pd.DataFrame(
+        [
+            {"command": "st.selectbox", "rating": 4, "is_widget": True},
+            {"command": "st.balloons", "rating": 5, "is_widget": False},
+            {"command": "st.time_input", "rating": 3, "is_widget": True},
+        ]
+    )
+
+
+df = load_data()
+edited_df = st.data_editor(df, use_container_width=True)
+
+favorite_command = edited_df.loc[edited_df["rating"].idxmax()]["command"]
+st.markdown(f"Your favorite command is **{favorite_command}** 🎈")

pages/data.data_editor1.py

@@ -0,0 +1,20 @@
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    return pd.DataFrame(
+        [
+            {"command": "st.selectbox", "rating": 4, "is_widget": True},
+            {"command": "st.balloons", "rating": 5, "is_widget": False},
+            {"command": "st.time_input", "rating": 3, "is_widget": True},
+        ]
+    )
+
+
+df = load_data()
+edited_df = st.data_editor(df, num_rows="dynamic", use_container_width=True)
+
+favorite_command = edited_df.loc[edited_df["rating"].idxmax()]["command"]
+st.markdown(f"Your favorite command is **{favorite_command}** 🎈")

pages/data.data_editor2.py

@@ -0,0 +1,17 @@
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    return pd.DataFrame(
+        [
+            {"command": "st.selectbox", "rating": 4, "is_widget": True},
+            {"command": "st.balloons", "rating": 5, "is_widget": False},
+            {"command": "st.time_input", "rating": 3, "is_widget": True},
+        ]
+    )
+
+
+df = load_data()
+st.dataframe(df, use_container_width=True)

pages/data.data_editor3.py

@@ -0,0 +1,32 @@
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    return pd.DataFrame(
+        {
+            "name": [
+                "Kelly Kelley",
+                "Nicole Nguyen MD",
+                "Ethan Turner",
+                "Todd Burton",
+                "Justin Garcia",
+            ],
+            "age": [75, 9, 39, 28, 89],
+            "gender": ["female", "other", "male", "female", "other"],
+            "is_active": [True, True, False, True, False],
+            "status": [0.71, 0.47, 0.6, 0.26, 0.9],
+            "homepage": [
+                "http://edwards.com/",
+                "https://www.cole.net/",
+                "https://www.baird-garner.info/",
+                "https://www.porter.biz/",
+                "http://ward-romero.org/",
+            ],
+        }
+    )
+
+
+df = load_data()
+edited_df = st.data_editor(df, use_container_width=True, num_rows="dynamic")

pages/data.data_editor4.py

@@ -0,0 +1,25 @@
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    data = {
+        "Animal": ["Lion", "Crocodile", "Elephant", "Giraffe", "Penguin"],
+        "Weight (kg)": [190, 430, 5000, 800, 4],
+        "Is Endangered": [True, True, True, False, False],
+        "Classification": ["Mammal", "Reptile", "Mammal", "Mammal", "Bird"],
+        "Average Lifespan (years)": [12, 70, 70, 25, 20],
+        "Habitat": ["Grassland", "Water", "Savannah", "Savannah", "Antarctica"],
+    }
+    df = pd.DataFrame(data)
+    df["Classification"] = df["Classification"].astype("category")
+    df["Habitat"] = df["Habitat"].astype("category")
+    return df
+
+
+df = load_data()
+
+st.data_editor(df, key="data_editor", num_rows="dynamic")
+st.write("Here's the session state:")
+st.write(st.session_state["data_editor"])

pages/data.data_editor_config.py

@@ -0,0 +1,37 @@
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    return pd.DataFrame(
+        [
+            {"command": "st.selectbox", "rating": 4, "is_widget": True},
+            {"command": "st.balloons", "rating": 5, "is_widget": False},
+            {"command": "st.time_input", "rating": 3, "is_widget": True},
+        ]
+    )
+
+
+df = load_data()
+
+edited_df = st.data_editor(
+    df,
+    column_config={
+        "command": "Streamlit Command",
+        "rating": st.column_config.NumberColumn(
+            "Your rating",
+            help="How much do you like this command (1-5)?",
+            min_value=1,
+            max_value=5,
+            step=1,
+            format="%d ⭐",
+        ),
+        "is_widget": "Widget ?",
+    },
+    disabled=["command", "is_widget"],
+    hide_index=True,
+)
+
+favorite_command = edited_df.loc[edited_df["rating"].idxmax()]["command"]
+st.markdown(f"Your favorite command is **{favorite_command}** 🎈")

pages/data.dataframe.py

@@ -1,12 +1,16 @@
-import streamlit as st
-import pandas as pd
 import numpy as np
+import pandas as pd
+import streamlit as st
+
 
-@st.experimental_memo
+@st.cache_data
 def load_data():
-    df = pd.DataFrame(np.random.randn(50, 20), columns=("col %d" % i for i in range(20)))
+    df = pd.DataFrame(
+        np.random.randn(50, 20), columns=("col %d" % i for i in range(20))
+    )
     return df
 
+
 df = load_data()
 
 st.dataframe(df)  # Same as st.write(df)

pages/data.dataframe1.py

@@ -1,12 +1,16 @@
-import streamlit as st
-import pandas as pd
 import numpy as np
+import pandas as pd
+import streamlit as st
+
 
-@st.experimental_memo
+@st.cache_data
 def load_data():
-    df = pd.DataFrame(np.random.randn(10, 20), columns=("col %d" % i for i in range(20)))
+    df = pd.DataFrame(
+        np.random.randn(10, 20), columns=("col %d" % i for i in range(20))
+    )
     return df
 
+
 df = load_data()
 
-st.dataframe(df.style.highlight_max(axis=0)) 
+st.dataframe(df.style.highlight_max(axis=0))

pages/data.dataframe2.py

@@ -3,7 +3,7 @@ import streamlit as st
 
 
 # Cache the dataframe so it's only loaded once
-@st.experimental_memo
+@st.cache_data
 def load_data():
     return pd.DataFrame(
         {

pages/data.dataframe_config.py

@@ -0,0 +1,42 @@
+import random
+
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    return pd.DataFrame(
+        {
+            "name": ["Roadmap", "Extras", "Issues"],
+            "url": [
+                "https://roadmap.streamlit.app",
+                "https://extras.streamlit.app",
+                "https://issues.streamlit.app",
+            ],
+            "stars": [random.randint(0, 1000) for _ in range(3)],
+            "views_history": [
+                [random.randint(0, 5000) for _ in range(30)] for _ in range(3)
+            ],
+        }
+    )
+
+
+df = load_data()
+
+st.dataframe(
+    df,
+    column_config={
+        "name": "App name",
+        "stars": st.column_config.NumberColumn(
+            "Github Stars",
+            help="Number of stars on GitHub",
+            format="%d ⭐",
+        ),
+        "url": st.column_config.LinkColumn("App URL"),
+        "views_history": st.column_config.LineChartColumn(
+            "Views (past 30 days)", y_min=0, y_max=5000
+        ),
+    },
+    hide_index=True,
+)

pages/data.date_column.py

@@ -0,0 +1,35 @@
+from datetime import date
+
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    return pd.DataFrame(
+        {
+            "birthday": [
+                date(1980, 1, 1),
+                date(1990, 5, 3),
+                date(1974, 5, 19),
+                date(2001, 8, 17),
+            ]
+        }
+    )
+
+
+data_df = load_data()
+
+st.data_editor(
+    data_df,
+    column_config={
+        "birthday": st.column_config.DateColumn(
+            "Birthday",
+            min_value=date(1900, 1, 1),
+            max_value=date(2005, 1, 1),
+            format="DD.MM.YYYY",
+            step=1,
+        ),
+    },
+    hide_index=True,
+)

pages/data.datetime_column.py

@@ -0,0 +1,35 @@
+from datetime import datetime
+
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    return pd.DataFrame(
+        {
+            "appointment": [
+                datetime(2024, 2, 5, 12, 30),
+                datetime(2023, 11, 10, 18, 0),
+                datetime(2024, 3, 11, 20, 10),
+                datetime(2023, 9, 12, 3, 0),
+            ]
+        }
+    )
+
+
+data_df = load_data()
+
+st.data_editor(
+    data_df,
+    column_config={
+        "appointment": st.column_config.DatetimeColumn(
+            "Appointment",
+            min_value=datetime(2023, 6, 1),
+            max_value=datetime(2025, 1, 1),
+            format="D MMM YYYY, h:mm a",
+            step=60,
+        ),
+    },
+    hide_index=True,
+)

pages/data.image_column.py

@@ -0,0 +1,29 @@
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    return pd.DataFrame(
+        {
+            "apps": [
+                "https://storage.googleapis.com/s4a-prod-share-preview/default/st_app_screenshot_image/5435b8cb-6c6c-490b-9608-799b543655d3/Home_Page.png",
+                "https://storage.googleapis.com/s4a-prod-share-preview/default/st_app_screenshot_image/ef9a7627-13f2-47e5-8f65-3f69bb38a5c2/Home_Page.png",
+                "https://storage.googleapis.com/s4a-prod-share-preview/default/st_app_screenshot_image/31b99099-8eae-4ff8-aa89-042895ed3843/Home_Page.png",
+                "https://storage.googleapis.com/s4a-prod-share-preview/default/st_app_screenshot_image/6a399b09-241e-4ae7-a31f-7640dc1d181e/Home_Page.png",
+            ],
+        }
+    )
+
+
+data_df = load_data()
+
+st.data_editor(
+    data_df,
+    column_config={
+        "apps": st.column_config.ImageColumn(
+            "Preview Image", help="Streamlit app preview screenshots"
+        )
+    },
+    hide_index=True,
+)

pages/data.linechart_column.py

@@ -0,0 +1,33 @@
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    return pd.DataFrame(
+        {
+            "sales": [
+                [0, 4, 26, 80, 100, 40],
+                [80, 20, 80, 35, 40, 100],
+                [10, 20, 80, 80, 70, 0],
+                [10, 100, 20, 100, 30, 100],
+            ],
+        }
+    )
+
+
+data_df = load_data()
+
+st.data_editor(
+    data_df,
+    column_config={
+        "sales": st.column_config.LineChartColumn(
+            "Sales (last 6 months)",
+            width="medium",
+            help="The sales volume in the last 6 months",
+            y_min=0,
+            y_max=100,
+        ),
+    },
+    hide_index=True,
+)

pages/data.link_column.py

@@ -0,0 +1,32 @@
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    return pd.DataFrame(
+        {
+            "apps": [
+                "https://roadmap.streamlit.app",
+                "https://extras.streamlit.app",
+                "https://issues.streamlit.app",
+                "https://30days.streamlit.app",
+            ],
+        }
+    )
+
+
+data_df = load_data()
+
+st.data_editor(
+    data_df,
+    column_config={
+        "apps": st.column_config.LinkColumn(
+            "Trending apps",
+            help="The top trending Streamlit apps",
+            validate="^https://[a-z]+\.streamlit\.app$",
+            max_chars=100,
+        )
+    },
+    hide_index=True,
+)

pages/data.list_column.py

@@ -0,0 +1,31 @@
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    return pd.DataFrame(
+        {
+            "sales": [
+                [0, 4, 26, 80, 100, 40],
+                [80, 20, 80, 35, 40, 100],
+                [10, 20, 80, 80, 70, 0],
+                [10, 100, 20, 100, 30, 100],
+            ],
+        }
+    )
+
+
+data_df = load_data()
+
+st.data_editor(
+    data_df,
+    column_config={
+        "sales": st.column_config.ListColumn(
+            "Sales (last 6 months)",
+            help="The sales volume in the last 6 months",
+            width="medium",
+        ),
+    },
+    hide_index=True,
+)

pages/data.number_column.py

@@ -0,0 +1,29 @@
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    return pd.DataFrame(
+        {
+            "price": [20, 950, 250, 500],
+        }
+    )
+
+
+data_df = load_data()
+
+st.data_editor(
+    data_df,
+    column_config={
+        "price": st.column_config.NumberColumn(
+            "Price (in USD)",
+            help="The price of the product in USD",
+            min_value=0,
+            max_value=1000,
+            step=1,
+            format="$%d",
+        )
+    },
+    hide_index=True,
+)

pages/data.progress_column.py

@@ -0,0 +1,28 @@
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    return pd.DataFrame(
+        {
+            "sales": [200, 550, 1000, 80],
+        }
+    )
+
+
+data_df = load_data()
+
+st.data_editor(
+    data_df,
+    column_config={
+        "sales": st.column_config.ProgressColumn(
+            "Sales volume",
+            help="The sales volume in USD",
+            format="$%f",
+            min_value=0,
+            max_value=1000,
+        ),
+    },
+    hide_index=True,
+)

pages/data.selectbox_column.py

@@ -0,0 +1,37 @@
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    return pd.DataFrame(
+        {
+            "category": [
+                "📊 Data Exploration",
+                "📈 Data Visualization",
+                "🤖 LLM",
+                "📊 Data Exploration",
+            ],
+        }
+    )
+
+
+data_df = load_data()
+
+st.data_editor(
+    data_df,
+    column_config={
+        "category": st.column_config.SelectboxColumn(
+            "App Category",
+            help="The category of the app",
+            width="medium",
+            options=[
+                "📊 Data Exploration",
+                "📈 Data Visualization",
+                "🤖 LLM",
+            ],
+            required=True,
+        )
+    },
+    hide_index=True,
+)

pages/data.table.py

@@ -1,12 +1,14 @@
-import streamlit as st
-import pandas as pd
 import numpy as np
+import pandas as pd
+import streamlit as st
+
 
-@st.experimental_memo
+@st.cache_data
 def load_data():
     df = pd.DataFrame(np.random.randn(10, 5), columns=("col %d" % i for i in range(5)))
     return df
 
+
 df = load_data()
 
 st.table(df)

pages/data.text_column.py

@@ -0,0 +1,28 @@
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    return pd.DataFrame(
+        {
+            "widgets": ["st.selectbox", "st.number_input", "st.text_area", "st.button"],
+        }
+    )
+
+
+data_df = load_data()
+
+st.data_editor(
+    data_df,
+    column_config={
+        "widgets": st.column_config.TextColumn(
+            "Widgets",
+            help="Streamlit **widget** commands 🎈",
+            default="st.",
+            max_chars=50,
+            validate="^st\.[a-z_]+$",
+        )
+    },
+    hide_index=True,
+)

pages/data.time_column.py

@@ -0,0 +1,35 @@
+from datetime import time
+
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data
+def load_data():
+    return pd.DataFrame(
+        {
+            "appointment": [
+                time(12, 30),
+                time(18, 0),
+                time(9, 10),
+                time(16, 25),
+            ]
+        }
+    )
+
+
+data_df = load_data()
+
+st.data_editor(
+    data_df,
+    column_config={
+        "appointment": st.column_config.TimeColumn(
+            "Appointment",
+            min_value=time(8, 0, 0),
+            max_value=time(19, 0, 0),
+            format="hh:mm a",
+            step=60,
+        ),
+    },
+    hide_index=True,
+)