【Python】Streamlit库学习：一款好用的Web框架

Streamlit简介

✨Streamlit是一个基于tornado框架的快速搭建Web应用的Python库，封装了大量常用组件方法，支持大量数据表、图表等对象的渲染，支持网格化、响应式布局。简单来说，可以让不了解前端的人搭建网页。
相比于同类产品PyWebIO，Streamlit的功能更加全面一些。

官方文档：https://docs.streamlit.io/

安装

安装前注意，python版本需满足：Python 3.7 - Python 3.11

pip install streamlit

安装完之后，终端输入：

streamlit hello

然后访问 http://localhost:8501，可以看到一些示例demo。

下面将通过官方文档中API的顺序来进行学习。

渲染元素：Write and magic

st.write

st.write是常规的渲染数据的手段。

下面来渲染一个hello world：

import streamlit as st

st.write('Hello, *World!* :sunglasses:')

输入完成之后，终端启动py文件：

streamlit run main.py

下面是个渲染pandas表格数据的实例：

import streamlit as st
import pandas as pd

st.write(pd.DataFrame({
    'first column': [1, 2, 3, 4],
    'second column': [10, 20, 30, 40],
}))

之前终端若无终止，这里可以直接刷新界面查看最新的渲染情况。
Streamlit对于表格型的pandas数据，自动提供了排序和缩放显示的功能。

Magic

Streamlit提供了一种魔法(Magic)，无需借助st.write就可以显示元素。

该方法默认是开启的，如果需要关闭该方法，可以修改~/.streamlit/config.toml的这个文件内容：

[runner]
magicEnabled = false

值得注意的是，Magic方法只能成功作用于启动的py文件，对于import之类的py文件，魔法会失效。

下面就用魔法来显示和上面一样的表格：

import pandas as pd
df = pd.DataFrame({
    'first column': [1, 2, 3, 4],
    'second column': [10, 20, 30, 40],
})
df  # 👈 Draw the dataframe

这里相当于输入变量名，直接自动调用st.write()方法，这一点有点类似于jupyter。

文本元素：Text elements

这一部分就是讲不同类型的文本元素，直接看示例即可。

markdown

import streamlit as st

st.markdown('Streamlit is **_really_ cool**.')
st.markdown("This text is :red[colored red], and this is **:blue[colored]** and bold.")
st.markdown(":green[$\sqrt{x^2+y^2}=1$] is a Pythagorean identity. :pencil:")

title

import streamlit as st

st.title('This is a title')
st.title('A title with _italics_ :blue[colors] and emojis :sunglasses:')

这里的title不是指H5里面的title来改选项卡名称，仅仅等同于一个h1标签。

header

import streamlit as st

st.header('This is a header')
st.header('A header with _italics_ :blue[colors] and emojis :sunglasses:')

header：比title小一号的字体。

subheader

import streamlit as st

st.subheader('This is a subheader')
st.subheader('A subheader with _italics_ :blue[colors] and emojis :sunglasses:')

caption

import streamlit as st

st.caption('This is a string that explains something above.')
st.caption('A caption with _italics_ :blue[colors] and emojis :sunglasses:')

caption：小号字体

code

import streamlit as st

code = '''def hello():
    print("Hello, Streamlit!")'''
st.code(code, language='python')

text

import streamlit as st

st.text('This is some text.')

text：普通字体

latex

import streamlit as st

st.latex(r'''
    a + ar + a r^2 + a r^3 + \cdots + a r^{n-1} =
    \sum_{k=0}^{n-1} ar^k =
    a \left(\frac{1-r^{n}}{1-r}\right)
    ''')

展示数据：Data display elements

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))

dataframe就是像excel中那种活动表，包含排序、搜索等功能。

table

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

df = pd.DataFrame(
   np.random.randn(10, 5),
   columns=('col %d' % i for i in range(5)))

st.table(df)

table是不包含特殊功能的普通表。

metric

metric指代的是网格(grid)布局。

import streamlit as st

col1, col2, col3 = st.columns(3)
col1.metric("Temperature", "70 °F", "1.2 °F")
col2.metric("Wind", "9 mph", "-8%")
col3.metric("Humidity", "86%", "4%")

json

import streamlit as st

st.json({
    'foo': 'bar',
    'baz': 'boz',
    'stuff': [
        'stuff 1',
        'stuff 2',
        'stuff 3',
        'stuff 5',
    ],
})

图表元素：Chart elements

折线图：line_chart

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

chart_data = pd.DataFrame(
    np.random.randn(20, 3),
    columns=['a', 'b', 'c'])

st.line_chart(chart_data)

折线面积图：area_chart

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

chart_data = pd.DataFrame(
    np.random.randn(20, 3),
    columns=['a', 'b', 'c'])

st.area_chart(chart_data)

柱状图：bar_chart

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

chart_data = pd.DataFrame(
    np.random.randn(20, 3),
    columns=["a", "b", "c"])

st.bar_chart(chart_data)

柱形面积图：pyplot

import streamlit as st
import matplotlib.pyplot as plt
import numpy as np

arr = np.random.normal(1, 1, size=100)
fig, ax = plt.subplots()
ax.hist(arr, bins=20)

st.pyplot(fig)

散点图：altair_chart

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

chart_data = pd.DataFrame(
    np.random.randn(20, 3),
    columns=['a', 'b', 'c'])

c = alt.Chart(chart_data).mark_circle().encode(
    x='a', y='b', size='c', color='c', tooltip=['a', 'b', 'c'])

st.altair_chart(c, use_container_width=True)

三维柱状图：pydeck_chart

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

chart_data = pd.DataFrame(
   np.random.randn(1000, 2) / [50, 50] + [37.76, -122.4],
   columns=['lat', 'lon'])

st.pydeck_chart(pdk.Deck(
    map_style=None,
    initial_view_state=pdk.ViewState(
        latitude=37.76,
        longitude=-122.4,
        zoom=11,
        pitch=50,
    ),
    layers=[
        pdk.Layer(
           'HexagonLayer',
           data=chart_data,
           get_position='[lon, lat]',
           radius=200,
           elevation_scale=4,
           elevation_range=[0, 1000],
           pickable=True,
           extruded=True,
        ),
        pdk.Layer(
            'ScatterplotLayer',
            data=chart_data,
            get_position='[lon, lat]',
            get_color='[200, 30, 0, 160]',
            get_radius=200,
        ),
    ],
))

二维散点地图：map

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

df = pd.DataFrame(
    np.random.randn(1000, 2) / [50, 50] + [37.76, -122.4],
    columns=['lat', 'lon'])

st.map(df)

这两个图和excel中的三维地图效果很相似。
此外，还有一些组合图、树状图，以及一些绘图参数说明，这里略过。

交互组件：Input widgets

按钮：button

import streamlit as st

if st.button('Say hello'):
    st.write('Why hello there')
else:
    st.write('Goodbye')

表格编辑器：experimental_data_editor

import streamlit as st
import pandas as pd

df = 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},
   ]
)
edited_df = st.experimental_data_editor(df)

favorite_command = edited_df.loc[edited_df["rating"].idxmax()]["command"]
st.markdown(f"Your favorite command is **{favorite_command}** 🎈")

可以由用户来编辑表格内容

下载按钮：download_button

这个是一个特殊按钮，用户点击之后可以下载文件。

下载DataFrame为CSV文件：

import streamlit as st

@st.cache
def convert_df(df):
    # IMPORTANT: Cache the conversion to prevent computation on every rerun
    return df.to_csv().encode('utf-8')

csv = convert_df(my_large_df)

st.download_button(
    label="Download data as CSV",
    data=csv,
    file_name='large_df.csv',
    mime='text/csv',
)

下载文本为txt文件：

import streamlit as st

text_contents = '''This is some text'''
st.download_button('Download some text', text_contents)

下载二进制文件：

import streamlit as st

binary_contents = b'example content'
# Defaults to 'application/octet-stream'
st.download_button('Download binary file', binary_contents)

下载图片：

import streamlit as st

with open("flower.png", "rb") as file:
    btn = st.download_button(
            label="Download image",
            data=file,
            file_name="flower.png",
            mime="image/png"
          )

勾选框：checkbox

import streamlit as st

agree = st.checkbox('I agree')

if agree:
    st.write('Great!')

单选按钮：radio

import streamlit as st

genre = st.radio(
    "What\'s your favorite movie genre",
    ('Comedy', 'Drama', 'Documentary'))

if genre == 'Comedy':
    st.write('You selected comedy.')
else:
    st.write("You didn\'t select comedy.")

单选框：selectbox

import streamlit as st

option = st.selectbox(
    'How would you like to be contacted?',
    ('Email', 'Home phone', 'Mobile phone'))

st.write('You selected:', option)

滑动块：slider

import streamlit as st

age = st.slider('How old are you?', 0, 130, 25)
st.write("I'm ", age, 'years old')

输入文本框：text_input

import streamlit as st

title = st.text_input('Movie title', 'Life of Brian')
st.write('The current movie title is', title)

输入数字框：number_input

import streamlit as st

number = st.number_input('Insert a number')
st.write('The current number is ', number)

输入日期框：date_input

import datetime
import streamlit as st

d = st.date_input(
    "When\'s your birthday",
    datetime.date(2019, 7, 6))
st.write('Your birthday is:', d)

点击可以唤起日历

文件上传按钮：file_uploader

上传单个文件：

import streamlit as st
import pandas as pd
from io import StringIO

uploaded_file = st.file_uploader("Choose a file")
if uploaded_file is not None:
    # To read file as bytes:
    bytes_data = uploaded_file.getvalue()
    st.write(bytes_data)

    # To convert to a string based IO:
    stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
    st.write(stringio)

    # To read file as string:
    string_data = stringio.read()
    st.write(string_data)

    # Can be used wherever a "file-like" object is accepted:
    dataframe = pd.read_csv(uploaded_file)
    st.write(dataframe)

上传多个文件：

import streamlit as st

uploaded_files = st.file_uploader("Choose a CSV file", accept_multiple_files=True)
for uploaded_file in uploaded_files:
    bytes_data = uploaded_file.read()
    st.write("filename:", uploaded_file.name)
    st.write(bytes_data)

此外，还有调用摄像头实时显示的camera_input，选择颜色color_picker，适用场景比较小，这里略过。

媒体元素：Media elements

图片：image

import streamlit as st
from PIL import Image

image = Image.open('sunrise.jpg')

st.image(image, caption='Sunrise by the mountains')

音频：audio

import streamlit as st
import numpy as np

audio_file = open('myaudio.ogg', 'rb')
audio_bytes = audio_file.read()

st.audio(audio_bytes, format='audio/ogg')

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)

视频：video

import streamlit as st

video_file = open('myvideo.mp4', 'rb')
video_bytes = video_file.read()

st.video(video_bytes)

布局和容器：Layouts and containers

侧边栏：sidebar

sidebar的以下两种调用方式等效：

# Object notation
st.sidebar.[element_name]

# 等效于
# "with" notation
with st.sidebar:
    st.[element_name]

使用示例：

import streamlit as st

# Using object notation
add_selectbox = st.sidebar.selectbox(
    "How would you like to be contacted?",
    ("Email", "Home phone", "Mobile phone")
)

# Using "with" notation
with st.sidebar:
    add_radio = st.radio(
        "Choose a shipping method",
        ("Standard (5-15 days)", "Express (2-5 days)")
    )

它可以将上述各种元素嵌到侧边栏中，侧边栏支持弹出和收缩。

行列布局：columns

示例：

import streamlit as st

col1, col2, col3 = st.columns(3)

with col1:
   st.header("A cat")
   st.image("https://static.streamlit.io/examples/cat.jpg")

with col2:
   st.header("A dog")
   st.image("https://static.streamlit.io/examples/dog.jpg")

with col3:
   st.header("An owl")
   st.image("https://static.streamlit.io/examples/owl.jpg")

标签界面：tabs

标签界面有点类似于Android里面的Fragment，相当于做了一个局部的界面切换。

import streamlit as st

tab1, tab2, tab3 = st.tabs(["Cat", "Dog", "Owl"])

with tab1:
   st.header("A cat")
   st.image("https://static.streamlit.io/examples/cat.jpg", width=200)

with tab2:
   st.header("A dog")
   st.image("https://static.streamlit.io/examples/dog.jpg", width=200)

with tab3:
   st.header("An owl")
   st.image("https://static.streamlit.io/examples/owl.jpg", width=200)

容器：container

容器的作用在于可以将一些元素组合起来，比如让一些元素一起不可见，此时，隐藏容器即可实现。
容器基本使用：

import streamlit as st

with st.container():
   st.write("This is inside the container")

   # You can call any Streamlit command, including custom components:
   st.bar_chart(np.random.randn(50, 3))

st.write("This is outside the container")

状态元素：Status elements

进度条：progress

import streamlit as st
import time

progress_text = "Operation in progress. Please wait."
my_bar = st.progress(0, text=progress_text)

for percent_complete in range(100):
    time.sleep(0.1)
    my_bar.progress(percent_complete + 1, text=progress_text)

加载圈：spinner

import time
import streamlit as st

with st.spinner('Wait for it...'):
    time.sleep(5)
st.success('Done!')

气球：balloon

挺有意思的一段过场动画，没有特别的实际意义。

import streamlit as st

st.balloons()

错误：error

import streamlit as st

st.error('This is an error', icon="🚨")

警告：warning

import streamlit as st

st.warning('This is a warning', icon="⚠️")

通知：info

import streamlit as st

st.info('This is a purely informational message', icon="ℹ️")

成功：success

import streamlit as st

st.success('This is a success message!', icon="✅")

异常：exception

import streamlit as st

e = RuntimeError('This is an exception of type RuntimeError')
st.exception(e)

控制流：Control flow

停止运行：stop

代码运行到st.stop的时候停止，类似于debug中的断点。
可以适用于判断用户输入的场景：

import streamlit as st

name = st.text_input('Name')
if not name:
  st.warning('Please input a name.')
  st.stop()
st.success('Thank you for inputting a name.')

这里用户输入不为空时，才执行success。

表单：form

这里的表单自带一个提交按钮，其它按钮不能添加到表单内部。

import streamlit as st

with st.form("my_form"):
   st.write("Inside the form")
   slider_val = st.slider("Form slider")
   checkbox_val = st.checkbox("Form checkbox")

   # Every form must have a submit button.
   submitted = st.form_submit_button("Submit")
   if submitted:
       st.write("slider", slider_val, "checkbox", checkbox_val)

st.write("Outside the form")

通用组件：Utilities

设置页面基本配置：set_page_config

这里可以设置页面的标题、图标，菜单信息

import streamlit as st

st.set_page_config(
    page_title="Ex-stream-ly Cool App",
    page_icon="🧊",
    layout="wide",
    initial_sidebar_state="expanded",
    menu_items={
        'Get Help': 'https://www.extremelycoolapp.com/help',
        'Report a bug': "https://www.extremelycoolapp.com/bug",
        'About': "# This is a header. This is an *extremely* cool app!"
    }
)

除了这个比较实用之外，这个模块包含了代码执行模块st.echo、显示函数帮助模块st.help等鸡肋模块，用处不大，暂且不表。

缓存：cache

缓存主要用来解决两个问题：

• 长时间运行的函数重复运行，这会减慢应用程序。
• 对象被重复创建，这使得它们很难在重新运行或会话中持久化。

在老版本的Streamlit中，缓存均通过装饰器st.cache来实现。
在新版本中，缓存分成了两个装饰器st.cache_data和st.cache_resource

缓存数据：cache_data

cache_data适合返回DataFrames、NumPy 数组、str、int、float或者其他可序列化类型的函数。

比如，这里有个函数需要下载数据集：

@st.cache_data
def load_data(url):
    df = pd.read_csv(url)  # 👈 Download the data
    return df

df = load_data("https://github.com/plotly/datasets/raw/master/uber-rides-data1.csv")
st.dataframe(df)

st.button("Rerun")

没有加@st.cache_data之前，每次运行都需要联网下载数据集，添加之后，只需要第一次运行去下载，之后，会将数据集序列化之后，存到缓存中，后续运行则可以直接读取缓存。

缓存资源：cache_resource

缓存资源通常作用于缓存数据库连接和 ML 模型这类全局可用的资源。

当函数的返回值不需要是可序列化的，比如数据库连接、文件句柄或线程，此时无法用cache_data，只能用cache_resource。

示例，缓存数据库连接：

@st.cache_resource
def init_connection():
    host = "hh-pgsql-public.ebi.ac.uk"
    database = "pfmegrnargs"
    user = "reader"
    password = "NWDMCE5xdipIjRrp"
    return psycopg2.connect(host=host, database=database, user=user, password=password)

conn = init_connection()

接口-内嵌Html

Streamlit预留了st.components.v1.html这个接口，可以解析html数据。
比如，可以通过这个接口，来内嵌b站视频iframe。

import streamlit.components.v1 as components

components.html(
    """
    <iframe src="//player.bilibili.com/player.html?aid=993781570&bvid=BV1fx4y1P7RA&cid=1061322233&page=1" scrolling="no" border="0" frameborder="no" framespacing="0" allowfullscreen="true"> </iframe>
    """,
    height=600,
)

总结

Streamlit就像是Markdown，不能指望它完全替代前端，来为生产项目提供服务。但是很大程度上简化了网页编辑操作，让构建一个简单网页的成本大大降低。文章来源地址https://www.toymoban.com/news/detail-427975.html

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