【Python】Streamlit库学习：一款好用的Web框架(python stream模块)

【Python】Streamlit库学习：一款好用的Web框架 Streamlit简介

推荐整理分享【Python】Streamlit库学习：一款好用的Web框架(python stream模块)，希望有所帮助，仅作参考，欢迎阅读内容。

文章相关热门搜索词:python stream流,python iostream,python streamlit,python streamlit,python streamhandler,python streaming,python streamlit,python streamlit,内容如对您有帮助，希望把文章链接给更多的朋友！

✨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 magicst.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 stst.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 stimport pandas as pdst.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 pddf = 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。

markdownimport streamlit as stst.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:")titleimport streamlit as stst.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。

headerimport streamlit as stst.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。

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

codeimport streamlit as stcode = '''def hello(): print("Hello, Streamlit!")'''st.code(code, language='python')textimport streamlit as stst.text('This is some text.')

text：普通字体

lateximport streamlit as stst.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 elementsdataframeimport streamlit as stimport pandas as pdimport numpy as npdf = 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。

tableimport streamlit as stimport pandas as pdimport numpy as npdf = 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 stcol1, col2, col3 = st.columns(3)col1.metric("Temperature", "70 °F", "1.2 °F")col2.metric("Wind", "9 mph", "-8%")col3.metric("Humidity", "86%", "4%")

jsonimport streamlit as stst.json({ 'foo': 'bar', 'baz': 'boz', 'stuff': [ 'stuff 1', 'stuff 2', 'stuff 3', 'stuff 5', ],})图表元素：Chart elements折线图：line_chartimport streamlit as stimport pandas as pdimport numpy as npchart_data = pd.DataFrame( np.random.randn(20, 3), columns=['a', 'b', 'c'])st.line_chart(chart_data)

折线面积图：area_chartimport streamlit as stimport pandas as pdimport numpy as npchart_data = pd.DataFrame( np.random.randn(20, 3), columns=['a', 'b', 'c'])st.area_chart(chart_data)

柱状图：bar_chartimport streamlit as stimport pandas as pdimport numpy as npchart_data = pd.DataFrame( np.random.randn(20, 3), columns=["a", "b", "c"])st.bar_chart(chart_data)

柱形面积图：pyplotimport streamlit as stimport matplotlib.pyplot as pltimport numpy as nparr = np.random.normal(1, 1, size=100)fig, ax = plt.subplots()ax.hist(arr, bins=20)st.pyplot(fig)

散点图：altair_chartimport streamlit as stimport pandas as pdimport numpy as npimport altair as altchart_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_chartimport streamlit as stimport pandas as pdimport numpy as npimport pydeck as pdkchart_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：mapimport streamlit as stimport pandas as pdimport numpy as npdf = 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：buttonimport streamlit as stif st.button('Say hello'): st.write('Why hello there')else: st.write('Goodbye')表格编辑器：experimental_data_editorimport streamlit as stimport pandas as pddf = 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.cachedef 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 sttext_contents = '''This is some text'''st.download_button('Download some text', text_contents)

下载二进制文件：

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

下载图片：

import streamlit as stwith 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：checkboximport streamlit as stagree = st.checkbox('I agree')if agree: st.write('Great!')单选按钮：radioimport streamlit as stgenre = 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：selectboximport streamlit as stoption = st.selectbox( 'How would you like to be contacted?', ('Email', 'Home phone', 'Mobile phone'))st.write('You selected:', option)

滑动块：sliderimport streamlit as stage = st.slider('How old are you?', 0, 130, 25)st.write("I'm ", age, 'years old')

输入文本框：text_inputimport streamlit as sttitle = st.text_input('Movie title', 'Life of Brian')st.write('The current movie title is', title)输入数字框：number_inputimport streamlit as stnumber = st.number_input('Insert a number')st.write('The current number is ', number)

输入日期框：date_inputimport datetimeimport streamlit as std = 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 stimport pandas as pdfrom io import StringIOuploaded_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 stuploaded_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：imageimport streamlit as stfrom PIL import Imageimage = Image.open('sunrise.jpg')st.image(image, caption='Sunrise by the mountains')音频：audioimport streamlit as stimport numpy as npaudio_file = open('myaudio.ogg', 'rb')audio_bytes = audio_file.read()st.audio(audio_bytes, format='audio/ogg')sample_rate = 44100 # 44100 samples per secondseconds = 2 # Note duration of 2 secondsfrequency_la = 440 # Our played note will be 440 Hz# Generate array with seconds*sample_rate steps, ranging between 0 and secondst = np.linspace(0, seconds, seconds * sample_rate, False)# Generate a 440 Hz sine wavenote_la = np.sin(frequency_la * t * 2 * np.pi)st.audio(note_la, sample_rate=sample_rate)视频：videoimport streamlit as stvideo_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 notationst.sidebar.[element_name]# 等效于# "with" notationwith st.sidebar: st.[element_name]

使用示例：

import streamlit as st# Using object notationadd_selectbox = st.sidebar.selectbox( "How would you like to be contacted?", ("Email", "Home phone", "Mobile phone"))# Using "with" notationwith 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 stcol1, col2, col3 = st.columns(3)with col1: st.header("A cat") st.image("https://www.yuucn.com/wp-content/uploads/2023/05/1683965599-b3aaee9f60b16df.jpg")with col2: st.header("A dog") st.image("https://www.yuucn.com/wp-content/uploads/2023/05/1683965606-b3aaee9f60b16df.jpg")with col3: st.header("An owl") st.image("https://www.yuucn.com/wp-content/uploads/2023/05/1683965612-b3aaee9f60b16df.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 sttab1, tab2, tab3 = st.tabs(["Cat", "Dog", "Owl"])with tab1: st.header("A cat") st.image("https://www.yuucn.com/wp-content/uploads/2023/05/1683965599-b3aaee9f60b16df.jpg", width=200)with tab2: st.header("A dog") st.image("https://www.yuucn.com/wp-content/uploads/2023/05/1683965606-b3aaee9f60b16df.jpg", width=200)with tab3: st.header("An owl") st.image("https://www.yuucn.com/wp-content/uploads/2023/05/1683965612-b3aaee9f60b16df.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 stwith 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：progressimport streamlit as stimport timeprogress_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：spinnerimport timeimport streamlit as stwith 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 stst.balloons()

错误：errorimport streamlit as stst.error('This is an error', icon="🚨")

警告：warningimport streamlit as stst.warning('This is a warning', icon="⚠️")通知：infoimport streamlit as stst.info('This is a purely informational message', icon="ℹ️")成功：successimport streamlit as stst.success('This is a success message!', icon="✅")异常：exceptionimport streamlit as ste = 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 stname = 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 stwith 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 stst.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_datadef load_data(url): df = pd.read_csv(url) # 👈 Download the data return dfdf = 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_resourcedef 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 componentscomponents.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。