不用学前端编程，你就能用 Python 简单高效写出漂亮的交互式 Web 应用，将你的数据分析成果立即展示给团队和客户 —— 少数派
Streamlit 介绍
Streamlit 是一个针对数据科学家设计的开源WEB框架，它可以使数据工程师能够围绕其数据，机器学习模型等几乎所有内容快速构建高度交互的Web应用程序。而且使用Streamlit开发自己的APP，只需要使用数据科学家最为熟悉Python语言，无需其他前端知识，使用它可以轻松展示展示数据，图片，JSON和代码等。
Streamlit 优点
- 只需使用Python语言； 无需HTML/JavaScript等前端知识！
- 使用少量的代码便可以创建漂亮的应用程序
- 使用caching优化了数据计算过程
环境配置
Prerequisites
- Python > 3.6
- PIP
Streamlit 安装
pip install streamlit
Streamlit 导入
import streamlit as st
在安装完streanlit以后，你便可以使用streanlit命令来运行你的代码
image.png
为了帮助你更好的理解streanlit的运行方式，Streamlit官方提供了一个Demo，你可以使用一下命令运行。
streamlit hello
接下来就可以使用访问本地的 http://localhost:8502/ 体验Demo的效果
image.png
streamlit的Demo共提供了四种不同的例子
image.png
-Animation Demo
该例子展示了如何使用streamlit来制作酷炫的动画
image.png
import numpy as np
# Interactive Streamlit elements, like these sliders, return their value.
# This gives you an extremely simple interaction model.
iterations = st.sidebar.slider("Level of detail", 2, 20, 10, 1)
separation = st.sidebar.slider("Separation", 0.7, 2.0, 0.7885)
# Non-interactive elements return a placeholder to their location
# in the app. Here we're storing progress_bar to update it later.
progress_bar = st.sidebar.progress(0)
# These two elements will be filled in later, so we create a placeholder
# for them using st.empty()
frame_text = st.sidebar.empty()
image = st.empty()
m, n, s = 960, 640, 400
x = np.linspace(-m / s, m / s, num=m).reshape((1, m))
y = np.linspace(-n / s, n / s, num=n).reshape((n, 1))
for frame_num, a in enumerate(np.linspace(0.0, 4 * np.pi, 100)):
# Here were setting value for these two elements.
progress_bar.progress(frame_num)
frame_text.text("Frame %i/100" % (frame_num + 1))
# Performing some fractal wizardry.
c = separation * np.exp(1j * a)
Z = np.tile(x, (n, 1)) + 1j * np.tile(y, (1, m))
C = np.full((n, m), c)
M = np.full((n, m), True, dtype=bool)
N = np.zeros((n, m))
for i in range(iterations):
Z[M] = Z[M] * Z[M] + C[M]
M[np.abs(Z) > 2] = False
N[M] = i
# Update the image placeholder by calling the image() function on it.
image.image(1.0 - (N / N.max()), use_column_width=True)
# We clear elements by calling empty on them.
progress_bar.empty()
frame_text.empty()
# Streamlit widgets automatically run the script from top to bottom. Since
# this button is not connected to any other logic, it just causes a plain
# rerun.
st.button("Re-run")
-
Plotting Demo
使用streamlit将绘图和动画与Streamlit结合来制作图表
image.png
import time
import numpy as np
progress_bar = st.sidebar.progress(0)
status_text = st.sidebar.empty()
last_rows = np.random.randn(1, 1)
chart = st.line_chart(last_rows)
for i in range(1, 101):
new_rows = last_rows[-1, :] + np.random.randn(5, 1).cumsum(axis=0)
status_text.text("%i%% Complete" % i)
chart.add_rows(new_rows)
progress_bar.progress(i)
last_rows = new_rows
time.sleep(0.05)
progress_bar.empty()
# Streamlit widgets automatically run the script from top to bottom. Since
# this button is not connected to any other logic, it just causes a plain
# rerun.
st.button("Re-run")
-
Mapping Demo
image.png
import pandas as pd
import pydeck as pdk
@st.cache
def from_data_file(filename):
url = (
"https://raw.githubusercontent.com/streamlit/"
"example-data/master/hello/v1/%s" % filename)
return pd.read_json(url)
try:
ALL_LAYERS = {
"Bike Rentals": pdk.Layer(
"HexagonLayer",
data=from_data_file("bike_rental_stats.json"),
get_position=["lon", "lat"],
radius=200,
elevation_scale=4,
elevation_range=[0, 1000],
extruded=True,
),
"Bart Stop Exits": pdk.Layer(
"ScatterplotLayer",
data=from_data_file("bart_stop_stats.json"),
get_position=["lon", "lat"],
get_color=[200, 30, 0, 160],
get_radius="[exits]",
radius_scale=0.05,
),
"Bart Stop Names": pdk.Layer(
"TextLayer",
data=from_data_file("bart_stop_stats.json"),
get_position=["lon", "lat"],
get_text="name",
get_color=[0, 0, 0, 200],
get_size=15,
get_alignment_baseline="bottom",
),
"Outbound Flow": pdk.Layer(
"ArcLayer",
data=from_data_file("bart_path_stats.json"),
get_source_position=["lon", "lat"],
get_target_position=["lon2", "lat2"],
get_source_color=[200, 30, 0, 160],
get_target_color=[200, 30, 0, 160],
auto_highlight=True,
width_scale=0.0001,
get_width="outbound",
width_min_pixels=3,
width_max_pixels=30,
),
}
except urllib.error.URLError as e:
st.error("""
**This demo requires internet access.**
Connection error: %s
""" % e.reason)
return
st.sidebar.markdown('### Map Layers')
selected_layers = [
layer for layer_name, layer in ALL_LAYERS.items()
if st.sidebar.checkbox(layer_name, True)]
if selected_layers:
st.pydeck_chart(pdk.Deck(
map_style="mapbox://styles/mapbox/light-v9",
initial_view_state={"latitude": 37.76, "longitude": -122.4, "zoom": 11, "pitch": 50},
layers=selected_layers,
))
else:
st.error("Please choose at least one layer above.")
-
DataFrame Demo
image.png
import sys
import pandas as pd
import altair as alt
if sys.version_info[0] < 3:
reload(sys) # noqa: F821 pylint:disable=undefined-variable
sys.setdefaultencoding("utf-8")
@st.cache
def get_UN_data():
AWS_BUCKET_URL = "https://streamlit-demo-data.s3-us-west-2.amazonaws.com"
df = pd.read_csv(AWS_BUCKET_URL + "/agri.csv.gz")
return df.set_index("Region")
try:
df = get_UN_data()
except urllib.error.URLError as e:
st.error(
"""
**This demo requires internet access.**
Connection error: %s
"""
% e.reason
)
return
countries = st.multiselect(
"Choose countries", list(df.index), ["China", "United States of America"]
)
if not countries:
st.error("Please select at least one country.")
return
data = df.loc[countries]
data /= 1000000.0
st.write("### Gross Agricultural Production ($B)", data.sort_index())
data = data.T.reset_index()
data = pd.melt(data, id_vars=["index"]).rename(
columns={"index": "year", "value": "Gross Agricultural Product ($B)"}
)
chart = (
alt.Chart(data)
.mark_area(opacity=0.3)
.encode(
x="year:T",
y=alt.Y("Gross Agricultural Product ($B):Q", stack=None),
color="Region:N",
)
)
st.altair_chart(chart, use_container_width=True)
参考链接
- Streamlit官网
- Streamlit 入门介绍
- 如何用 Python 和 Streamlit 做交互式数据分析产品？
- awesome-streamlit
- 机器学习工程师福音：超好用的Streamlit简介