怀化主要网站,做网站用什么服务器比较好,在线医生免费咨询,可以分4天做任务的网站st.map
显示一张叠加了散点图的地图。
它是 st.pydeck_chart 的包装器#xff0c;用于在地图上快速创建散点图表#xff0c;并具有自动居中和自动缩放功能。
使用该命令时#xff0c;Mapbox 会提供地图瓦片来渲染地图内容。请注意#xff0c;Mapbox 是第三方产品#x…st.map
显示一张叠加了散点图的地图。
它是 st.pydeck_chart 的包装器用于在地图上快速创建散点图表并具有自动居中和自动缩放功能。
使用该命令时Mapbox 会提供地图瓦片来渲染地图内容。请注意Mapbox 是第三方产品Streamlit 不对 Mapbox 或 Mapbox 提供的任何内容或信息承担任何责任。
Mapbox 要求用户注册并提供一个令牌然后用户才能请求地图碎片。目前Streamlit 会为您提供该令牌但该令牌随时可能变更。我们强烈建议所有用户创建并使用自己的个人 Mapbox 令牌以免影响使用体验。您可以使用 mapbox.token 配置选项来创建。Mapbox 的使用受 Mapbox 使用条款的约束。
要为自己获取一个令牌请在 https://mapbox.com 上创建一个帐户。有关如何设置配置选项的更多信息请参阅https://docs.streamlit.io/library/advanced-features/configuration。
Function signature[source] st.map(dataNone, *, latitudeNone, longitudeNone, colorNone, sizeNone, zoomNone, use_container_widthTrue) Parameters data (pandas.DataFrame, pandas.Styler, pyarrow.Table, pyspark.sql.DataFrame, snowflake.snowpark.dataframe.DataFrame, snowflake.snowpark.table.Table, Iterable, dict, or None) The data to be plotted. latitude (str or None) The name of the column containing the latitude coordinates of the datapoints in the chart. If None, the latitude data will come from any column named lat, latitude, LAT, or LATITUDE. longitude (str or None) The name of the column containing the longitude coordinates of the datapoints in the chart. If None, the longitude data will come from any column named lon, longitude, LON, or LONGITUDE. color (str or tuple or None) The color of the circles representing each datapoint. Can be: None, to use the default color.A hex string like #ffaa00 or #ffaa0088.An RGB or RGBA tuple with the red, green, blue, and alpha components specified as ints from 0 to 255 or floats from 0.0 to 1.0.The name of the column to use for the color. Cells in this column should contain colors represented as a hex string or color tuple, as described above. size (str or float or None) The size of the circles representing each point, in meters. This can be: None, to use the default size.A number like 100, to specify a single size to use for all datapoints.The name of the column to use for the size. This allows each datapoint to be represented by a circle of a different size. zoom (int) Zoom level as specified in https://wiki.openstreetmap.org/wiki/Zoom_levels. use_container_width (bool) Whether to override the maps native width with the width of the parent container. If use_container_width is False (default), Streamlit sets the width of the chart to fit its contents according to the plotting library, up to the width of the parent container. If use_container_width is True, Streamlit sets the width of the map to match the width of the parent container.
代码 这段代码使用了Streamlit库来创建一个交互式地图。首先它导入了streamlit、pandas和numpy库。然后它创建了一个包含随机数据的DataFrame其中包括1000行和2列列名分别为lat和lon。随后使用np.random.randn函数生成了随机数据并使用除以[50, 50]和加上[37.76, -122.4]的操作对数据进行转换。最后使用st.map函数将DataFrame中的经纬度数据显示在地图上。
import streamlit as st
import pandas as pd
import numpy as npdf pd.DataFrame(np.random.randn(1000, 2) / [50, 50] [37.76, -122.4],columns[lat, lon])st.map(df) 您还可以自定义数据点的大小和颜色
st.map(df, size20, color#0044ff)
最后您还可以为经纬度组件选择不同的列并根据其他列动态设置每个数据点的大小和颜色 这段代码使用了Streamlit库来创建一个交互式地图。首先它导入了streamlit、pandas和numpy库。然后它使用pandas和numpy创建了一个包含四列数据的DataFrame。接下来它使用streamlit的map函数来将DataFrame中的数据显示在地图上。它指定了经度列为col1纬度列为col2大小列为col3颜色列为col4。这样就可以在地图上显示数据的位置、大小和颜色使用户可以通过交互方式来探索数据。
import streamlit as st
import pandas as pd
import numpy as npdf 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(),
})st.map(df,latitudecol1,longitudecol2,sizecol3,colorcol4) element.add_rows
将一个数据帧连接到当前数据帧的底部。
import streamlit as st
import pandas as pd
import numpy as npdf1 pd.DataFrame(np.random.randn(50, 20), columns(col %d % i for i in range(20)))my_table st.table(df1)df2 pd.DataFrame(np.random.randn(50, 20), columns(col %d % i for i in range(20)))my_table.add_rows(df2)
# Now the table shown in the Streamlit app contains the data for
# df1 followed by the data for df2.
您也可以对曲线图做同样的处理。例如如果您想在折线图中添加更多数据
# Assuming df1 and df2 from the example above still exist...
my_chart st.line_chart(df1)
my_chart.add_rows(df2)
# Now the chart shown in the Streamlit app contains the data for
# df1 followed by the data for df2.
对于数据集已命名的绘图可以使用关键字参数传递数据关键字就是名称
# Assuming df1 and df2 from the example above still exist...
my_chart st.line_chart(df1)
my_chart.add_rows(df2)
# Now the chart shown in the Streamlit app contains the data for
# df1 followed by the data for df2.
st.altair_chart
使用 Vega-Altair 库显示图表。
Vega-Altair 是基于 Vega 和 Vega-Lite 的 Python 声明式统计可视化库。
Function signature[source] st.altair_chart(altair_chart, *, use_container_widthFalse, themestreamlit, keyNone, on_selectignore, selection_modeNone) Returns (element or dict) If on_select is ignore (default), this method returns an internal placeholder for the chart element that can be used with the .add_rows() method. Otherwise, this method returns a dictionary-like object that supports both key and attribute notation. The attributes are described by the VegaLiteState dictionary schema. Parameters altair_chart (altair.Chart) The Altair chart object to display. See Example Gallery — Vega-Altair 5.3.0 documentation for examples of graph descriptions. use_container_width (bool) Whether to override the figures native width with the width of the parent container. If use_container_width is False (default), Streamlit sets the width of the chart to fit its contents according to the plotting library, up to the width of the parent container. If use_container_width is True, Streamlit sets the width of the figure to match the width of the parent container. theme (streamlit or None) The theme of the chart. If theme is streamlit (default), Streamlit uses its own design default. If theme is None, Streamlit falls back to the default behavior of the library. key (str) An optional string to use for giving this element a stable identity. If key is None (default), this elements identity will be determined based on the values of the other parameters. Additionally, if selections are activated and key is provided, Streamlit will register the key in Session State to store the selection state. The selection state is read-only. on_select (ignore, rerun, or callable) How the figure should respond to user selection events. This controls whether or not the figure behaves like an input widget. on_select can be one of the following: ignore (default): Streamlit will not react to any selection events in the chart. The figure will not behave like an input widget.rerun: Streamlit will rerun the app when the user selects data in the chart. In this case, st.altair_chart will return the selection data as a dictionary.A callable: Streamlit will rerun the app and execute the callable as a callback function before the rest of the app. In this case, st.altair_chart will return the selection data as a dictionary.To use selection events, the object passed to altair_chart must include selection paramters. To learn about defining interactions in Altair and how to declare selection-type parameters, see Interactive Charts in Altairs documentation. selection_mode (str or Iterable of str) The selection parameters Streamlit should use. If selection_mode is None (default), Streamlit will use all selection parameters defined in the charts Altair spec. When Streamlit uses a selection parameter, selections from that parameter will trigger a rerun and be included in the selection state. When Streamlit does not use a selection parameter, selections from that parameter will not trigger a rerun and not be included in the selection state. Selection parameters are identified by their name property.
代码
import streamlit as st
import pandas as pd
import numpy as np
import altair as altchart_data pd.DataFrame(np.random.randn(20, 3), columns[a, b, c])c (alt.Chart(chart_data).mark_circle().encode(xa, yb, sizec, colorc, tooltip[a, b, c])
)st.altair_chart(c, use_container_widthTrue) 这段代码使用了Streamlit库来创建一个交互式的数据可视化应用。首先导入了所需的库包括streamlit、pandas、numpy和altair。然后创建了一个包含随机数据的DataFrame并使用Altair库创建了一个散点图。散点图的x轴和y轴分别对应DataFrame中的a和b列点的大小和颜色分别对应DataFrame中的c列同时鼠标悬停在点上时会显示a、b和c的数值。最后使用streamlit的altair_chart函数将这个图表展示在应用中并设置了use_container_widthTrue以自适应容器宽度。 Chart selections
VegaLiteState 流式版本 Vega-Lite 事件状态的模式。
事件状态存储在一个类似字典的对象中该对象同时支持键和属性符号。事件状态不能通过会话状态进行编程更改或设置。
目前只支持选择事件。
代码
以下两个示例具有等效定义。每个示例的图表定义中都包含一个点和区间选择参数。点选择参数名为 point_selection点选择。区间或方框选择参数名为 interval_selection。
下面的示例使用的是 st.altair_chart
import streamlit as st
import pandas as pd
import numpy as np
import altair as altif data not in st.session_state:st.session_state.data pd.DataFrame(np.random.randn(20, 3), columns[a, b, c])
df st.session_state.datapoint_selector alt.selection_point(point_selection)
interval_selector alt.selection_interval(interval_selection)
chart (alt.Chart(df).mark_circle().encode(xa,yb,sizec,colorc,tooltip[a, b, c],fillOpacityalt.condition(point_selector, alt.value(1), alt.value(0.3)),).add_params(point_selector, interval_selector)
)event st.altair_chart(chart, keyalt_chart, on_selectrerun)event
这段代码使用了Streamlit和Altair库来创建一个交互式数据可视化界面。首先代码导入了所需的库streamlit、pandas、numpy和altair。
接下来代码检查了会话状态中是否存在名为data的数据。如果不存在就创建一个包含20行3列随机数的DataFrame并将其存储在会话状态中。然后将数据存储在变量df中。
接着代码创建了两个选择器point_selector和interval_selector。point_selector用于选择单个数据点而interval_selector用于选择数据区间。
然后代码使用Altair库创建了一个散点图。散点图的x轴和y轴分别对应DataFrame中的a和b列点的大小和颜色分别对应DataFrame中的c列。另外还添加了tooltip来显示数据点的具体数值并设置了点的透明度根据选择器的状态来调整透明度。
最后代码使用Streamlit的altair_chart函数将图表显示在界面上并添加了on_select参数来指定当用户进行选择操作时触发重新运行。最后一行代码将事件显示在界面上。
下面的示例使用了 st.vega_lite_chart
import streamlit as st
import pandas as pd
import numpy as npif data not in st.session_state:st.session_state.data pd.DataFrame(np.random.randn(20, 3), columns[a, b, c])spec {mark: {type: circle, tooltip: True},params: [{name: interval_selection, select: interval},{name: point_selection, select: point},],encoding: {x: {field: a, type: quantitative},y: {field: b, type: quantitative},size: {field: c, type: quantitative},color: {field: c, type: quantitative},fillOpacity: {condition: {param: point_selection, value: 1},value: 0.3,},},
}event st.vega_lite_chart(st.session_state.data, spec, keyvega_chart, on_selectrerun)event
请尝试在这个交互式示例中选择点。单击点时选择将显示在属性 point_selection 下这是点选择参数的名称。同样当您进行区间选择时它将显示在属性 interval_selection 下。如果需要您还可以为选择参数赋予其他名称。
如果在选择点时按住 Shift 键现有的点选择将被保留。在进行其他选择时不会保留区间选择。 element.add_rows
import streamlit as st
import pandas as pd
import numpy as npdf1 pd.DataFrame(np.random.randn(50, 20), columns(col %d % i for i in range(20)))my_table st.table(df1)df2 pd.DataFrame(np.random.randn(50, 20), columns(col %d % i for i in range(20)))my_table.add_rows(df2)
# Now the table shown in the Streamlit app contains the data for
# df1 followed by the data for df2.
您也可以对曲线图做同样的处理。例如如果您想在折线图中添加更多数据
# Assuming df1 and df2 from the example above still exist...
my_chart st.line_chart(df1)
my_chart.add_rows(df2)
# Now the chart shown in the Streamlit app contains the data for
# df1 followed by the data for df2.
对于数据集已命名的绘图可以使用关键字参数传递数据关键字就是名称
my_chart st.vega_lite_chart({mark: line,encoding: {x: a, y: b},datasets: {some_fancy_name: df1, # -- named dataset},data: {name: some_fancy_name},
}),
my_chart.add_rows(some_fancy_namedf2) # -- name used as keyword
设计
Altair 图表默认使用 Streamlit 主题显示。该主题时尚、用户友好并采用了 Streamlit 的调色板。这样做的额外好处是图表可以更好地与应用程序的其他设计融为一体。
从 Streamlit 1.16.0 开始Streamlit 主题可通过 themestreamlit 关键字参数使用。要禁用它并使用 Altair 的本地主题请使用 themeNone 代替。
让我们来看一个使用 Streamlit 主题和 Altair 原生主题的图表示例
代码
import altair as alt
from vega_datasets import datasource data.cars()chart alt.Chart(source).mark_circle().encode(xHorsepower,yMiles_per_Gallon,colorOrigin,
).interactive()tab1, tab2 st.tabs([Streamlit theme (default), Altair native theme])with tab1:# Use the Streamlit theme.# This is the default. So you can also omit the theme argument.st.altair_chart(chart, themestreamlit, use_container_widthTrue)
with tab2:# Use the native Altair theme.st.altair_chart(chart, themeNone, use_container_widthTrue) 点击下面互动应用程序中的标签查看启用和禁用 Streamlit 主题的图表。 如果您想知道自己的自定义配置是否仍会被考虑在内不用担心您仍然可以更改图表配置。换句话说虽然我们现在默认启用了 Streamlit 主题但你可以用自定义颜色或字体覆盖它。例如如果你想让图表线变成绿色而不是默认的红色你就可以这么做
下面是一个 Altair 图表的示例其中手动传递了颜色并得到了反映
import altair as alt
import streamlit as st
from vega_datasets import datasource data.seattle_weather()scale alt.Scale(domain[sun, fog, drizzle, rain, snow],range[#e7ba52, #a7a7a7, #aec7e8, #1f77b4, #9467bd],
)
color alt.Color(weather:N, scalescale)# We create two selections:
# - a brush that is active on the top panel
# - a multi-click that is active on the bottom panel
brush alt.selection_interval(encodings[x])
click alt.selection_multi(encodings[color])# Top panel is scatter plot of temperature vs time
points (alt.Chart().mark_point().encode(alt.X(monthdate(date):T, titleDate),alt.Y(temp_max:Q,titleMaximum Daily Temperature (C),scalealt.Scale(domain[-5, 40]),),coloralt.condition(brush, color, alt.value(lightgray)),sizealt.Size(precipitation:Q, scalealt.Scale(range[5, 200])),).properties(width550, height300).add_selection(brush).transform_filter(click)
)# Bottom panel is a bar chart of weather type
bars (alt.Chart().mark_bar().encode(xcount(),yweather:N,coloralt.condition(click, color, alt.value(lightgray)),).transform_filter(brush).properties(width550,).add_selection(click)
)chart alt.vconcat(points, bars, datasource, titleSeattle Weather: 2012-2015)tab1, tab2 st.tabs([Streamlit theme (default), Altair native theme])with tab1:st.altair_chart(chart, themestreamlit, use_container_widthTrue)
with tab2:st.altair_chart(chart, themeNone, use_container_widthTrue) 请注意即使启用了 Streamlit 主题自定义颜色仍然反映在图表中 。 柱状图
