空间数据可视化神器，Pydeck！

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小张Python 2022-02-15 17:54:20

文章标签 json html ico 文章分类 数据可视化人工智能

大家好，我是小F的朋友小张。

今天给大家介绍一款超赞的空间（地理）数据可视化神器：Pydeck。

Pydeck库通过deck.gl对数据进行空间可视化渲染，对3D的可视化支持非常强。

使用文档：

https://pydeck.gl/index.html

GitHub：

https://github.com/visgl/deck.gl/tree/master/bindings/pydeck

空间数据可视化神器，Pydeck！_html_02

通过下面的命令进行安装。

# 安装
pip install pydeck -i https://mirror.baidu.com/pypi/simple

可以在jupyter notebook及IDE上运行，其中jupyter上需要安装相关的插件。

# 启用Pydeck
jupyter nbextension install --sys-prefix --symlink --overwrite --py pydeck
jupyter nbextension enable --sys-prefix --py pydeck

接下来，就给大家介绍一下相关的可视化案例。

使用的数据及代码都已上传，可在文末获取~

① 弧形图

空间数据可视化神器，Pydeck！_html_03

打工人下班后的通勤情况，起点位于旧金山市中心(绿色)，终点为目的地(红色)。

数据由美国人口普查局收集。

地址https://lehd.ces.census.gov/data/

代码如下。

import pydeck as pdk
import pandas as pd


DATA_URL = "https://raw.githubusercontent.com/ajduberstein/sf_public_data/master/bay_area_commute_routes.csv"
# A bounding box for downtown San Francisco, to help filter this commuter data
DOWNTOWN_BOUNDING_BOX = [
    -122.43135291617365,
    37.766492914983864,
    -122.38706428091974,
    37.80583561830737,
]




def in_bounding_box(point):
    """Determine whether a point is in our downtown bounding box"""
    lng, lat = point
    in_lng_bounds = DOWNTOWN_BOUNDING_BOX[0] <= lng <= DOWNTOWN_BOUNDING_BOX[2]
    in_lat_bounds = DOWNTOWN_BOUNDING_BOX[1] <= lat <= DOWNTOWN_BOUNDING_BOX[3]
    return in_lng_bounds and in_lat_bounds




df = pd.read_csv(DATA_URL)
# Filter to bounding box
df = df[df[["lng_w", "lat_w"]].apply(lambda row: in_bounding_box(row), axis=1)]


GREEN_RGB = [0, 255, 0, 40]
RED_RGB = [240, 100, 0, 40]


# Specify a deck.gl ArcLayer
arc_layer = pdk.Layer(
    "ArcLayer",
    data=df,
    get_width="S000 * 2",
    get_source_positinotallow=["lng_h", "lat_h"],
    get_target_positinotallow=["lng_w", "lat_w"],
    get_tilt=15,
    get_source_color=RED_RGB,
    get_target_color=GREEN_RGB,
    pickable=True,
    auto_highlight=True,
)


view_state = pdk.ViewState(latitude=37.7576171, lnotallow=-122.5776844, bearing=45, pitch=50, zoom=8,)




TOOLTIP_TEXT = {"html": "{S000} jobs <br /> Home of commuter in red; work location in green"}
r = pdk.Deck(arc_layer, initial_view_state=view_state, tooltip=TOOLTIP_TEXT)
r.to_html("arc_layer.html")

② 3D柱状图

空间数据可视化神器，Pydeck！_ico_04

加拿大温哥华的房价情况，输入数据是GeoJSON格式的。

代码如下。

import pydeck as pdk


DATA_URL = "https://raw.githubusercontent.com/visgl/deck.gl-data/master/examples/geojson/vancouver-blocks.json"
LAND_COVER = [[[-123.0, 49.196], [-123.0, 49.324], [-123.306, 49.324], [-123.306, 49.196]]]


INITIAL_VIEW_STATE = pdk.ViewState(latitude=49.254, lnotallow=-123.13, zoom=11, max_zoom=16, pitch=45, bearing=0)


polygon = pdk.Layer(
    "PolygonLayer",
    LAND_COVER,
    stroked=False,
    # processes the data as a flat longitude-latitude pair
    get_polygnotallow="-",
    get_fill_color=[0, 0, 0, 20],
)


geojson = pdk.Layer(
    "GeoJsonLayer",
    DATA_URL,
    opacity=0.8,
    stroked=False,
    filled=True,
    extruded=True,
    wireframe=True,
    get_elevatinotallow="properties.valuePerSqm / 20",
    get_fill_color="[255, 255, properties.growth * 255]",
    get_line_color=[255, 255, 255],
)


r = pdk.Deck(layers=[polygon, geojson], initial_view_state=INITIAL_VIEW_STATE)


r.to_html("geojson_layer.html")

③ 图标图

空间数据可视化神器，Pydeck！_json_05

德国啤酒花园的位置分布情况。

代码如下。

import pydeck as pdk
import pandas as pd




# Data from OpenStreetMap, accessed via osmpy
DATA_URL = "https://raw.githubusercontent.com/ajduberstein/geo_datasets/master/biergartens.json"
ICON_URL = "https://upload.wikimedia.org/wikipedia/commons/c/c4/Projet_bi%C3%A8re_logo_v2.png"


icon_data = {
    # Icon from Wikimedia, used the Creative Commons Attribution-Share Alike 3.0
    # Unported, 2.5 Generic, 2.0 Generic and 1.0 Generic licenses
    "url": ICON_URL,
    "width": 242,
    "height": 242,
    "anchorY": 242,
}


data = pd.read_json(DATA_URL)
data["icon_data"] = None
for i in data.index:
    data["icon_data"][i] = icon_data


view_state = pdk.data_utils.compute_view(data[["lon", "lat"]], 0.1)


icon_layer = pdk.Layer(
    type="IconLayer",
    data=data,
    get_icnotallow="icon_data",
    get_size=4,
    size_scale=15,
    get_positinotallow=["lon", "lat"],
    pickable=True,
)


r = pdk.Deck(layers=[icon_layer], initial_view_state=view_state, tooltip={"text": "{tags}"})
r.to_html("icon_layer.html")

④ 流向图

空间数据可视化神器，Pydeck！_html_06

从希思罗机场飞往欧洲各地的航班路线图。

代码如下。

import pydeck as pdk


DATA_URL = {
    "AIRPORTS": "https://raw.githubusercontent.com/visgl/deck.gl-data/master/examples/line/airports.json",
    "FLIGHT_PATHS": "https://raw.githubusercontent.com/visgl/deck.gl-data/master/examples/line/heathrow-flights.json",  # noqa
}


INITIAL_VIEW_STATE = pdk.ViewState(latitude=47.65, lnotallow=7, zoom=4.5, max_zoom=16, pitch=50, bearing=0)


# RGBA value generated in Javascript by deck.gl's Javascript expression parser
GET_COLOR_JS = [
    "255 * (1 - (start[2] / 10000) * 2)",
    "128 * (start[2] / 10000)",
    "255 * (start[2] / 10000)",
    "255 * (1 - (start[2] / 10000))",
]


scatterplot = pdk.Layer(
    "ScatterplotLayer",
    DATA_URL["AIRPORTS"],
    radius_scale=20,
    get_positinotallow="coordinates",
    get_fill_color=[255, 140, 0],
    get_radius=60,
    pickable=True,
)


line_layer = pdk.Layer(
    "LineLayer",
    DATA_URL["FLIGHT_PATHS"],
    get_source_positinotallow="start",
    get_target_positinotallow="end",
    get_color=GET_COLOR_JS,
    get_width=10,
    highlight_color=[255, 255, 0],
    picking_radius=10,
    auto_highlight=True,
    pickable=True,
)


layers = [scatterplot, line_layer]


r = pdk.Deck(layers=layers, initial_view_state=INITIAL_VIEW_STATE)
r.to_html("line_layer.html")

⑤ 路径图

空间数据可视化神器，Pydeck！_html_07

湾区快速交通线路图。

代码如下。

import pandas as pd
import pydeck as pdk


DATA_URL = "https://raw.githubusercontent.com/visgl/deck.gl-data/master/website/bart-lines.json"
df = pd.read_json(DATA_URL)




def hex_to_rgb(h):
    h = h.lstrip("#")
    return tuple(int(h[i : i + 2], 16) for i in (0, 2, 4))




df["color"] = df["color"].apply(hex_to_rgb)




view_state = pdk.ViewState(latitude=37.782556, lnotallow=-122.3484867, zoom=10)


layer = pdk.Layer(
    type="PathLayer",
    data=df,
    pickable=True,
    get_color="color",
    width_scale=20,
    width_min_pixels=2,
    get_path="path",
    get_width=5,
)


r = pdk.Deck(layers=[layer], initial_view_state=view_state, tooltip={"text": "{name}"})
r.to_html("path_layer.html")

⑥ 散点图

空间数据可视化神器，Pydeck！_json_08

旧金山市内各地铁站的出口数量图。

代码如下。

import pydeck as pdk
import pandas as pd
import math


SCATTERPLOT_LAYER_DATA = "https://raw.githubusercontent.com/visgl/deck.gl-data/master/website/bart-stations.json"
df = pd.read_json(SCATTERPLOT_LAYER_DATA)


# Use pandas to calculate additional data
df["exits_radius"] = df["exits"].apply(lambda exits_count: math.sqrt(exits_count))


# Define a layer to display on a map
layer = pdk.Layer(
    "ScatterplotLayer",
    df,
    pickable=True,
    opacity=0.8,
    stroked=True,
    filled=True,
    radius_scale=6,
    radius_min_pixels=1,
    radius_max_pixels=100,
    line_width_min_pixels=1,
    get_positinotallow="coordinates",
    get_radius="exits_radius",
    get_fill_color=[255, 140, 0],
    get_line_color=[0, 0, 0],
)


# Set the viewport location
view_state = pdk.ViewState(latitude=37.7749295, lnotallow=-122.4194155, zoom=10, bearing=0, pitch=0)


# Render
r = pdk.Deck(layers=[layer], initial_view_state=view_state, tooltip={"text": "{name}\n{address}"})
r.to_html("scatterplot_layer.html")