以小写(值为代码颜色)更改dict的值，以便在plotly.graph.object中接受为颜色参数

Question

因此，我试图从字典'Disaster_type‘中提取颜色，根据灾害的类型，在地线上绘制标记。

基本上，我想用它的颜色代码在图形中再现自然的弥散器。这是一种火山活动，把它涂成“橙色”。我想改变标记的大小，也取决于灾难的程度，但这还需要一天的时间。

以下是数据集的链接：https://www.kaggle.com/datasets/brsdincer/all-natural-disasters-19002021-eosdis

import plotly.graph_objects as go 
import pandas as pd
import plotly as plt

df = pd.read_csv('1900_2021_DISASTERS - main.csv')
df.head()
df.tail()
disaster_set = {disaster for disaster in df['Disaster Type']}
disaster_type = {'Storm':'aliceblue', 
 'Volcanic activity':'orange', 
 'Flood':'royalblue', 
 'Mass movement (dry)':'darkorange',
 'Landslide':'#C76114',
 'Extreme temperature':'#FF0000',
 'Animal accident':'gray55',
 'Glacial lake outburst':'#7D9EC0',
 'Earthquake':'#CD8C95',
 'Insect infestation':'#EEE8AA', 
 'Wildfire':'   #FFFF00', 
 'Fog':'#00E5EE', 
 'Drought':'#FFEFD5', 
 'Epidemic':'#00CD66    ', 
 'Impact':'#FF6347'}
# disaster_type_lower = {(k, v.lower()) for k, v in disaster_type.items()}
# print(disaster_type_lower)
# for values in disaster_type.values():
#     disaster_type[values] = disaster_type.lowercase()


fig = go.Figure(data=go.Scattergeo(
    lon = df['Longitude'], 
    lat = df['Latitude'],
    text = df['Country'],
    mode = 'markers',
    marker_color = disaster_type_.values()
        )
  )             
fig.show()

我想不出是怎么回事，在我试着做那件事之后，我留下了评论。它改变了它们的小写，但我知道我不知道热得them...My大脑完全融化

Answer 1

• 这是熊猫图的一个简单案例
• 在kaggle上找到了与您的数据相同的数据，所以使用了
• 一种类型是未映射的极端温度，因此使用fillna("red")消除任何错误。
• gray55给了我一个错误，因此将它替换为RGB等效。

import kaggle.cli
import sys
import pandas as pd
from zipfile import ZipFile
import urllib
import plotly.graph_objects as go

# fmt: off
# download data set
url = "https://www.kaggle.com/brsdincer/all-natural-disasters-19002021-eosdis"
sys.argv = [sys.argv[0]] + f"datasets download {urllib.parse.urlparse(url).path[1:]}".split(" ")
kaggle.cli.main()
zfile = ZipFile(f'{urllib.parse.urlparse(url).path.split("/")[-1]}.zip')
dfs = {f.filename: pd.read_csv(zfile.open(f)) for f in zfile.infolist()}
# fmt: on
df = dfs["DISASTERS/1970-2021_DISASTERS.xlsx - emdat data.csv"]

disaster_type = {
    "Storm": "aliceblue",
    "Volcanic activity": "orange",
    "Flood": "royalblue",
    "Mass movement (dry)": "darkorange",
    "Landslide": "#C76114",
    "Extreme temperature": "#FF0000",
    "Animal accident": "#8c8c8c",  # gray55
    "Glacial lake outburst": "#7D9EC0",
    "Earthquake": "#CD8C95",
    "Insect infestation": "#EEE8AA",
    "Wildfire": "   #FFFF00",
    "Fog": "#00E5EE",
    "Drought": "#FFEFD5",
    "Epidemic": "#00CD66    ",
    "Impact": "#FF6347",
}


fig = go.Figure(
    data=go.Scattergeo(
        lon=df["Longitude"],
        lat=df["Latitude"],
        text=df["Country"],
        mode="markers",
        marker_color=df["Disaster Type"].map(disaster_type).fillna("red"),
    )
)
fig.show()

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