英文:
Plotting a graph with different marker colors based on attributes
问题
我有一个数据框，看起来像这样：
| 纬度 | 经度 | 邮政编码 | |
|---|---|---|---|
| 0 | 40.244350 | -8.443650 | 3025-151 |
| 2 | 40.626130 | -8.645250 | 3810-419 |
| 1 | 40.626130 | -8.645250 | 3810-498 |
| 3 | 40.626130 | -8.645250 | 3810-419 |
我想绘制一个图表，使用纬度和经度坐标（线图），其中标记的面颜色基于邮政编码标签而不同。
我想到了以下解决方案：
color_labels = df['cod_postal'].unique()
# RGB颜色列表
rgb_values = sns.color_palette('Set2', 8)
# 将标签分配给RGB代码
color_map = dict(zip(color_labels, rgb_values))
plt.plot(df['latitude'], df['longitude'], marker='o', markerfacecolor=df['cod_postal'].map(color_map))
但是我遇到了以下错误：
ValueError: RGBA sequence should have length 3 or 4
如何修复这个问题？
我知道如果我绘制散点图，它会起作用，但对于我的情况，绘制线图是必不可少的。
提前致谢！
编辑：
为我的期望输出添加了一张参考图片。
英文:
I have a df that looks like this:
| latitude | longitude | cod_postal | |
|---|---|---|---|
| 0 | 40.244350 | -8.443650 | 3025-151 |
| 2 | 40.626130 | -8.645250 | 3810-419 |
| 1 | 40.626130 | -8.645250 | 3810-498 |
| 3 | 40.626130 | -8.645250 | 3810-419 |
I want to plot a graph with the lat and lon coordinates (a line plot) where the marker face color is different based on the cod_postal label.
I have come up with this:
color_labels=df['cod_postal'].unique()
#list of rgb
rgb_values=sns.color_palette('Set2', 8)
#Assign label to a rgb code
color_map=dict(zip(color_labels, rgb_values))
plt.plot(df['latitude'], df['longitude'], marker='o', mfc=df['cod_postal'].map(color_map))
But I get an error saying:
ValueError: RGBA sequence should have length 3 or 4
How can I fix this?
I know that if I was plotting a scatter it would work, but for my case, being a line plot is essential.
Thanks in advance!
EDIT:
Adding a reference picture for my expected output.
答案1
得分: 1
plt.plot() 期望 mfc 参数只有一个颜色。
使用 marker="o"，看起来你试图创建一个散点图。如果是这种情况，你应该使用 plt.scatter。现在可以将多个颜色传递给 c= 参数（每个散点一个颜色）。
color_labels = df['cod_postal'].unique()
# RGB颜色列表
rgb_values = sns.color_palette('Set2', 8)
# 将标签分配给RGB代码
color_map = dict(zip(color_labels, rgb_values))
fig, ax = plt.subplots()
# 创建散点图，每个“cod_postal”使用一个颜色
plt.scatter(df['latitude'], df['longitude'], c=df['cod_postal'].map(color_map).values)
项1、2和3具有相同的坐标，因此它们在图上重叠。
关于提供的参考图像的编辑。
我建议您分两步进行。首先按照上述说明创建散点图，然后创建连接的黑线图。
plt.scatter(df['latitude'], df['longitude'], c=df['cod_postal'].map(color_map).values, zorder=3)
plt.plot(df['latitude'], df['longitude'], c="k")
我调整了数据帧以使图更有趣。
df = pd.DataFrame([
[40.244350, -8.443650, "3025-151"],
[41.244350, -9.443650, "3025-151"],
[42.244350, -8.443650, "3025-151"],
[40.626130, -8.645250, "3810-419"],
[41.626130, -8.645250, "3810-419"],
], columns=["latitude", "longitude", "cod_postal"])
英文:
plt.plot() is expecting a single color for the mfc parameter.
Using marker="o", it looks like you're trying to create a scatter plot. If it's the case, you should use plt.scatter. You can now pass multiple colors to the c= parameter (one for each scatter point).
color_labels=df['cod_postal'].unique()
#list of rgb
rgb_values=sns.color_palette('Set2', 8)
#Assign label to a rgb code
color_map=dict(zip(color_labels, rgb_values))
fig, ax = plt.subplots()
# Create scatter plot, with one color 'cod_postal'
plt.scatter(df['latitude'], df['longitude'], c=df['cod_postal'].map(color_map).values)
items 1, 2, and 3 have the same coordinates so they overlap on the figure.
Edit regarding the reference image provided.
I would then suggest you to do that in 2 steps. First create the scatter plot as explained above. Then create a line plot for the connecting black line.
plt.scatter(df['latitude'], df['longitude'], c=df['cod_postal'].map(color_map).values, zorder=3)
plt.plot(df['latitude'], df['longitude'], c="k")
I adjusted the dataframe to make the figure a bit more interesting
df = pd.DataFrame([
[40.244350, -8.443650, "3025-151"],
[41.244350, -9.443650, "3025-151"],
[42.244350, -8.443650, "3025-151"],
[40.626130, -8.645250, "3810-419"],
[41.626130, -8.645250, "3810-419"],
], columns=["latitude", "longitude", "cod_postal"])
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