將圖例添加到 Seaborn 點圖

Question

我正在使用seaborn將多個數據seaborn繪制為點圖。 我也在同一軸上繪制所有數據框。

我將如何在情節中添加圖例？

我的代碼獲取每個數據框並在同一個圖形上一個接一個地繪制它。

每個數據框都有相同的列

date        count
2017-01-01  35
2017-01-02  43
2017-01-03  12
2017-01-04  27 

我的代碼：

f, ax = plt.subplots(1, 1, figsize=figsize)
x_col='date'
y_col = 'count'
sns.pointplot(ax=ax,x=x_col,y=y_col,data=df_1,color='blue')
sns.pointplot(ax=ax,x=x_col,y=y_col,data=df_2,color='green')
sns.pointplot(ax=ax,x=x_col,y=y_col,data=df_3,color='red')

這在同一個圖上繪制了 3 條線。 然而，傳說不見了。 該文檔不接受label參數。

一種有效的解決方法是創建一個新的數據框並使用hue argument 。

df_1['region'] = 'A'
df_2['region'] = 'B'
df_3['region'] = 'C'
df = pd.concat([df_1,df_2,df_3])
sns.pointplot(ax=ax,x=x_col,y=y_col,data=df,hue='region')

但我想知道是否有一種方法可以為代碼創建圖例，該代碼首先向圖中添加順序點圖，然后添加圖例。

示例輸出：

Answer 1

我建議不要使用 seaborn pointplot進行繪圖。 這使事情變得不必要地復雜。
而是使用 matplotlib plot_date 。 這允許為繪圖設置標簽，並使用ax.legend()將它們自動放入圖例中。

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np

date = pd.date_range("2017-03", freq="M", periods=15)
count = np.random.rand(15,4)
df1 = pd.DataFrame({"date":date, "count" : count[:,0]})
df2 = pd.DataFrame({"date":date, "count" : count[:,1]+0.7})
df3 = pd.DataFrame({"date":date, "count" : count[:,2]+2})

f, ax = plt.subplots(1, 1)
x_col='date'
y_col = 'count'

ax.plot_date(df1.date, df1["count"], color="blue", label="A", linestyle="-")
ax.plot_date(df2.date, df2["count"], color="red", label="B", linestyle="-")
ax.plot_date(df3.date, df3["count"], color="green", label="C", linestyle="-")

ax.legend()

plt.gcf().autofmt_xdate()
plt.show()

---

如果仍然有興趣獲得點圖的圖例，這里有一種方法：

 sns.pointplot(ax=ax,x=x_col,y=y_col,data=df1,color='blue') sns.pointplot(ax=ax,x=x_col,y=y_col,data=df2,color='green') sns.pointplot(ax=ax,x=x_col,y=y_col,data=df3,color='red') ax.legend(handles=ax.lines[::len(df1)+1], labels=["A","B","C"]) ax.set_xticklabels([t.get_text().split("T")[0] for t in ax.get_xticklabels()]) plt.gcf().autofmt_xdate() plt.show()

Answer 2

老問題，但有一個更簡單的方法。

sns.pointplot(x=x_col,y=y_col,data=df_1,color='blue')
sns.pointplot(x=x_col,y=y_col,data=df_2,color='green')
sns.pointplot(x=x_col,y=y_col,data=df_3,color='red')
plt.legend(labels=['legendEntry1', 'legendEntry2', 'legendEntry3'])

這使您可以按順序添加圖，而不必擔心除了定義圖例項之外的任何 matplotlib 廢話。

Answer 3

我嘗試使用 Adam B 的答案，但是，它對我不起作用。 相反，我找到了以下解決方法來向點圖添加圖例。

import matplotlib.patches as mpatches
red_patch = mpatches.Patch(color='#bb3f3f', label='Label1')
black_patch = mpatches.Patch(color='#000000', label='Label2')

在點圖中，可以按照前面的答案中所述指定顏色。 一旦設置了對應於不同圖的這些補丁，

plt.legend(handles=[red_patch, black_patch])

並且圖例應該出現在點圖中。

Answer 4

這又有點超出了原來的問題，同時也建立在@PSub的東西更普遍反應---我知道在Matplotlib一些這方面是比較容易直接，但許多默認樣式Seaborn選項都相當不錯，所以我想工作，你怎么能有一個情節點（或其他Seaborn積）超過一個傳說，而不落入Matplotlib在一開始。

這是一種解決方案：

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# We will need to access some of these matplotlib classes directly
from matplotlib.lines import Line2D # For points and lines
from matplotlib.patches import Patch # For KDE and other plots
from matplotlib.legend import Legend

from matplotlib import cm

# Initialise random number generator
rng = np.random.default_rng(seed=42)

# Generate sample of 25 numbers
n = 25
clusters = []

for c in range(0,3):
    
    # Crude way to get different distributions
    # for each cluster
    p = rng.integers(low=1, high=6, size=4)
    
    df = pd.DataFrame({
        'x': rng.normal(p[0], p[1], n),
        'y': rng.normal(p[2], p[3], n),
        'name': f"Cluster {c+1}"
    })
    clusters.append(df)

# Flatten to a single data frame
clusters = pd.concat(clusters)

# Now do the same for data to feed into
# the second (scatter) plot... 
n = 8
points = []

for c in range(0,2):
    
    p = rng.integers(low=1, high=6, size=4)
    
    df = pd.DataFrame({
        'x': rng.normal(p[0], p[1], n),
        'y': rng.normal(p[2], p[3], n),
        'name': f"Group {c+1}"
    })
    points.append(df)

points = pd.concat(points)

# And create the figure
f, ax = plt.subplots(figsize=(8,8))

# The KDE-plot generates a Legend 'as usual'
k = sns.kdeplot(
    data=clusters,
    x='x', y='y',
    hue='name',
    shade=True,
    thresh=0.05,
    n_levels=2,
    alpha=0.2,
    ax=ax,
)

# Notice that we access this legend via the
# axis to turn off the frame, set the title, 
# and adjust the patch alpha level so that
# it closely matches the alpha of the KDE-plot
ax.get_legend().set_frame_on(False)
ax.get_legend().set_title("Clusters")
for lh in ax.get_legend().get_patches(): 
    lh.set_alpha(0.2)

# You would probably want to sort your data 
# frame or set the hue and style order in order
# to ensure consistency for your own application
# but this works for demonstration purposes
groups  = points.name.unique()
markers = ['o', 'v', 's', 'X', 'D', '<', '>']
colors  = cm.get_cmap('Dark2').colors

# Generate the scatterplot: notice that Legend is
# off (otherwise this legend would overwrite the 
# first one) and that we're setting the hue, style,
# markers, and palette using the 'name' parameter 
# from the data frame and the number of groups in 
# the data.
p = sns.scatterplot(
    data=points,
    x="x",
    y="y",
    hue='name',
    style='name',
    markers=markers[:len(groups)],
    palette=colors[:len(groups)],
    legend=False,
    s=30,
    alpha=1.0
)

# Here's the 'magic' -- we use zip to link together 
# the group name, the color, and the marker style. You
# *cannot* retreive the marker style from the scatterplot
# since that information is lost when rendered as a 
# PathCollection (as far as I can tell). Anyway, this allows
# us to loop over each group in the second data frame and 
# generate a 'fake' Line2D plot (with zero elements and no
# line-width in our case) that we can add to the legend. If
# you were overlaying a line plot or a second plot that uses
# patches you'd have to tweak this accordingly.
patches = []
for x in zip(groups, colors[:len(groups)], markers[:len(groups)]):
    patches.append(Line2D([0],[0], linewidth=0.0, linestyle='', 
                   color=x[1], markerfacecolor=x[1],
                   marker=x[2], label=x[0], alpha=1.0))

# And add these patches (with their group labels) to the new
# legend item and place it on the plot.
leg = Legend(ax, patches, labels=groups, 
             loc='upper left', frameon=False, title='Groups')
ax.add_artist(leg);

# Done
plt.show();

這是輸出：