pandas 带有两个 y 轴的 Seaborn 条形图
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Seaborn barplot with two y-axis
提问by Flo1895
considering the following pandas DataFrame:
考虑以下Pandas数据帧：
labels values_a values_b values_x values_y
0 date1 1 3 150 170
1 date2 2 6 200 180
It is easy to plot this with Seaborn (see example code below). However, due to the big difference between values_a/values_b and values_x/values_y, the bars for values_a and values_b are not easily visible (actually, the dataset given above is just a sample and in my real dataset the difference is even bigger). Therefore, I would like to use two y-axis, i.e., one y-axis for values_a/values_b and one for values_x/values_y. I tried to use plt.twinx() to get a second axis but unfortunately, the plot shows only two bars for values_x and values_y, even though there are at least two y-axis with the right scaling. :) Do you have an idea how to fix that and get four bars for each label whereas the values_a/values_b bars relate to the left y-axis and the values_x/values_y bars relate to the right y-axis?
使用 Seaborn 很容易绘制此图（请参见下面的示例代码）。然而，由于 values_a/values_b 和 values_x/values_y 之间的巨大差异，values_a 和 values_b 的条形不容易看到（实际上，上面给出的数据集只是一个样本，在我的真实数据集中差异更大）。因此，我想使用两个 y 轴，即一个 y 轴用于 values_a/values_b，一个用于 values_x/values_y。我尝试使用 plt.twinx() 来获取第二个轴，但不幸的是，该图仅显示了 values_x 和 values_y 的两个条形，即使至少有两个 y 轴具有正确的缩放比例。:) 您是否知道如何解决这个问题并为每个标签获得四个条形，而 values_a/values_b 条形与左侧 y 轴相关，而 values_x/values_y 条形与右侧 y 轴相关？
Thanks in advance!
提前致谢！
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
columns = ["labels", "values_a", "values_b", "values_x", "values_y"]
test_data = pd.DataFrame.from_records([("date1", 1, 3, 150, 170),\
("date2", 2, 6, 200, 180)],\
columns=columns)
# working example but with unreadable values_a and values_b
test_data_melted = pd.melt(test_data, id_vars=columns[0],\
var_name="source", value_name="value_numbers")
g = sns.barplot(x=columns[0], y="value_numbers", hue="source",\
data=test_data_melted)
plt.show()
# values_a and values_b are not displayed
values1_melted = pd.melt(test_data, id_vars=columns[0],\
value_vars=["values_a", "values_b"],\
var_name="source1", value_name="value_numbers1")
values2_melted = pd.melt(test_data, id_vars=columns[0],\
value_vars=["values_x", "values_y"],\
var_name="source2", value_name="value_numbers2")
g1 = sns.barplot(x=columns[0], y="value_numbers1", hue="source1",\
data=values1_melted)
ax2 = plt.twinx()
g2 = sns.barplot(x=columns[0], y="value_numbers2", hue="source2",\
data=values2_melted, ax=ax2)
plt.show()
采纳答案by ALollz
This is probably best suited for multiple sub-plots, but if you are truly set on a single plot, you can scale the data before plotting, create another axis and then modify the tick values.
这可能最适合多个子图，但如果您真的设置在单个图上，则可以在绘图前缩放数据，创建另一个轴，然后修改刻度值。
Sample Data
样本数据
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
columns = ["labels", "values_a", "values_b", "values_x", "values_y"]
test_data = pd.DataFrame.from_records([("date1", 1, 3, 150, 170),\
("date2", 2, 6, 200, 180)],\
columns=columns)
test_data_melted = pd.melt(test_data, id_vars=columns[0],\
var_name="source", value_name="value_numbers")
Code:
代码：
# Scale the data, just a simple example of how you might determine the scaling
mask = test_data_melted.source.isin(['values_a', 'values_b'])
scale = int(test_data_melted[~mask].value_numbers.mean()
/test_data_melted[mask].value_numbers.mean())
test_data_melted.loc[mask, 'value_numbers'] = test_data_melted.loc[mask, 'value_numbers']*scale
# Plot
fig, ax1 = plt.subplots()
g = sns.barplot(x=columns[0], y="value_numbers", hue="source",\
data=test_data_melted, ax=ax1)
# Create a second y-axis with the scaled ticks
ax1.set_ylabel('X and Y')
ax2 = ax1.twinx()
# Ensure ticks occur at the same positions, then modify labels
ax2.set_ylim(ax1.get_ylim())
ax2.set_yticklabels(np.round(ax1.get_yticks()/scale,1))
ax2.set_ylabel('A and B')
plt.show()
