绘制多个 Y 轴

我知道熊猫支持一个次要的 Y 轴，但我很好奇，如果有人知道一个方法，把第三个 Y 轴的阴谋。目前我正在使用 numpy + pyplot 实现这一点，但是对于大型数据集来说速度很慢。

这是为了在同一图表上绘制不同单位的不同测量值，以便于比较(例如: 相对湿度/温度/电导率)。

所以真的只是好奇，如果有人知道这是否可能在 pandas没有太多的工作。

[编辑]我怀疑有一种方法可以做到这一点(没有太多的开销) ，但我希望被证明是错误的，因为这可能是 matplotlib 的一个限制。

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最佳答案

I think this might work:

import matplotlib.pyplot as plt
import numpy as np
from pandas import DataFrame
df = DataFrame(np.random.randn(5, 3), columns=['A', 'B', 'C'])


fig, ax = plt.subplots()
ax3 = ax.twinx()
rspine = ax3.spines['right']
rspine.set_position(('axes', 1.15))
ax3.set_frame_on(True)
ax3.patch.set_visible(False)
fig.subplots_adjust(right=0.7)


df.A.plot(ax=ax, style='b-')
# same ax as above since it's automatically added on the right
df.B.plot(ax=ax, style='r-', secondary_y=True)
df.C.plot(ax=ax3, style='g-')


# add legend --> take advantage of pandas providing us access
# to the line associated with the right part of the axis
ax3.legend([ax.get_lines()[0], ax.right_ax.get_lines()[0], ax3.get_lines()[0]],\
['A','B','C'], bbox_to_anchor=(1.5, 0.5))

Output:

Output

小开

A simpler solution without plt:

ax1 = df1.plot()


ax2 = ax1.twinx()
ax2.spines['right'].set_position(('axes', 1.0))
df2.plot(ax=ax2)


ax3 = ax1.twinx()
ax3.spines['right'].set_position(('axes', 1.1))
df3.plot(ax=ax3)


....

Using function to achieve this:

def plot_multi(data, cols=None, spacing=.1, **kwargs):


from pandas.plotting._matplotlib.style import get_standard_colors


# Get default color style from pandas - can be changed to any other color list
if cols is None: cols = data.columns
if len(cols) == 0: return
colors = get_standard_colors(num_colors=len(cols))


# First axis
ax = data.loc[:, cols[0]].plot(label=cols[0], color=colors[0], **kwargs)
ax.set_ylabel(ylabel=cols[0])
lines, labels = ax.get_legend_handles_labels()


for n in range(1, len(cols)):
# Multiple y-axes
ax_new = ax.twinx()
ax_new.spines['right'].set_position(('axes', 1 + spacing * (n - 1)))
data.loc[:, cols[n]].plot(ax=ax_new, label=cols[n], color=colors[n % len(colors)], **kwargs)
ax_new.set_ylabel(ylabel=cols[n])
        

# Proper legend position
line, label = ax_new.get_legend_handles_labels()
lines += line
labels += label


ax.legend(lines, labels, loc=0)
return ax

Example:

from random import randrange


data = pd.DataFrame(dict(
s1=[randrange(-1000, 1000) for _ in range(100)],
s2=[randrange(-100, 100) for _ in range(100)],
s3=[randrange(-10, 10) for _ in range(100)],
))


plot_multi(data.cumsum(), figsize=(10, 5))

Output:

Multiple Y-Axes

小开

I modified the above answer a bit to make it accept custom x column, well-documented, and more flexible.

You can copy this snippet and use it as a function:

from typing import List, Union


import matplotlib.axes
import pandas as pd


def plot_multi(
data: pd.DataFrame,
x: Union[str, None] = None,
y: Union[List[str], None] = None,
spacing: float = 0.1,
**kwargs
) -> matplotlib.axes.Axes:
"""Plot multiple Y axes on the same chart with same x axis.


Args:
data: dataframe which contains x and y columns
x: column to use as x axis. If None, use index.
y: list of columns to use as Y axes. If None, all columns are used
except x column.
spacing: spacing between the plots
**kwargs: keyword arguments to pass to data.plot()


Returns:
a matplotlib.axes.Axes object returned from data.plot()


Example:
>>> plot_multi(df, figsize=(22, 10))
>>> plot_multi(df, x='time', figsize=(22, 10))
>>> plot_multi(df, y='price qty value'.split(), figsize=(22, 10))
>>> plot_multi(df, x='time', y='price qty value'.split(), figsize=(22, 10))
>>> plot_multi(df[['time price qty'.split()]], x='time', figsize=(22, 10))


See Also:
This code is mentioned in https://stackoverflow.com/q/11640243/2593810
"""
from pandas.plotting._matplotlib.style import get_standard_colors


# Get default color style from pandas - can be changed to any other color list
if y is None:
y = data.columns


# remove x_col from y_cols
if x:
y = [col for col in y if col != x]


if len(y) == 0:
return
colors = get_standard_colors(num_colors=len(y))


if "legend" not in kwargs:
kwargs["legend"] = False  # prevent multiple legends


# First axis
ax = data.plot(x=x, y=y[0], color=colors[0], **kwargs)
ax.set_ylabel(ylabel=y[0])
lines, labels = ax.get_legend_handles_labels()


for i in range(1, len(y)):
# Multiple y-axes
ax_new = ax.twinx()
ax_new.spines["right"].set_position(("axes", 1 + spacing * (i - 1)))
data.plot(
ax=ax_new, x=x, y=y[i], color=colors[i % len(colors)], **kwargs
)
ax_new.set_ylabel(ylabel=y[i])


# Proper legend position
line, label = ax_new.get_legend_handles_labels()
lines += line
labels += label


ax.legend(lines, labels, loc=0)
return ax

Here's one way to use it:

plot_multi(df, x='time', y='price qty value'.split(), figsize=(22, 10))

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