Each Answer to this Q is separated by one/two green lines.

I know pandas supports a secondary Y axis, but I’m curious if anyone knows a way to put a tertiary Y axis on plots. Currently I am achieving this with numpy+pyplot, but it is slow with large data sets.

This is to plot different measurements with distinct units on the same graph for easy comparison (eg: Relative Humidity/Temperature/ and Electrical Conductivity).

So really just curious if anyone knows if this is possible in `pandas`

without too much work.

[Edit] I doubt that there is a way to do this(without too much overhead) however I hope to be proven wrong, as this may be a limitation of matplotlib.

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:

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:

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))
```