I have an existing plot that was created with pandas like this:

``````df['myvar'].plot(kind='bar')
``````

The y axis is format as float and I want to change the y axis to percentages. All of the solutions I found use ax.xyz syntax and I can only place code below the line above that creates the plot (I cannot add ax=ax to the line above.)

How can I format the y axis as percentages without changing the line above?

Here is the solution I found but requires that I redefine the plot:

``````import matplotlib.pyplot as plt
import numpy as np
import matplotlib.ticker as mtick

data = [8,12,15,17,18,18.5]
perc = np.linspace(0,100,len(data))

fig = plt.figure(1, (7,4))

ax.plot(perc, data)

fmt="%.0f%%" # Format you want the ticks, e.g. '40%'
xticks = mtick.FormatStrFormatter(fmt)
ax.xaxis.set_major_formatter(xticks)

plt.show()
``````

Link to the above solution: Pyplot: using percentage on x axis

This is a few months late, but I have created PR#6251 with matplotlib to add a new `PercentFormatter` class. With this class you just need one line to reformat your axis (two if you count the import of `matplotlib.ticker`):

``````import ...
import matplotlib.ticker as mtick

ax = df['myvar'].plot(kind='bar')
ax.yaxis.set_major_formatter(mtick.PercentFormatter())
``````

`PercentFormatter()` accepts three arguments, `xmax`, `decimals`, `symbol`. `xmax` allows you to set the value that corresponds to 100% on the axis. This is nice if you have data from 0.0 to 1.0 and you want to display it from 0% to 100%. Just do `PercentFormatter(1.0)`.

The other two parameters allow you to set the number of digits after the decimal point and the symbol. They default to `None` and `'%'`, respectively. `decimals=None` will automatically set the number of decimal points based on how much of the axes you are showing.

Update

`PercentFormatter` was introduced into Matplotlib proper in version 2.1.0.

pandas dataframe plot will return the `ax` for you, And then you can start to manipulate the axes whatever you want.

``````import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(100,5))

# you get ax from here
ax = df.plot()
type(ax)  # matplotlib.axes._subplots.AxesSubplot

# manipulate
vals = ax.get_yticks()
ax.set_yticklabels(['{:,.2%}'.format(x) for x in vals])
``````

Jianxun‘s solution did the job for me but broke the y value indicator at the bottom left of the window.

I ended up using `FuncFormatter`instead (and also stripped the uneccessary trailing zeroes as suggested here):

``````import pandas as pd
import numpy as np
from matplotlib.ticker import FuncFormatter

df = pd.DataFrame(np.random.randn(100,5))

ax = df.plot()
ax.yaxis.set_major_formatter(FuncFormatter(lambda y, _: '{:.0%}'.format(y)))
``````

Generally speaking I’d recommend using `FuncFormatter` for label formatting: it’s reliable, and versatile.

For those who are looking for the quick one-liner:

``````plt.gca().set_yticklabels([f'{x:.0%}' for x in plt.gca().get_yticks()])
``````

this assumes

• import: `from matplotlib import pyplot as plt`
• Python >=3.6 for f-String formatting. For older versions, replace `f'{x:.0%}'` with `'{:.0%}'.format(x)`

I’m late to the game but I just realize this: `ax` can be replaced with `plt.gca()` for those who are not using axes and just subplots.

Echoing @Mad Physicist answer, using the package `PercentFormatter` it would be:

``````import matplotlib.ticker as mtick

plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(1))
#if you already have ticks in the 0 to 1 range. Otherwise see their answer
``````

I propose an alternative method using `seaborn`

Working code:

``````import pandas as pd
import seaborn as sns
data=np.random.rand(10,2)*100
df = pd.DataFrame(data, columns=['A', 'B'])
ax= sns.lineplot(data=df, markers= True)
ax.set(xlabel="xlabel", ylabel="ylabel", title="title")
#changing ylables ticks
y_value=['{:,.2f}'.format(x) + '%' for x in ax.get_yticks()]
ax.set_yticklabels(y_value)
``````

Based on the answer of @erwanp, you can use the formatted string literals of Python 3,

``````x = '2'
percentage = f'{x}%' # 2%
``````

inside the `FuncFormatter()` and combined with a lambda expression.

All wrapped:

``````ax.yaxis.set_major_formatter(FuncFormatter(lambda y, _: f'{y}%'))
``````

You can do this in one line without importing anything:
`plt.gca().yaxis.set_major_formatter(plt.FuncFormatter('{}%'.format))`

If you want integer percentages, you can do:
`plt.gca().yaxis.set_major_formatter(plt.FuncFormatter('{:.0f}%'.format))`

You can use either `ax.yaxis` or `plt.gca().yaxis`. `FuncFormatter` is still part of `matplotlib.ticker`, but you can also do `plt.FuncFormatter` as a shortcut.

Another one line solution if the yticks are between 0 and 1:

``````plt.yticks(plt.yticks()[0], ['{:,.0%}'.format(x) for x in plt.yticks()[0]])
``````