python - How to format seaborn/matplotlib axis tick labels from number to thousands or Millions? (125,436 to 125.4K)

24
votes
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import seaborn as sns
import pandas as pd
sns.set(style="darkgrid")    
fig, ax = plt.subplots(figsize=(8, 5))    
palette = sns.color_palette("bright", 6)
g = sns.scatterplot(ax=ax, x="Area", y="Rent/Sqft", hue="Region", marker='o', data=df, s=100, palette= palette)
g.legend(bbox_to_anchor=(1, 1), ncol=1)
g.set(xlim = (50000,250000))

enter image description here

How can I can change the axis format from a number to custom format? For example, 125000 to 125.00K

4
pythonmatplotlibseaborn

4 Answers

33
votes

IIUC you can format the xticks and set these:

In[60]:
#generate some psuedo data
df = pd.DataFrame({'num':[50000, 75000, 100000, 125000], 'Rent/Sqft':np.random.randn(4), 'Region':list('abcd')})
df

Out[60]: 
      num  Rent/Sqft Region
0   50000   0.109196      a
1   75000   0.566553      b
2  100000  -0.274064      c
3  125000  -0.636492      d

In[61]:
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import seaborn as sns
import pandas as pd
sns.set(style="darkgrid")    
fig, ax = plt.subplots(figsize=(8, 5))    
palette = sns.color_palette("bright", 4)
g = sns.scatterplot(ax=ax, x="num", y="Rent/Sqft", hue="Region", marker='o', data=df, s=100, palette= palette)
g.legend(bbox_to_anchor=(1, 1), ncol=1)
g.set(xlim = (50000,250000))
xlabels = ['{:,.2f}'.format(x) + 'K' for x in g.get_xticks()/1000]
g.set_xticklabels(xlabels)

Out[61]:

enter image description here

The key bit here is this line:

xlabels = ['{:,.2f}'.format(x) + 'K' for x in g.get_xticks()/1000]
g.set_xticklabels(xlabels)

So this divides all the ticks by 1000 and then formats them and sets the xtick labels

UPDATE Thanks to @ScottBoston who has suggested a better method:

ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: '{:,.2f}'.format(x/1000) + 'K'))

see the docs

20
votes

The canonical way of formatting the tick labels in the standard units is to use an EngFormatter. There is also an example in the matplotlib docs.

Here it might look as follows.

import numpy as np; np.random.seed(42)
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import seaborn as sns
import pandas as pd

df = pd.DataFrame({"xaxs" : np.random.randint(50000,250000, size=20),
                   "yaxs" : np.random.randint(7,15, size=20),
                   "col"  : np.random.choice(list("ABC"), size=20)})

fig, ax = plt.subplots(figsize=(8, 5))    
palette = sns.color_palette("bright", 6)
sns.scatterplot(ax=ax, x="xaxs", y="yaxs", hue="col", data=df, 
                marker='o', s=100, palette="magma")
ax.legend(bbox_to_anchor=(1, 1), ncol=1)
ax.set(xlim = (50000,250000))

ax.xaxis.set_major_formatter(ticker.EngFormatter())

plt.show()

enter image description here

6
votes

Using Seaborn without importing matplotlib:

import seaborn as sns
sns.set()

chart = sns.relplot(x="x_val", y="y_val", kind="line", data=my_data)

ticks = chart.axes[0][0].get_xticks()

xlabels = ['$' + '{:,.0f}'.format(x) for x in ticks]

chart.set_xticklabels(xlabels)
chart.fig

Thank you to EdChum's answer above for getting me 90% there.

1
votes

Here's how I'm solving this: (similar to ScottBoston)

from matplotlib.ticker import FuncFormatter

f = lambda x, pos: f'{x/10**3:,.0f}K'
ax.xaxis.set_major_formatter(FuncFormatter(f))