Relabelling ticks on Seaborn axes?
Question:
I’m doing a log-log plot with Seaborn; the data is actually derived from a StackOverflow developer survey. I tried using the built-in log scale, but the results didn’t make sense, so this simply calculates the logs before plotting.
df = pd.DataFrame( {'company_size_range': {7800: 7.0, 7801: 700.0, 7802: 7.0, 7803: 20000.0, 7805: 200.0, 7806: 20000.0, 7808: 2000.0, 7809: 2000.0, 7810: 7.0, 7811: 200.0, 7812: 50.0, 7813: 20000.0, 7816: 2.0, 7819: 200.0, 7820: 2000.0, 7824: 2.0, 7825: 2.0, 7827: 2.0, 7828: 50.0, 7830: 14.0, 7831: 50.0, 7833: 200.0, 7834: 50.0, 7835: 50.0, 7838: 2.0, 7840: 50.0, 7841: 50.0, 7842: 7000.0, 7843: 20000.0, 7844: 14.0, 7846: 2.0, 7850: 20000.0, 7851: 700.0, 7852: 200.0, 7853: 200.0, 7855: 200.0, 7856: 7.0, 7857: 50.0, 7858: 700.0, 7861: 20000.0, 7863: 20000.0, 7865: 20000.0, 7867: 700.0, 7868: 20000.0, 7870: 50.0, 7871: 2000.0, 7872: 50.0, 7873: 20000.0, 7874: 200.0, 7876: 14.0, 7877: 20000.0, 7879: 50.0, 7880: 50.0 }, 'team_size_range': {7800: 7.0, 7801: 7.0, 7802: 7.0, 7803: 2.0, 7805: 7.0, 7806: 2.0, 7808: 7.0, 7809: 7.0, 7810: 2.0, 7811: 17.0, 7812: 7.0, 7813: 2.0, 7816: 2.0, 7819: 7.0, 7820: 30.0, 7824: 2.0, 7825: 2.0, 7827: 2.0, 7828: 2.0, 7830: 2.0, 7831: 7.0, 7833: 2.0, 7834: 2.0, 7835: 7.0, 7838: 2.0, 7840: 7.0, 7841: 30.0, 7842: 7.0, 7843: 7.0, 7844: 2.0, 7846: 2.0, 7850: 7.0, 7851: 11.0, 7852: 7.0, 7853: 7.0, 7855: 2.0, 7856: 7.0, 7857: 7.0, 7858: 11.0, 7861: 7.0, 7863: 2.0, 7865: 30.0, 7867: 7.0, 7868: 7.0, 7870: 2.0, 7871: 17.0, 7872: 7.0, 7873: 17.0, 7874: 7.0, 7876: 2.0, 7877: 7.0, 7879: 17.0, 7880: 7.0}} )
g=sns.jointplot(x=np.log10(df['company_size_range']+1),
y=np.log10(df['team_size_range']+1), kind='kde', color='g')
That’s fine, but the axes show the log values, not the underlying values. The X-axis, for example, is:
-1, 1, 2, 3, 4, 5, 6
So I added this to fix it, using the X position of the labels as the X values:
g.ax_joint.set_xticklabels(["{:.0f}".format(10**label.get_position()[0]-1)
for label in g.ax_joint.get_xticklabels()])
The trouble is the resulting X-axis labels are nonsense:
1, 2, 3, 5, 9, 0, 0, 0
What is going on, and how best to fix it, please?
Answers:
Try the following by first using the canvas.draw(). Also, I do not understand why you are subtracting 1
g.fig.canvas.draw()
g.ax_joint.set_xticklabels(["{:.0f}".format(10**label.get_position()[0]-1)
for label in g.ax_joint.get_xticklabels()]);
You could make use of a FuncFormatter. The benefit would be that the ticks are always drawn right also after resizing the window.
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
import numpy as np
import pandas as pd
import seaborn as sns
def tickformat_pow10(value, tick_number):
return f'{10**value:,.0f}'
# df = ...
g = sns.jointplot(x=np.log10(df['company_size_range'] + 1),
y=np.log10(df['team_size_range'] + 1), kind='kde', color='g')
g.ax_joint.xaxis.set_major_formatter(FuncFormatter(tickformat_pow10))
g.ax_joint.yaxis.set_major_formatter(FuncFormatter(tickformat_pow10))
I’m doing a log-log plot with Seaborn; the data is actually derived from a StackOverflow developer survey. I tried using the built-in log scale, but the results didn’t make sense, so this simply calculates the logs before plotting.
df = pd.DataFrame( {'company_size_range': {7800: 7.0, 7801: 700.0, 7802: 7.0, 7803: 20000.0, 7805: 200.0, 7806: 20000.0, 7808: 2000.0, 7809: 2000.0, 7810: 7.0, 7811: 200.0, 7812: 50.0, 7813: 20000.0, 7816: 2.0, 7819: 200.0, 7820: 2000.0, 7824: 2.0, 7825: 2.0, 7827: 2.0, 7828: 50.0, 7830: 14.0, 7831: 50.0, 7833: 200.0, 7834: 50.0, 7835: 50.0, 7838: 2.0, 7840: 50.0, 7841: 50.0, 7842: 7000.0, 7843: 20000.0, 7844: 14.0, 7846: 2.0, 7850: 20000.0, 7851: 700.0, 7852: 200.0, 7853: 200.0, 7855: 200.0, 7856: 7.0, 7857: 50.0, 7858: 700.0, 7861: 20000.0, 7863: 20000.0, 7865: 20000.0, 7867: 700.0, 7868: 20000.0, 7870: 50.0, 7871: 2000.0, 7872: 50.0, 7873: 20000.0, 7874: 200.0, 7876: 14.0, 7877: 20000.0, 7879: 50.0, 7880: 50.0 }, 'team_size_range': {7800: 7.0, 7801: 7.0, 7802: 7.0, 7803: 2.0, 7805: 7.0, 7806: 2.0, 7808: 7.0, 7809: 7.0, 7810: 2.0, 7811: 17.0, 7812: 7.0, 7813: 2.0, 7816: 2.0, 7819: 7.0, 7820: 30.0, 7824: 2.0, 7825: 2.0, 7827: 2.0, 7828: 2.0, 7830: 2.0, 7831: 7.0, 7833: 2.0, 7834: 2.0, 7835: 7.0, 7838: 2.0, 7840: 7.0, 7841: 30.0, 7842: 7.0, 7843: 7.0, 7844: 2.0, 7846: 2.0, 7850: 7.0, 7851: 11.0, 7852: 7.0, 7853: 7.0, 7855: 2.0, 7856: 7.0, 7857: 7.0, 7858: 11.0, 7861: 7.0, 7863: 2.0, 7865: 30.0, 7867: 7.0, 7868: 7.0, 7870: 2.0, 7871: 17.0, 7872: 7.0, 7873: 17.0, 7874: 7.0, 7876: 2.0, 7877: 7.0, 7879: 17.0, 7880: 7.0}} )
g=sns.jointplot(x=np.log10(df['company_size_range']+1),
y=np.log10(df['team_size_range']+1), kind='kde', color='g')
That’s fine, but the axes show the log values, not the underlying values. The X-axis, for example, is:
-1, 1, 2, 3, 4, 5, 6
So I added this to fix it, using the X position of the labels as the X values:
g.ax_joint.set_xticklabels(["{:.0f}".format(10**label.get_position()[0]-1)
for label in g.ax_joint.get_xticklabels()])
The trouble is the resulting X-axis labels are nonsense:
1, 2, 3, 5, 9, 0, 0, 0
What is going on, and how best to fix it, please?
Try the following by first using the canvas.draw(). Also, I do not understand why you are subtracting 1
g.fig.canvas.draw()
g.ax_joint.set_xticklabels(["{:.0f}".format(10**label.get_position()[0]-1)
for label in g.ax_joint.get_xticklabels()]);
You could make use of a FuncFormatter. The benefit would be that the ticks are always drawn right also after resizing the window.
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
import numpy as np
import pandas as pd
import seaborn as sns
def tickformat_pow10(value, tick_number):
return f'{10**value:,.0f}'
# df = ...
g = sns.jointplot(x=np.log10(df['company_size_range'] + 1),
y=np.log10(df['team_size_range'] + 1), kind='kde', color='g')
g.ax_joint.xaxis.set_major_formatter(FuncFormatter(tickformat_pow10))
g.ax_joint.yaxis.set_major_formatter(FuncFormatter(tickformat_pow10))