Matplotlib.pyplot.hist() very slow

Question

I'm plotting about 10,000 items in an array. They are of around 1,000 unique values.

The plotting has been running half an hour now. I made sure rest of the code works.

Is it that slow? This is my first time plotting histograms with pyplot.

Answer 1

To plot histograms using matplotlib quickly you need to pass the histtype='step' argument to pyplot.hist. For example:

plt.hist(np.random.exponential(size=1000000,bins=10000))
plt.show()

takes ~15 seconds to draw and roughly 5-10 seconds to update when you pan or zoom.

In contrast, plotting with histtype='step':

plt.hist(np.random.exponential(size=1000000),bins=10000,histtype='step')
plt.show()

plots almost immediately and can be panned and zoomed with no delay.

Answer 2

It will be instant to plot the histogram after flattening the numpy array. Try the below demo code:

import numpy as np

array2d = np.random.random_sample((512,512))*100
plt.hist(array2d.flatten())
plt.hist(array2d.flatten(), bins=1000)

Answer 3

Importing seaborn somewhere in the code may cause pyplot.hist to take a really long time.

If the problem is seaborn, it can be solved by resetting the matplotlib settings:

import seaborn as sns
sns.reset_orig()

Answer 4

For me, the problem is that the data type of pd.Series, say S, is 'object' rather than 'float64'. After I use S = np.float64(S), then plt.hist(S) is very quick.

Answer 5

I was facing the same problem using Pandas .hist() method. For me the solution was:

pd.to_numeric(df['your_data']).hist()

Which worked instantly.

Answer 6

Since several answers already mention the issue of slowness with pandas.hist(), note that it may be due to dealing with non-numerical data. An issue easily solved by using value_counts() :

df['colour'].value_counts().plot(kind='bar')

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