Why do these plots with same parameters look so different? logging in matplotlib vs seaborn

With seaborn.histplot:

import seaborn as sns

plot = sns.histplot(data = adata.obs, x = 'n_counts',  bins=50, log_scale=True)
plot.set_xlim(1, 100000)

With plt.hist

adata = org_1
data = adata.obs['n_counts']
plt.hist(data, bins=50, range=(1, 100000))
plt.xscale("log")

With plt.hist, but logging the data before passing it to plotting function:
Tangent - how can I get the x axis to be in 10^n notation? (as in first plot)

data = np.log10(adata.obs['n_counts'])
plt.hist(data, bins=50)
plt.xlabel('log nUMI')

With plt.hist, but logging the data before passing it to plotting function, but specifying range to be as in plots 1 and 2:

data = np.log10(adata.obs['n_counts'])
plt.hist(data, bins=50, range = (1, 10000))
plt.xlabel('log nUMI')

(I don't have enough reputation to comment, but I would like to assist)

> With plt.hist

Here you have exactly 50 buckets! They are each 2_000 units. (see how the first bucket ends at 2*10^3) Because you have plotted them on a logscale, the buckets to the left appear unnaturally wide which is an artifact of log axes.

> data = np.log10(adata.obs['n_counts'])

As a general rule in most (if not all) plotting utilities that I have used, taking the log of the x-axis does not lend well to getting the variable tick spacing you desire. If you want the variable tick spacing, don't take the log for the plot, let the plot do it.

> plt.hist(data, bins=50, range = (1, 10000))

Once you take the log, then the range must be scaled back. You need to go from log(1) to log(10000). There are no data points whose value after the log is 10000 (this would imply you had 10000 digit numbers to begin with)

Your plots do not have the same parameters.

When you pass plt.hist some data and ask for 50 bins, it has no way of knowing that you are later going to change the axis scale, so it computes 50 linearly-spaced breaks.

Because you passed log_scale=True to sns.histplot, it knows the scale at the time that it computes the bin breaks, and it can make them evenly spaced in log intervals.

(You could also set the axis scale to log before calling sns.histplot and without passing log_scale=True, but plt.hist does not work this way).

When you log the data first, there is no way for the function to know that the numbers represent log values. So you do get bins that appear evenly spaced (because everything matplotlib does happens on a linear scale now) and represent a lognormal distribution well, but those bins no longer correspond to the range covered by the original data, and you would need to manually change any tick labels to represent the original magnitudes.