Custom Histogram Bins Using the breaks Parameter

import pandas as pd

from lets_plot import *

LetsPlot.setup_html()

df = pd.read_csv("https://raw.githubusercontent.com/JetBrains/lets-plot-docs/refs/heads/master/data/diamonds.csv")
print(df.shape)
df.head()

(53940, 10)

	carat	cut	color	clarity	depth	table	price	x	y	z
0	0.23	Ideal	E	SI2	61.5	55.0	326	3.95	3.98	2.43
1	0.21	Premium	E	SI1	59.8	61.0	326	3.89	3.84	2.31
2	0.23	Good	E	VS1	56.9	65.0	327	4.05	4.07	2.31
3	0.29	Premium	I	VS2	62.4	58.0	334	4.20	4.23	2.63
4	0.31	Good	J	SI2	63.3	58.0	335	4.34	4.35	2.75

Default Bins

ggplot(df, aes("price")) + geom_histogram(color="black", fill="gray80")

Equi-probable Bins

price_bins = df["price"].quantile(q=[i/10 for i in range(11)], interpolation="linear")
ggplot(df, aes("price")) + geom_histogram(breaks=price_bins, color="black", fill="gray80")

Identity Stat

breaks = [2**d for d in range(16)]
bin_x = [(breaks[i] + breaks[i + 1]) / 2.0 for i in range(len(breaks) - 1)]             # bin centers
agg_df = df.assign(
    bin_x=pd.cut(df["price"], bins=breaks, labels=bin_x)                                # bin center, corresponding to current price
).groupby("bin_x", observed=True)["price"].count().to_frame("count").reset_index()      # aggregated dataframe: bin center -> size of bin
agg_df["bin_x"] = agg_df["bin_x"].astype(float)
agg_df

	bin_x	count
0	384.0	2004
1	768.0	13041
2	1536.0	9471
3	3072.0	10474
4	6144.0	11605
5	12288.0	6367
6	24576.0	978

ggplot(agg_df) + \
    geom_histogram(aes("bin_x", "count"), stat='identity', breaks=breaks, color="black", fill="gray80") + \
    xlab("price")