Histogram

A histogram shows data distribution with bars.

Histogram

A histogram shows the distribution of a single variable: the value range is split into bins (bins) and the number of examples in each bin is drawn as a bar. Skew, multiple peaks and outliers show up here.

fig, ax = plt.subplots()
ax.hist(x, bins=20, edgecolor="black")
sayi, sinir = np.histogram(x, bins=20)   # same counts, no drawing

np.histogram gives the same bin counts and edges without drawing.

Bin count and comparison

Few bins hide detail, many bins produce noise. To compare classes, histograms are drawn semi-transparent on the same axis:

for k in [0, 1]:
    ax.hist(X[y == k, 0], bins=20, alpha=0.5, label=f"sınıf {k}")
ax.legend()

If the two classes' histograms overlap a lot, that feature is weak at separating the classes.

Exercise Count the distribution

Draw a 4-bin histogram of x and save it. Get the same counts with np.histogram(x, bins=4); print the counts and the edges on separate lines.

Show example solution

import numpy as np
import matplotlib.pyplot as plt

x = np.array([1, 2, 2, 3, 3, 3, 4, 4, 5, 9])

fig, ax = plt.subplots()
ax.hist(x, bins=4)
fig.savefig("hist.png")
sayi, sinir = np.histogram(x, bins=4)
print(sayi)
print(sinir)

Test yourself

How many bin edges does <code>np.histogram(x, bins=10)</code> return?
- 9
- 20
- 11
- 10
Show answer

11 — The starts and ends of 10 bins: 11 edges; the count array has 10 elements.
Show the legend.
```
ax.____()
```
Show answer
```
ax.legend()
```
Match the concepts.

bins

Number of bins
np.histogram

Counting without drawing
label

Legend text
overlapping histogram

Weak separating feature

Next lesson: Line plot

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