In addition to its plotting tools, Pandas also offers a convenient `.value_counts()`

method that computes a histogram of non-null values to a Pandas `Series`

:

```
>>> import pandas as pd
>>> data = np.random.choice(np.arange(10), size=10000,
... p=np.linspace(1, 11, 10) / 60)
>>> s = pd.Series(data)
>>> s.value_counts()
9 1831
8 1624
7 1423
6 1323
5 1089
4 888
3 770
2 535
1 347
0 170
dtype: int64
>>> s.value_counts(normalize=True).head()
9 0.1831
8 0.1624
7 0.1423
6 0.1323
5 0.1089
dtype: float64
```

Elsewhere, `pandas.cut()`

is a convenient way to bin values into arbitrary intervals. Let’s say you have some data on ages of individuals and want to bucket them sensibly:

```
>>> ages = pd.Series(
... [1, 1, 3, 5, 8, 10, 12, 15, 18, 18, 19, 20, 25, 30, 40, 51, 52])
>>> bins = (0, 10, 13, 18, 21, np.inf) # The edges
>>> labels = ('child', 'preteen', 'teen', 'military_age', 'adult')
>>> groups = pd.cut(ages, bins=bins, labels=labels)
>>> groups.value_counts()
child 6
adult 5
teen 3
military_age 2
preteen 1
dtype: int64
>>> pd.concat((ages, groups), axis=1).rename(columns={0: 'age', 1: 'group'})
age group
0 1 child
1 1 child
2 3 child
3 5 child
4 8 child
5 10 child
6 12 preteen
7 15 teen
8 18 teen
9 18 teen
10 19 military_age
11 20 military_age
12 25 adult
13 30 adult
14 40 adult
15 51 adult
16 52 adult
```

What’s nice is that both of these operations ultimately utilize Cython code that makes them competitive on speed while maintaining their flexibility.