# staircase.Stairs.hist#

Stairs.hist(bins='unit', closed='left', stat='sum')#

Calculates histogram data for the corresponding step function values

Parameters
bins“unit”, sequence or `pandas.IntervalIndex`

If bins is “unit” then the histogram bins will have unit length and cover the range of step function values. If bins is a sequence, it defines a monotonically increasing array of bin edges. If bins are defined by `pandas.IntervalIndex` they should be non-overlapping and monotonic increasing.

closed{“left”, “right”}, default “left”

Indicates whether the histogram bins are left-closed right-open or right-closed left-open. Only relevant when bins is not a `pandas.IntervalIndex`

stat{“sum”, “frequency”, “density”, “probability”}, default “sum”
The aggregate statistic to compute in each bin. Inspired by `seaborn.histplot()` stat parameter.
• `sum` the magnitude of observations

• `frequency` values of the histogram are divided by the corresponding bin width

• `density` normalises values of the histogram so that the area is 1

• `probability` normalises values so that the histogram values sum to 1

Returns
`pandas.DataFrame`

Examples

```>>> s1.plot(arrows=True)
```
```>>> s1.hist()
[-1, 0)    1.0
[0, 1)     1.0
[1, 2)     2.0
dtype: float64
```
```>>> s1.hist(closed="right")
(-2, -1]    1.0
(-1, 0]     1.0
(0, 1]      2.0
dtype: float64
```
```>>> s1.hist(bins=[-1,1,2])
[-1, 1)    2.0
[1, 2)     2.0
dtype: float64
```
```>>> s1.hist(bins=[-1,1,2], stat="frequency")
[-1, 1)    1.0
[1, 2)     2.0
dtype: float64
```
```>>> s1.hist(bins=[-1,1,2], stat="density")
[-1, 1)    0.333333
[1, 2)     0.333333
dtype: float64
```
```>>> s1.hist(bins=[-1,1,2], stat="probability")
[-1, 1)    0.5
[1, 2)     0.5
dtype: float64
```