Is there a way to tell matplotlib to "normalize" a histogram such that its area equals a specified value (other than 1)?

The option "normed = 0" in

```
n, bins, patches = plt.hist(x, 50, normed=0, histtype='stepfilled')
```

just brings it back to a frequency distribution.

Just calculate it and normalize it to any value you'd like, then use `bar`

to plot the histogram.

On a side note, this will normalize things such that the *area* of all the bars is `normed_value`

. The raw sum will *not* be `normed_value`

(though it's easy to have that be the case, if you'd like).

E.g.

```
import numpy as np
import matplotlib.pyplot as plt
x = np.random.random(100)
normed_value = 2
hist, bins = np.histogram(x, bins=20, density=True)
widths = np.diff(bins)
hist *= normed_value
plt.bar(bins[:-1], hist, widths)
plt.show()
```

So, in this case, if we were to integrate (sum the height multiplied by the width) the bins, we'd get 2.0 instead of 1.0. (i.e. `(hist * widths).sum()`

will yield `2.0`

)

You can pass a `weights`

argument to `hist`

instead of using `normed`

. For example, if your bins cover the interval `[minval, maxval]`

, you have `n`

bins, and you want to normalize the area to `A`

, then I think

```
weights = np.empty_like(x)
weights.fill(A * n / (maxval-minval) / x.size)
plt.hist(x, bins=n, range=(minval, maxval), weights=weights)
```

should do the trick.

EDIT: The `weights`

argument must be the same size as `x`

, and its effect is to make each value in x contribute the corresponding value in `weights`

towards the bin count, instead of 1.

I think the `hist`

function could probably do with a greater ability to control normalization, though. For example, I think as it stands, values outside the binned range are ignored when normalizing, which isn't generally what you want.

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