1 | initial version |

Use a Python function, either using `def`

, or using `lambda`

.

This will let you evaluate the function, and plot it.

Here, using `lambda`

:

```
sage: W = RealDistribution('lognormal',[1.5, .6])
sage: def N(x): return W.cum_distribution_function(x)
sage: N(4)
0.4248467949573992
sage: plot(N, (x, 0, 10))
```

Here, using `def`

:

```
sage: W = RealDistribution('lognormal',[1.5, .6])
sage: N = lambda x: W.cum_distribution_function(x)
sage: N(4)
0.4248467949573992
sage: plot(N, (x, 0, 10))
```

2 | No.2 Revision |

Use a Python function, either using `def`

, or using `lambda`

.

This will let you evaluate the function, and plot it.

Here, using `lambda`

:

```
sage: W = RealDistribution('lognormal',[1.5, .6])
sage: def N(x): return W.cum_distribution_function(x)
sage: N(4)
0.4248467949573992
sage: plot(N, (x, 0, 10))
```

Here, using `def`

:

```
sage: W = RealDistribution('lognormal',[1.5, .6])
sage: N = lambda x: W.cum_distribution_function(x)
sage: N(4)
0.4248467949573992
sage: plot(N, (x, 0, 10))
```

The down side is you need to do that for every variation based on N.

For instance, to plot `1 - N`

,

```
sage: NN = lambda x: 1 - N(x)
sage: plot (NN, (x, 0, 10))
```

Of course, to eg differentiate it, you would really need a symbolic function.

Maybe someone knows how to turn it into a symbolic function.

Maybe it's worth opening a Sage trac ticket to make cumulative distribution functions symbolic.

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