Hi,

@ Warren: I was thinking of using federico method as its quite simple. I
know the mu and sigma of log(values) and I need to plot a normal
distribution based on that. Anything inaccurate in doing that?

@ Sebastian: Thanks for your suggestion. I got to know more about powerlaw
distributions.  But, I dont think my values have a long tail. do you think
it is still relevant? What are the potential applications of the same?

Thanks & Regards,
Sanant

On Thu, May 26, 2016 at 7:50 PM, Sebastian Benthall <[email protected]>
wrote:

> You may also be interested in the 'powerlaw' Python package, which detects
> the tail cutoff.
> On May 26, 2016 5:46 AM, "Warren Weckesser" <[email protected]>
> wrote:
>
>>
>>
>> On Thu, May 26, 2016 at 2:08 AM, Startup Hire <[email protected]>
>> wrote:
>>
>>> Hi all,
>>>
>>> Hope you are doing good.
>>>
>>> I am working on a project where I need to do the following things:
>>>
>>> 1. I need to fit a lognormal distribution to a set of values [I know its
>>> lognormal by a simple XY scatter plot in excel]
>>>
>>>
>>
>> The probability distributions in scipy have a fit() method, and
>> scipy.stats.lognorm implements the log-normal distribution (
>> http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.lognorm.html)
>> so you can use scipy.lognorm.fit().  See, for example,
>> http://stackoverflow.com/questions/26406056/a-lognormal-distribution-in-python
>> or http://stackoverflow.com/
>> /questions/15630647/fitting-lognormal-distribution-using-scipy-vs-matlab
>>
>> Warren
>>
>>
>>
>>> 2. I need to find the intersection of the lognormal distribution so that
>>> I can decide cut-off values based on that.
>>>
>>>
>>> Can you guide me on (1) and (2) can be achieved in python?
>>>
>>> Regards,
>>> Sanant
>>>
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>>> https://mail.python.org/mailman/listinfo/scikit-learn
>>>
>>>
>>
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