CODEWITHSUNDEEP
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Eric Broda
Eric Broda

Reputation: 7231

Find a python transformation function or numpy matrix to transform skewed normal distribution to normal distribution

I have an input dataset (DataFrame / numpy matrix) that has a skewed normal distribution. I am trying to find the python transformation function (or numpy matrix) which will transform the input dataset to a normal distribution with no skew.

I have looked at curve_fit (in scipy.optimize) and am not sure how I would go about applying it.

Is there a simple method of doing this?

Upvotes: 2

Views: 1357

Answers (1)

piRSquared
piRSquared

Reputation: 294228

I've done one of 2 things:

  • Use box-cox transformations. This requires you find the appropriate power or lambda that transforms you data to having zero skew.
  • Force a normal distribution.

Example

from scipy.stats import norm

df = pd.DataFrame(np.random.rand(1000), columns=['Uniform'])
df['Normal'] = norm.ppf((df.Uniform.rank() - .5) / len(df))
df.plot(kind='kde')

enter image description here

df.skew()

Uniform    2.392991e-02
Normal     2.114051e-15
dtype: float64

Upvotes: 1

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