# Pca question
**URL:** https://discourse.julialang.org/t/pca-question/63337
**Category:** Optimization (Mathematical)
**Created:** [June 21, 2021, 11:07pm UTC](https://discourse.julialang.org/t/pca-question/63337 "2021-06-21T23:07:51Z")
**Posts on this page:** 3
**Page:** 1
### Author:  [@Kevin\_25](https://discourse.julialang.org/u/Kevin_25)
#### Post date: [June 21, 2021, 11:07pm UTC](https://discourse.julialang.org/t/pca-question/63337/1 "2021-06-21T23:07:51Z")
I was wondering if there is a command or a way to produce the result of the “explained\_variance\_ratio\_” command from scikitlearn in order to plot it.
Thanks.
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### Author:  [@odow](https://discourse.julialang.org/u/odow)
#### Post date: [June 22, 2021, 8:31pm UTC](https://discourse.julialang.org/t/pca-question/63337/2 "2021-06-22T20:31:58Z")
Hi @Kevin_25,
Since this is your first post, please read [Please read: make it easier to help you](https://discourse.julialang.org/t/please-read-make-it-easier-to-help-you/14757).
Unless I’m misunderstanding your question, you’ve stumbled onto a Julia forum instead of a Python/scikit-learn forum. I suggest you consult the scikit-learn documentation or ask on their forums.
---
### Author:  [@ElOceanografo](https://discourse.julialang.org/u/ElOceanografo)
#### Post date: [June 22, 2021, 9:32pm UTC](https://discourse.julialang.org/t/pca-question/63337/3 "2021-06-22T21:32:19Z")
@Kevin_25, if you are talking about PCA as implemented in MultivariateStats.jl, you can get the proportion of total variance explained by each component like so:
```julia
using MultivariateStats
X = randn(10, 1000)
M = fit(PCA, X)
principalvars(M) / tvar(M)
```
See the documentation here: [Principal Component Analysis — MultivariateStats 0.1.0 documentation](https://multivariatestatsjl.readthedocs.io/en/stable/pca.html)
If you’re doing it “by hand,” you can get the same info like this:
```julia
using LinearAlgebra, Statistics
λ, Φ = eigen(cov(X, dims=2))
λ / sum(λ)
```
(Note that `eigen` returns the eigenvalues in ascending order.)