Principal components analysis using pandas dataframe

How can I calculate Principal Components Analysis from data in a pandas dataframe?

Most sklearn objects work with pandas dataframes just fine, would something like this work for you?

import pandas as pd
import numpy as np
from sklearn.decomposition import PCA

df = pd.DataFrame(data=np.random.normal(0, 1, (20, 10)))

pca = PCA(n_components=5)
pca.fit(df)

You can access the components themselves with

pca.components_ 

import pandas
from sklearn.decomposition import PCA
import numpy
import matplotlib.pyplot as plot

df = pandas.DataFrame(data=numpy.random.normal(0, 1, (20, 10)))

# You must normalize the data before applying the fit method
df_normalized=(df - df.mean()) / df.std()
pca = PCA(n_components=df.shape[1])
pca.fit(df_normalized)

# Reformat and view results
loadings = pandas.DataFrame(pca.components_.T,
columns=['PC%s' % _ for _ in range(len(df_normalized.columns))],
index=df.columns)
print(loadings)

plot.plot(pca.explained_variance_ratio_)
plot.ylabel('Explained Variance')
plot.xlabel('Components')
plot.show()