Finding count of distinct elements in DataFrame in each column

I am trying to find the count of distinct values in each column using Pandas. This is what I did.

import pandas as pd

df = pd.read_csv('train.csv')
# print(df)

a = pd.unique(df.values.ravel())
print(a)

It counts unique elements in the DataFrame irrespective of rows/columns, but I need to count for each column with output formatted as below.

policyID              0
statecode             0
county                0
eq_site_limit         0
hu_site_limit         454
fl_site_limit         647
fr_site_limit         0
tiv_2011              0
tiv_2012              0
eq_site_deductible    0
hu_site_deductible    0
fl_site_deductible    0
fr_site_deductible    0
point_latitude        0
point_longitude       0
line                  0
construction          0
point_granularity     0

What would be the most efficient way to do this, as this method will be applied to files which have size greater than 1.5GB?

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Based upon the answers, df.apply(lambda x: len(x.unique())) is the fastest.

In[23]: %timeit df.apply(pd.Series.nunique)
1 loops, best of 3: 1.45 s per loop
In[24]: %timeit df.apply(lambda x: len(x.unique()))
1 loops, best of 3: 335 ms per loop
In[25]: %timeit df.T.apply(lambda x: x.nunique(), axis=1)
1 loops, best of 3: 1.45 s per loop