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
import numpy as np

# Generate data.
NROW = 10000
NCOL = 100
df = pd.DataFrame(np.random.randint(1, 100000, (NROW, NCOL)),
                  columns=['col' + x for x in np.arange(NCOL).astype(str)])

I need to count the number of distinct elements for each column, like this:

col0    9538
col1    9505
col2    9524

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?

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

%timeit df.apply(lambda x: len(x.unique())) 10 loops, best of 3: 49.5 ms per loop %timeit df.nunique() 10 loops, best of 3: 59.7 ms per loop %timeit df.apply(pd.Series.nunique) 10 loops, best of 3: 60.3 ms per loop %timeit df.T.apply(lambda x: x.nunique(), axis=1) 10 loops, best of 3: 60.5 ms per loop

As of pandas 0.20 we can use nunique directly on DataFrames, i.e.:

df.nunique()
a    4
b    5
c    1
dtype: int64

Other legacy options:

You could do a transpose of the df and then using apply call nunique row-wise:

In [205]:
df = pd.DataFrame({'a':[0,1,1,2,3],'b':[1,2,3,4,5],'c':[1,1,1,1,1]})
df

Out[205]:
   a  b  c
0  0  1  1
1  1  2  1
2  1  3  1
3  2  4  1
4  3  5  1

In [206]:
df.T.apply(lambda x: x.nunique(), axis=1)

Out[206]:
a    4
b    5
c    1
dtype: int64

EDIT

As pointed out by @ajcr the transpose is unnecessary:

In [208]:
df.apply(pd.Series.nunique)

Out[208]:
a    4
b    5
c    1
dtype: int64

A Pandas.Series has a .value_counts() function that provides exactly what you want to. Check out the documentation for the function.

Already some great answers here :) but this one seems to be missing:

df.apply(lambda x: x.nunique())

As of pandas 0.20.0, DataFrame.nunique() is also available.

Recently, I have same issues of counting unique value of each column in DataFrame, and I found some other function that runs faster than the apply function:

#Select the way how you want to store the output, could be pd.DataFrame or Dict, I will use Dict to demonstrate:
col_uni_val={}
for i in df.columns:
    col_uni_val[i] = len(df[i].unique())

#Import pprint to display dic nicely:
import pprint
pprint.pprint(col_uni_val)

This works for me almost twice faster than df.apply(lambda x: len(x.unique()))