find and select the most frequent data of column in pandas DataFrame

Question

I have a dataframe with the following column:

file[\'DirViento\']

Fecha
2011-01-01    ENE
2011-01-02    ENE
2011-01-03    ENE
2011-01-04    NNE 
2011-01-05          


        

Answer 1

This is not as straightforward as it could be (should be).

As you probably know, the statistics jargon for the most common value is the "mode." Numpy does not have a built-in function for this, but scipy does. Import it like so:

from scipy.stats.mstats import mode

It does more than simply return the most common value, as you can read about in the docs, so it's convenient to define a function that uses mode to just get the most common value.

f = lambda x: mode(x, axis=None)[0]

And now, instead of value_counts(), use apply(f). Here is an example:

In [20]: DataFrame([1,1,2,2,2,3], index=[1,1,1,2,2,2]).groupby(level=0).apply(f)
Out[20]: 
1    1.0
2    2.0
dtype: object

Update: Scipy's mode does not work with strings. For your string data, you'll need to define a more general mode function. This answer should do the trick.

Answer 2

Pandas 0.15.2 has a DataFrame.mode() method. It might be of use to someone looking for this as I was.

Here are the docs.

Edit: For the Value:

DataFrame.mode()[0]

Answer 3

1. For whole dataframe, you can use:

   dataframe.mode()
   

2. For specific column:

   dataframe.mode()['Column'][0]
   

Second case is more useful in imputing the values.