Find And Select The Most Frequent Data Of Column In Pandas Dataframe

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 ENE 2011-01-06 ENE

Solution 1:

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]

Solution 2:

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.mstatsimport 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]: 
11.022.0
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.

Solution 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.