python - When is it appropriate to use df.value_counts() vs df.groupby('...').count()?

Question

I've heard in Pandas there's often multiple ways to do the same thing, but I was wondering –

If I'm trying to group data by a value within a specific column and count the number of items with that value, when does it make sense to use df.groupby('colA').count() and when does it make sense to use df['colA'].value_counts() ?

Answer 1

There is difference value_counts return:

The resulting object will be in descending order so that the first element is the most frequently-occurring element.

but count not, it sort output by index (created by column in groupby('col')).

---

df.groupby('colA').count() 

is for aggregate all columns of df by function count. So it count values excluding NaNs.

So if need count only one column need:

df.groupby('colA')['colA'].count() 

Sample:

df = pd.DataFrame({'colB':list('abcdefg'),
                   'colC':[1,3,5,7,np.nan,np.nan,4],
                   'colD':[np.nan,3,6,9,2,4,np.nan],
                   'colA':['c','c','b','a',np.nan,'b','b']})

print (df)
  colA colB  colC  colD
0    c    a   1.0   NaN
1    c    b   3.0   3.0
2    b    c   5.0   6.0
3    a    d   7.0   9.0
4  NaN    e   NaN   2.0
5    b    f   NaN   4.0
6    b    g   4.0   NaN

print (df['colA'].value_counts())
b    3
c    2
a    1
Name: colA, dtype: int64

print (df.groupby('colA').count())
      colB  colC  colD
colA                  
a        1     1     1
b        3     2     2
c        2     2     1

print (df.groupby('colA')['colA'].count())
colA
a    1
b    3
c    2
Name: colA, dtype: int64