如何对熊猫中的多个列进行分组

Question

I have the following sample dataframe in Python pandas:

+---+------+------+------+
|   | col1 | col2 | col3 |
+---+------+------+------+
| 0 |   a  |   d  |   b  |
+---+------+------+------+
| 1 |   a  |   c  |   b  |
+---+------+------+------+
| 2 |   c  |   b  |   c  |
+---+------+------+------+
| 3 |   b  |   b  |   c  |
+---+------+------+------+
| 4 |   a  |   a  |   d  |
+---+------+------+------+

I would like to perform a count of all the 'a,' 'b,' 'c,' and 'd' values across columns 1-3 so that I would end up with a dataframe like this:

+---+--------+-------+
|   | letter | count |
+---+--------+-------+
| 0 |    a   |   4   |
+---+--------+-------+
| 1 |    b   |   5   |
+---+--------+-------+
| 2 |    c   |   4   |
+---+--------+-------+
| 3 |    d   |   2   |
+---+--------+-------+

One way I can do this is stack the columns on top of each other and THEN do a groupby count, but I feel like there has to be a better way. Can someone help me with this?

Answer 1

You can stack() the dataframe to put all columns into rows and then do value_counts :

df.stack().value_counts()

b    5
c    4
a    4
d    2
dtype: int64

Answer 2

You can apply value_counts with sum :

print (df.apply(pd.value_counts))
   col1  col2  col3
a   3.0     1   NaN
b   1.0     2   2.0
c   1.0     1   2.0
d   NaN     1   1.0

df1 = df.apply(pd.value_counts).sum(1).reset_index()
df1.columns = ['letter','count']
df1['count'] = df1['count'].astype(int)
print (df1)
  letter  count
0      a      4
1      b      5
2      c      4
3      d      2