Supposing that I have a DataFrame along the lines of:
| David | Maxime | |
|---|---|---|
| 0 | cat | duck |
| 1 | dog | cat |
| 2 | horse | duck |
| 3 | cat | dog |
How to obtain the following result ?
| Animals | Total |
|---|---|
| cat | 3 |
| duck | 2 |
| dog | 2 |
| horse | 1 |
I found many codes but not for the case where a result is return according to number of occurrences of datas in unique column.
You can use
pd.concat([df.David.value_counts(), df.Maxine.value_counts()], axis=1).fillna(0).sum(axis=1)
This creates a dataframe with the value counts along each column, then sums the rows.
One option is to melt the dataframe, and use value_counts:
(df.melt(value_name = 'Animal')
.value_counts(subset='Animal')
.reset_index(name='Total')
)
Animal Total
0 cat 3
1 dog 2
2 duck 2
3 horse 1
