Count True/False/Null in columns per row in Pandas DataFrame

For Pandas 1.5

I have a DataFrame like this:

| A     | B     | C     | D     |
| ----- | ----- | ----- | ----- |
| False | True  | 63.56 | True  |
| False | True  | 57.13 | <NA>  |
| True  | True  | 87.31 | True  |
| False | <NA>  | 71.09 | True  |
| False | False | 11.78 | False |
| <NA>  | False | 5.07  | True  |
| True  | False | 5.11  | True  |
| False | False | 9.55  | True  |

The value types of column A/B/D are nullable Boolean (True/False/null).

I want to count the total numbers of True/False/null in Column A/B/D for every row like this:

| A     | B     | C     | D     | TrueCount | FalseCount | NullCount |
| ----- | ----- | ----- | ----- | --------- | ---------- | --------- |
| False | True  | 63.56 | True  | 2         | 1          | 0         |
| False | True  | 57.13 | <NA>  | 1         | 1          | 1         |
| True  | True  | 87.31 | True  | 3         | 0          | 0         |
| False | <NA>  | 71.09 | True  | 1         | 1          | 1         |
| False | False | 11.78 | False | 0         | 3          | 0         |
| <NA>  | False | 5.07  | True  | 1         | 1          | 1         |
| True  | False | 5.11  | True  | 2         | 1          | 0         |
| False | False | 9.55  | True  | 1         | 2          | 0         |

Is there any method/way similar to COUNTIF() in Excel which could let me write code like this pseudo-code: df['TrueCount'] = df.COUNTIF(columns=['A','B','D'], True).

P.S. I have large data (100k+ rows), so I also need to care the performance.

>Solution :

You can just sum across axis=1

 df['TrueCount'] = df[['A', 'B', 'D']].sum(axis=1)
 df['FalseCount'] = (df==False).sum(axis=1)
 df['NullCount '] = df.isna().sum(axis=1)