I have a DataFrame like this:
index column1 column2 column3
1 30 55 62
2 69 20 40
3 23 62 23
...
May I know how to count the number of values which are > 50 for all elements in the above table?
I'm trying below method:
count = 0
for column in df.items():
count += df[df[column] > 50][column].count()
Is this a proper way to do it? Or any other more effective suggestion?
You can just check all the values at once and then sum() them since True evaluates to 1 and False to 0:
df.gt(50).sum().sum()
(df > 54).values.sum() will do what you're looking for here is the total code to get the results:
>>> df = pd.DataFrame(np.random.randint(0,100,size=(5, 2)), columns=list('AB'))
>>> df
A B
0 68 92
1 47 53
2 5 35
3 75 82
4 51 89
>>> (df > 54).values.sum()
5
>>>
Basically what I'm doing is creating a mask of true false values of the entire data frame based on the condition in this case > 54 and then just rolling up the data frame because true/false is equal to 1/0 when added.
