Find the rows in which the (aboslute) value is bigger than
Question:
I have a pandas dataframe like:
one
two
three
1
3
4
2
4
6
1
3
4
10
3
4
2
4
5
0
3
4
-10
3
4
Now observing the first column (labeled ‘one’) I would like to find the rows where the value is bigger than say 9. (in this case it would be the fourth )
Ideally, I also would like to find the rows where the absolute value of the value is bigger than say 9 (so that would be fourth and seventh)
How can I do this? (So far I only covert the columns into series and even into series of truths and false but my problem is that my dataframe is huge and I cannot visually inspect it. I need to get the row numbers automatically
Answers:
you can apply abs and compare and filter by loc:
out = df.loc[df['one'].abs() > 9]
output :
>>>
one two three
3 10 3 4
6 -10 3 4
You could use abs() pandas-abs
df = pd.DataFrame({
'a': [1, 4, -6, 3, 7],
'b': [2, 3, 5, 3, 1],
'c': [4, 2, 7, 1, 3]
})
df[df.a.abs() > 5]
returns two rows 2, 4.
row = {}
for column in df:
row_temp = {}
index = df.loc[df[column].abs()>=9].index
row.update({column:list(index)})
I have a pandas dataframe like:
| one | two | three |
|---|---|---|
| 1 | 3 | 4 |
| 2 | 4 | 6 |
| 1 | 3 | 4 |
| 10 | 3 | 4 |
| 2 | 4 | 5 |
| 0 | 3 | 4 |
| -10 | 3 | 4 |
Now observing the first column (labeled ‘one’) I would like to find the rows where the value is bigger than say 9. (in this case it would be the fourth )
Ideally, I also would like to find the rows where the absolute value of the value is bigger than say 9 (so that would be fourth and seventh)
How can I do this? (So far I only covert the columns into series and even into series of truths and false but my problem is that my dataframe is huge and I cannot visually inspect it. I need to get the row numbers automatically
you can apply abs and compare and filter by loc:
out = df.loc[df['one'].abs() > 9]
output :
>>>
one two three
3 10 3 4
6 -10 3 4
You could use abs() pandas-abs
df = pd.DataFrame({
'a': [1, 4, -6, 3, 7],
'b': [2, 3, 5, 3, 1],
'c': [4, 2, 7, 1, 3]
})
df[df.a.abs() > 5]
returns two rows 2, 4.
row = {}
for column in df:
row_temp = {}
index = df.loc[df[column].abs()>=9].index
row.update({column:list(index)})