Columns greater than a threshold
How can I retrieve the columns in which at least once appears a value < threshold?
For instance:
THRESHOLD = 0
print(df)
Col_1 Col_2 Col_3 Col_4
1 3 5 -9
1 3 5 -9
1 -2 5 -9
print(final_df)
Col_2 Col_4
3 -9
3 -9
-2 -9
I tried with:
df[(df < 0).any(1)]
But it reports the rows, not the columns, in which at least one element < 0 appears.
python pandas threshold
edited Nov 23 '18 at 15:20
D Manokhin
599219
asked Nov 23 '18 at 15:16
Alessandro CeccarelliAlessandro Ceccarelli
259211
add a comment |
How can I retrieve the columns in which at least once appears a value < threshold?
For instance:
THRESHOLD = 0
print(df)
Col_1 Col_2 Col_3 Col_4
1 3 5 -9
1 3 5 -9
1 -2 5 -9
print(final_df)
Col_2 Col_4
3 -9
3 -9
-2 -9
I tried with:
df[(df < 0).any(1)]
But it reports the rows, not the columns, in which at least one element < 0 appears.
python pandas threshold
edited Nov 23 '18 at 15:20
D Manokhin
599219
asked Nov 23 '18 at 15:16
Alessandro CeccarelliAlessandro Ceccarelli
259211
you can transpose but it's probably not the best solution.df.T[(df.T < 0).any(1)].T
– gyx-hh
Nov 23 '18 at 15:21
add a comment |
How can I retrieve the columns in which at least once appears a value < threshold?
For instance:
THRESHOLD = 0
print(df)
Col_1 Col_2 Col_3 Col_4
1 3 5 -9
1 3 5 -9
1 -2 5 -9
print(final_df)
Col_2 Col_4
3 -9
3 -9
-2 -9
I tried with:
df[(df < 0).any(1)]
But it reports the rows, not the columns, in which at least one element < 0 appears.
python pandas threshold
edited Nov 23 '18 at 15:20
D Manokhin
599219
asked Nov 23 '18 at 15:16
Alessandro CeccarelliAlessandro Ceccarelli
259211
How can I retrieve the columns in which at least once appears a value < threshold?
For instance:
THRESHOLD = 0
print(df)
Col_1 Col_2 Col_3 Col_4
1 3 5 -9
1 3 5 -9
1 -2 5 -9
print(final_df)
Col_2 Col_4
3 -9
3 -9
-2 -9
I tried with:
df[(df < 0).any(1)]
But it reports the rows, not the columns, in which at least one element < 0 appears.
python pandas threshold
python pandas threshold
edited Nov 23 '18 at 15:20
D Manokhin
599219
asked Nov 23 '18 at 15:16
Alessandro CeccarelliAlessandro Ceccarelli
259211
edited Nov 23 '18 at 15:20
D Manokhin
599219
asked Nov 23 '18 at 15:16
Alessandro CeccarelliAlessandro Ceccarelli
259211
edited Nov 23 '18 at 15:20
D Manokhin
599219
edited Nov 23 '18 at 15:20
D Manokhin
599219
edited Nov 23 '18 at 15:20
D Manokhin
599219
599219
asked Nov 23 '18 at 15:16
Alessandro CeccarelliAlessandro Ceccarelli
259211
asked Nov 23 '18 at 15:16
Alessandro CeccarelliAlessandro Ceccarelli
259211
asked Nov 23 '18 at 15:16
Alessandro CeccarelliAlessandro Ceccarelli
259211
259211
you can transpose but it's probably not the best solution.df.T[(df.T < 0).any(1)].T
– gyx-hh
Nov 23 '18 at 15:21
add a comment |
you can transpose but it's probably not the best solution.df.T[(df.T < 0).any(1)].T
– gyx-hh
Nov 23 '18 at 15:21
you can transpose but it's probably not the best solution.
df.T[(df.T < 0).any(1)].T – gyx-hh
Nov 23 '18 at 15:21
you can transpose but it's probably not the best solution.
df.T[(df.T < 0).any(1)].T – gyx-hh
Nov 23 '18 at 15:21
add a comment |
2 Answers
2
active
oldest
votes
You can issue df.loc[:, (df < 0).any(0)].
>>> df
Col_1 Col_2 Col_3 Col_4
0 1 3 5 -9
1 1 3 5 -9
2 1 -2 5 -9
>>>
>>> df.loc[:, (df < 0).any(0)]
Col_2 Col_4
0 3 -9
1 3 -9
2 -2 -9
Details:
(df < 0).any(0) will give you the columns that have a value lower than zero, because any(0) operates along the rows.
>>> df < 0
Col_1 Col_2 Col_3 Col_4
0 False False False True
1 False False False True
2 False True False True
>>>
>>> (df < 0).any(0)
Col_1 False
Col_2 True
Col_3 False
Col_4 True
dtype: bool
Then df.loc[:, (df < 0).any(0)] selects all rows and the columns for which df < 0).any(0) is True by boolean indexing.
answered Nov 23 '18 at 15:22
timgebtimgeb
50.8k116493
add a comment |
Using axis=0 with .loc
df.loc[:,(df < 0).any(0)]
Out[215]:
Col_2 Col_4
0 3 -9
1 3 -9
2 -2 -9
Or we using .iloc with nonzero
df.iloc[:,(df<0).any().nonzero()[0]]
Out[230]:
Col_2 Col_4
0 3 -9
1 3 -9
2 -2 -9
answered Nov 23 '18 at 15:22
Wen-BenWen-Ben
110k83266
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
You can issue df.loc[:, (df < 0).any(0)].
>>> df
Col_1 Col_2 Col_3 Col_4
0 1 3 5 -9
1 1 3 5 -9
2 1 -2 5 -9
>>>
>>> df.loc[:, (df < 0).any(0)]
Col_2 Col_4
0 3 -9
1 3 -9
2 -2 -9
Details:
(df < 0).any(0) will give you the columns that have a value lower than zero, because any(0) operates along the rows.
>>> df < 0
Col_1 Col_2 Col_3 Col_4
0 False False False True
1 False False False True
2 False True False True
>>>
>>> (df < 0).any(0)
Col_1 False
Col_2 True
Col_3 False
Col_4 True
dtype: bool
Then df.loc[:, (df < 0).any(0)] selects all rows and the columns for which df < 0).any(0) is True by boolean indexing.
answered Nov 23 '18 at 15:22
timgebtimgeb
50.8k116493
add a comment |
You can issue df.loc[:, (df < 0).any(0)].
>>> df
Col_1 Col_2 Col_3 Col_4
0 1 3 5 -9
1 1 3 5 -9
2 1 -2 5 -9
>>>
>>> df.loc[:, (df < 0).any(0)]
Col_2 Col_4
0 3 -9
1 3 -9
2 -2 -9
Details:
(df < 0).any(0) will give you the columns that have a value lower than zero, because any(0) operates along the rows.
>>> df < 0
Col_1 Col_2 Col_3 Col_4
0 False False False True
1 False False False True
2 False True False True
>>>
>>> (df < 0).any(0)
Col_1 False
Col_2 True
Col_3 False
Col_4 True
dtype: bool
Then df.loc[:, (df < 0).any(0)] selects all rows and the columns for which df < 0).any(0) is True by boolean indexing.
answered Nov 23 '18 at 15:22
timgebtimgeb
50.8k116493
add a comment |
You can issue df.loc[:, (df < 0).any(0)].
>>> df
Col_1 Col_2 Col_3 Col_4
0 1 3 5 -9
1 1 3 5 -9
2 1 -2 5 -9
>>>
>>> df.loc[:, (df < 0).any(0)]
Col_2 Col_4
0 3 -9
1 3 -9
2 -2 -9
Details:
(df < 0).any(0) will give you the columns that have a value lower than zero, because any(0) operates along the rows.
>>> df < 0
Col_1 Col_2 Col_3 Col_4
0 False False False True
1 False False False True
2 False True False True
>>>
>>> (df < 0).any(0)
Col_1 False
Col_2 True
Col_3 False
Col_4 True
dtype: bool
Then df.loc[:, (df < 0).any(0)] selects all rows and the columns for which df < 0).any(0) is True by boolean indexing.
answered Nov 23 '18 at 15:22
timgebtimgeb
50.8k116493
You can issue df.loc[:, (df < 0).any(0)].
>>> df
Col_1 Col_2 Col_3 Col_4
0 1 3 5 -9
1 1 3 5 -9
2 1 -2 5 -9
>>>
>>> df.loc[:, (df < 0).any(0)]
Col_2 Col_4
0 3 -9
1 3 -9
2 -2 -9
Details:
(df < 0).any(0) will give you the columns that have a value lower than zero, because any(0) operates along the rows.
>>> df < 0
Col_1 Col_2 Col_3 Col_4
0 False False False True
1 False False False True
2 False True False True
>>>
>>> (df < 0).any(0)
Col_1 False
Col_2 True
Col_3 False
Col_4 True
dtype: bool
Then df.loc[:, (df < 0).any(0)] selects all rows and the columns for which df < 0).any(0) is True by boolean indexing.
answered Nov 23 '18 at 15:22
timgebtimgeb
50.8k116493
answered Nov 23 '18 at 15:22
timgebtimgeb
50.8k116493
answered Nov 23 '18 at 15:22
timgebtimgeb
50.8k116493
answered Nov 23 '18 at 15:22
timgebtimgeb
50.8k116493
50.8k116493
add a comment |
add a comment |
Using axis=0 with .loc
df.loc[:,(df < 0).any(0)]
Out[215]:
Col_2 Col_4
0 3 -9
1 3 -9
2 -2 -9
Or we using .iloc with nonzero
df.iloc[:,(df<0).any().nonzero()[0]]
Out[230]:
Col_2 Col_4
0 3 -9
1 3 -9
2 -2 -9
answered Nov 23 '18 at 15:22
Wen-BenWen-Ben
110k83266
add a comment |
Using axis=0 with .loc
df.loc[:,(df < 0).any(0)]
Out[215]:
Col_2 Col_4
0 3 -9
1 3 -9
2 -2 -9
Or we using .iloc with nonzero
df.iloc[:,(df<0).any().nonzero()[0]]
Out[230]:
Col_2 Col_4
0 3 -9
1 3 -9
2 -2 -9
answered Nov 23 '18 at 15:22
Wen-BenWen-Ben
110k83266
add a comment |
Using axis=0 with .loc
df.loc[:,(df < 0).any(0)]
Out[215]:
Col_2 Col_4
0 3 -9
1 3 -9
2 -2 -9
Or we using .iloc with nonzero
df.iloc[:,(df<0).any().nonzero()[0]]
Out[230]:
Col_2 Col_4
0 3 -9
1 3 -9
2 -2 -9
answered Nov 23 '18 at 15:22
Wen-BenWen-Ben
110k83266
Using axis=0 with .loc
df.loc[:,(df < 0).any(0)]
Out[215]:
Col_2 Col_4
0 3 -9
1 3 -9
2 -2 -9
Or we using .iloc with nonzero
df.iloc[:,(df<0).any().nonzero()[0]]
Out[230]:
Col_2 Col_4
0 3 -9
1 3 -9
2 -2 -9
answered Nov 23 '18 at 15:22
Wen-BenWen-Ben
110k83266
edited Nov 23 '18 at 15:27
answered Nov 23 '18 at 15:22
Wen-BenWen-Ben
110k83266
answered Nov 23 '18 at 15:22
Wen-BenWen-Ben
110k83266
answered Nov 23 '18 at 15:22
Wen-BenWen-Ben
110k83266
110k83266
add a comment |
add a comment |
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you can transpose but it's probably not the best solution.
df.T[(df.T < 0).any(1)].T– gyx-hh
Nov 23 '18 at 15:21