Understand A value is trying to be set on a copy of a slice from a DataFrame
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3
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I am trying to understand when do I get a copy vs view when using .loc method to index the dataframe. Getting a view to me means any changes on view will be carried over to the original df. I expect using .loc gives me a copy.
In below example, I am indexing the df using the same method, but one gives me a copy, the other one gives me a view.
Can someone point out what I am missing?
a = pd.DataFrame([[1,2, 1], [2, 4,1], [3,4,1]])
b = a.loc[:, 1:3]
b.loc[:, 'tag'] = 'test' # this is a copy?
b seems to be a copy, since the 'tag' column shows only on b, not on a.
b
Out[70]:
1 2 tag
0 2 1 test
1 4 1 test
2 4 1 test
a
Out[71]:
0 1 2
0 1 2 1
1 2 4 1
2 3 4 1
c = a.loc[0:1, :]
c.loc[0, :] = 100 # why this is a view??
c
c seems to be a view of b. Because the changes on b is carried over to a.
Out[74]: c
0 1 2
0 100 100 100
1 2 4 1
a
Out[75]: a
0 1 2
0 100 100 100
1 2 4 1
2 3 4 1
Any why a.loc[:, 1:3] gives me 3 rows but a.loc[0:1, :] gives me 2 rows??
python pandas
asked Oct 17 at 17:10
Lisa
85311432
add a comment |
up vote
3
down vote
favorite
I am trying to understand when do I get a copy vs view when using .loc method to index the dataframe. Getting a view to me means any changes on view will be carried over to the original df. I expect using .loc gives me a copy.
In below example, I am indexing the df using the same method, but one gives me a copy, the other one gives me a view.
Can someone point out what I am missing?
a = pd.DataFrame([[1,2, 1], [2, 4,1], [3,4,1]])
b = a.loc[:, 1:3]
b.loc[:, 'tag'] = 'test' # this is a copy?
b seems to be a copy, since the 'tag' column shows only on b, not on a.
b
Out[70]:
1 2 tag
0 2 1 test
1 4 1 test
2 4 1 test
a
Out[71]:
0 1 2
0 1 2 1
1 2 4 1
2 3 4 1
c = a.loc[0:1, :]
c.loc[0, :] = 100 # why this is a view??
c
c seems to be a view of b. Because the changes on b is carried over to a.
Out[74]: c
0 1 2
0 100 100 100
1 2 4 1
a
Out[75]: a
0 1 2
0 100 100 100
1 2 4 1
2 3 4 1
Any why a.loc[:, 1:3] gives me 3 rows but a.loc[0:1, :] gives me 2 rows??
python pandas
asked Oct 17 at 17:10
Lisa
85311432
2
See pandas.pydata.org/pandas-docs/stable/… "Outside of simple cases, it’s very hard to predict whether it will return a view or a copy (it depends on the memory layout of the array, about which pandas makes no guarantees), and therefore whether the setitem will modify dfmi or a temporary object that gets thrown out immediately afterward. That’s what SettingWithCopy is warning you about!"
– ayhan
Oct 17 at 17:18
Also github site github.com/pandas-dev/pandas/issues/6149
– W-B
Oct 17 at 17:21
add a comment |
up vote
3
down vote
favorite
up vote
3
down vote
favorite
I am trying to understand when do I get a copy vs view when using .loc method to index the dataframe. Getting a view to me means any changes on view will be carried over to the original df. I expect using .loc gives me a copy.
In below example, I am indexing the df using the same method, but one gives me a copy, the other one gives me a view.
Can someone point out what I am missing?
a = pd.DataFrame([[1,2, 1], [2, 4,1], [3,4,1]])
b = a.loc[:, 1:3]
b.loc[:, 'tag'] = 'test' # this is a copy?
b seems to be a copy, since the 'tag' column shows only on b, not on a.
b
Out[70]:
1 2 tag
0 2 1 test
1 4 1 test
2 4 1 test
a
Out[71]:
0 1 2
0 1 2 1
1 2 4 1
2 3 4 1
c = a.loc[0:1, :]
c.loc[0, :] = 100 # why this is a view??
c
c seems to be a view of b. Because the changes on b is carried over to a.
Out[74]: c
0 1 2
0 100 100 100
1 2 4 1
a
Out[75]: a
0 1 2
0 100 100 100
1 2 4 1
2 3 4 1
Any why a.loc[:, 1:3] gives me 3 rows but a.loc[0:1, :] gives me 2 rows??
python pandas
asked Oct 17 at 17:10
Lisa
85311432
I am trying to understand when do I get a copy vs view when using .loc method to index the dataframe. Getting a view to me means any changes on view will be carried over to the original df. I expect using .loc gives me a copy.
In below example, I am indexing the df using the same method, but one gives me a copy, the other one gives me a view.
Can someone point out what I am missing?
a = pd.DataFrame([[1,2, 1], [2, 4,1], [3,4,1]])
b = a.loc[:, 1:3]
b.loc[:, 'tag'] = 'test' # this is a copy?
b seems to be a copy, since the 'tag' column shows only on b, not on a.
b
Out[70]:
1 2 tag
0 2 1 test
1 4 1 test
2 4 1 test
a
Out[71]:
0 1 2
0 1 2 1
1 2 4 1
2 3 4 1
c = a.loc[0:1, :]
c.loc[0, :] = 100 # why this is a view??
c
c seems to be a view of b. Because the changes on b is carried over to a.
Out[74]: c
0 1 2
0 100 100 100
1 2 4 1
a
Out[75]: a
0 1 2
0 100 100 100
1 2 4 1
2 3 4 1
Any why a.loc[:, 1:3] gives me 3 rows but a.loc[0:1, :] gives me 2 rows??
python pandas
python pandas
asked Oct 17 at 17:10
Lisa
85311432
asked Oct 17 at 17:10
Lisa
85311432
edited Nov 20 at 4:21
asked Oct 17 at 17:10
Lisa
85311432
asked Oct 17 at 17:10
Lisa
85311432
asked Oct 17 at 17:10
Lisa
85311432
85311432
2
See pandas.pydata.org/pandas-docs/stable/… "Outside of simple cases, it’s very hard to predict whether it will return a view or a copy (it depends on the memory layout of the array, about which pandas makes no guarantees), and therefore whether the setitem will modify dfmi or a temporary object that gets thrown out immediately afterward. That’s what SettingWithCopy is warning you about!"
– ayhan
Oct 17 at 17:18
Also github site github.com/pandas-dev/pandas/issues/6149
– W-B
Oct 17 at 17:21
add a comment |
2
See pandas.pydata.org/pandas-docs/stable/… "Outside of simple cases, it’s very hard to predict whether it will return a view or a copy (it depends on the memory layout of the array, about which pandas makes no guarantees), and therefore whether the setitem will modify dfmi or a temporary object that gets thrown out immediately afterward. That’s what SettingWithCopy is warning you about!"
– ayhan
Oct 17 at 17:18
Also github site github.com/pandas-dev/pandas/issues/6149
– W-B
Oct 17 at 17:21
2
2
See pandas.pydata.org/pandas-docs/stable/… "Outside of simple cases, it’s very hard to predict whether it will return a view or a copy (it depends on the memory layout of the array, about which pandas makes no guarantees), and therefore whether the setitem will modify dfmi or a temporary object that gets thrown out immediately afterward. That’s what SettingWithCopy is warning you about!"
– ayhan
Oct 17 at 17:18
See pandas.pydata.org/pandas-docs/stable/… "Outside of simple cases, it’s very hard to predict whether it will return a view or a copy (it depends on the memory layout of the array, about which pandas makes no guarantees), and therefore whether the setitem will modify dfmi or a temporary object that gets thrown out immediately afterward. That’s what SettingWithCopy is warning you about!"
– ayhan
Oct 17 at 17:18
Also github site github.com/pandas-dev/pandas/issues/6149
– W-B
Oct 17 at 17:21
Also github site github.com/pandas-dev/pandas/issues/6149
– W-B
Oct 17 at 17:21
add a comment |
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2
See pandas.pydata.org/pandas-docs/stable/… "Outside of simple cases, it’s very hard to predict whether it will return a view or a copy (it depends on the memory layout of the array, about which pandas makes no guarantees), and therefore whether the setitem will modify dfmi or a temporary object that gets thrown out immediately afterward. That’s what SettingWithCopy is warning you about!"
– ayhan
Oct 17 at 17:18
Also github site github.com/pandas-dev/pandas/issues/6149
– W-B
Oct 17 at 17:21