Fill missing value by averaging previous row value
7
I want to fill missing value with the average of previous N row value, example is shown below:
N=2
df = pd.DataFrame([[np.nan, 2, np.nan, 0],
[3, 4, np.nan, 1],
[np.nan, np.nan, np.nan, 5],
[np.nan, 3, np.nan, np.nan]],
columns=list('ABCD'))
DataFrame is like:
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 NaN NaN NaN 5
3 NaN 3.0 NaN NaN
Result should be:
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 NaN (4+2)/2 NaN 5
3 NaN 3.0 NaN (1+5)/2
I am wondering if there is elegant and fast way to achieve this without for loop.
python python-3.x pandas
edited Nov 16 '18 at 10:57
jpp
102k2165116
asked Nov 16 '18 at 10:48
GarveyGarvey
419314
add a comment |
7
I want to fill missing value with the average of previous N row value, example is shown below:
N=2
df = pd.DataFrame([[np.nan, 2, np.nan, 0],
[3, 4, np.nan, 1],
[np.nan, np.nan, np.nan, 5],
[np.nan, 3, np.nan, np.nan]],
columns=list('ABCD'))
DataFrame is like:
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 NaN NaN NaN 5
3 NaN 3.0 NaN NaN
Result should be:
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 NaN (4+2)/2 NaN 5
3 NaN 3.0 NaN (1+5)/2
I am wondering if there is elegant and fast way to achieve this without for loop.
python python-3.x pandas
edited Nov 16 '18 at 10:57
jpp
102k2165116
asked Nov 16 '18 at 10:48
GarveyGarvey
419314
add a comment |
7
7
7
0
I want to fill missing value with the average of previous N row value, example is shown below:
N=2
df = pd.DataFrame([[np.nan, 2, np.nan, 0],
[3, 4, np.nan, 1],
[np.nan, np.nan, np.nan, 5],
[np.nan, 3, np.nan, np.nan]],
columns=list('ABCD'))
DataFrame is like:
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 NaN NaN NaN 5
3 NaN 3.0 NaN NaN
Result should be:
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 NaN (4+2)/2 NaN 5
3 NaN 3.0 NaN (1+5)/2
I am wondering if there is elegant and fast way to achieve this without for loop.
python python-3.x pandas
edited Nov 16 '18 at 10:57
jpp
102k2165116
asked Nov 16 '18 at 10:48
GarveyGarvey
419314
I want to fill missing value with the average of previous N row value, example is shown below:
N=2
df = pd.DataFrame([[np.nan, 2, np.nan, 0],
[3, 4, np.nan, 1],
[np.nan, np.nan, np.nan, 5],
[np.nan, 3, np.nan, np.nan]],
columns=list('ABCD'))
DataFrame is like:
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 NaN NaN NaN 5
3 NaN 3.0 NaN NaN
Result should be:
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 NaN (4+2)/2 NaN 5
3 NaN 3.0 NaN (1+5)/2
I am wondering if there is elegant and fast way to achieve this without for loop.
python python-3.x pandas
python python-3.x pandas
edited Nov 16 '18 at 10:57
jpp
102k2165116
asked Nov 16 '18 at 10:48
GarveyGarvey
419314
edited Nov 16 '18 at 10:57
jpp
102k2165116
asked Nov 16 '18 at 10:48
GarveyGarvey
419314
edited Nov 16 '18 at 10:57
jpp
102k2165116
edited Nov 16 '18 at 10:57
jpp
102k2165116
edited Nov 16 '18 at 10:57
jpp
102k2165116
102k2165116
asked Nov 16 '18 at 10:48
GarveyGarvey
419314
asked Nov 16 '18 at 10:48
GarveyGarvey
419314
asked Nov 16 '18 at 10:48
GarveyGarvey
419314
419314
add a comment |
add a comment |
1 Answer
1
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oldest
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6
rolling + mean + shift
You will need to modify the below logic to interpret the mean of NaN and another value, in the case where one of the previous two values are null.
df = df.fillna(df.rolling(2).mean().shift())
print(df)
A B C D
0 NaN 2.0 NaN 0.0
1 3.0 4.0 NaN 1.0
2 NaN 3.0 NaN 5.0
3 NaN 3.0 NaN 3.0
answered Nov 16 '18 at 10:57
jppjpp
102k2165116
seems rolling(2) will not consider the all previous n values if the rows are greater than 4 :-| @jpp
– Naga Kiran
Nov 16 '18 at 11:50
1
@NagaKiran, Yes, it does. The issue is withNaNtreatment. OP hasn't defined what the average ofNaNand another value should be, so I haven't second-guessed.
– jpp
Nov 16 '18 at 11:50
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
6
rolling + mean + shift
You will need to modify the below logic to interpret the mean of NaN and another value, in the case where one of the previous two values are null.
df = df.fillna(df.rolling(2).mean().shift())
print(df)
A B C D
0 NaN 2.0 NaN 0.0
1 3.0 4.0 NaN 1.0
2 NaN 3.0 NaN 5.0
3 NaN 3.0 NaN 3.0
answered Nov 16 '18 at 10:57
jppjpp
102k2165116
seems rolling(2) will not consider the all previous n values if the rows are greater than 4 :-| @jpp
– Naga Kiran
Nov 16 '18 at 11:50
1
@NagaKiran, Yes, it does. The issue is withNaNtreatment. OP hasn't defined what the average ofNaNand another value should be, so I haven't second-guessed.
– jpp
Nov 16 '18 at 11:50
add a comment |
6
rolling + mean + shift
You will need to modify the below logic to interpret the mean of NaN and another value, in the case where one of the previous two values are null.
df = df.fillna(df.rolling(2).mean().shift())
print(df)
A B C D
0 NaN 2.0 NaN 0.0
1 3.0 4.0 NaN 1.0
2 NaN 3.0 NaN 5.0
3 NaN 3.0 NaN 3.0
answered Nov 16 '18 at 10:57
jppjpp
102k2165116
seems rolling(2) will not consider the all previous n values if the rows are greater than 4 :-| @jpp
– Naga Kiran
Nov 16 '18 at 11:50
1
@NagaKiran, Yes, it does. The issue is withNaNtreatment. OP hasn't defined what the average ofNaNand another value should be, so I haven't second-guessed.
– jpp
Nov 16 '18 at 11:50
add a comment |
6
6
6
rolling + mean + shift
You will need to modify the below logic to interpret the mean of NaN and another value, in the case where one of the previous two values are null.
df = df.fillna(df.rolling(2).mean().shift())
print(df)
A B C D
0 NaN 2.0 NaN 0.0
1 3.0 4.0 NaN 1.0
2 NaN 3.0 NaN 5.0
3 NaN 3.0 NaN 3.0
answered Nov 16 '18 at 10:57
jppjpp
102k2165116
rolling + mean + shift
You will need to modify the below logic to interpret the mean of NaN and another value, in the case where one of the previous two values are null.
df = df.fillna(df.rolling(2).mean().shift())
print(df)
A B C D
0 NaN 2.0 NaN 0.0
1 3.0 4.0 NaN 1.0
2 NaN 3.0 NaN 5.0
3 NaN 3.0 NaN 3.0
answered Nov 16 '18 at 10:57
jppjpp
102k2165116
edited Nov 16 '18 at 11:04
answered Nov 16 '18 at 10:57
jppjpp
102k2165116
answered Nov 16 '18 at 10:57
jppjpp
102k2165116
answered Nov 16 '18 at 10:57
jppjpp
102k2165116
102k2165116
seems rolling(2) will not consider the all previous n values if the rows are greater than 4 :-| @jpp
– Naga Kiran
Nov 16 '18 at 11:50
1
@NagaKiran, Yes, it does. The issue is withNaNtreatment. OP hasn't defined what the average ofNaNand another value should be, so I haven't second-guessed.
– jpp
Nov 16 '18 at 11:50
add a comment |
seems rolling(2) will not consider the all previous n values if the rows are greater than 4 :-| @jpp
– Naga Kiran
Nov 16 '18 at 11:50
1
@NagaKiran, Yes, it does. The issue is withNaNtreatment. OP hasn't defined what the average ofNaNand another value should be, so I haven't second-guessed.
– jpp
Nov 16 '18 at 11:50
seems rolling(2) will not consider the all previous n values if the rows are greater than 4 :-| @jpp
– Naga Kiran
Nov 16 '18 at 11:50
seems rolling(2) will not consider the all previous n values if the rows are greater than 4 :-| @jpp
– Naga Kiran
Nov 16 '18 at 11:50
1
1
@NagaKiran, Yes, it does. The issue is with
NaN treatment. OP hasn't defined what the average of NaN and another value should be, so I haven't second-guessed.– jpp
Nov 16 '18 at 11:50
@NagaKiran, Yes, it does. The issue is with
NaN treatment. OP hasn't defined what the average of NaN and another value should be, so I haven't second-guessed.– jpp
Nov 16 '18 at 11:50
add a comment |
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