pandas filling nans by mean of before and after non-nan values
I would like to fill df's nan with an average of adjacent elements.
Consider a dataframe:
df = pd.DataFrame({'val': [1,np.nan, 4, 5, np.nan, 10, 1,2,5, np.nan, np.nan, 9]})
val
0 1.0
1 NaN
2 4.0
3 5.0
4 NaN
5 10.0
6 1.0
7 2.0
8 5.0
9 NaN
10 NaN
11 9.0
My desired output is:
val
0 1.0
1 2.5
2 4.0
3 5.0
4 7.5
5 10.0
6 1.0
7 2.0
8 5.0
9 7.0 <<< deadend
10 7.0 <<< deadend
11 9.0
I've looked into other solutions such as Fill cell containing NaN with average of value before and after, but this won't work in case of two or more consecutive np.nans.
Any help is greatly appreciated!
python pandas
asked 21 mins ago
ChrisChris
1,186213
add a comment |
I would like to fill df's nan with an average of adjacent elements.
Consider a dataframe:
df = pd.DataFrame({'val': [1,np.nan, 4, 5, np.nan, 10, 1,2,5, np.nan, np.nan, 9]})
val
0 1.0
1 NaN
2 4.0
3 5.0
4 NaN
5 10.0
6 1.0
7 2.0
8 5.0
9 NaN
10 NaN
11 9.0
My desired output is:
val
0 1.0
1 2.5
2 4.0
3 5.0
4 7.5
5 10.0
6 1.0
7 2.0
8 5.0
9 7.0 <<< deadend
10 7.0 <<< deadend
11 9.0
I've looked into other solutions such as Fill cell containing NaN with average of value before and after, but this won't work in case of two or more consecutive np.nans.
Any help is greatly appreciated!
python pandas
asked 21 mins ago
ChrisChris
1,186213
add a comment |
I would like to fill df's nan with an average of adjacent elements.
Consider a dataframe:
df = pd.DataFrame({'val': [1,np.nan, 4, 5, np.nan, 10, 1,2,5, np.nan, np.nan, 9]})
val
0 1.0
1 NaN
2 4.0
3 5.0
4 NaN
5 10.0
6 1.0
7 2.0
8 5.0
9 NaN
10 NaN
11 9.0
My desired output is:
val
0 1.0
1 2.5
2 4.0
3 5.0
4 7.5
5 10.0
6 1.0
7 2.0
8 5.0
9 7.0 <<< deadend
10 7.0 <<< deadend
11 9.0
I've looked into other solutions such as Fill cell containing NaN with average of value before and after, but this won't work in case of two or more consecutive np.nans.
Any help is greatly appreciated!
python pandas
asked 21 mins ago
ChrisChris
1,186213
I would like to fill df's nan with an average of adjacent elements.
Consider a dataframe:
df = pd.DataFrame({'val': [1,np.nan, 4, 5, np.nan, 10, 1,2,5, np.nan, np.nan, 9]})
val
0 1.0
1 NaN
2 4.0
3 5.0
4 NaN
5 10.0
6 1.0
7 2.0
8 5.0
9 NaN
10 NaN
11 9.0
My desired output is:
val
0 1.0
1 2.5
2 4.0
3 5.0
4 7.5
5 10.0
6 1.0
7 2.0
8 5.0
9 7.0 <<< deadend
10 7.0 <<< deadend
11 9.0
I've looked into other solutions such as Fill cell containing NaN with average of value before and after, but this won't work in case of two or more consecutive np.nans.
Any help is greatly appreciated!
python pandas
python pandas
asked 21 mins ago
ChrisChris
1,186213
asked 21 mins ago
ChrisChris
1,186213
asked 21 mins ago
ChrisChris
1,186213
asked 21 mins ago
ChrisChris
1,186213
asked 21 mins ago
ChrisChris
1,186213
1,186213
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Use ffill + bfill and divide by 2:
df = (df.ffill()+df.bfill())/2
print(df)
val
0 1.0
1 2.5
2 4.0
3 5.0
4 7.5
5 10.0
6 1.0
7 2.0
8 5.0
9 7.0
10 7.0
11 9.0
answered 16 mins ago
Sandeep KadapaSandeep Kadapa
6,823629
3
That is just brilliant. Thanks a ton :)
– Chris
14 mins ago
@Chris Glad to help.
– Sandeep Kadapa
8 mins ago
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
Use ffill + bfill and divide by 2:
df = (df.ffill()+df.bfill())/2
print(df)
val
0 1.0
1 2.5
2 4.0
3 5.0
4 7.5
5 10.0
6 1.0
7 2.0
8 5.0
9 7.0
10 7.0
11 9.0
answered 16 mins ago
Sandeep KadapaSandeep Kadapa
6,823629
3
That is just brilliant. Thanks a ton :)
– Chris
14 mins ago
@Chris Glad to help.
– Sandeep Kadapa
8 mins ago
add a comment |
Use ffill + bfill and divide by 2:
df = (df.ffill()+df.bfill())/2
print(df)
val
0 1.0
1 2.5
2 4.0
3 5.0
4 7.5
5 10.0
6 1.0
7 2.0
8 5.0
9 7.0
10 7.0
11 9.0
answered 16 mins ago
Sandeep KadapaSandeep Kadapa
6,823629
3
That is just brilliant. Thanks a ton :)
– Chris
14 mins ago
@Chris Glad to help.
– Sandeep Kadapa
8 mins ago
add a comment |
Use ffill + bfill and divide by 2:
df = (df.ffill()+df.bfill())/2
print(df)
val
0 1.0
1 2.5
2 4.0
3 5.0
4 7.5
5 10.0
6 1.0
7 2.0
8 5.0
9 7.0
10 7.0
11 9.0
answered 16 mins ago
Sandeep KadapaSandeep Kadapa
6,823629
Use ffill + bfill and divide by 2:
df = (df.ffill()+df.bfill())/2
print(df)
val
0 1.0
1 2.5
2 4.0
3 5.0
4 7.5
5 10.0
6 1.0
7 2.0
8 5.0
9 7.0
10 7.0
11 9.0
answered 16 mins ago
Sandeep KadapaSandeep Kadapa
6,823629
edited 2 mins ago
answered 16 mins ago
Sandeep KadapaSandeep Kadapa
6,823629
answered 16 mins ago
Sandeep KadapaSandeep Kadapa
6,823629
answered 16 mins ago
Sandeep KadapaSandeep Kadapa
6,823629
6,823629
3
That is just brilliant. Thanks a ton :)
– Chris
14 mins ago
@Chris Glad to help.
– Sandeep Kadapa
8 mins ago
add a comment |
3
That is just brilliant. Thanks a ton :)
– Chris
14 mins ago
@Chris Glad to help.
– Sandeep Kadapa
8 mins ago
3
3
That is just brilliant. Thanks a ton :)
– Chris
14 mins ago
That is just brilliant. Thanks a ton :)
– Chris
14 mins ago
@Chris Glad to help.
– Sandeep Kadapa
8 mins ago
@Chris Glad to help.
– Sandeep Kadapa
8 mins ago
add a comment |
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