I have a dataFrame (python) like this:
x y z time
0 0.730110 4.091428 7.833503 1618237788537
1 0.691825 4.024428 7.998608 1618237788537
2 0.658325 3.998107 8.195119 1618237788537
3 0.658325 4.002893 8.408080 1618237788537
4 0.677468 4.017250 8.561220 1618237788537
I want to add column to this dataFrame called computed. This column includes values computed as for:
row 0: (0.730110-0)^2 (4.091428-0)^2 (7.833503-0)^2
row 1: (0.691825 -0.730110)^2 (4.024428- 4.091428)^2 (7.998608-7.833503)^2
etc
How can do that please.
CodePudding user response：
TL;DR:
df['computed'] = df.diff().pow(2).sum(axis=1)
df.at[0, 'computed'] = df.loc[0].pow(2).sum()
Step by step:
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6], 'b': [1, 1, 2, 3, 5, 8], 'c': [1, 4, 9, 16, 25, 36]})
df
# a b c
# 0 1 1 1
# 1 2 1 4
# 2 3 2 9
# 3 4 3 16
# 4 5 5 25
# 5 6 8 36
df.diff()
# a b c
# 0 NaN NaN NaN
# 1 1.0 0.0 3.0
# 2 1.0 1.0 5.0
# 3 1.0 1.0 7.0
# 4 1.0 2.0 9.0
# 5 1.0 3.0 11.0
df.diff().pow(2)
# a b c
# 0 NaN NaN NaN
# 1 1.0 0.0 9.0
# 2 1.0 1.0 25.0
# 3 1.0 1.0 49.0
# 4 1.0 4.0 81.0
# 5 1.0 9.0 121.0
df.diff().pow(2).sum(axis=1)
# 0 0.0
# 1 10.0
# 2 27.0
# 3 51.0
# 4 86.0
# 5 131.0
df['computed'] = df.diff().pow(2).sum(axis=1)
df
# a b c computed
# 0 1 1 1 0.0
# 1 2 1 4 10.0
# 2 3 2 9 27.0
# 3 4 3 16 51.0
# 4 5 5 25 86.0
# 5 6 8 36 131.0
df.at[0, 'computed'] = df.loc[0].pow(2).sum()
df
# a b c computed
# 0 1 1 1 3.0
# 1 2 1 4 10.0
# 2 3 2 9 27.0
# 3 4 3 16 51.0
# 4 5 5 25 86.0
# 5 6 8 36 131.0
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