问题
if I have a date frame like this: N
EG_00_04 NEG_04_08 NEG_08_12 NEG_12_16 NEG_16_20 NEG_20_24 \
datum_von
2017-10-12 21.69 15.36 0.87 1.42 0.76 0.65
2017-10-13 11.85 8.08 1.39 2.86 1.02 0.55
2017-10-14 7.83 5.88 1.87 2.04 2.29 2.18
2017-10-15 14.64 11.28 2.62 3.35 2.13 1.25
2017-10-16 5.11 5.82 -0.30 -0.38 -0.24 -0.10
2017-10-17 12.09 9.61 0.20 1.09 0.39 0.57
And I wanna check the values that are above 0 and change them to zero when they are lower.
Not sure how should I use the function iterrows() and the loc() function to do so.
回答1:
clip_lower and mask solutions are good.
Here is another one with applymap:
df.applymap(lambda x: max(0.0, x))
回答2:
you can try:
df1 = df[df > 0].fillna(0)
as result:
In [24]: df
Out[24]:
EG_00_04 NEG_04_08 NEG_08_12 NEG_12_16 NEG_16_20 NEG_20_24 \
0 2017-10-12 21.69 15.36 0.87 1.42 0.76
1 2017-10-13 11.85 8.08 1.39 2.86 1.02
2 2017-10-14 7.83 5.88 1.87 2.04 2.29
3 2017-10-15 14.64 11.28 2.62 3.35 2.13
4 2017-10-16 5.11 5.82 -0.30 -0.38 -0.24
5 2017-10-17 12.09 9.61 0.20 1.09 0.39
datum_von
0 0.65
1 0.55
2 2.18
3 1.25
4 -0.10
5 0.57
In [25]: df1 = df[df > 0].fillna(0)
In [26]: df1
Out[26]:
EG_00_04 NEG_04_08 NEG_08_12 NEG_12_16 NEG_16_20 NEG_20_24 \
0 2017-10-12 21.69 15.36 0.87 1.42 0.76
1 2017-10-13 11.85 8.08 1.39 2.86 1.02
2 2017-10-14 7.83 5.88 1.87 2.04 2.29
3 2017-10-15 14.64 11.28 2.62 3.35 2.13
4 2017-10-16 5.11 5.82 0.00 0.00 0.00
5 2017-10-17 12.09 9.61 0.20 1.09 0.39
datum_von
0 0.65
1 0.55
2 2.18
3 1.25
4 0.00
5 0.57
回答3:
Use clip_lower:
df = df.clip_lower(0)
print (df)
G_00_04 NEG_04_08 NEG_08_12 NEG_12_16 NEG_16_20 NEG_20_24
datum_von
2017-10-12 21.69 15.36 0.87 1.42 0.76 0.65
2017-10-13 11.85 8.08 1.39 2.86 1.02 0.55
2017-10-14 7.83 5.88 1.87 2.04 2.29 2.18
2017-10-15 14.64 11.28 2.62 3.35 2.13 1.25
2017-10-16 5.11 5.82 0.00 0.00 0.00 0.00
2017-10-17 12.09 9.61 0.20 1.09 0.39 0.57
If first column is not index:
df = df.set_index('datum_von').clip_lower(0)
print (df)
G_00_04 NEG_04_08 NEG_08_12 NEG_12_16 NEG_16_20 NEG_20_24
datum_von
2017-10-12 21.69 15.36 0.87 1.42 0.76 0.65
2017-10-13 11.85 8.08 1.39 2.86 1.02 0.55
2017-10-14 7.83 5.88 1.87 2.04 2.29 2.18
2017-10-15 14.64 11.28 2.62 3.35 2.13 1.25
2017-10-16 5.11 5.82 0.00 0.00 0.00 0.00
2017-10-17 12.09 9.61 0.20 1.09 0.39 0.57
Alternative solution:
df = df.mask(df < 0, 0)
print (df)
G_00_04 NEG_04_08 NEG_08_12 NEG_12_16 NEG_16_20 NEG_20_24
datum_von
2017-10-12 21.69 15.36 0.87 1.42 0.76 0.65
2017-10-13 11.85 8.08 1.39 2.86 1.02 0.55
2017-10-14 7.83 5.88 1.87 2.04 2.29 2.18
2017-10-15 14.64 11.28 2.62 3.35 2.13 1.25
2017-10-16 5.11 5.82 0.00 0.00 0.00 0.00
2017-10-17 12.09 9.61 0.20 1.09 0.39 0.57
Timings:
df = pd.concat([df]*10000).reset_index(drop=True)
In [240]: %timeit (df.applymap(lambda x: max(0.0, x)))
10 loops, best of 3: 164 ms per loop
In [241]: %timeit (df[df > 0].fillna(0))
100 loops, best of 3: 7.05 ms per loop
In [242]: %timeit (df.clip_lower(0))
1000 loops, best of 3: 1.96 ms per loop
In [243]: %timeit df.mask(df < 0, 0)
100 loops, best of 3: 5.18 ms per loop
来源：https://stackoverflow.com/questions/46970671/interacting-over-a-dateframe-with-functions