使用quantile
與numpy.where
和自定義功能:
def c(gr):
ser=gr['gdp']
#q=0.5 is by default, so can be omit
p = ser.quantile()
gr['perf'] = np.where(ser > p, 'high', 'low')
return gr
df = df.groupby('Group').apply(c)
這可以通過transform
被簡化:
q = df.groupby('Group')['gdp'].transform('quantile')
df['perf1'] = np.where(df['gdp'] > q, 'high', 'low')
樣品:
np.random.seed(12)
N = 15
L = list('abcd')
df = pd.DataFrame({'Group': np.random.choice(L, N),
'gdp': np.random.rand(N)})
df = df.sort_values('Group').reset_index(drop=True)
df.loc[[0,4,5,10,13,14], 'gdp'] = np.nan
#print (df)
def c(gr):
ser=gr['gdp']
#q=0.5 is by default, so can be omit
p = ser.quantile()
gr['perf'] = np.where(ser > p, 'high', 'low')
return gr
df = df.groupby('Group').apply(c)
q = df.groupby('Group')['gdp'].transform('quantile')
df['perf1'] = np.where(df['gdp'] > q, 'high', 'low')
print (df)
Group gdp perf perf1
0 a NaN low low
1 a 0.907267 high high
2 a 0.456051 low low
3 b 0.675998 low low
4 b NaN low low
5 b NaN low low
6 b 0.563141 low low
7 b 0.801265 high high
8 c 0.372834 low low
9 c 0.481530 high high
10 c NaN low low
11 d 0.082524 low low
12 d 0.725954 high high
13 d NaN low low
14 d NaN low low
類似你看着'pd.cut'? –
嗨,我給的解決方案,它是類似的R :) – Wen