我有一个数据框。我汇总如下。但是,我想将它们区分为最大值 - 最小值
dnm=df.groupby('Type').agg({'Vehicle_Age': ['max','min']})
期望:
np.ptp
,这会为您进行max - min
计算:
df.groupby('Type').agg({'Vehicle_Age': np.ptp})
或者,
df.groupby('Type')['Vehicle_Age'].agg(np.ptp)
如果您将系列作为输出。
比较一下两者:
grouping = df.groupby('Type')
dnm = grouping.max() - grouping.min()
@cs95的答案是正确的做法,而且也有更好的时机! :
设置:
df = pd.DataFrame({'a':np.arange(100),'Type':[1 if i %2 ==0 else 0 for i in range(100)]})
@cs95:
%timeit df.groupby('Type').agg({'a': np.ptp})
1.29 ms ± 39.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
对
%%timeit
grouping = df.groupby('Type')
dnm = grouping.max() - grouping.min()
1.57 ms ± 299 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
您应该对表的列执行基本的逐元素操作,您可以这样做:
import pandas as pd
# This is just setup to replicate your example
df = pd.DataFrame([[14, 7], [15, .25], [14, 9], [13, 2], [14, 4]], index=['Large SUV', 'Mid-size', 'Minivan', 'Small', 'Small SUV'], columns = ['max', 'min'])
print(df)
# max min
# Large SUV 14 7.00
# Mid-size 15 0.25
# Minivan 14 9.00
# Small 13 2.00
# Small SUV 14 4.00
# This is the operation that will give you the values you want
diff = df['max'] - df['min']
print(diff)
# Large SUV 7.00
# Mid-size 14.75
# Minivan 5.00
# Small 11.00
# Small SUV 10.00