我有这个数组:
np = "8 12 16 20 24 28 32"
do for [i=1:7] {
plot for [IDX=0:4] sprintf("run-1/%s.t-0.dat", word(np, i)) ...
第一个索引包含动物的名称,第二个索引包含动物的名称,第三个索引是种群。我需要获取每个区域中物种的平均值,并获取每个区域中每种物种的最大值和最小值。因此,对于“紫色海雀”,平均值应为(125 + 143 + 128)/ 3 = 132 。
我对如何获取numpy数组仅计算每个区域的人口感到非常困惑。
将2d数组分为多个2d数组会更好还是更容易?
答案 0 :(得分:3)
这看起来更像是熊猫的任务,我们可以先构建一个数据框:
import pandas as pd
df = pd.DataFrame([
['Burgundy Bichon Frise','1','137'],
['Pumpkin Pomeranian','1','182'],
['Purple Puffin','1','125'],
['Wisteria Wombat','1','109'],
['Burgundy Bichon Frise','2','168'],
['Pumpkin Pomeranian','2','141'],
['Purple Puffin','2','143'],
['Wisteria Wombat','2','167'],
['Burgundy Bichon Frise','3','154'],
['Pumpkin Pomeranian','3','175'],
['Purple Puffin','3','128'],
['Wisteria Wombat','3','167']], columns=['animal', 'region', 'n'])
接下来,我们可以将region
和n
转换为数字,这将使统计数据的计算更加容易:
df.region = pd.to_numeric(df.region)
df.n = pd.to_numeric(df.n)
最后,我们可以执行.groupby(..)
,然后计算总计,例如:
>>> df[['animal', 'n']].groupby(('animal')).min()
n
animal
Burgundy Bichon Frise 137
Pumpkin Pomeranian 141
Purple Puffin 125
Wisteria Wombat 109
>>> df[['animal', 'n']].groupby(('animal')).max()
n
animal
Burgundy Bichon Frise 168
Pumpkin Pomeranian 182
Purple Puffin 143
Wisteria Wombat 167
>>> df[['animal', 'n']].groupby(('animal')).mean()
n
animal
Burgundy Bichon Frise 153.000000
Pumpkin Pomeranian 166.000000
Purple Puffin 132.000000
Wisteria Wombat 147.666667
编辑:获取每只动物 的最小行
我们可以使用idxmin
/ idxmax
获取每个动物的最小/最大行的索引号,然后使用df.iloc[..]
获取这些行,例如:
>>> df.ix[df.groupby(('animal'))['n'].idxmin()]
animal region n
0 Burgundy Bichon Frise 1 137
5 Pumpkin Pomeranian 2 141
2 Purple Puffin 1 125
3 Wisteria Wombat 1 109
>>> df.ix[df.groupby(('animal'))['n'].idxmax()]
animal region n
4 Burgundy Bichon Frise 2 168
1 Pumpkin Pomeranian 1 182
6 Purple Puffin 2 143
7 Wisteria Wombat 2 167
0, 5, 2, 3
(用于idxmin
)是数据帧的“行号”。
答案 1 :(得分:2)
以下是使用numpy将数据a
转换为2D表的方法:
>>> unqr, invr = np.unique(a[:, 0], return_inverse=True)
>>> unqc, invc = np.unique(a[:, 1], return_inverse=True)
# initialize with nans in case there are missing values
# these are then treated correctly by nanmean etc.:
>>> out = np.full((unqr.size, unqc.size), np.nan)
>>> out[invr, invc] = a[:, 2]
>>>
# now we have a table
>>> out
array([[137., 168., 154.],
[182., 141., 175.],
[125., 143., 128.],
[109., 167., 167.]])
# with rows
>>> unqr
array(['Burgundy Bichon Frise', 'Pumpkin Pomeranian', 'Purple Puffin',
'Wisteria Wombat'], dtype='<U21')
# and columns
>>> unqc
array(['1', '2', '3'], dtype='<U21')
>>>
# find the mean for 'Purple Puffin':
>>> np.nanmean(out[unqr.searchsorted('Purple Puffin')])
132.0
# find the max for region '2'
>>> np.nanmax(out[:, unqc.searchsorted('2')])
168.0