Data.csv文件(样本数据)
Taluka Crop Village Area
T1 C1 V1 11
T1 C1 V2 15
T1 C1 V3 3
T1 C1 V4 1
T1 C1 V5 2
T1 C2 V1 12
T1 C2 V2 16
T1 C2 V3 4
T1 C2 V4 100
T1 C2 V5 52
T1 C3 V1 47
T1 C3 V2 15
T1 C3 V3 21
T1 C3 V4 5
T1 C3 V5 7
T1 C4 V1 20
T1 C4 V2 14
T1 C4 V3 18
T1 C4 V4 5
T1 C4 V5 24
T2 C1 V1 21
T2 C1 V2 20
T2 C1 V3 14
T2 C1 V4 7
T2 C1 V5 8
T2 C2 V1 18
T2 C2 V2 3
T2 C2 V3 12
T2 C2 V4 78
T2 C2 V5 56
T2 C3 V1 16
T2 C3 V2 11
T2 C3 V3 15
T2 C3 V2 45
T2 C3 V3 2
T2 C4 V1 3
T2 C4 V2 12
T2 C4 V3 12
T2 C4 V4 44
T2 C4 V5 10
我想知道,
哪些村庄有高风险,中等风险和低风险地区的特定作物的特定taluka。
我总共有500个塔鲁卡,500个以下的塔鲁卡有10到14种作物,每个塔鲁卡将有100到200个村庄。
所以,我想找出,对于Taluka-1(即e-Thane)和Crop-1(即稻田),哪些村庄处于高风险、中风险和低风险下。采用百分位数法。
我做了一些工作。但问题是我的代码不是动态的。我需要输入每种塔鲁卡-每种作物,有这么多的组合。所以我需要使用一些循环(例如for循环、if循环)动态地完成这项工作,但是我被困在这部分。
请看我的密码。
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df=pd.read_csv("/home/desktop/Data.csv")
df.head()
##part-1 Partition taluka's
T1= df[df['Taluka'] == 'T1']
T2= df[df['Taluka'] == 'T2']
##Part-2 Partition crop wise in each taluka's
T1_C1= T1[T1['Crop'] == 'C1']
T1_C2= T1[T1['Crop'] == 'C2']
T1_C3= T1[T1['Crop'] == 'C3']
T1_C4= T1[T1['Crop'] == 'C4']
T2_C1= T2[T2['Crop'] == 'C1']
T2_C2= T2[T2['Crop'] == 'C2']
T2_C3= T2[T2['Crop'] == 'C3']
T2_C4= T2[T2['Crop'] == 'C4']
##Descending order
T1_C1 = T1_C1.sort('Area', ascending=False)
T1_C2 = T1_C2.sort('Area', ascending=False)
T1_C3 = T1_C3.sort('Area', ascending=False)
T1_C4 = T1_C4.sort('Area', ascending=False)
T2_C1 = T2_C1.sort('Area', ascending=False)
T2_C2 = T2_C2.sort('Area', ascending=False)
T2_C3 = T2_C3.sort('Area', ascending=False)
T2_C4 = T2_C4.sort('Area', ascending=False)
#####Add levels for for each crops in each taluka's
T1_C1['Level'] = pd.qcut(T1_C1['Area'], 3, ['Low Risk','Medium Risk','High Risk'])
T1_C2['Level'] = pd.qcut(T1_C2['Area'], 3, ['Low Risk','Medium Risk','High Risk'])
T1_C3['Level'] = pd.qcut(T1_C3['Area'], 3, ['Low Risk','Medium Risk','High Risk'])
T1_C4['Level'] = pd.qcut(T1_C4['Area'], 3, ['Low Risk','Medium Risk','High Risk'])
T2_C1['Level'] = pd.qcut(T2_C1['Area'], 3, ['Low Risk','Medium Risk','High Risk'])
T2_C2['Level'] = pd.qcut(T2_C2['Area'], 3, ['Low Risk','Medium Risk','High Risk'])
T2_C3['Level'] = pd.qcut(T2_C3['Area'], 3, ['Low Risk','Medium Risk','High Risk'])
T2_C4['Level'] = pd.qcut(T2_C4['Area'], 3, ['Low Risk','Medium Risk','High Risk'])
print(T1_C1)
所以,这里我将得到作物C1,taluka T1,哪些村庄位于高风险地区,低风险地区...
如何将其放入循环中?在那里我有reduce代码。代码将用于500塔鲁卡?
我认为您需要groupby
和apply
以及自定义功能:
def f(x):
labels = ['Low Risk','Medium Risk','High Risk']
x['Level'] = pd.qcut(x['Area'].sort_values(ascending=False), 3, labels = labels)
return x
df1 = df.groupby(['Taluka','Crop']).apply(f)
print (df1)
Taluka Crop Village Area Level
0 T1 C1 V1 11 High Risk
1 T1 C1 V2 15 High Risk
2 T1 C1 V3 3 Medium Risk
3 T1 C1 V4 1 Low Risk
4 T1 C1 V5 2 Low Risk
5 T1 C2 V1 12 Low Risk
6 T1 C2 V2 16 Medium Risk
7 T1 C2 V3 4 Low Risk
8 T1 C2 V4 100 High Risk
9 T1 C2 V5 52 High Risk
10 T1 C3 V1 47 High Risk
11 T1 C3 V2 15 Medium Risk
12 T1 C3 V3 21 High Risk
13 T1 C3 V4 5 Low Risk
14 T1 C3 V5 7 Low Risk
15 T1 C4 V1 20 High Risk
16 T1 C4 V2 14 Low Risk
17 T1 C4 V3 18 Medium Risk
18 T1 C4 V4 5 Low Risk
19 T1 C4 V5 24 High Risk
20 T2 C1 V1 21 High Risk
21 T2 C1 V2 20 High Risk
22 T2 C1 V3 14 Medium Risk
23 T2 C1 V4 7 Low Risk
24 T2 C1 V5 8 Low Risk
25 T2 C2 V1 18 Medium Risk
26 T2 C2 V2 3 Low Risk
27 T2 C2 V3 12 Low Risk
28 T2 C2 V4 78 High Risk
29 T2 C2 V5 56 High Risk
30 T2 C3 V1 16 High Risk
31 T2 C3 V2 11 Low Risk
32 T2 C3 V3 15 Medium Risk
33 T2 C3 V2 45 High Risk
34 T2 C3 V3 2 Low Risk
35 T2 C4 V1 3 Low Risk
36 T2 C4 V2 12 Medium Risk
37 T2 C4 V3 12 Medium Risk
38 T2 C4 V4 44 High Risk
39 T2 C4 V5 10 Low Risk
编辑:可能添加sort_values
最后:
df1 = df1.sort_values(['Taluka','Crop', 'Area'], ascending=[True, True, False])
print (df1)
Taluka Crop Village Area Level
1 T1 C1 V2 15 High Risk
0 T1 C1 V1 11 High Risk
2 T1 C1 V3 3 Medium Risk
4 T1 C1 V5 2 Low Risk
3 T1 C1 V4 1 Low Risk
8 T1 C2 V4 100 High Risk
9 T1 C2 V5 52 High Risk
6 T1 C2 V2 16 Medium Risk
5 T1 C2 V1 12 Low Risk
7 T1 C2 V3 4 Low Risk
10 T1 C3 V1 47 High Risk
12 T1 C3 V3 21 High Risk
11 T1 C3 V2 15 Medium Risk
14 T1 C3 V5 7 Low Risk
13 T1 C3 V4 5 Low Risk
19 T1 C4 V5 24 High Risk
15 T1 C4 V1 20 High Risk
17 T1 C4 V3 18 Medium Risk
16 T1 C4 V2 14 Low Risk
18 T1 C4 V4 5 Low Risk
20 T2 C1 V1 21 High Risk
21 T2 C1 V2 20 High Risk
22 T2 C1 V3 14 Medium Risk
24 T2 C1 V5 8 Low Risk
23 T2 C1 V4 7 Low Risk
28 T2 C2 V4 78 High Risk
29 T2 C2 V5 56 High Risk
25 T2 C2 V1 18 Medium Risk
27 T2 C2 V3 12 Low Risk
26 T2 C2 V2 3 Low Risk
33 T2 C3 V2 45 High Risk
30 T2 C3 V1 16 High Risk
32 T2 C3 V3 15 Medium Risk
31 T2 C3 V2 11 Low Risk
34 T2 C3 V3 2 Low Risk
38 T2 C4 V4 44 High Risk
36 T2 C4 V2 12 Medium Risk
37 T2 C4 V3 12 Medium Risk
39 T2 C4 V5 10 Low Risk
35 T2 C4 V1 3 Low Risk
or(slwier)在每个循环中排序:
def f(x):
labels = ['Low Risk','Medium Risk','High Risk']
x = x.sort_values('Area', ascending=False)
x['Level'] = pd.qcut(x['Area'], 3, labels = labels)
return x
df1 = df.groupby(['Taluka','Crop']).apply(f).reset_index(drop=True)
print (df1)
Taluka Crop Village Area Level
0 T1 C1 V2 15 High Risk
1 T1 C1 V1 11 High Risk
2 T1 C1 V3 3 Medium Risk
3 T1 C1 V5 2 Low Risk
4 T1 C1 V4 1 Low Risk
5 T1 C2 V4 100 High Risk
6 T1 C2 V5 52 High Risk
7 T1 C2 V2 16 Medium Risk
8 T1 C2 V1 12 Low Risk
9 T1 C2 V3 4 Low Risk
10 T1 C3 V1 47 High Risk
11 T1 C3 V3 21 High Risk
12 T1 C3 V2 15 Medium Risk
13 T1 C3 V5 7 Low Risk
14 T1 C3 V4 5 Low Risk
15 T1 C4 V5 24 High Risk
16 T1 C4 V1 20 High Risk
17 T1 C4 V3 18 Medium Risk
18 T1 C4 V2 14 Low Risk
19 T1 C4 V4 5 Low Risk
20 T2 C1 V1 21 High Risk
21 T2 C1 V2 20 High Risk
22 T2 C1 V3 14 Medium Risk
23 T2 C1 V5 8 Low Risk
24 T2 C1 V4 7 Low Risk
25 T2 C2 V4 78 High Risk
26 T2 C2 V5 56 High Risk
27 T2 C2 V1 18 Medium Risk
28 T2 C2 V3 12 Low Risk
29 T2 C2 V2 3 Low Risk
30 T2 C3 V2 45 High Risk
31 T2 C3 V1 16 High Risk
32 T2 C3 V3 15 Medium Risk
33 T2 C3 V2 11 Low Risk
34 T2 C3 V3 2 Low Risk
35 T2 C4 V4 44 High Risk
36 T2 C4 V2 12 Medium Risk
37 T2 C4 V3 12 Medium Risk
38 T2 C4 V5 10 Low Risk
39 T2 C4 V1 3 Low Risk
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