Python 包含序列中特定值的过滤器df-1
我有一个对df进行子集划分的复杂过程。对于给定的序列或行,我希望返回特定的值。具体地说,使用下面的df,序列的开始用Python 包含序列中特定值的过滤器df-1,python,pandas,Python,Pandas,我有一个对df进行子集划分的复杂过程。对于给定的序列或行,我希望返回特定的值。具体地说,使用下面的df,序列的开始用开始a,开始B,开始C表示。如果X或Y位于给定序列内,我想为同一序列返回Up或Down。如果在包含X或Y的序列中未找到Up或Down,则返回Left或Right。如果未发现Up或Down或X或Y,则打印错误 import pandas as pd df = pd.DataFrame({ 'Num' : [1,2,3,4,6,7,9,10,12,13,14,15
开始a
,开始B
,开始C
表示。如果X
或Y
位于给定序列内,我想为同一序列返回Up
或Down
。如果在包含X
或Y
的序列中未找到Up
或Down
,则返回Left
或Right
。如果未发现Up
或Down
或X
或Y
,则打印错误
import pandas as pd
df = pd.DataFrame({
'Num' : [1,2,3,4,6,7,9,10,12,13,14,15,17,18,19,21,22,23,25,26,27,28,30,31,32],
'Item' : ['Start_A','AB','CD','Left','Start_C','CD','X','Up','Right','Start_C','EF','AB','Y','AB','Down','Left','Start_B','AB','Y','CD','Left','Start_A','AB','CD','Right'],
})
m1 = df['Item'].isin(['X','Y']).cumsum().gt(0)
m2 = df['Item'].isin(['Up','Down']).iloc[::-1].cumsum().gt(0)
df1 = df[m1 & m2]
原始df:
Num Item
0 1 Start_A # No X,Y within sequence. drop all
1 2 AB
2 3 CD
3 4 Left
4 6 Start_C # X and Up within sequence.
5 7 CD
6 9 X
7 10 Up
8 12 Right
9 13 Start_C # Y and Down within sequence.
10 14 EF
11 15 AB
12 17 Y
13 18 AB
14 19 Down
15 21 Left
16 22 Start_B # Y within sequence. No Up/Down. But Left is.
17 23 AB
18 25 Y
19 26 CD
20 27 AB
21 27 Left
22 28 Start_A # No X,Y within sequence. drop all
23 30 AB
24 31 CD
25 32 Right
预期产出:
Num Item
6 9 X
7 10 Up
12 17 Y
14 19 Down
18 25 Y
21 27 Left
下面是一种方法:
import pandas as pd
import numpy as np
df = pd.DataFrame({
'Num' : [1,2,3,4,6,7,9,10,12,13,14,15,17,18,19,21,22,23,25,26,27,28,30,31,32],
'Item' : ['Start_A','AB','CD','Left','Start_C','CD','X','Up','Right','Start_C','EF','AB','Y','AB','Down','Left','Start_B','AB','Y','CD','Left','Start_A','AB','CD','Right'],
})
grp = df['Item'].str.startswith('Start_').cumsum()
df['X_Y'] = df['Item'].isin(['X', 'Y'])
df['Up_Down'] = df['Item'].isin(['Up', 'Down'])
df['Left_Right'] = df['Item'].isin(['Left', 'right'])
def f(x):
if x['X_Y'].any():
return pd.concat([x[x['X_Y']], x[x['Up_Down']], x[x['Left_Right']]]).head(2)
df.groupby(grp, group_keys=False).apply(f).drop(['X_Y', 'Up_Down', 'Left_Right'], axis=1)
输出:
Num Item
6 9 X
7 10 Up
12 17 Y
14 19 Down
18 25 Y
20 27 Left
详细信息:
- 首先,使用cumsum和startswith'Start\创建组grp
- 接下来,创建三个布尔序列,分别标记为“xy”、“Up Down”和“Left” 对
- 然后,创建一个自定义函数,该函数接受每个组(如果该组 包含“X_Y”的真实记录,然后构建数据帧 将“X_Y”、“Up_Down”和“Left_Right”按该顺序连接起来。使用 头(2)只获得每组的前两条记录
- 从中生成结果数据帧后删除辅助列 群比
# thanks to Scott Boston for a simpler syntax here
(df.assign(counter = df.Item.str.startswith("Start_").cumsum(),
boolean = lambda df: df.groupby('counter').transform(",".join),
#first phase, X or Y should be present
# if absent, nulls will be introduced
boolean_1 = lambda df: df.boolean.str.extract(r"(X|Y)")
)
.dropna()
# next phase, get them in order of Up, Down, Left, Right
# use extract, since it returns the first match
.assign(boolean_2 = lambda df: df.boolean
.str.extract(r"(Up|Down|Left|Right)"))
# filter and keep the original columns
.query("Item == boolean_1 or Item == boolean_2")
.filter(['Num', 'Item'])
)
Num Item
6 9 X
7 10 Up
12 17 Y
14 19 Down
18 25 Y
20 27 Left
# thanks to Scott Boston for a simpler syntax here
(df.assign(counter = df.Item.str.startswith("Start_").cumsum(),
boolean = lambda df: df.groupby('counter').transform(",".join),
#first phase, X or Y should be present
# if absent, nulls will be introduced
boolean_1 = lambda df: df.boolean.str.extract(r"(X|Y)")
)
.dropna()
# next phase, get them in order of Up, Down, Left, Right
# use extract, since it returns the first match
.assign(boolean_2 = lambda df: df.boolean
.str.extract(r"(Up|Down|Left|Right)"))
# filter and keep the original columns
.query("Item == boolean_1 or Item == boolean_2")
.filter(['Num', 'Item'])
)
Num Item
6 9 X
7 10 Up
12 17 Y
14 19 Down
18 25 Y
20 27 Left