我的数据框如下所示:
id state level
0 1 [p, t] [dsd]
1 3 [t, t] [dsds, dsd]
2 4 [l, l] [jgddf, vdv]
3 6 [u, c] [cxxc, jgddf]
我试图做的是检查level
列在列表中是否包含部分或整个字符串,并在此基础上添加一个新列。这就是我试图实现这一目标的方式(包括如何创建数据帧以及如何对每行中的元素进行排序、过滤和合并):
import numpy as np
import pandas as pd
something = [[1, "p", "dsd"], [3, "t", "dsd"], [6, "u", "jgddf"], [1, "p", "dsd"], [4, "l", "jgddf"], [1, "t", "dsd"],
[3, "t", "dsds"], [6, "c", "cxxc"], [1, "p", "dsd"], [4, "l", "vdv"]]
test = pd.DataFrame(something)
test = test.drop_duplicates()
test.columns = ['id', 'state', 'level']
test = test.sort_values(by=['id'], ascending=True)
test_unique = test["id"].unique()
df_aslist = test.groupby(['id']).aggregate(lambda x: list(x)).reset_index()
#making it a set to remove duplicates
df_aslist['level'] = df_aslist['level'].apply(lambda x: list(set(x)))
print(df_aslist)
conditions = [(df_aslist["level"].str.contains("ds") & df_aslist["level"].str.contains("sd")),
(df_aslist["level"].str.contains("cx") & df_aslist["level"].str.contains("vd"))]
values = ["term 1", "term 2"]
df_aslist["label"] = np.select(conditions, values)
print(df_aslist)
输出:
id state level label
0 1 [p, t] [tere] 0
1 3 [t, t] [dsds, dsd] 0
2 4 [l, l] [vdv, jgddf] 0
3 6 [u, c] [cxxc, jgddf] 0
理想情况下,它应该显示以下内容,其中不符合条件的行应该消失,其余的保留新标签
id state level label
1 3 [t, t] [dsds, dsd] term 1
2 4 [l, l] [vdv, jgddf] term 2
3 6 [u, c] [cxxc, jgddf] term 2
尝试使用
astype()
方法:最后筛选出您的数据帧:
如果您打印
df_aslist
,您将获得所需的输出注意:如果您想要返回这些列表,请使用
pd.eval()
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