Python Pandas：如何根据基于另一列的条件创建列？

Question

给定以下数据框：

df_test = pd.DataFrame(
    [[1, "BURGLARY"], [2, "PETIT LARCENY"], [3, "DANGEROUS DRUGS"], [4, "LOITERING FOR DRUG PURPOSES"], [5, "DANGEROUS WEAPONS"]],
      columns = ['id','ofns_desc']
)

我想添加一个新列来简化列中的描述ofns_desc。我做了以下事情：

THEFT = ["BURGLARY", "PETIT LARCENY"]
df_test.loc[df_test.ofns_desc.isin(THEFT), 'category'] = "THEFT"

DRUGS = ["DANGEROUS DRUGS", "LOITERING FOR DRUG PURPOSES"]
df_test.loc[df_test.ofns_desc.isin(DRUGS), 'category'] = "DRUGS"

到目前为止，上面的代码有效：

但是当我尝试"OTHER"为该category列创建一个值时，该列中的每个值category都会被覆盖：

ALL_CAT = [THEFT, DRUGS]
df_test.loc[~df_test.ofns_desc.isin(ALL_CAT), 'category'] = "OTHER"

我究竟做错了什么？

Answer 1

问题是你测试嵌套列表，所以所有的值都失败了，你需要连接列表+而不是传递给[]喜欢的更改：

ALL_CAT = [THEFT, DRUGS]

到：

ALL_CAT = THEFT + DRUGS

另一个想法是创建字典和Series.map，最后替换缺失值Series.fillna：

THEFT = ["BURGLARY", "PETIT LARCENY"]
DRUGS = ["DANGEROUS DRUGS", "LOITERING FOR DRUG PURPOSES"]
d = {"THEFT":THEFT, 'DRUGS':DRUGS}

#swap key values in dict
#http://stackoverflow.com/a/31674731/2901002
d1 = {k: oldk for oldk, oldv in d.items() for k in oldv}
print (d1)
{'BURGLARY': 'THEFT', 'PETIT LARCENY': 'THEFT',
 'DANGEROUS DRUGS': 'DRUGS', 'LOITERING FOR DRUG PURPOSES': 'DRUGS'}

df_test['category'] = df_test['ofns_desc'].map(d1).fillna("OTHER")
print (df_test)
   id                    ofns_desc category
0   1                     BURGLARY    THEFT
1   2                PETIT LARCENY    THEFT
2   3              DANGEROUS DRUGS    DRUGS
3   4  LOITERING FOR DRUG PURPOSES    DRUGS
4   5            DANGEROUS WEAPONS    OTHER