Question

我有两个数据帧，第一个是：

DF1

col_one col_two
ABBC1  (1, 2, 3)
DFFG2  (3, 5, 1)
JJKS3  (5, 2, 5)

DF2

    col_1
operate ABBC1 1 to 2, JJKS3 3 to 5
operate JJKS3, FOM

所需的输出df2：

  col_1                col_2
operate ABBC1, to 2  (1, 2, 3)
operate JJKS3, FOM   (5, 2, 5)

我尝试了多种方法，但最接近的是：

for values, map_col in df1[['col_one', 'col_two']].values:
    for val in df2['col_1']:
        if ("%s" %values) in df2['col_1'] :
            df2['col_2'] = "%s" %(map_col,)

我认为这会很好，但我对所有行都有完全相同的值。

欢迎任何帮助。感谢

Answer 1

您可以按extract创建新列并按其合并：

pat = '|'.join(r"\b{}\b".format(x) for x in df1['col_one'].unique())
df2['col_one'] = df2['col_1'].str.extract('(' + pat + ')')
print (df2)
                 col_1 col_one
0  operate ABBC1, to 2   ABBC1
1   operate JJKS3, FOM   JJKS3

df = df1.merge(df2, on='col_one')
print (df)
  col_one    col_two                col_1
0   ABBC1  (1, 2, 3)  operate ABBC1, to 2
1   JJKS3  (5, 2, 5)   operate JJKS3, FOM

编辑：

如果可以匹配多个值，请使用findall并创建新的DataFrame：

pat = '|'.join(r"\b{}\b".format(x) for x in df1['col_one'].unique())
s = df2['col_1'].str.findall('(' + pat + ')')
print (s)
0    [ABBC1, JJKS3]
1           [JJKS3]
Name: col_1, dtype: object

lens = s.str.len()
a = np.repeat(df2['col_1'], lens)
b = np.concatenate(s)
df2 = pd.DataFrame({'col_1':a, 'col_one':b})
print (df2)
                               col_1 col_one
0  operate ABBC1, to 2  JJKS3 3 to 5   ABBC1
0  operate ABBC1, to 2  JJKS3 3 to 5   JJKS3
1                 operate JJKS3, FOM   JJKS3

df = df1.merge(df2, on='col_one')
print (df)
  col_one    col_two                              col_1
0   ABBC1  (1, 2, 3)  operate ABBC1, to 2  JJKS3 3 to 5
1   JJKS3  (5, 2, 5)  operate ABBC1, to 2  JJKS3 3 to 5
2   JJKS3  (5, 2, 5)                 operate JJKS3, FOM

如果字符串包含Pandas中其他列的值，则映射列

1 个答案: