在python中使用pandas在列上映射匹配的单词数 [英] mapping matching word count on a column using pandas in python

问题描述

我有一个df，

Name    Step     Description
Ram        1     Ram is oNe of the good cricketer
Ram        2     gopal one
Sri        1     Sri is one of the member
Sri        2     ravi good 
Kumar      1     Kumar is a keeper
Madhu      1     good boy
Vignesh    1     oNe little
Pechi      1     one book
mario      1     good randokm
Roger      1     one milita good
bala       1     looks good
raj        1     more one
venk       1     likes good

和一个列表

my_list=["one","good"]

我正在尝试从my_list中获取具有至少一个关键字的行.

I am trying to get the rows which are having atleast one keyword from my_list.

我尝试过， mask = df ["Description"].str.contains("|" .join(my_list)，na = False) 我得到了output_df，

I tried, mask=df["Description"].str.contains("|".join(my_list),na=False) I am getting the output_df,

Name    Description
Ram     Ram is one of the good cricketer
Sri     Sri is one of the member        

我还想将说明"中存在的关键字及其计数添加到单独的列中，

I also want to add the keywords present in the "Description" and its counts in a separate columns,

即使df ["Name"]不是第一次出现，说明"中也包含关键字，也不应将关键字复制到我的期望"列中，

Even the "Description" contains a keyword when the df["Name"] is not a first time occureance it should not copy the keyword in keys column My desired output is,

my_desired输出是

my_desired output is,

 Name   Step    Description                          keys        count
 Ram     1     Ram is one of the good cricketer      one,good    2
 Ram     2     gopal one
 Sri     1     Sri is one of the member              one         1
 Sri     2     ravi good
 Kumar   1     Kumar is a keeper
 Madhu   1     good boy                              good        1
 Vignesh 1     oNe little                            oNe         1
 Pechi   1     one book                              one         1 
 mario   1     good randokm good                     good        1
 Roger   1     one milita good                       one,good    2
 bala    1     looks good                            good        1
 raj     1     more one                              one         1
 venk    1     likes good                            good        1

推荐答案

创建新蒙版并应用它:

my_list=["one","good"]

mask=df["Description"].str.contains("|".join(my_list),na=False,flags=re.IGNORECASE ) & \
     (df.groupby('Name').cumcount() == 0)
print (mask)
0      True
1     False
2      True
3     False
4     False
5      True
6      True
7      True
8      True
9      True
10     True
11     True
12     True
dtype: bool

---

extracted = df['Description'].str.findall('(' + '|'.join(my_list) + ')', flags=re.IGNORECASE)
df.loc[mask, 'keys'] = extracted.str.join(',')
df.loc[mask, 'count'] = extracted.str.len()
print (df)
       Name  Step                       Description      keys  count
0       Ram     1  Ram is oNe of the good cricketer  oNe,good    2.0
1       Ram     2                         gopal one       NaN    NaN
2       Sri     1          Sri is one of the member       one    1.0
3       Sri     2                        ravi good        NaN    NaN
4     Kumar     1                 Kumar is a keeper       NaN    NaN
5     Madhu     1                          good boy      good    1.0
6   Vignesh     1                        oNe little       oNe    1.0
7     Pechi     1                          one book       one    1.0
8     mario     1                      good randokm      good    1.0
9     Roger     1                   one milita good  one,good    2.0
10     bala     1                        looks good      good    1.0
11      raj     1                          more one       one    1.0
12     venk     1                        likes good      good    1.0

#transform all values if need same size of original
s = df.groupby('Name')['Description'].transform(','.join)
print (s)
0     Ram is oNe of the good cricketer,gopal one
1     Ram is oNe of the good cricketer,gopal one
2            Sri is one of the member,ravi good 
3            Sri is one of the member,ravi good 
4                              Kumar is a keeper
5                                       good boy
6                                     oNe little
7                                       one book
8                              good randokm good
9                                one milita good
10                                    looks good
11                                      more one
12                                    likes good
Name: Description, dtype: object

---

#for mask use new Series s
mask=s.str.contains("|".join(my_list),na=False,flags=re.IGNORECASE ) & \
     (df.groupby('Name').cumcount() == 0)
print (mask)
0      True
1     False
2      True
3     False
4     False
5      True
6      True
7      True
8      True
9      True
10     True
11     True
12     True
dtype: bool

---

#extract from new Series s
extracted = s.str.findall('(' + '|'.join(my_list) + ')', flags=re.IGNORECASE).apply(set)
df.loc[mask, 'keys'] = extracted.str.join(',')
df.loc[mask, 'count'] = extracted.str.len()
print (df)
       Name  Step                       Description          keys  count
0       Ram     1  Ram is oNe of the good cricketer  good,oNe,one    3.0
1       Ram     2                         gopal one           NaN    NaN
2       Sri     1          Sri is one of the member      good,one    2.0
3       Sri     2                        ravi good            NaN    NaN
4     Kumar     1                 Kumar is a keeper           NaN    NaN
5     Madhu     1                          good boy          good    1.0
6   Vignesh     1                        oNe little           oNe    1.0
7     Pechi     1                          one book           one    1.0
8     mario     1                 good randokm good          good    1.0
9     Roger     1                   one milita good      good,one    2.0
10     bala     1                        looks good          good    1.0
11      raj     1                          more one           one    1.0
12     venk     1                        likes good          good    1.0

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