[英]Create a new column in a dataframe if the column contains a string from a column of another dataframe
I want to create a new column in my dataframe if the column contains any of the values from a column of a second dataframe. 如果该列包含第二个数据框的列中的任何值,我想在数据框中创建一个新列。
First dataframe 第一个数据框
WXYnineZAB
EFGsixHIJ
QRSeightTUV
GHItwoJKL
YZAfiveBCD
EFGsixHIJ
MNOthreePQR
ABConeDEF
MNOthreePQR
MNOthreePQR
YZAfiveBCD
WXYnineZAB
GHItwoJKL
KLMsevenNOP
EFGsixHIJ
ABConeDEF
KLMsevenNOP
QRSeightTUV
STUfourVWX
STUfourVWX
KLMsevenNOP
WXYnineZAB
CDEtenFGH
YZAfiveBCD
CDEtenFGH
QRSeightTUV
ABConeDEF
STUfourVWX
CDEtenFGH
GHItwoJKL
Second Dataframe 第二个数据框
one
three
five
seven
nine
Output DataFrame 输出数据框
WXYnineZAB,nine
EFGsixHIJ,***
QRSeightTUV,***
GHItwoJKL,***
YZAfiveBCD,five
EFGsixHIJ,***
MNOthreePQR,three
ABConeDEF,one
MNOthreePQR,three
MNOthreePQR,three
YZAfiveBCD,five
WXYnineZAB,nine
GHItwoJKL,***
KLMsevenNOP,seven
EFGsixHIJ,***
ABConeDEF,one
KLMsevenNOP,seven
QRSeightTUV,***
STUfourVWX,***
STUfourVWX,***
KLMsevenNOP,seven
WXYnineZAB,nine
CDEtenFGH,***
YZAfiveBCD,five
CDEtenFGH,***
QRSeightTUV,***
ABConeDEF,one
STUfourVWX,***
CDEtenFGH,***
GHItwoJKL,***
To explain it easily I made the first dataframe be 3chars + search string + 3chars, but my actual file doesn't have any consistency like this. 为了易于解释,我将第一个数据帧设置为3chars +搜索字符串+ 3chars,但是我的实际文件没有这样的一致性。
Source DFs: 源DF:
In [172]: d1
Out[172]:
txt
0 WXYnineZAB
1 EFGsixHIJ
2 QRSeightTUV
3 GHItwoJKL
4 YZAfiveBCD
.. ...
25 QRSeightTUV
26 ABConeDEF
27 STUfourVWX
28 CDEtenFGH
29 GHItwoJKL
[30 rows x 1 columns]
In [173]: d2
Out[173]:
word
0 one
1 three
2 five
3 seven
4 nine
generate RegEx pattern from the second DataFrame: 从第二个DataFrame生成RegEx模式:
In [174]: pat = r'({})'.format(d2['word'].str.cat(sep='|'))
In [175]: pat
Out[175]: '(one|three|five|seven|nine)'
extract words matching the RegEx pattern and assign them as a new column: 提取与RegEx模式匹配的单词并将其分配为新列:
In [176]: d1['new'] = d1['txt'].str.extract(pat, expand=False)
In [177]: d1
Out[177]:
txt new
0 WXYnineZAB nine
1 EFGsixHIJ NaN
2 QRSeightTUV NaN
3 GHItwoJKL NaN
4 YZAfiveBCD five
.. ... ...
25 QRSeightTUV NaN
26 ABConeDEF one
27 STUfourVWX NaN
28 CDEtenFGH NaN
29 GHItwoJKL NaN
[30 rows x 2 columns]
you can also fill NaN's if you want in the same step: 您也可以在同一步骤中填写NaN:
In [178]: d1['new'] = d1['txt'].str.extract(pat, expand=False).fillna('***')
In [179]: d1
Out[179]:
txt new
0 WXYnineZAB nine
1 EFGsixHIJ ***
2 QRSeightTUV ***
3 GHItwoJKL ***
4 YZAfiveBCD five
.. ... ...
25 QRSeightTUV ***
26 ABConeDEF one
27 STUfourVWX ***
28 CDEtenFGH ***
29 GHItwoJKL ***
[30 rows x 2 columns]
If you want to avoid RegEx, here is a purely list-based solution: 如果要避免使用RegEx,请使用以下纯粹基于列表的解决方案:
# Sample DataFrames (structure is borrowed from MaxU)
d1 = pd.DataFrame({'txt':['WXYnineZAB','EFGsixHIJ','QRSeightTUV','GHItwoJKL']})
d2 = pd.DataFrame({'word':['two','six']})
# Check if word exists in any txt (1-liner).
exists = [list(d2.word[[word in txt for word in d2.word]])[0] if sum([word in txt for word in d2.word]) == 1 else '***' for txt in d1.txt]
# Resulting output
res = pd.DataFrame(zip(d1.txt,exists), columns = ['text','word'])
Result: 结果:
text word
0 WXYnineZAB ***
1 EFGsixHIJ six
2 QRSeightTUV ***
3 GHItwoJKL two
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