Python 多次匹配后合并数据帧
我有两个数据帧,其中我根据一列(Python 多次匹配后合并数据帧,python,pandas,dataframe,Python,Pandas,Dataframe,我有两个数据帧,其中我根据一列(tld)找到了常见的匹配项,如果找到了匹配项(在source和destination中的一列之间),我将列(uuid)的值从源复制到destination数据帧。 我还检查另一列是否匹配。(company\u name)然后提取uuid 现在我需要比较不同的列(类似的公司),并提取uuid 数据帧1:源 uuid website company_name tld 0 1a www.facebook.com
tld
)找到了常见的匹配项,如果找到了匹配项(在source
和destination
中的一列之间),我将列(uuid
)的值从源复制到destination
数据帧。
我还检查另一列是否匹配。(company\u name
)然后提取uuid
现在我需要比较不同的列(类似的公司
),并提取uuid
数据帧1:源
uuid website company_name tld
0 1a www.facebook.com facebook facebook.com
1 2b www.yahoo.com yahoo inc yahoo.com
2 3c www.google.com Google google.com
3 4d www.cisco.com Cisco cisco.com
数据帧2:目的地
id website company_name tld match uuid
0 a www.facebook.com facebook facebook.com False NaN
1 b www.y.com Yahoo Inc y.com False NaN
2 c www.g.com Google g.com False NaN
3 d www.g.com Google Inc g.com False NaN
4 e www.facebook.com Facebook Inc facebook.com False NaN
期望输出:
id website company_name tld match similar_companies
0 a www.facebook.com facebook facebook.com True Facebook
1 b www.y.com Yahoo Inc y.com False None
2 c www.g.com Google g.com True None
3 d www.g.com Google Inc g.com False None
4 e www.facebook.com Facebook facebook.com True facebook
5 f www.face.uk Facebook Inc face.uk True facebook
uuid
0 1a
1 NaN
2 3c
3 NaN
4 1a
5 1a
当前代码:
# Find if TLD is the same.
match_tld = destination.tld.isin(source.tld)
# Find if Company name is the same.
match_company_name = destination.company_name.isin(
source.company_name)
# Find similar source.
destination[
_SIMILAR_COMPANIES] = destination.company_name.apply(
_FindSimilarCompanies, args=(destination,))
# Find if Company name is the same from similar source.
match_similar_companies = destination.similar_companies.isin(
source.company_name)
# Update match column if TLD or company_name matches.
destination['match'] = match_tld | match_company_name | match_similar_companies
# Extract UUID for TLD matches.
merge_tld = destination.merge(
source[['tld', 'uuid']], on='tld', how='left')
# Extract UUID for company name matches.
destination = destination.merge(
source[['company_name', 'uuid']], on='company_name', how='left')
# I insert new line here!!!
# Combine dataframes.
destination['uuid'] = destination['uuid'].combine_first(merge_tld['uuid'])
logging.info(source)
logging.info(destination)
上面的代码适用于2列,但是当我尝试合并新列时,我得到一个keyrerror:(我在插入新代码的地方添加了一条注释)
错误:
KeyError: 'similar_companies'
我认为问题在于
源代码中没有列类似的公司,所以有必要重命名:
#for sample data column
_SIMILAR_COMPANIES = 'similar_companies'
destination[_SIMILAR_COMPANIES] = destination.company_name.str.extract('([fF]acebook)')
对不起,\u相似的公司
是相似的公司
?是的,没错。再次非常感谢
#for sample data column
_SIMILAR_COMPANIES = 'similar_companies'
destination[_SIMILAR_COMPANIES] = destination.company_name.str.extract('([fF]acebook)')
destination1 = destination.merge(
source[['company_name', 'uuid']], on='company_name', how='left')
destination2 = (destination.merge(
source[['company_name', 'uuid']].rename(columns={'company_name':'similar_companies'}),
on='similar_companies', how='left'))
# Combine dataframes.
merge_tld['uuid'] = (merge_tld['uuid'].combine_first(destination1['uuid'])
.combine_first(destination2['uuid']))
print (merge_tld)
id website company_name tld match similar_companies \
0 a www.facebook.com facebook facebook.com True facebook
1 b www.y.com Yahoo Inc y.com False NaN
2 c www.g.com Google g.com True NaN
3 d www.g.com Google Inc g.com False NaN
4 e www.facebook.com Facebook Inc facebook.com True Facebook
uuid
0 1a
1 NaN
2 3c
3 NaN
4 1a