![](/img/trans.png)
[英]Copy rows from a dataframe to another dataframe in pandas
[英]Pandas copy values from another dataframe into my dataframe
我有 2 个数据df_mentions
: df_mentions
,我有网址, media
,我有一些期刊的信息。 我需要使用媒体中包含的信息不断更新df_mentions
。
Mentions=['https://www.lemonde.fr/football/article/2019/07/08/coupe-du-monde-feminine-2109-au-sein-de-chaque-equipe-j-ai-vu-de-grandes-joueuses_5486741_1616938.html','https://www.telegraph.co.uk/world-cup/2019/06/12/womens-world-cup-2019-groups-complete-guide-teams-players-rankings/','https://www.washingtonpost.com/sports/dcunited/us-womens-world-cup-champs-arrive-home-ahead-of-parade/2019/07/08/48df1a84-a1e3-11e9-a767-d7ab84aef3e9_story.html?utm_term=.8f474bba8a1a']
Date=['08/07/2019','08/07/2019','08/07/2019']
Publication=['','','']
Country=['','','']
Foundation=['','','']
Is_in_media=['','','']
df_mentions=pd.DataFrame()
df_mentions['Mentions']=Mentions
df_mentions['Date']=Date
df_mentions['Source']=Source
df_mentions['Country']=Country
df_mentions['Foundation']=Foundation
df_mentions['Is_in_media']=Is_in_media
Source=['New York times','Lemonde','Washington Post']
Link=['https://www.nytimes.com/','https://www.lemonde.fr/','https://www.washingtonpost.com/']
Country=['USA','France','USA']
Foundation=['1851','1944','1877']
media=pd.DataFrame()
media['Source']=Source
media['Link']=Link
media['Country']=Country
media['Foundation']=Foundation
media
媒体
我需要检查链接的来源是否包含在媒体中并从中提取数据以填充 df_mentions 并获得以下结果:
而我所做的是:
for index in range(0,len(media)):
for index2 in range(0,len(df_mentions)):
if str(media['Link'][index])in str(df_mentions['Mentions'][index2]):
df_mentions['Publication'][index2]=media['Publication'][index]
df_mentions['Country'][index2]=media['Country'][index]
df_mentions['Foundation'][index2]=media['Foundation'][index]
df_mentions['Is_in_media'][index2]='Yes'
else:
df_mentions['Is_in_media'][index2]='No'
df_mentions
但是它在我的笔记本上运行一次,如果我关闭笔记本会给我错误,我使用的是 Pandas 0.24.0。 有没有更好的方法来做到这一点并一直允许工作?
提前致谢! 所有帮助将不胜感激!
您可以做的一件事是提取df_mentions
的 URL 并将其用作合并的键
起始数据(删除了df_mentions
的空列):
print(df_mentions)
Mentions Date
0 https://www.lemonde.fr/football/article/2019/0... 08/07/2019
1 https://www.telegraph.co.uk/world-cup/2019/06/... 08/07/2019
2 https://www.washingtonpost.com/sports/dcunited... 08/07/2019
print(media)
Source Link Country Foundation
0 New York times https://www.nytimes.com/ USA 1851
1 Lemonde https://www.lemonde.fr/ France 1944
2 Washington Post https://www.washingtonpost.com/ USA 1877
创建一个包含基本 url 的新列:
df_mentions['url'] = df_mentions['Mentions'].str.extract(r'(http[s]?:\/\/.+?\/)')
Mentions Date url
0 https://www.lemonde.fr/football/articl... 08/07/2019 https://www.lemonde.fr/
1 https://www.telegraph.co.uk/world-cup/... 08/07/2019 https://www.telegraph.co.uk/
2 https://www.washingtonpost.com/sports/... 08/07/2019 https://www.washingtonpost.com/
合并时使用该新列作为键:
df_mentions.merge(media,
left_on='url',
right_on='Link',
how='left').drop(columns=['url', 'Link'])
Mentions Date Source Country Foundation
0 https://www.lemonde.fr/football/art... 08/07/2019 Lemonde France 1944
1 https://www.telegraph.co.uk/world-c... 08/07/2019 NaN NaN NaN
2 https://www.washingtonpost.com/spor... 08/07/2019 Washington Post USA 1877
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.