我有以下格式的两个数据集&想要将它们合并到基于City + Age + Gender的单个数据集中。提前致谢
数据集1:
City Age Gender Source Count
0 California 15-24 Female Amazon Prime Video 14629
1 California 15-24 Female Fubo TV 3840
2 California 15-24 Female Hulu 54067
3 California 15-24 Female Netflix 11713
4 California 15-24 Female Sling TV 10642
Dataset2:
City Age Gender Source Feeds
0 California 15-24 Female Blogs 150
1 California 15-24 Female Customsite 57
2 California 15-24 Female Discussions 28
3 California 15-24 Female Facebook Comment 555
4 California 15-24 Female Google+ 19
预期的结果数据集:
City Age Gender Source Count
California 15-24 Female Amazon Prime Video 14629
California 15-24 Female Fubo TV 3840
California 15-24 Female Hulu 54067
California 15-24 Female Netflix 11713
California 15-24 Female Sling TV 10642
California 15-24 Female Blogs 150
California 15-24 Female Customsite 57
California 15-24 Female Discussions 28
California 15-24 Female Facebook Comment 555
California 15-24 Female Google+ 19
注意:Feed / Count表示相同的含义。所以可以将它们中的任何一个作为合并数据集中的列名。
答案 0 :(得分:1)
使用pandas.concat
列与rename
列对齐列 - both DataFrames
中需要相同的列:
df = pd.concat([df1, df2.rename(columns={'Feeds':'Count'})], ignore_index=True)
print (df)
City Age Gender Source Count
0 California 15-24 Female Amazon Prime Video 14629
1 California 15-24 Female Fubo TV 3840
2 California 15-24 Female Hulu 54067
3 California 15-24 Female Netflix 11713
4 California 15-24 Female Sling TV 10642
5 California 15-24 Female Blogs 150
6 California 15-24 Female Customsite 57
7 California 15-24 Female Discussions 28
8 California 15-24 Female Facebook Comment 555
9 California 15-24 Female Google+ 19
替代DataFrame.append
- 不是纯python append
:
df = df1.append(df2.rename(columns={'Feeds':'Count'}), ignore_index=True)
print (df)
City Age Gender Source Count
0 California 15-24 Female Amazon Prime Video 14629
1 California 15-24 Female Fubo TV 3840
2 California 15-24 Female Hulu 54067
3 California 15-24 Female Netflix 11713
4 California 15-24 Female Sling TV 10642
5 California 15-24 Female Blogs 150
6 California 15-24 Female Customsite 57
7 California 15-24 Female Discussions 28
8 California 15-24 Female Facebook Comment 555
9 California 15-24 Female Google+ 19