在 Python Pandas 中合并两个数据集答案

【问题标题】：Merge two data-sets in Python Pandas在 Python Pandas 中合并两个数据集
【发布时间】：2017-10-31 16:08:10
【问题描述】：

我有以下格式的两个数据集，并希望将它们合并为一个基于 City+Age+Gender 的数据集。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

数据集2：

         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：Feeds/Count 表示相同的含义。可以将它们中的任何一个作为合并数据集中的列名。

【问题讨论】：

抱歉，因为我在度假，所以耽搁了。今天检查了一下，效果很好..谢谢
没问题，不客气！

标签： python pandas merge

【解决方案1】：

使用 pandas.concat 和 rename 列来对齐列 - 需要 both DataFrames 中的相同列：

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

DataFrame.append 的替代品 - 不纯 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

【讨论】：