在Pandas数据框中将列转换为行 | 在Pandas数据框中将列转换为行

本文介绍了在Pandas数据框中将列转换为行的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我有两列的Pandas数据框.一个是唯一标识符，第二个是附加到此唯一标识符的产品名称.我有重复的标识符和产品名称值.我想将一列产品名称转换为几列而不重复标识符.也许我需要通过标识符来汇总产品名称.

I have Pandas dataframe with two columns. One is unique identifier and second is the name of product attached to this unique identifier. I have duplicate values for identifier and product names. I want to convert one column of product names into several columns without duplicating identifier. Maybe I need to aggregate product names through identifier.

我的数据框如下:

ID  Product_Name
100  Apple
100  Banana
200  Cherries
200  Apricots
200  Apple
300  Avocados

我想拥有这样的数据框:

ID
100  Apple Banana
200  Cherries Apricots Apple
300  Avocados

每个标识符旁边的每个产品都必须在单独的列中

Each product along each identifier has to be in separate column

我尝试了pd.melt，pd.pivot，pd.pivot_table，但只有错误，并且此错误表示No numeric types to aggregate

I tried pd.melt, pd.pivot, pd.pivot_table but only errors and this errors says No numeric types to aggregate

有什么想法吗?

推荐答案

使用 cumcount ，用于将MultiIndex的新列名.DataFrame.set_index.html"rel =" nofollow noreferrer> set_index 并通过 unstack :

Use cumcount for new columns names to MultiIndex by set_index and reshape by unstack:

df = df.set_index(['ID',df.groupby('ID').cumcount()])['Product_Name'].unstack()

或通过构造器创建list的Series和新的DataFrame:

Or create Series of lists and new DataFrame by contructor:

s = df.groupby('ID')['Product_Name'].apply(list)
df = pd.DataFrame(s.values.tolist(), index=s.index)

print (df)
            0         1      2
ID
100     Apple    Banana    NaN
200  Cherries  Apricots  Apple
300  Avocados       NaN    NaN

但是如果要2列DataFrame:

df1 = df.groupby('ID')['Product_Name'].apply(' '.join).reset_index(name='new')
print (df1)
    ID                      new
0  100             Apple Banana
1  200  Cherries Apricots Apple
2  300                 Avocados

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