我正面临下一个情况。我有两个数据框，比如说 df1 和 df2，我需要通过一个键（ ID_ed ， ID ）加入它们，第二个数据框可能有不止一次出现的键，我需要的是加入这两个数据框，并添加重复出现的键作为新列（如下图所示）
我尝试使用merge = df2.join( df1 , lsuffix='_ZID', rsuffix='_IID' , how = "left" ) 和 concat 操作，但到目前为止没有运气。似乎它只保留最后一次出现（好像它正在覆盖数据）
非常感谢您对此的任何帮助，并在此先感谢。
【问题讨论】：
print(df.to_string()) 的输出，而不是电子表格的屏幕截图。
另一种方法是在调用pivot_table 之前先调用create a serial counter for the ID_ed column、set_index 和unstack。 pivot_table 聚合将是 first。这种方法与this SO answer 非常相似
生成数据
import pandas as pd
import numpy as np
a = [['ID_ed','color'],[1,5],[2,8],[3,7]]
b = [['ID','code'],[1,1],[1,5],
[2,np.nan],[2,20],[2,74],
[3,10],[3,98],[3,85],
[3,21],[3,45]]
df1 = pd.DataFrame(a[1:], columns=a[0])
df2 = pd.DataFrame(b[1:], columns=b[0])
print(df1)
ID_ed color
0 1 5
1 2 8
2 3 7
print(df2)
ID code
0 1 1.0
1 1 5.0
2 2 NaN
3 2 20.0
4 2 74.0
5 3 10.0
6 3 98.0
7 3 85.0
8 3 21.0
9 3 45.0
先合并和解栈
# Merge and add a serial counter column
df = df1.merge(df2, how='inner', left_on='ID_ed', right_on='ID')
df['counter'] = df.groupby('ID_ed').cumcount()+1
print(df)
ID_ed color ID code counter
0 1 5 1 1.0 1
1 1 5 1 5.0 2
2 2 8 2 NaN 1
3 2 8 2 20.0 2
4 2 8 2 74.0 3
5 3 7 3 10.0 1
6 3 7 3 98.0 2
7 3 7 3 85.0 3
8 3 7 3 21.0 4
9 3 7 3 45.0 5
# Set index and unstack
df.set_index(['ID_ed','color','counter']).\
unstack().\
swaplevel(1,0,axis=1).\
sort_index(level=0,axis=1).add_prefix('counter_')
print(df)
counter counter_1 counter_2 \
counter_ID counter_code counter_ID counter_code\
ID_ed color \
1 5 1.0 1.0 1.0 5.0\
2 8 2.0 NaN 2.0 20.0\
3 7 3.0 10.0 3.0 98.0 \
counter_3 counter_4 counter_5
counter_ID counter_code counter_ID counter_code counter_ID counter_code
NaN NaN NaN NaN NaN NaN
2.0 74.0 NaN NaN NaN NaN
3.0 85.0 3.0 21.0 3.0 45.0
接下来生成数据透视表
# Pivot table with 'first' aggregation
dfp = pd.pivot_table(df, index=['ID_ed','color'],
columns=['counter'],
values=['ID', 'code'],
aggfunc='first')
print(dfp)
ID code
counter 1 2 3 4 5 1 2 3 4 5
ID_ed color
1 5 1.0 1.0 NaN NaN NaN 1.0 5.0 NaN NaN NaN
2 8 2.0 2.0 2.0 NaN NaN NaN 20.0 74.0 NaN NaN
3 7 3.0 3.0 3.0 3.0 3.0 10.0 98.0 85.0 21.0 45.0
最后重命名列并按部分列名切片
# Rename columns
level_1_names = list(dfp.columns.get_level_values(1))
level_0_names = list(dfp.columns.get_level_values(0))
new_cnames = [b+'_'+str(f) for f, b in zip(level_1_names, level_0_names)]
dfp.columns = new_cnames
# Slice by new column names
print(dfp.loc[:, dfp.columns.str.contains('code')].reset_index(drop=False))
ID_ed color code_1 code_2 code_3 code_4 code_5
0 1 5 1.0 5.0 NaN NaN NaN
1 2 8 NaN 20.0 74.0 NaN NaN
2 3 7 10.0 98.0 85.0 21.0 45.0
【讨论】：
我会使用 cumcount 和 pivot_table：
In [11]: df1
Out[11]:
ID color
0 1 5
1 2 8
2 3 7
In [12]: df2
Out[12]:
ID code
0 1 1.0
1 1 5.0
2 2 NaN
3 2 20.0
4 2 74.0
In [13]: res = df1.merge(df2) # This is a merge if the column names match
In [14]: res
Out[14]:
ID color code
0 1 5 1.0
1 1 5 5.0
2 2 8 NaN
3 2 8 20.0
4 2 8 74.0
In [15]: res['count'] = res.groupby('ID').cumcount()
In [16]: res.pivot_table('code', ['ID', 'color'], 'count')
Out[16]:
count 0 1 2
ID color
1 5 1.0 5.0 NaN
2 8 NaN 20.0 74.0
【讨论】：