我有这个数据框,我想将具有相同ID的行转换为一行:
set_property(
TARGETS MY-TARGET
PROPERTY CXX_INCLUDE_WHAT_YOU_USE ${iwyu_path}
)
结果应为:
ID TYPE1 TYPE2 GROUP STARTIME
1 A C Q1 10:25
1 A C Q2 11:00
1 A C Q3 11:30
2 B D Y1 12:00
2 B D Y2 12:30
这是我目前的代码:
ID TYPE1 TYPE2 G1 G2 G3 START_G1 START_G2 START_G3
1 A C Q1 Q2 Q3 10:25 11:00 11:30
2 B D Y1 Y2 NaN 12:00 12:30 NaN
但是列df_transposed = df.pivot_table(index= ['ID','GROUP']).unstack()
df_transposed = df_transposed.sort_index(axis=1, level=1)
df_transposed.columns = ['_'.join((col[0], str(col[1]))) for col in df_transposed]
df_transposed = df_transposed.reset_index(level=0)
df_transposed.head()
和TYPE1
对于ID 1重复3次,对于ID 2重复2次。我希望它们是单个列,如预期结果中所示,因为它们始终具有相同ID的相同值。另外,我得到TYPE2
这样的列,但我想要GROUP_Q1
,Group_1
等。
答案 0 :(得分:1)
您可以将pivot_table
与cumcount
一起用于计算群组:
df_transposed = df.pivot_table(index= ['ID','TYPE1', 'TYPE2'],
columns=df.groupby(['ID','TYPE1', 'TYPE2']).cumcount() + 1,
values=['GROUP','STARTIME'], aggfunc='first')
df_transposed.columns = ['_'.join((col[0], str(col[1]))) for col in df_transposed]
print (df_transposed)
GROUP_1 GROUP_2 GROUP_3 STARTIME_1 STARTIME_2 STARTIME_3
ID TYPE1 TYPE2
1 A C Q1 Q2 Q3 10:25 11:00 11:30
2 B D Y1 Y2 None 12:00 12:30 None
如果需要重命名列:
df = df.rename(columns={'GROUP':'G','STARTIME':'START'})
df_transposed = df.pivot_table(index= ['ID','TYPE1', 'TYPE2'],
columns=df.groupby(['ID','TYPE1', 'TYPE2']).cumcount() + 1,
values=['G','START'], aggfunc='first')
df_transposed.columns = ['_'.join((col[0], str(col[1]))) for col in df_transposed]
print (df_transposed.reset_index())
ID TYPE1 TYPE2 G_1 G_2 G_3 START_1 START_2 START_3
0 1 A C Q1 Q2 Q3 10:25 11:00 11:30
1 2 B D Y1 Y2 None 12:00 12:30 None