Python:一次包含重复值的多个列的 Pandas 数据透视表
问题描述
有一个包含名称、学校和标记列的 pandas 数据框
have a pandas dataframme with columns name , school and marks
name school marks
tom HBS 55
tom HBS 55
tom HBS 14
mark HBS 28
mark HBS 19
lewis HBS 88
如何转置和转换成这样的
How to transpose and convert into like this
name school marks_1 marks_2 marks_3
tom HBS 55 55 14
mark HBS 28 19
lewis HBS 88
试过这个:
df = df.pivot_table(index='name', values='marks', columns='school')
.reset_index()
.rename_axis(None, axis=1)
print(df)
df = df.pivot('name','marks','school')
检查了这些链接
https://stackoverflow.com/questions/22798934/pandas-long-to-wide-reshape-by-two-variables
https://stackoverflow.com/questions/62391419/pandas-group-by-and-convert-rows-into-multiple-columns
https://stackoverflow.com/questions/60698109/pandas-multiple-rows-to-single-row-with-multiple-columns-on-2-indexes
由于重复值而出现此错误.如果存在重复如何处理,我们必须保留它们
getting this error due to duplicate values. how to handle if duplicate exists and we have to keep them
ValueError: Index contains duplicate entries, cannot reshape
解决方案
尝试使用 set_index
和 unstack
与 groupby
和 cumcount
:
Try using set_index
and unstack
with groupby
and cumcount
:
df_out = df.set_index(['name',
'school',
df.groupby(['name','school'])
.cumcount() +1]).unstack()
df_out.columns = [f'{i}_{j}' for i, j in df_out.columns]
df_out = df_out.reset_index()
df_out
输出:
name school marks_1 marks_2 marks_3
0 lewis HBS 88.0 NaN NaN
1 mark HBS 28.0 19.0 NaN
2 tom HBS 55.0 55.0 14.0
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