我有这个数据集:
ARTID INFO_1 INFO_2
00001 some_info_11 some_info_21
00002 some_info_12 some_info_22
00003 some_info_13 some_info_23
我想这样转变
ARTID some_info_11 some_info_12 some_info_13 some_info_21 some_info_22 some_info_23
00001 1 0 0 1 0 0
00002 0 1 0 0 1 0
但我需要是一个稀疏矩阵。最“内存友好”的方法是什么？
最佳答案
使用pd.get_dummies()和 pd.concat()
df1 = pd.concat([df.ARTID,pd.get_dummies(df[['INFO_1','INFO_2']],prefix='',prefix_sep='')],axis=1)
print(df1)
ARTID some_info_11 some_info_12 some_info_13 some_info_21 \
0 00001 1 0 0 1
1 00002 0 1 0 0
2 00003 0 0 1 0
some_info_22 some_info_23
0 0 0
1 1 0
2 0 1
如果允许 ARTID 作为索引，则可以使用:
pd.get_dummies(df[['INFO_1','INFO_2']],prefix='',prefix_sep='').set_index(df.ARTID)
some_info_11 some_info_12 some_info_13 some_info_21 some_info_22 \
ARTID
00001 1 0 0 1 0
00002 0 1 0 0 1
00003 0 0 1 0 0
some_info_23
ARTID
00001 0
00002 0
00003 1
关于python - 将 pandas df 从长转换为宽，然后转换为稀疏矩阵，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/54197329/