ascending=True时，按升序排列.
normalize=True时，可计算出不同字符出现的频率，画柱状图统计时可以⽤到.
# trian中标签的⽐例
label_proportion = train['label'].value_counts(normalize=True).reset_index().sort_values(by=['index'])
# index label
# 5 1 0.029851
# 2 2 0.199005
# 0 3 0.298507
# 1 4 0.248756
# 3 5 0.149254
# 4 6 0.074627
df1= DataFrame( {"a":[3,4,5,6,2,3,4,4], "b":[2,4,5,6,5,4,3,4]} )
print(df1)
#dataframe要借助apply来应⽤value_counts()
记住我print(df1.apply(pd.value_counts))
# map中括号内是series类型,key是a列的数,values是出现的次数
print(df1['a'].map(df1['a'].value_counts()))
print(df1['a'].value_counts()) #加括号时可直接统计出a列每个元素出现的次数
a b
0 3 2
1 4 4
2 5 5
3 6 6
4 2 5
5 3 4
6 4 3
7 4 4
a b
2 1 1
3 2 1
4 3 3
5 1 2
6 1 1
0 2
1 3
2 1
3 1
4 1
5 2
6 3
7 3
Name: a, dtype: int64
输出
blog.csdn/qq_20412595/article/details/79921849
blog.csdn/qq_42665335/article/details/81177699
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