给定一个无序的值列表,比如
a = [5, 1, 2, 2, 4, 3, 1, 2, 3, 1, 1, 5, 2]
我怎样才能得到出现在列表中的每个值的频率,就像这样?
# `a` has 4 instances of `1`, 4 of `2`, 2 of `3`, 1 of `4,` 2 of `5`
b = [4, 4, 2, 1, 2] # expected output
给定一个无序的值列表,比如
a = [5, 1, 2, 2, 4, 3, 1, 2, 3, 1, 1, 5, 2]
我怎样才能得到出现在列表中的每个值的频率,就像这样?
# `a` has 4 instances of `1`, 4 of `2`, 2 of `3`, 1 of `4,` 2 of `5`
b = [4, 4, 2, 1, 2] # expected output
当前回答
str1='the cat sat on the hat hat'
list1=str1.split();
list2=str1.split();
count=0;
m=[];
for i in range(len(list1)):
t=list1.pop(0);
print t
for j in range(len(list2)):
if(t==list2[j]):
count=count+1;
print count
m.append(count)
print m
count=0;
#print m
其他回答
a=[1,2,3,4,5,1,2,3]
b=[0,0,0,0,0,0,0]
for i in range(0,len(a)):
b[a[i]]+=1
另一种方法是使用较重但功能强大的库——NLTK。
import nltk
fdist = nltk.FreqDist(a)
fdist.values()
fdist.most_common()
对于您的第一个问题,迭代列表并使用字典跟踪元素的存在。
对于你的第二个问题,只需使用集合操作符。
你可以这样做:
import numpy as np
a = [1,1,1,1,2,2,2,2,3,3,4,5,5]
np.unique(a, return_counts=True)
输出:
(array([1, 2, 3, 4, 5]), array([4, 4, 2, 1, 2], dtype=int64))
第一个数组是值,第二个数组是具有这些值的元素的数量。
所以如果你想要得到一个数字数组,你应该使用这个:
np.unique(a, return_counts=True)[1]
from collections import Counter
a=["E","D","C","G","B","A","B","F","D","D","C","A","G","A","C","B","F","C","B"]
counter=Counter(a)
kk=[list(counter.keys()),list(counter.values())]
pd.DataFrame(np.array(kk).T, columns=['Letter','Count'])