如何在數據框Python中獲取具有特定值的最常見單詞

Question

我有一個得分為0和1的數據框以及相應的評論，我想找到0分和1分的評論中最常見的單詞。 我嘗試了這個，但它給出了所有單詞的計數：

count = defaultdict(int)
l = df['Summary']
for number in l:
    count[number] += 1

print(count)

如何從1分和0分的所有行中找到最常見的值？

Answer 1

嘗試使用頻率字典。 如果您的列可以被視為列表列表：

data = [[0, "text samle 1"], [0, "text sample 2"], [1, "text sample 3"]]

...那么你也能：

fd0 = dict()
fd1 = dict()
for list_item in data:
    associated_value = list_item[0]

    #note the split(' ') splits the string into a list of words
    for word in list_item[1].split(' '):
        if associated_value == 0:
            fd0[word] = 1 if word not in fd0 else fd0[word] + 1
        elif associated_value == 1:
            fd1[word] = 1 if word not in fd1 else fd1[word] + 1

在循環結束時，fd0應具有標簽0的頻率，fd1應具有標簽1的頻率。

Answer 2

假設您的數據看起來像這樣

            review  score
0       bad review      0
1      good review      1
2  very bad review      0
3   movie was good      1

你可以做點什么

words = pd.concat([pd.Series(row['score'], row['review'].split(' '))              
    for _, row in df.iterrows()]).reset_index()
words.columns = ['word', 'score']
print(words.groupby(['score', 'word']).size())

給你的

score  word
0      bad       2
       review    2
       very      1
1      good      2
       movie     1
       review    1
       was       1
dtype: int64

Answer 3

most_common_0 = ''
most_common_1 = ''

for text, score in zip(df['Summary'], df['Score']):
    if score == 1:
        most_common_1 += ' ' + text
    else:
        most_common_0 += ' ' + text

from collections import Counter
c = Counter(most_common_1.split())
print(c.most_common(2)) # change this 2 to the number you want to analyze

產量

[('good', 2), ('and', 1)]