计算熊猫数据框中特定单词的出现次数

易于使用 defaultDict 中的 Counter 或 collections：

words = ['uno','dos','one','two','tres','quatro','yes','wooly','bully','watch','come','git','matty','told','hattie','thing','saw','big','horns','jaw','drive','lets','dont','take','chance','lseven','learn','dance','yeah','thats','get','someone','really','pull','wool','got','got']


from collections import defaultDict
Dict_count = defaultDict(int)
for item in words:
    Dict_count[item] += 1

或者：

from collections import Counter
counts = Counter(words)

要计算字符串中子字符串出现的次数，您可以这样做

String.count(subString)

因此，您可以将此函数应用于包含字符串的列：

String_occurrences = df.Token.apply(lambda x: sum([x.count(subString) for subsTing in ['wooly','girl']])

然后你只需要对计数求和

@R_19_10586@l_occurrences = String_occurrences.sum()

Counter 的返回是字典。

df["Count"] = (
    df['Token'].str.split()
    .apply(Counter)
    .apply(lambda counts: sum([counts[word] for word in words]))
)

如果 Token 列中的值已经是列表，则无需使用 str.split()

df["Count"] = (
    df['Token']
    .apply(Counter)
    .apply(lambda counts: sum([counts[word] for word in words]))
)

要计算 pandas 数据框中特定单词的出现次数，您可以使用以下代码。

from collections import Counter
    
def most_common_words(labels,quantity):

    words = [i.split(" ",1)[0] for i in labels]
    counter = Counter(words).most_common(quantity)
    df = pd.DataFrame(counter,columns=["Word","occurrence number"])\
                        .sort_values(by="occurrence number",ascending=TruE)
    return df

    
# Identify the most common words
df_most_common_words = most_common_words(words,20)
print(df_most_common_words)