python - 计算 pandas DataFrame 中的子词频率

Question

我有一个 pandas.DataFrame，其中有 2 列，其中包含酒精类型(即伏特加 80 PROOF、加拿大威士忌、香料朗姆酒)和售出的瓶子数量。我想首先将其分类为不太精细的类别，即(威士忌、伏特加、朗姆酒)，然后将每个类别销售的所有瓶子相加。

我的代码不允许我隔离“VODKA”等标签，而是返回“VODKA 80 Proof”等类别的频率。

在:

top_N = 10 # top 10 most used categories

word_dist = nltk.FreqDist(df['Category Name'])

print('All frequencies:')
print('=' * 60)
rslt = pd.DataFrame(word_dist.most_common(top_N),
                    columns=['Word', 'Frequency'])
print(rslt)
print('=' * 60)

df= df.groupby('Category Name')['Bottles Sold'].sum()

输出:

All frequencies:
============================================================
                               Word  Frequency
0                    VODKA 80 PROOF      35373
1                 CANADIAN WHISKIES      27087
2         STRAIGHT BOURBON WHISKIES      15342
3                        SPICED RUM      14631
4                    VODKA FLAVORED      14001
5                           TEQUILA      12109
6                  BLENDED WHISKIES      11547
7                   WHISKEY LIQUEUR      10902
8                    IMPORTED VODKA      10668
9  PUERTO RICO & VIRGIN ISLANDS RUM      10062
============================================================

有什么想法吗？

Answer 1

您是否考虑过添加匹配单词的类别？像这样的东西:

代码:

categories = {'VODKA', 'WHISKIES', 'RUM', 'TEQUILA', 'LIQUEUR'}
df['category'] = df['product'].apply(lambda x:
                                     [c for c in categories if c in x]

测试代码:

data = [
    ['VODKA 80 PROOF', '35373'],
    ['CANADIAN WHISKIES', '27087'],
    ['STRAIGHT BOURBON WHISKIES', '15342'],
    ['SPICED RUM', '14631'],
    ['VODKA FLAVORED', '14001'],
    ['TEQUILA', '12109'],
    ['BLENDED WHISKIES', '11547'],
    ['WHISKEY LIQUEUR', '10902'],
    ['IMPORTED VODKA', '10668'],
    ['PUERTO RICO & VIRGIN ISLANDS RUM', '10062'],
]
df = pd.DataFrame(data, columns=['product', 'count'], dtype=int)

categories = {'VODKA', 'WHISKIES', 'RUM', 'TEQUILA', 'LIQUEUR'}
df['category'] = df['product'].apply(lambda x:
                                     [c for c in categories if c in x][0])
print(df)
print(df.groupby('category')['count'].sum())

结果:

                            product  count  category
0                    VODKA 80 PROOF  35373     VODKA
1                 CANADIAN WHISKIES  27087  WHISKIES
2         STRAIGHT BOURBON WHISKIES  15342  WHISKIES
3                        SPICED RUM  14631       RUM
4                    VODKA FLAVORED  14001     VODKA
5                           TEQUILA  12109   TEQUILA
6                  BLENDED WHISKIES  11547  WHISKIES
7                   WHISKEY LIQUEUR  10902   LIQUEUR
8                    IMPORTED VODKA  10668     VODKA
9  PUERTO RICO & VIRGIN ISLANDS RUM  10062       RUM

category
LIQUEUR     10902
RUM         24693
TEQUILA     12109
VODKA       60042
WHISKIES    53976
Name: count, dtype: int32