對(duì)于當(dāng)前的項(xiàng)目，我計(jì)劃將 Pandas DataFrame 分組為stock_symbol第一標(biāo)準(zhǔn)和quarter第二標(biāo)準(zhǔn)。從其他線程中，我已經(jīng)看到類似的結(jié)構(gòu)group_data = df.groupby(['stock_symbol', 'quarter'])可能是這一點(diǎn)的可能解決方案。在給定的情況下，我只收到終端輸出<pandas.core.groupby.generic.DataFrameGroupBy object at 0x11fdcbf10>。有沒有人發(fā)現(xiàn)我這條線的思維錯(cuò)誤？相關(guān)代碼部分如下所示：# Datetime conversiondf['date'] = pd.to_datetime(df['date'])# Adding of 'Quarter' columndf['quarter'] = df['date'].dt.to_period('Q')# Grouping both the Stock Symbol and the Quarter columngroup_data = df.groupby(['stock_symbol', 'quarter'])print(group_data)在操作中要調(diào)用的函數(shù)突出顯示如下：# Word frequency analysisdef get_top_n_bigram(corpus, n=None): vec = CountVectorizer(ngram_range=(2, 2), stop_words='english').fit(corpus) bag_of_words = vec.transform(corpus) sum_words = bag_of_words.sum(axis=0) words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()] words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True) return words_freq[:n]
1 回答
慕斯王
TA貢獻(xiàn)1864條經(jīng)驗(yàn) 獲得超2個(gè)贊
這是實(shí)現(xiàn)您所追求的目標(biāo)的一種方法：
自定義函數(shù)：
def get_top_n_bigram(row):
corpus = row['txt_main'] + row['txt_pro'] + row['txt_con'] + row['txt_adviceMgmt']
n = 2 % the top n
vec = CountVectorizer(ngram_range=(2, 2), stop_words='english').fit(corpus)
bag_of_words = vec.transform(corpus)
sum_words = bag_of_words.sum(axis=0)
words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]
words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)
return words_freq[:n]
使用定義的函數(shù)調(diào)用groupbywith ：apply
df['date'] = pd.to_datetime(df['date'])
df['quarter'] = df['date'].dt.to_period('Q')
newdf = df.groupby(['stock_symbol', 'quarter']).apply(get_top_n_bigram).to_frame(name = 'frequencies')
print(newdf)
frequencies
stock_symbol quarter
AMG 2011Q3 [(smart driven, 2), (driven risk, 2)]
2013Q1 [(asset management, 2), (smart working, 1)]
2014Q1 [(audit firm, 3), (employment agency, 2)]
MMM 2017Q2 [(working 3m, 1), (3m time, 1)]
添加回答
舉報(bào)
0/150
提交
取消