Title_1 Title_2 Type He heard it from space A quick story about sounds from space Fiction The end of all time A sad poem about the end of time Non-Fiction The perfect beginning A story about friendship Non-Fiction我正在嘗試計算所有小說、非小說類型并計算 Title_1 和 Title_2 中相應類型的單詞數。我想要的輸出是：Type Count Num-Words Non-Fiction 2 20Fiction 1 12這是我到目前為止所擁有的：fopen = open(file_name, 'r')fhand = csv.reader(fopen)next(fhand)category_sum = dict()for row in fhand: col_0=len(row[0].split()) col_1=len(row[1].split()) print( col_1 + col_1) if row[2] in category_sum.keys(): category_sum[row[2]]+=1 else: category_sum[row[2]]=1我可以在一本不錯的字典中獲得類型的總數，但我似乎無法弄清楚如何將字數分配給適當的類型作為字典中的值。有任何想法嗎？
4 回答
小怪獸愛吃肉
TA貢獻1852條經驗 獲得超1個贊
這就是我最終使用的：
fhand = csv.reader(fopen)
next(fhand)
category_sum = dict()
word_sum = dict()
for row in fhand:
num_words = len(row[0].split(" ")) + len(row[1].split(" "))
if row[2] in category_sum.keys():
category_sum[row[2]]+=1
word_sum[row[2]]+=num_words
else:
category_sum[row[2]]=1
word_sum[row[2]]=num_words
combined = {key:[category_sum[key],word_sum[key]] for key in category_sum}
#print(combined)
print("Category | # Titles | # of Words\n---------------------------------")
for key in combined:
print("{} | {} | {} ".format(key,combined[key][0],combined[key][1]))
MMMHUHU
TA貢獻1834條經驗 獲得超8個贊
你可以這樣做：
import csv
file_name = 'book_titles.csv'
with open(file_name, 'r', newline='') as fopen:
reader = csv.reader(fopen)
next(reader) # Skip header.
category_sum = {}
for row in reader:
category_sum[row[2]] = category_sum.get(row[2], 0) + 1
print(category_sum) # -> {'Fiction': 1, 'Non-Fiction': 2}
紅糖糍粑
TA貢獻1815條經驗 獲得超6個贊
您可以將字典保存為其中一個鍵Count和另一個鍵所在的值Num-Words。因此，您的字典值分配可能如下所示：
# num_of_words =
if row[2] in category_sum.keys():
category_sum[row[2]]['Count']+=1
category_sum[row[2]]['Num-Words']+=num_of_words
else:
category_sum[row[2]]={}
蝴蝶不菲
使用
TA貢獻1810條經驗 獲得超4個贊
使用pandas：
創建數據框
合并兩個標題，按空格分割并計算由創建的列表中的單詞
splitgroupbyonType，然后聚合count和sum函數。reset_index并rename獲得所需的確切形式。
import pandas as pd
# read the file in
df = pd.read_csv('file.csv')
Title_1 Title_2 Type
He heard it from space A quick story about sounds from space Fiction
The end of all time A sad poem about the end of time Non-Fiction
The perfect beginning A story about friendship Non-Fiction
# count the words in Title_1 & Title_2
df['num_words'] = df[['Title_1', 'Title_2']].apply(lambda x: len(f'{x[0]} {x[1]}'.split()), axis=1)
Title_1 Title_2 Type num_words
He heard it from space A quick story about sounds from space Fiction 12
The end of all time A sad poem about the end of time Non-Fiction 13
The perfect beginning A story about friendship Non-Fiction 7
# create your desired output
test = df[['Type', 'num_words']].groupby('Type')['num_words'].agg(['count', 'sum']).reset_index().rename(columns={'count': 'Count', 'sum': 'Num-words'})
Type Count Num-words
Fiction 1 12
Non-Fiction 2 20
只需 3 行代碼即可獲得所需的輸出
使用數據框中的數據，如果需要，您可以更輕松地執行其他類型的文本分析（例如文本分析：使用 python 查找列中最常見的單詞）
在 a 中獲取輸出dict：
test.to_dict('list')
>>> {'Type': ['Fiction', 'Non-Fiction'], 'Count': [1, 2], 'Num-words': [12, 20]}
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