我正在努力抓取數(shù)據(jù)并解析字符串中的名稱。例如，我正在使用類似于以下內(nèi)容的字符串：Dr. Sharif Amlani, UC Davis Health, Dr. Joe Biden, UC San Francisco, and Dr. Elton John, Public Health Director for Davis County和Dr. Winston Bishop from UC San Francisco and Usain Bolt from UC San Francisco 是否有代碼可以獲取此類文本并將其轉(zhuǎn)換為數(shù)據(jù)集？這樣，數(shù)據(jù)看起來像這樣： Name AffiliationSharif Amlani UC Davis HealthJoe Biden UC San FranciscoElton John Public Health Director for Davis CountyWinston Bishop UC San FranciscoUsain Bolt UC San Francisco謝謝
4 回答
慕少森
TA貢獻(xiàn)2019條經(jīng)驗(yàn) 獲得超9個(gè)贊
如果您的字符串始終采用 format name from place and name from place，您可以這樣做：
import pandas as pd
# your consistently formatted string
s = "Dr. Winston Bishop from UC San Francisco and Usain Bolt from UC San Francisco"
l = list() # a list to keep track of data - I am sure there's a better way to do this
for row in s.split('and'): # each row looks like "name from affiliation"
# l = [(name, affiliation), ...]
l.append(n.split((n.strip() for n in row.split('from'))
# then create the DataFrame
df = pd.DataFrame(data = l, columns = ['Name', 'Affiliation'])
# you might want to strip the names and affiliations using pandas DataFrame using a lambda expression
眼眸繁星
TA貢獻(xiàn)1873條經(jīng)驗(yàn) 獲得超9個(gè)贊
您可以進(jìn)行正則表達(dá)式匹配并創(chuàng)建 df. 此處顯示一個(gè)字符串的示例方法：
text = "Dr. Sharif Amlani, UC Davis Health, Dr. Joe Biden, UC San Francisco, and Dr.
Elton John, Public Health Director for Davis County"
text = text.replace(', and' ,',')
re.findall("([\w\s]+),([\w\s]+)",text)
df = pd.DataFrame(r)
df.columns = ("Name", "Affiliation")
print(df)
輸出：
Name Affiliation
0 Sharif Amlani UC Davis Health
1 Joe Biden UC San Francisco
2 Elton John Public Health Director for Davis County
侃侃無極
TA貢獻(xiàn)2051條經(jīng)驗(yàn) 獲得超10個(gè)贊
在抓取過程中，一切都?xì)w結(jié)為模式匹配。如果字符串的格式不一致，可能會(huì)非常痛苦。不幸的是，就你而言，情況似乎就是這樣。因此，我建議根據(jù)具體情況進(jìn)行處理。
我可以觀察到這樣一種模式，除了一個(gè)例外，所有名字都以“博士”開頭。您可以使用它通過正則表達(dá)式提取名稱。
import re
text = "Dr. Sharif Amlani, UC Davis Health, Dr. Joe Biden, UC San Francisco, and Dr. Elton John, Public Health Director for Davis County"
regex = '((Dr.)( [A-Z]{1}[a-z]+)+)' # this will return three groups of matches
names = [match[0] for match in re.findall(regex, text)] #simply extracting the first group of matches, which is the name
您可以將其應(yīng)用于其他字符串，但正如我上面提到的，限制是它只能捕獲以“Dr.”開頭的名稱。您也可以對附屬關(guān)系使用類似的策略。請注意，“，”分隔名稱和從屬關(guān)系，以便我們可以使用它。
import re
text = "Dr. Sharif Amlani, UC Davis Health, Dr. Joe Biden, UC San Francisco, and Dr. Elton John, Public Health Director for Davis County"
affiliations = [term for term in text.split(',') if 'Dr.' not in term] # splitting the text by a comma and then excluding results that contain a 'Dr.'
同樣，您必須根據(jù)特定文本調(diào)整您的解決方案，但希望這可以幫助您思考問題。最后，您可以使用 pandas 將結(jié)果合并到數(shù)據(jù)框中：
import pandas as pd
data = pd.DataFrame(list(zip(names, affiliations)), columns = ['Name', 'Affiliation'])
慕沐林林
TA貢獻(xiàn)2016條經(jīng)驗(yàn) 獲得超9個(gè)贊
以下是此示例文本的示例代碼：
text = "\
Sharif Amlani UC Davis Health\n\
Joe Biden UC San Francisco\n\
Elton John Public Health Director for Davis County\n\
Winston Bishop UC San Francisco\n\
Usain Bolt UC San Francisco"
lines = text.split('\n')
df = pd.concat([pd.DataFrame([[line[0:16].strip(),line[16:].strip()]]) for line in lines])
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