英文:
how to convert from extractedInformation (using pytesseract.image_to_string) to dataframe?
问题
I have this image:
I have extracted the text using pytesseract
# Use the image_to_string method to extract text from the imageextractedInformation = pytesseract.image_to_string(image, lang='eng')# Print the extracted textprint(extractedInformation)
This is the output:
MoustafaEngineerUpwork CompanyAhmedTeacherSchool
How to convert the output to a dataframe with 3 columns cols = ["name","job","company"]?
I tried to convert extractedInformation to a list by
list_word = extractedInformation.split('\n')
and this was the output:
['Moustafa','Engineer','Upwork Company','','Ahmed','Teacher','School','\x0c']
but still couldn't convert it to a dataframe with 3 columns.
英文:
I have this image:
I have extracted the text using pytesseract
# Use the image_to_string method to extract text from the imageextractedInformation = pytesseract.image_to_string(image, lang = 'eng')# Print the extract textprint(extractedInformation)
This is the output:
MoustafaEngineerUpwork CompanyAhmedTeacherSchool
How to convert the output to dataframe that has 3 columns cols = ["name","job","company"]?
I tried to convert extractedInformation to list by
list_word = extractedInformation.split('\n')
and this was the output:
['Moustafa','Engineer','Upwork Company','','Ahmed','Teacher','School','\x0c']
but still couldn't convert it to dataframe with 3 columns.
答案1
得分: 0
你可以使用：
import numpy as nplist_word =展开收缩df = pd.DataFrame(np.array(list_word).reshape(-1, 3), columns=["name", "job", "company"])
另一种变体（不使用 [tag:numpy]）：
data = [list_word[i:i+3] for i in range(0, len(list_word), 3)]df = pd.DataFrame(data, columns=["name", "job", "company"])
输出：
print(df)name job company0 Moustafa Engineer Upwork Company1 Ahmed Teacher School
英文:
You can use :
import numpy as nplist_word =展开收缩df = pd.DataFrame(np.array(list_word).reshape(-1, 3), columns=["name", "job", "company"])
Another variant (without [tag:numpy]) :
data = [list_word[i:i+3] for i in range(0, len(list_word), 3)]df = pd.DataFrame(data, columns=["name", "job", "company"])
Output :
print(df)name job company0 Moustafa Engineer Upwork Company1 Ahmed Teacher School
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