Recently I tried using tabula to parse a table in the pdf that contains no lines within each fields of the table.
This results in a creation of a list that combines all the different fields into one (example of output).
How do i convert this single string into a dataframe so i can manipulate the numbers? Thank you very much
There is no dummy file given in the question to test, but if there is no separation line in between columns of the pdf table, and the table is merging in one column after extracting from tabula, try to use parameter 'columns' in tabula.read_pdf.
According to Tabula Documentation, this parameter works like this:
columns (list, optional) –
X coordinates of column boundaries.
So, if the format of the PDF is same for every PDF, you can find X coordinates of columns from which you want to separate the data. For that you can use any PDF tool like Adobe, or you can hit and trial also.
Still doubt, please attach dummy PDF so one can look into it.
Related
I am trying to extract a table like this into a Dataframe. How to do that (and extract even the names splitted on several lines) with Python?
Also, I want this to be general and to be applied on each table (even if it doesn't this structure), so giving the coordinates for each separate and different table won't work that well.
I don't know about your exact problem but if you want to extract data or tables from PDF then try the camelot-py library, it is easy and gives almost more than 90% accuracy.
I am also working on the same project.
import camelot
tables = camelot.read_pdf(PDF_file_Path, flavor='stream', pages='1', table_areas=['5,530,620,180'])
tables[0].parsing_report
df = tables[0].df
The parameters of camelot.read_pdf are:
PDF_File the give file path;
table_areas is optional if you get an exact table then provide a location otherwise it can get whole data & all tables;
pages number of pages.
.parsing_report show the result description, e.g., accuracy and whitespace.
.df can show the table as a data frame. Index 0 refer to the 1st table. It depends on your data.
You can read more about them in the camelot documentation.
I am working on this PDF file to parse the tabular data out of it. I was hoping to use tabula or PyPDF2 to extract tables out of it but the data in PDF is not stored in tables. So, I chose pdfplumber to extract text out of it. Until now, I am able to read text line by line. But I can not figure out a universal pattern that I can use to extract the pricing list rows which I can store in a pandas dataframe and write to an excel file.
Can you help me if I should construct a regular expression or anything else that I can use to extract the pricing list out of this PDF? Because I can not think of any particular regular expression that would fit the messy nature of data inside the PDF, is there any better approach to take? Or simply it's not possible?
Code
Using the following code, I am able to extract all lines of text but the problem is, one price entry is spread across two rows. Consider current row is where most details about the entry are listed, how can I decide if the previous or next row also has information related to current entry.
If I could somehow figure that out, what might be the right approach to deal with the column values, they can be from 6-13 per line, how can I decide if at this particular location in current line, the column value resides?
import pdfplumber as scrapper
text = []
with scrapper.open('./report.pdf') as pdf:
for page in pdf.pages:
text.append(page.extract_text())
The PDF file I am working with:
https://drive.google.com/file/d/1GtjBf9FcKJCOJVNcGA9mvAshJ6t0oFca/view?usp=sharing
Sample Pictures demonstrating which data should fit in which fields:
I get data measurements from instruments. These measurements depend on several parameters, and a pivot table is a good solution to represent the data. Every measurement can be associated to a scope screenshoot to be more explicit. I get all the data in the following csv format :
The number of measurements and parameters can change.
I am trying to write a Python script (for now with Pandas lib) which allows me to create a pivot table in Excel. With Pandas, I can color the data in and out of a defined range. However, I would like also to to create a link on every cell who can send me to the corresponding screenshot. But I am stuck here.
I would like a result like the following (but with the link to the corresponding screenshot) :
Actually, I found out a way to add the link thanks to the =HYPERLINK() Excel function to all the cells with the apply() Pandas function.
However, I cannot apply a conditional formatting thanks to xlsxWriter anymore because the cells don't have a numerical content anymore
I can apply the conditional formatting first and then iterate through the whole sheet to add a link, but it will be a total mess to retrieve the relation between the data and the different parameters measurement
I would like your help to find ideas and efficient ways to do what I would like
xlsxwriter has a function called write_url ,but first while creating new worksheet you must apply write_url and then use openyxl to insert your pandas data frame
1)create worksheet and insert write_url
2)use openyxl to write data into already formatted cells.
I'm pretty new to Pandas and Python, but have solid coding background. I've decided to pick this up because it will help me automate certain financial reports at work..
To give you a basic background of my issue, I'm taking a PDF and using Tabula to reformat it into a CSV file, which is working fine but giving me certain formatting issues. The reports come in about 60 page PDF files, which I am exporting to a CSV and then trying to manipulate the data in Python using Pandas.
The issue: when I reformat the data, I get a CSV file that looks something like this -
The issue here is that certain tables are shifting and I think it is due to the amount of pages and multiple headings within those.
Would it be possible for me to reformat this data using Pandas, and basically create a set of rules for how it gets reformatted?
Basically, I would like to shift the rows that are misplaced back into their respective places based on something like blank spaces.
Is it possible for me to delete rows with certain strings - deleting extra/unnecessary headers.
Can I somehow save the 'Total' data at the bottom by searching for the row with 'Total' and placing it somewhere else?
In essence, is there a way to partition this data by a set of commands (without specifying row numbers - because this changes daily) and then reposition it accordingly so that I can manipulate the data however necessary?
I have got a spreadsheet, in which some cells are marked as Input cells. I would like to extract only those cells into a Python variable using, for example, the excel_read() function from pandas.
Is this possible at all?
Sure, if you know beforehand where they are you can specify which columns to use by invoking the parse_cols parameter. But it doesn't look like by reading through the pandas.read_excel function docs that you can programmatically select certain cells within the function call.
However, you could always read in everything and then discard what you don't need based on how Input cells are represented in the DataFrame. Without an example it would hard to guess how to do this currently, but pandas is good for this type of data cleaning.