pythonpdfpython-camelot

Extracting PDF Data into a Dataframe

I am trying to take this data and turn it into a dataframe in pandas:

enter image description here

I am using camelot and it is "working" however, I am only getting 2 columns with this code:

import camelot


tables = camelot.read_pdf('Inventory_Summary.pdf', flavor='stream')
print(tables[0])

what is happening is it is considering everything on the left side 1 columns and the blacked out information the only information in the 2nd column

I want just the informaiton below the date into a dataframe

any help you can provide whould be great!

Thanks!

-littlejiver

Solution

  • This is how I solved it...

    import PyPDF2
import pandas as pd
import numpy as np
 
 
lines = []
sites = []
kinds = []
total_offqc_wip_inv = []
total_offqc_scale_inv = []
total_offqc_truck_inv = []
total_offqc_rail_inv = []
total_offqc_boat_inv = []
 
 
# creating a pdf file object
pdfFileObj = open('PDFs/Inventory_Summary.pdf', 'rb')
 
# creating a pdf reader object
pdfReader = PyPDF2.PdfFileReader(pdfFileObj)
count = pdfReader.numPages
 
# creating a page object
 
pageObj0 = pdfReader.getPage(0)
pageObj1 = pdfReader.getPage(1)
pageObj2 = pdfReader.getPage(2)
pageObj3 = pdfReader.getPage(3)
pageObj4 = pdfReader.getPage(4)
pageObj5 = pdfReader.getPage(5)
 
# extracting text from page
page0 = pageObj0.extractText().strip()
page1 = pageObj1.extractText().strip()
page2 = pageObj2.extractText().strip()
page3 = pageObj3.extractText().strip()
page4 = pageObj4.extractText().strip()
page5 = pageObj5.extractText().strip()
 
corrected_page0 = page0.split('07:43am')[+1]
corrected_page1 = page1.split('07:43am')[+1]
corrected_page2 = page2.split('07:43am')[+1]
corrected_page3 = page3.split('07:43am')[+1]
corrected_page4 = page4.split('07:43am')[+1]
corrected_page5 = page5.split('07:43am')[+1]
 
for line in page0.splitlines():
    if 'Site' in line:
       for word in line.split():
           if word != 'Site': 
                sites.append(word)
    if 'All Shifts' in line:
        for word in line.split():
            if word != 'All':
                if word != 'Shifts': 
                    kinds.append(word)
    if 'Total OffQc WIP Inv' in line:
        for word in line.split():
            if word != 'Total':
                if word != 'OffQc':
                    if word != 'WIP':
                        if word != 'Inv':
                            total_offqc_wip_inv.append(word)
    if 'Total OffQc Scale Inv' in line:
        for word in line.split():
            if word != 'Total':
                if word != 'OffQc':
                    if word != 'Scale':
                        if word != 'Inv':
                            total_offqc_scale_inv.append(word)
    if 'Total OffQc Truck Inv' in line:
        for word in line.split():
            if word != 'Total':
                if word != 'OffQc':
                    if word != 'Truck':
                        if word != 'Inv':
                            total_offqc_truck_inv.append(word)
for line in page1.splitlines():
    if 'Total OffQc Rail Inv' in line:
        for word in line.split():
            if word != 'Total':
                if word != 'OffQc':
                    if word != 'Rail':
                        if word != 'Inv':
                            total_offqc_rail_inv.append(word)
    if 'Total OffQc Boat Inv' in line:
        for word in line.split():
            if word != 'Total':
                if word != 'OffQc':
                    if word != 'Boat':
                        if word != 'Inv':
                            total_offqc_boat_inv.append(word)
for line in page3.splitlines():
    if 'Site' in line:
        for word in line.split():
           if word != 'Site': 
                sites.append(word)
    if 'All Shifts' in line:
        for word in line.split():
            if word != 'All':
                if word != 'Shifts': 
                    kinds.append(word)
    if 'Total OffQc WIP Inv' in line:
        for word in line.split():
            if word != 'Total':
                if word != 'OffQc':
                    if word != 'WIP':
                        if word != 'Inv':
                            total_offqc_wip_inv.append(word)
    if 'Total OffQc Scale Inv' in line:
        for word in line.split():
            if word != 'Total':
                if word != 'OffQc':
                    if word != 'Scale':
                        if word != 'Inv':
                            total_offqc_scale_inv.append(word)
    if 'Total OffQc Truck Inv' in line:
        for word in line.split():
            if word != 'Total':
                if word != 'OffQc':
                    if word != 'Truck':
                        if word != 'Inv':
                            total_offqc_truck_inv.append(word)
for line in page4.splitlines():
    if 'Total OffQc Rail Inv' in line:
        for word in line.split():
            if word != 'Total':
                if word != 'OffQc':
                    if word != 'Rail':
                        if word != 'Inv':
                            total_offqc_rail_inv.append(word)
    if 'Total OffQc Boat Inv' in line:
        for word in line.split():
            if word != 'Total':
                if word != 'OffQc':
                    if word != 'Boat':
                        if word != 'Inv':
                            total_offqc_boat_inv.append(word)
sites.append("Total")
 
d = np.column_stack([sites, kinds, total_offqc_wip_inv, total_offqc_scale_inv, total_offqc_truck_inv, total_offqc_rail_inv, total_offqc_boat_inv])
            
 
df = pd.DataFrame(d)
 
# closing the pdf file object
pdfFileObj.close()