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How to write a large .txt file to a csv for Biq Query dump?

I have a dataset that is 86 million rows x 20 columns with a header, and I need to convert it to a csv in order to dump it into big query (adding multiple tags from that). The logical solution is reading the .txt file with pd.read_csv but I don't have 86 million rows of memory on my device and it will crash jupyter.
I'm aware of other threads such as (How to convert a tab delimited text file to a csv file in Python) but my issue seems rather niche.
Is there a way I could go about this? I thought about Vaex but I have total unfamiliarity with the toolkit, and it doesn't seem to have a writer within.
Current thoughts would be:
csv_path = r'csv_test.csv'
txt_path = r'txt_test.txt'
with open(txt_path, "r") as in_text:
in_reader = csv.reader(in_text, delimiter="|", skipinitialspace=True)
with open(csv_path, "w") as out_csv:
out_writer = csv.writer(out_csv, delimiter = ',')
for row in in_reader:
out_writer.writerow(row)
Currently, I am receiving an error stating:
Error: field larger than field limit (131072)
It seems it's the maximum row count in a single column, so I'm quite a bit off.
I've gotten a csv of smaller files to generate (only using 3 of the 35 total .txt files) but when I attempt to use all, it fails with code above.
Update: I have expanded the sys.maxsize and am still receiving this same error
I have no way to verify if this works due to the sheer size of the dataset, but it seems like it /should/ work. Trying to read it with Vaex would work if I wasn't getting parsing errors due to there being commas within the data.
So I have 3 questions:
Is there a way I can write a larger sized csv?
Is there a way to dump in the large pipe delimited .txt file to Big Query in chunks as different csv's?
Can I dump 35 csv's into Big Query in one upload?
Edit:
here is a short dataframe sample:
|CMTE_ID| AMNDT_IND| RPT_TP| TRANSACTION_PGI| IMAGE_NUM| TRANSACTION_TP| ENTITY_TP| NAME| CITY| STATE| ZIP_CODE| EMPLOYER| OCCUPATION| TRANSACTION_DT| TRANSACTION_AMT| OTHER_ID| TRAN_ID| FILE_NUM| MEMO_CD| MEMO_TEXT| SUB_ID
0|C00632562|N|M4|P|202204139496092475|15E|IND|NAME, NAME|PALO ALTO|CA|943012820.0|NOT EMPLOYED|RETIRED|3272022|5|C00401224|VTEKDYJ78M3|1581595||* EARMARKED CONTRIBUTION: SEE BELOW|4041920221470955005
1|C00632562|N|M4|P|202204139496092487|15E|IND|NAME, NAME|DALLAS|TX|752054324.0|SELF EMPLOYED|PHOTOGRAPHER|3272022|500|C00401224|VTEKDYJ7BD4|1581595||* EARMARKED CONTRIBUTION: SEE BELOW|4041920221470955041

I think there is some red-herring going on here:
Is there a way I can write a larger sized csv?
Yes, the reader and writer iterator style should be able to read any length of file, they step through incrementally, and at no stage do they attempt to read the whole file. Something else is going wrong in your example.
Is there a way to dump in the large tab-delimited .txt file to Big Query in chunks as different csv's?
You shouldn't need to.
Can I dump 35 csv's into Big Query in one upload?
That's more a Big Query api question, so I wont attempt to answer that here.
In your code, your text delimiter is set to a pipe, but in your question number 2, you describe it as being tab delimited. If you're giving the wrong delimiter to the code, it might try to read more content into a field than it's expecting, and fail when it hits some field-size limit. This sounds like it might be what's going on in your case.
Also, watch out when piping your file out and changing delimiters - in the data sample you post, there are some commas embedded in the text, this might result in a corrupted file when it comes to reading it in again on the other side. Take some time to think about your target CSV dialect, in terms of text quoting, chosen delimiters etc.
Try replacing the | with \t and see if that helps.
If you're only changing the delimiter from one thing to another, is that a useful process? Maybe forget the whole CSV nature of the file, and read lines iteratively, and write them without modifying them any, you could use readline and writeline for this, probably speeding things up in the process. Again, because they're iterative, you wont have to worry about loading the whole file into RAM, and just stream from one source to your target. Beware how long it might take to do this, and if you've a patchy network, it can all go horribly wrong. But at least it's a different error!

how can I reliably access a single key-value pair from a JSON file that's too large to load into memory?

I am trying to retrieve the names of the people from my file. The file size is 201GB
import json
with open("D:/dns.json", "r") as fh:
for l in fh:
d = json.loads(l)
print(d["name"])
Whenever I try to run this program on windows, I encounter a Memory error, which says insufficient memory.
Is there a reliable way to parse a single key, value pair without loading the whole file? I have reading the file in chunks in mind, but I don't know how to start.
Here is sample: test.json
Every line is seperated by newline. Hope this helps.

You may want to give ijson a try : https://pypi.python.org/pypi/ijson

Unfortunately there is no guarantee that each line of a JSON file will make any sense to the parser on its own. I'm afraid JSON was never intended for multi-gigabyte data exchange, precisely because each JSON file contains an integral data structure. In the XML world people have written incremental event-driven (SAX-based) parsers. I'm not aware of such a library for JSON.

How can I read four specific lines of a file without reading the whole file in python?

I need to read 4 specific lines of a file in python. I don't want to read all the file and then get four out of it ( for the sake of menory). Does anyone know how to do that?
Thanks!
P. S. I used the following code but apparently it reads all the file and then take 4 out of it.
a=open("file", "r")
b=a.readlines() [c:d]

you have to read at least to the lines you are interested in ... you can use islice to grab a slice
interesting_lines = list(itertools.islice(a,c,d))
but it still reads up to those lines

Files, at least on Macs and Windows and Linux and other UNIXy systems, are just streams of bytes; there's no concept of "line" in the file structure, just bytes that happen to represent newline characters. So the only way to find the Nth line in the file is to start at the beginning and read until you've found (N-1) newlines. You don't have to store all the content you scan through, but you do have to read it.
Then you have to read and store from that point until you find 4 more newlines.
You can do this in Python, but it's not clear to me that it's a win compared to using the straightforward approach that reads more than it needs to; feels like premature optimization to me.

Reading lines in text files using python

I am currently programming a game that requires reading and writing lines in a text file. I was wondering if there is a way to read a specific line in the text file (i.e. the first line in the text file). Also, is there a way to write a line in a specific location (i.e. change the first line in the file, write a couple of other lines and then change the first line again)? I know that we can read lines sequentially by calling:
f.readline()
Edit: Based on responses, apparently there is no way to read specific lines if they are different lengths. I am only working on a small part of a large group project and to change the way I'm storing data would mean a lot of work.
But is there a method to change specifically the first line of the file? I know calling:
f.write('text')
Writes something into the file, but it writes the line at the end of the file instead of the beginning. Is there a way for me to specifically rewrite the text at the beginning?

If all your lines are guaranteed to be the same length, then you can use f.seek(N) to position the file pointer at the N'th byte (where N is LINESIZE*line_number) and then f.read(LINESIZE). Otherwise, I'm not aware of any way to do it in an ordinary ASCII file (which I think is what you're asking about).
Of course, you could store some sort of record information in the header of the file and read that first to let you know where to seek to in your file -- but at that point you're better off using some external library that has already done all that work for you.
Unless your text file is really big, you can always store each line in a list:
with open('textfile','r') as f:
lines=[L[:-1] for L in f.readlines()]
(note I've stripped off the newline so you don't have to remember to keep it around)
Then you can manipulate the list by adding entries, removing entries, changing entries, etc.
At the end of the day, you can write the list back to your text file:
with open('textfile','w') as f:
f.write('\n'.join(lines))
Here's a little test which works for me on OS-X to replace only the first line.
test.dat
this line has n characters
this line also has n characters
test.py
#First, I get the length of the first line -- if you already know it, skip this block
f=open('test.dat','r')
l=f.readline()
linelen=len(l)-1
f.close()
#apparently mode='a+' doesn't work on all systems :( so I use 'r+' instead
f=open('test.dat','r+')
f.seek(0)
f.write('a'*linelen+'\n') #'a'*linelen = 'aaaaaaaaa...'
f.close()

These days, jumping within files in an optimized fashion is a task for high performance applications that manage huge files.
Are you sure that your software project requires reading/writing random places in a file during runtime? I think you should consider changing the whole approach:
If the data is small, you can keep / modify / generate the data at runtime in memory within appropriate container formats (list or dict, for instance) and then write it entirely at once (on change, or only when your program exits). You could consider looking at simple databases. Also, there are nice data exchange formats like JSON, which would be the ideal format in case your data is stored in a dictionary at runtime.
An example, to make the concept more clear. Consider you already have data written to gamedata.dat:
[{"playtime": 25, "score": 13, "name": "rudolf"}, {"playtime": 300, "score": 1, "name": "peter"}]
This is utf-8-encoded and JSON-formatted data. Read the file during runtime of your Python game:
with open("gamedata.dat") as f:
s = f.read().decode("utf-8")
Convert the data to Python types:
gamedata = json.loads(s)
Modify the data (add a new user):
user = {"name": "john", "score": 1337, "playtime": 1}
gamedata.append(user)
John really is a 1337 gamer. However, at this point, you also could have deleted a user, changed the score of Rudolf or changed the name of Peter, ... In any case, after the modification, you can simply write the new data back to disk:
with open("gamedata.dat", "w") as f:
f.write(json.dumps(gamedata).encode("utf-8"))
The point is that you manage (create/modify/remove) data during runtime within appropriate container types. When writing data to disk, you write the entire data set in order to save the current state of the game.

Get the inputs from Excel and use those inputs in python script

How to get the inputs from excel and use those inputs in python.

Take a look at xlrd
This is the best reference I found for learning how to use it: http://www.dev-explorer.com/articles/excel-spreadsheets-and-python

Not sure if this is exactly what you're talking about, but:
If you have a very simple excel file (i.e. basically just one table filled with string-values, nothing fancy), and all you want to do is basic processing, then I'd suggest just converting it to a csv (comma-seperated value file). This can be done by "saving as..." in excel and selecting csv.
This is just a file with the same data as the excel, except represented by lines seperated with commas:
cell A:1, cell A:2, cell A:3
cell B:1, cell B:2, cell b:3
This is then very easy to parse using standard python functions (i.e., readlines to get each line of the file, then it's just a list that you can split on ",").
This if of course only helpful in some situations, like when you get a log from a program and want to quickly run a python script which handles it.
Note: As was pointed out in the comments, splitting the string on "," is actually not very good, since you run into all sorts of problems. Better to use the csv module (which another answer here teaches how to use).

import win32com
Excel=win32com.client.Dispatch("Excel.Application")
Excel.Workbooks.Open(file path)
Cells=Excel.ActiveWorkBook.ActiveSheet.Cells
Cells(row,column).Value=Input
Output=Cells(row,column).Value

If you can save as a csv file with headers:
Attrib1, Attrib2, Attrib3
value1.1, value1.2, value1.3
value2,1,...
Then I would highly recommend looking at built-in the csv module
With that you can do things like:
csvFile = csv.DictReader(open("csvFile.csv", "r"))
for row in csvFile:
print row['Attrib1'], row['Attrib2']