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How to extract data after specific string in csv files of pandas

I want to read data after specific string in csv file of pandas i know this can be acheive through indexing but data length is changing every time how do i acheive it by using pandas ?

How do I convert the following .csv data into bi-grams?

I have some data in the .csv format file given in the link below.
https://drive.google.com/file/d/1kBtK-uBhZEyCMQ2ndHpQ1Rqd6LZ3sVJJ/view?usp=sharing
I have converted it into a pandas dataframe. My question is how do I convert it into bi-grams as in a pandas dataframe fo bigrams?
(Usually we use [i:i+n] for text but here I am dealing with columns)
Picture of the pandas dataframe I currently to make it easier for you

reading a large number from excel with pandas

I am reading a xlsx file with pandas and a Column contain 18 digit number for example 360000036011012000
after reading the number is converted to 360000036011011968
my code
import pandas as pd
df = pd.read_excel("Book1.xlsx")
I also tried converting the column to string but the results are same
df = pd.read_excel("Book1.xlsx",dtype = {"column_name":"str" })
also tried with engine = 'openpyxl'
also if the same number is in csv file there is no problem reading works fine but I have to read it from excel only.

That is an Excel problem, not a pandas problem. See here:
The yellow marked entries, are actually the number below * 10 +1 so should not end on 0.
What happens under the hood in Excel seems to be a number limit of 18. But the last two numbers are interpreted as decimals. Since this is a Excel not a CSV problem, a csv will work just fine.
Solution:
Format the numbers in Excel as Text, as shown in the first picture with: =Text(CELL,0).
Pandas can then import it as string, but you will lose the information of the last digits. Therefore Excel should not be used for numbers with more than 18 digits. Use a different file, like csv, insert the numbers directly as strings into excel by using a leading: ' symbol.

Working with csv files that have delimiters

I have to read data from a csv file and I want to convert two columns by making use of one hot encoding.
The csv files data has one column with ';' in between the data (E.g. CITY;MONTH;SALES_AMOUNT). How do I load this in pandas dataframe in separate columns?
Desired result : E.g CITY MONTH SALES_AMOUNT
Instead of: CITY;MONTH;SALES_AMOUNT

You can use the delimiter parameter when reading the CSV file.
import pandas as pd
pd.read_csv('dataset.csv', delimiter = ';')

Column size issue : read_csv

I have a dataframe that has 4 columns. I have to convert this dataframe to csv for working in my local computer. when I convert dataframe to csv I have only one column:
df = pd.read_csv("final.csv")
print df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20479 entries, 0 to 20478
Data columns (total 1 columns)
How can I convert this csv to dataframe with 4 columns?

Question is inconcise. Are you aiming to write a pandas dataframe object to a csv file, or create a dataframe object from an existing csv file?
Pandas Dataframe to CSV this link should be sufficient to write a df to a csv file, and vice versa listed here Dataframe from CSV.
A csv file (comma separated values) is separated by commas so make sure the separator is consistent.

When you read in your dataframe, you might have to explicitly state what type of separator is being used. I would open the csv in a text editor and see what the separator is. If, for example, the separator used was "|", I would use the following code:
df = pd.read_csv('final.csv', sep='|')
Then, to save to a .csv the code should be as simple as:
df.to_csv('path/to/file/csvFileName.csv', index=False)
I would recommend using index=False like I did, otherwise the pandas index will be included as a column in your csv file. Cheers.