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importing a csv file with clean columns using pandas?

so i'm trying to import this csv file and each value is seperated by a comma but how do i make new rows and columns from the imported data?
I tried importing it as normal and printing the data frame in different ways.

try the same with
df = pd.read_csv('file_name.csv', sep = ',')
this might work

Unable to parse string quoted csv data using pandas

I am trying to parse this CSV data which has quotes in between in unusual pattern and semicolon in the end of each row.
I am not able to parse this file correctly using pandas.
Here is the link of data (The pastebin was for some reason not recognizing as text / csv so picked up any random formatting please ignore that)
https://paste.gnome.org/pr1pmw4w2
I have tried using the "," as delimiter, and normal call of pandas dataframe object construction by only giving file name as parameter.
header = ["Organization_Name","Organization_Name_URL","Categories","Headquarters_Location","Description","Estimated_Revenue_Range","Operating_Status","Founded_Date","Founded_Date_Precision","Contact_Email","Phone_Number","Full_Description","Investor_Type","Investment_Stage","Number_of_Investments","Number_of_Portfolio_Organizations","Accelerator_Program_Type","Number_of_Founders_(Alumni)","Number_of_Alumni","Number_of_Funding_Rounds","Funding_Status","Total_Funding_Amount","Total_Funding_Amount_Currency","Total_Funding_Amount_Currency_(in_USD)","Total_Equity_Funding_Amount","Total_Equity_Funding_Amount_Currency","Total_Equity_Funding_Amount_Currency_(in_USD)","Number_of_Lead_Investors","Number_of_Investors","Number_of_Acquisitions","Transaction_Name","Transaction_Name_URL","Acquired_by","Acquired_by_URL","Announced_Date","Announced_Date_Precision","Price","Price_Currency","Price_Currency_(in_USD)","Acquisition_Type","IPO_Status,Number_of_Events","SimilarWeb_-_Monthly_Visits","Number_of_Founders","Founders","Number_of_Employees"]
pd.read_csv("data.csv", sep=",", encoding="utf-8", names=header)

First, you can just read the data normally. Now all data would be in the first column. You can use pyparsing module to split based on ',' and assign it back. I hope this solves your query. You just need to do this for all the rows.
import pyparsing as pp
import pandas as pd
df = pd.read_csv('input.csv')
df.loc[0] = pp.commaSeparatedList.parseString(df['Organization Name'][0]).asList()
Output
df #(since there are 42 columns, pasting just a snipped)

How to convert data from txt files to Excel files using python

I have a text file that contains data like this. It is is just a small example, but the real one is pretty similar.
I am wondering how to display such data in an "Excel Table" like this using Python?

The pandas library is wonderful for reading csv files (which is the file content in the image you linked). You can read in a csv or a txt file using the pandas library and output this to excel in 3 simple lines.
import pandas as pd
df = pd.read_csv('input.csv') # if your file is comma separated
or if your file is tab delimited '\t':
df = pd.read_csv('input.csv', sep='\t')
To save to excel file add the following:
df.to_excel('output.xlsx', 'Sheet1')
complete code:
import pandas as pd
df = pd.read_csv('input.csv') # can replace with df = pd.read_table('input.txt') for '\t'
df.to_excel('output.xlsx', 'Sheet1')
This will explicitly keep the index, so if your input file was:
A,B,C
1,2,3
4,5,6
7,8,9
Your output excel would look like this:
You can see your data has been shifted one column and your index axis has been kept. If you do not want this index column (because you have not assigned your df an index so it has the arbitrary one provided by pandas):
df.to_excel('output.xlsx', 'Sheet1', index=False)
Your output will look like:
Here you can see the index has been dropped from the excel file.

You do not need python! Just rename your text file to CSV and voila, you get your desired output :)
If you want to rename using python then -
You can use os.rename function
os.rename(src, dst)
Where src is the source file and dst is the destination file

XLWT
I use the XLWT library. It produces native Excel files, which is much better than simply importing text files as CSV files. It is a bit of work, but provides most key Excel features, including setting column widths, cell colors, cell formatting, etc.

saving this is:
df.to_excel("testfile.xlsx")

Combining multiple .csv files using pandas and keeping the original structure

I have around 60 .csv files which i would like to combine in pandas. So far i've used this:
import pandas as pd
import glob
total_files = glob.glob("something*.csv")
data = []
for csv in total_files:
list = pd.read_csv(csv, encoding="utf-8", sep='delimiter', engine='python')
data.append(list)
biggerlist = pd.concat(data, ignore_index=True)
biggerlist.to_csv("output.csv")
This works somewhat, only the files I would like to combine all have the same structure of 15 columns with the same headers. When I use this code, only one column is filled with info of the entire row, and every column name is add-up of all column names (e.g. SEARCH_ROW, DATE, TEXT, etc.).
How can I combine these csv files, while keeping the same structure of the original files?
Edit:
So perhaps I should be a bit more specific regarding my data. This is a snapshot of one of the .csv files i'm using:
As you can see it is just newspaper-data, where the last column is 'TEXT', which isn't shown completely when you open the file.
This is a part of how it looks when i have combined the data using my code.
Apart, i can read any of these .csv files no problem using
data = pd.read_csv("something.csv",encoding="utf-8", sep='delimiter', engine='python')

I solved it!
The problem was the amount of comma's in the text part of my .csv files. So after removing all comma's (just using search/replace), I used:
import pandas
import glob
filenames = glob.glob("something*.csv")
df = pandas.DataFrame()
for filename in filenames:
df = df.append(pandas.read_csv(filename, encoding="utf-8", sep=";"))
Thanks for all the help.

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.