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Importing csv formatted for excel into a dataframe

I am receiving datafiles from 2 different people and the files are coming through with different formats despite both users using the same system and the same browser.
I would like to be able to make my code smart enough to read either format but so far I have been unsuccessful.
The data coming through I am having issues with looks like this
+----------------+---------------+--------------+
| Customer Name | Customer code | File Ref |
+----------------+---------------+--------------+
| ACCOUNT SET UP | ="35" | R2I0025715 |
+----------------+---------------+--------------+
| Xenox | ="4298" | ="913500999" |
+----------------+---------------+--------------+
and the data that is importing cleanly looks like this
+----------------+---------------+------------+
| Customer Name | Customer code | File Ref |
+----------------+---------------+------------+
| ACCOUNT SET UP | 35 | R2I0025715 |
+----------------+---------------+------------+
| Xenox | 4298 | 913500999 |
+----------------+---------------+------------+
I am trying to import the data with the following code pd.read_csv(f, encoding='utf-8', dtype={"Customer Name": "string", "Customer code": "string", "File Ref": "string"})
A workaround that I am using is opening each csv in excel, and saving. But when this involves hundreds of files, it isn't really a workaround.
Can anyone help?

You could use the standard strip() function to remove leading and trailing = and " characters on all of your columns.
For example:
import pandas as pd
data = {
'Customer Name' : ['ACCOUNT SET UP', 'Xenox', 'ACCOUNT SET UP', 'Xenox'],
'Customer Code': ['="35"', '="4298"', '35', '4298'],
'File Ref': ['R2I0025715', '="913500999"', 'R2I0025715', '913500999']
}
df = pd.DataFrame(data)
for col in df.columns:
df[col] = df[col].str.strip('="')
print(df)
Giving you:
Customer Name Customer Code File Ref
0 ACCOUNT SET UP 35 R2I0025715
1 Xenox 4298 913500999
2 ACCOUNT SET UP 35 R2I0025715
3 Xenox 4298 913500999
If you just want to apply it to specific columns, use:
for col in ['Customer Code', 'File Ref']:
df[col] = df[col].str.strip('="')

My Solution:
import re
import pandas as pd
def removechar(x):
x = str(x)
out = re.sub('="', '', x)
return(out)
def removechar2(x):
x = str(x)
out = re.sub('"', '', x)
out = int(out) #could use float(), depends on what you want
return(out)
#then use applymap from pandas
Example:
datas = {'feature1': ['="23"', '="24"', '="23"', '="83"'], 'feature2': ['="23"', '="2"', '="3"', '="23"']}
test = pd.DataFrame(datas) # Example dataframe
test
Out[1]:
feature1 feature2
0 ="23" ="23"
1 ="24" ="2"
2 ="23" ="3"
3 ="83" ="23"
#applymap my functions
test = test.applymap(removechar)
test = test.applymap(removechar2)
test
Out[2]:
feature1 feature2
0 23 23
1 24 2
2 23 3
3 83 23
#fixed
Note you could probably do it just one line of applymap and one function running re.sub, try googling and reading the documentation for re.sub, this was something quick I whipped up.

store values from a list into an array after each iteration consists of specified column of files in python

For a file in files:
This is my list which consists of values from 3 files after each iteration.
import pandas files = [r"C:\Users\Anjana\Documents\radar\HeightVsDopplr\EXP_DBS_CH4_24Apr2017_10_49_10_Beam2_W1_Az_90.00_Oz_10.00.mmts",r"C:\Users\Anjana\Documents\radar\HeightVsDopplr\EXP_DBS_CH4_24Apr2017_10_49_10_Beam4_W1_Az_180.00_Oz_10.00.mmts", r"C:\Users\Anjana\Documents\radar\HeightVsDopplr\EXP_DBS_CH4_24Apr2017_10_49_10_Beam1_W1_Az_0.00_Oz_0.00.mmts"]
for file in files:
if file.endswith(".mmts"):
csvfiles.append(str(file))
a = pd.read_csv(file)
x = list(a0[:][:]['Mean'])
matrix = np.empty((a0.shape[0],3))
matrix.fill(np.nan)
I need the output somewhat like this
matrix(row1,col1) should be value of first value from file 1
matrix(row2,col1) should be value of first value from file 2
matrix(row3,col1) should be value of first value from file 3
sample input:
file 1
+--------+----------+
| Height | Mean |
+--------+----------+
| 3.33 | -0.41005 |
+--------+----------+
| 3.51 | 0.15782 |
+--------+----------+
| 3.69 | 0.12896 |
+--------+----------+
file 2
+--------+--------+
| Height | Mean |
+--------+--------+
| 3.33 | 1.8867 |
+--------+--------+
| 3.51 | 2.3108 |
+--------+--------+
| 3.69 | 2.5924 |
+--------+--------+
output
array[-0.41005,0.15782 ,0.12896]
[1.8867 ,2.3108 ,2.5924]

Your question lacks the context, so I'm assuming that
You've three files to read.
So I've 3 files, a.txt, b.txt, c.txt.
a.txt
1
2
3
b.txt
paul
sleeba
harry
a.txt
23
25
34
You need to get individual access these file contents so that you can enlist them in particular columns.
with open("a.txt", "r") as a, open("b.txt", "r") as b, open("c.txt", "r") as c:
num = a.readlines()
name = b.readlines()
age = c.readlines()
print(num[0], name[0], age[0])
Output
1
paul
23
I see that you're using numpy, but I can't help about it since the plot of question is hardly helping to understand the context.
Update: I still don't understand your confusion with different files since question is vague, but follow the code for each column in a file.
My CSV
import pandas as pd
import numpy as np
a = pd.read_csv("data.csv")
out_list = []
for col in a.columns.values:
out_list.append(list(a[col]))
out_list = np.array(out_list)
print(out_list)
Output
array([['All industries ', 'All industries ', 'All industries ',
'All industries ', 'All industries ', 'All industries ',
'All industries ', 'All industries ', 'All industries ',
'All industries '],
['H01', 'H04', 'H05', 'H07', 'H08', 'H09', 'H10', 'H11', 'H12',
'H12'],
['644159', '567080', '59317', '17762', '560665', '33474', '6890',
'18730', '99874', '99874']], dtype='<U16')
UPDATE : From different files
out_list = []
files = ["1.csv", "2.csv"]
for file in files:
df = pd.read_csv(file)
out_list.append(list(df["Mean"]))
out_list = np.array(out_list)
out_list
Output
array([[-0.41005, 0.15782, 0.12896],
[ 1.8867 , 2.3108 , 2.5924 ]])

Your question is very general. Try to provide a minimal complete verifiable example otherwise it is difficult to help you.
What is in the files? Is each line a number? Too few information.
In Python you can use multiple iteration variables.
#open the files first
f1 = open("file1", "r")
f2 = open("file2", "r")
f3 = open("file3", "r")
#Assuming the files have the same number of lines:
for linef1, linef2, linef3 in zip(file1,file2,file3):
#Assuming each line is a number
matrix[i,0]= int(linef1)
matrix[i,1]= int(linef1)
matrix[i,2]= int(linef1)

Top 5 scores using csv

I am having difficulty with presenting the top 5 scores from a csv file.
This is my current code:
def leaderboards():
with open('userData.csv', 'r') as data:
reader = csv.reader(data)
for idx, row in enumerate(reader):
print('{} | {} | {}'.format(str(idx), row[0], row[2]))
This is what I currently get:
>>> leaderboards()
0 | Name | Score
1 | Ryan | 3
2 | Liam | 12
3 | Steve | 3
4 | Donald | 3
5 | Alan | 3
6 | Harry | 1
There are two main elements that I would like to implement but don't know how.
They are to sort the score so that the highest score is at the top, and to limit the number of players the leaderboards show to 5.
Current code:
with open('userData.csv', 'r') as data:
reader = csv.DictReader(data)
newList = sorted(reader, key=lambda row: row['Score'],
reverse=False)[0:5]
print (' | Name | Score')
for i, r in enumerate(newList):
print('{} | {} | {}'.format(str(i), r['Name'], r['Score']))
Current result:
| Name | Score
0 | Liam | 12
1 | Harry | 2
2 | Ryan | 3
3 | Steve | 3
4 | Donald | 3
It works for the most part, however, the 2 is higher up than the 3. I'm not really sure why but maybe you might realise something. Once again, thank you so much and I'm sorry that I'm just asking so much but this is out of my league in terms of coding knowledge. :D

If you're trying to sort on the last element of each row, the blank space is throwing an error. Also, I think you're script will also consider the header row for sorting, so if you're not looking to consider that, you should skip that row when sorting.
If you want to print out the score the way you want according to your file structure, you could do something like the following:
with open('...file name here...', 'r') as data:
reader = csv.reader(data)
for idx, row in enumerate(reader):
print '{} | {} | {}'.format(str(idx), row[0], row[2])
That will print things out line by line according to how you've indexed your csv. Since you have a header row, I would also suggest that you take a look at DictReader within the Python csv module. That way you won't have to rely on index position in the future.
Also, if you've having issues with extra lines in csv's, take a look at this answer.
Update for sorting based on the request in comments:
To sort your reader you can just use lambda based on the appropriate key in your csv columns. Assuming that Score is the column you wish to sort on and that Score is of index 2, you can do:
newList = sorted(reader, key=lambda row: row[2], reverse=True)
That will give you a sorted list in descending order by the 2nd index. Remove reverse=True for an ascending list order.
Then, if you want to take only the first five entries, you can just slice the list:
slicedList = newList[0:5]
Then, you can print out your list as usual.
Update 2 to accommodate second request:
with open('...name of your file...', 'r') as data:
reader = csv.reader(data)
newList = sorted(reader, key=lambda row: row[2], reverse=True)[0:5]
for i, r in enumerate(newList):
print('{} | {} | {}'.format(str(i), r[0], r[2]))
The above code assumes (from your original csv file) that r[0] is the "Name" column and r[2] is the "Score" column.
Also, as an alternative, you could use csv.DictReader just in case your column headers change index. The alternative code would be something like:
with open('...your file name here...', 'r') as data:
reader = csv.DictReader(data)
#### COMMENTING THIS OUT in favor of the newList below
# newList = sorted(reader, key=lambda row: row['Score'], reverse=True)[0:5]
##### EDIT to evaluate the Score as an int()
# cast row['Score'] to an int() below so that sorted() treats the row like and int rather than str
newList = sorted(reader, key=lambda row: int(row['Score']), reverse=True)[0:5]
print (' | Name | Score')
for i, r in enumerate(newList):
print('{} | {} | {}'.format(str(i), r['Name'], r['Score']))

How to read in only rows with specific values on a specific column? [duplicate]

I'm trying to parse through a csv file and extract the data from only specific columns.
Example csv:
ID | Name | Address | City | State | Zip | Phone | OPEID | IPEDS |
10 | C... | 130 W.. | Mo.. | AL... | 3.. | 334.. | 01023 | 10063 |
I'm trying to capture only specific columns, say ID, Name, Zip and Phone.
Code I've looked at has led me to believe I can call the specific column by its corresponding number, so ie: Name would correspond to 2 and iterating through each row using row[2] would produce all the items in column 2. Only it doesn't.
Here's what I've done so far:
import sys, argparse, csv
from settings import *
# command arguments
parser = argparse.ArgumentParser(description='csv to postgres',\
fromfile_prefix_chars="#" )
parser.add_argument('file', help='csv file to import', action='store')
args = parser.parse_args()
csv_file = args.file
# open csv file
with open(csv_file, 'rb') as csvfile:
# get number of columns
for line in csvfile.readlines():
array = line.split(',')
first_item = array[0]
num_columns = len(array)
csvfile.seek(0)
reader = csv.reader(csvfile, delimiter=' ')
included_cols = [1, 2, 6, 7]
for row in reader:
content = list(row[i] for i in included_cols)
print content
and I'm expecting that this will print out only the specific columns I want for each row except it doesn't, I get the last column only.

The only way you would be getting the last column from this code is if you don't include your print statement in your for loop.
This is most likely the end of your code:
for row in reader:
content = list(row[i] for i in included_cols)
print content
You want it to be this:
for row in reader:
content = list(row[i] for i in included_cols)
print content
Now that we have covered your mistake, I would like to take this time to introduce you to the pandas module.
Pandas is spectacular for dealing with csv files, and the following code would be all you need to read a csv and save an entire column into a variable:
import pandas as pd
df = pd.read_csv(csv_file)
saved_column = df.column_name #you can also use df['column_name']
so if you wanted to save all of the info in your column Names into a variable, this is all you need to do:
names = df.Names
It's a great module and I suggest you look into it. If for some reason your print statement was in for loop and it was still only printing out the last column, which shouldn't happen, but let me know if my assumption was wrong. Your posted code has a lot of indentation errors so it was hard to know what was supposed to be where. Hope this was helpful!

import csv
from collections import defaultdict
columns = defaultdict(list) # each value in each column is appended to a list
with open('file.txt') as f:
reader = csv.DictReader(f) # read rows into a dictionary format
for row in reader: # read a row as {column1: value1, column2: value2,...}
for (k,v) in row.items(): # go over each column name and value
columns[k].append(v) # append the value into the appropriate list
# based on column name k
print(columns['name'])
print(columns['phone'])
print(columns['street'])
With a file like
name,phone,street
Bob,0893,32 Silly
James,000,400 McHilly
Smithers,4442,23 Looped St.
Will output
>>>
['Bob', 'James', 'Smithers']
['0893', '000', '4442']
['32 Silly', '400 McHilly', '23 Looped St.']
Or alternatively if you want numerical indexing for the columns:
with open('file.txt') as f:
reader = csv.reader(f)
next(reader)
for row in reader:
for (i,v) in enumerate(row):
columns[i].append(v)
print(columns[0])
>>>
['Bob', 'James', 'Smithers']
To change the deliminator add delimiter=" " to the appropriate instantiation, i.e reader = csv.reader(f,delimiter=" ")

Use pandas:
import pandas as pd
my_csv = pd.read_csv(filename)
column = my_csv.column_name
# you can also use my_csv['column_name']
Discard unneeded columns at parse time:
my_filtered_csv = pd.read_csv(filename, usecols=['col1', 'col3', 'col7'])
P.S. I'm just aggregating what other's have said in a simple manner. Actual answers are taken from here and here.

You can use numpy.loadtext(filename). For example if this is your database .csv:
ID | Name | Address | City | State | Zip | Phone | OPEID | IPEDS |
10 | Adam | 130 W.. | Mo.. | AL... | 3.. | 334.. | 01023 | 10063 |
10 | Carl | 130 W.. | Mo.. | AL... | 3.. | 334.. | 01023 | 10063 |
10 | Adolf | 130 W.. | Mo.. | AL... | 3.. | 334.. | 01023 | 10063 |
10 | Den | 130 W.. | Mo.. | AL... | 3.. | 334.. | 01023 | 10063 |
And you want the Name column:
import numpy as np
b=np.loadtxt(r'filepath\name.csv',dtype=str,delimiter='|',skiprows=1,usecols=(1,))
>>> b
array([' Adam ', ' Carl ', ' Adolf ', ' Den '],
dtype='|S7')
More easily you can use genfromtext:
b = np.genfromtxt(r'filepath\name.csv', delimiter='|', names=True,dtype=None)
>>> b['Name']
array([' Adam ', ' Carl ', ' Adolf ', ' Den '],
dtype='|S7')

With pandas you can use read_csv with usecols parameter:
df = pd.read_csv(filename, usecols=['col1', 'col3', 'col7'])
Example:
import pandas as pd
import io
s = '''
total_bill,tip,sex,smoker,day,time,size
16.99,1.01,Female,No,Sun,Dinner,2
10.34,1.66,Male,No,Sun,Dinner,3
21.01,3.5,Male,No,Sun,Dinner,3
'''
df = pd.read_csv(io.StringIO(s), usecols=['total_bill', 'day', 'size'])
print(df)
total_bill day size
0 16.99 Sun 2
1 10.34 Sun 3
2 21.01 Sun 3

Context: For this type of work you should use the amazing python petl library. That will save you a lot of work and potential frustration from doing things 'manually' with the standard csv module. AFAIK, the only people who still use the csv module are those who have not yet discovered better tools for working with tabular data (pandas, petl, etc.), which is fine, but if you plan to work with a lot of data in your career from various strange sources, learning something like petl is one of the best investments you can make. To get started should only take 30 minutes after you've done pip install petl. The documentation is excellent.
Answer: Let's say you have the first table in a csv file (you can also load directly from the database using petl). Then you would simply load it and do the following.
from petl import fromcsv, look, cut, tocsv
#Load the table
table1 = fromcsv('table1.csv')
# Alter the colums
table2 = cut(table1, 'Song_Name','Artist_ID')
#have a quick look to make sure things are ok. Prints a nicely formatted table to your console
print look(table2)
# Save to new file
tocsv(table2, 'new.csv')

I think there is an easier way
import pandas as pd
dataset = pd.read_csv('table1.csv')
ftCol = dataset.iloc[:, 0].values
So in here iloc[:, 0], : means all values, 0 means the position of the column.
in the example below ID will be selected
ID | Name | Address | City | State | Zip | Phone | OPEID | IPEDS |
10 | C... | 130 W.. | Mo.. | AL... | 3.. | 334.. | 01023 | 10063 |

import pandas as pd
csv_file = pd.read_csv("file.csv")
column_val_list = csv_file.column_name._ndarray_values

Thanks to the way you can index and subset a pandas dataframe, a very easy way to extract a single column from a csv file into a variable is:
myVar = pd.read_csv('YourPath', sep = ",")['ColumnName']
A few things to consider:
The snippet above will produce a pandas Series and not dataframe.
The suggestion from ayhan with usecols will also be faster if speed is an issue.
Testing the two different approaches using %timeit on a 2122 KB sized csv file yields 22.8 ms for the usecols approach and 53 ms for my suggested approach.
And don't forget import pandas as pd

If you need to process the columns separately, I like to destructure the columns with the zip(*iterable) pattern (effectively "unzip"). So for your example:
ids, names, zips, phones = zip(*(
(row[1], row[2], row[6], row[7])
for row in reader
))

import pandas as pd
dataset = pd.read_csv('Train.csv')
X = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values
X is a a bunch of columns, use it if you want to read more that one column
y is single column, use it to read one column
[:, 1:-1] are [row_index : to_row_index, column_index : to_column_index]

SAMPLE.CSV
a, 1, +
b, 2, -
c, 3, *
d, 4, /
column_names = ["Letter", "Number", "Symbol"]
df = pd.read_csv("sample.csv", names=column_names)
print(df)
OUTPUT
Letter Number Symbol
0 a 1 +
1 b 2 -
2 c 3 *
3 d 4 /
letters = df.Letter.to_list()
print(letters)
OUTPUT
['a', 'b', 'c', 'd']

import csv
with open('input.csv', encoding='utf-8-sig') as csv_file:
# the below statement will skip the first row
next(csv_file)
reader= csv.DictReader(csv_file)
Time_col ={'Time' : []}
#print(Time_col)
for record in reader :
Time_col['Time'].append(record['Time'])
print(Time_col)

From CSV File Reading and Writing you can import csv and use this code:
with open('names.csv', newline='') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
print(row['first_name'], row['last_name'])

To fetch column name, instead of using readlines() better use readline() to avoid loop & reading the complete file & storing it in the array.
with open(csv_file, 'rb') as csvfile:
# get number of columns
line = csvfile.readline()
first_item = line.split(',')

Read specific columns from a csv file with csv module?

I'm trying to parse through a csv file and extract the data from only specific columns.
Example csv:
ID | Name | Address | City | State | Zip | Phone | OPEID | IPEDS |
10 | C... | 130 W.. | Mo.. | AL... | 3.. | 334.. | 01023 | 10063 |
I'm trying to capture only specific columns, say ID, Name, Zip and Phone.
Code I've looked at has led me to believe I can call the specific column by its corresponding number, so ie: Name would correspond to 2 and iterating through each row using row[2] would produce all the items in column 2. Only it doesn't.
Here's what I've done so far:
import sys, argparse, csv
from settings import *
# command arguments
parser = argparse.ArgumentParser(description='csv to postgres',\
fromfile_prefix_chars="#" )
parser.add_argument('file', help='csv file to import', action='store')
args = parser.parse_args()
csv_file = args.file
# open csv file
with open(csv_file, 'rb') as csvfile:
# get number of columns
for line in csvfile.readlines():
array = line.split(',')
first_item = array[0]
num_columns = len(array)
csvfile.seek(0)
reader = csv.reader(csvfile, delimiter=' ')
included_cols = [1, 2, 6, 7]
for row in reader:
content = list(row[i] for i in included_cols)
print content
and I'm expecting that this will print out only the specific columns I want for each row except it doesn't, I get the last column only.

The only way you would be getting the last column from this code is if you don't include your print statement in your for loop.
This is most likely the end of your code:
for row in reader:
content = list(row[i] for i in included_cols)
print content
You want it to be this:
for row in reader:
content = list(row[i] for i in included_cols)
print content
Now that we have covered your mistake, I would like to take this time to introduce you to the pandas module.
Pandas is spectacular for dealing with csv files, and the following code would be all you need to read a csv and save an entire column into a variable:
import pandas as pd
df = pd.read_csv(csv_file)
saved_column = df.column_name #you can also use df['column_name']
so if you wanted to save all of the info in your column Names into a variable, this is all you need to do:
names = df.Names
It's a great module and I suggest you look into it. If for some reason your print statement was in for loop and it was still only printing out the last column, which shouldn't happen, but let me know if my assumption was wrong. Your posted code has a lot of indentation errors so it was hard to know what was supposed to be where. Hope this was helpful!

import csv
from collections import defaultdict
columns = defaultdict(list) # each value in each column is appended to a list
with open('file.txt') as f:
reader = csv.DictReader(f) # read rows into a dictionary format
for row in reader: # read a row as {column1: value1, column2: value2,...}
for (k,v) in row.items(): # go over each column name and value
columns[k].append(v) # append the value into the appropriate list
# based on column name k
print(columns['name'])
print(columns['phone'])
print(columns['street'])
With a file like
name,phone,street
Bob,0893,32 Silly
James,000,400 McHilly
Smithers,4442,23 Looped St.
Will output
>>>
['Bob', 'James', 'Smithers']
['0893', '000', '4442']
['32 Silly', '400 McHilly', '23 Looped St.']
Or alternatively if you want numerical indexing for the columns:
with open('file.txt') as f:
reader = csv.reader(f)
next(reader)
for row in reader:
for (i,v) in enumerate(row):
columns[i].append(v)
print(columns[0])
>>>
['Bob', 'James', 'Smithers']
To change the deliminator add delimiter=" " to the appropriate instantiation, i.e reader = csv.reader(f,delimiter=" ")

Use pandas:
import pandas as pd
my_csv = pd.read_csv(filename)
column = my_csv.column_name
# you can also use my_csv['column_name']
Discard unneeded columns at parse time:
my_filtered_csv = pd.read_csv(filename, usecols=['col1', 'col3', 'col7'])
P.S. I'm just aggregating what other's have said in a simple manner. Actual answers are taken from here and here.

You can use numpy.loadtext(filename). For example if this is your database .csv:
ID | Name | Address | City | State | Zip | Phone | OPEID | IPEDS |
10 | Adam | 130 W.. | Mo.. | AL... | 3.. | 334.. | 01023 | 10063 |
10 | Carl | 130 W.. | Mo.. | AL... | 3.. | 334.. | 01023 | 10063 |
10 | Adolf | 130 W.. | Mo.. | AL... | 3.. | 334.. | 01023 | 10063 |
10 | Den | 130 W.. | Mo.. | AL... | 3.. | 334.. | 01023 | 10063 |
And you want the Name column:
import numpy as np
b=np.loadtxt(r'filepath\name.csv',dtype=str,delimiter='|',skiprows=1,usecols=(1,))
>>> b
array([' Adam ', ' Carl ', ' Adolf ', ' Den '],
dtype='|S7')
More easily you can use genfromtext:
b = np.genfromtxt(r'filepath\name.csv', delimiter='|', names=True,dtype=None)
>>> b['Name']
array([' Adam ', ' Carl ', ' Adolf ', ' Den '],
dtype='|S7')

With pandas you can use read_csv with usecols parameter:
df = pd.read_csv(filename, usecols=['col1', 'col3', 'col7'])
Example:
import pandas as pd
import io
s = '''
total_bill,tip,sex,smoker,day,time,size
16.99,1.01,Female,No,Sun,Dinner,2
10.34,1.66,Male,No,Sun,Dinner,3
21.01,3.5,Male,No,Sun,Dinner,3
'''
df = pd.read_csv(io.StringIO(s), usecols=['total_bill', 'day', 'size'])
print(df)
total_bill day size
0 16.99 Sun 2
1 10.34 Sun 3
2 21.01 Sun 3

Context: For this type of work you should use the amazing python petl library. That will save you a lot of work and potential frustration from doing things 'manually' with the standard csv module. AFAIK, the only people who still use the csv module are those who have not yet discovered better tools for working with tabular data (pandas, petl, etc.), which is fine, but if you plan to work with a lot of data in your career from various strange sources, learning something like petl is one of the best investments you can make. To get started should only take 30 minutes after you've done pip install petl. The documentation is excellent.
Answer: Let's say you have the first table in a csv file (you can also load directly from the database using petl). Then you would simply load it and do the following.
from petl import fromcsv, look, cut, tocsv
#Load the table
table1 = fromcsv('table1.csv')
# Alter the colums
table2 = cut(table1, 'Song_Name','Artist_ID')
#have a quick look to make sure things are ok. Prints a nicely formatted table to your console
print look(table2)
# Save to new file
tocsv(table2, 'new.csv')

I think there is an easier way
import pandas as pd
dataset = pd.read_csv('table1.csv')
ftCol = dataset.iloc[:, 0].values
So in here iloc[:, 0], : means all values, 0 means the position of the column.
in the example below ID will be selected
ID | Name | Address | City | State | Zip | Phone | OPEID | IPEDS |
10 | C... | 130 W.. | Mo.. | AL... | 3.. | 334.. | 01023 | 10063 |

import pandas as pd
csv_file = pd.read_csv("file.csv")
column_val_list = csv_file.column_name._ndarray_values

Thanks to the way you can index and subset a pandas dataframe, a very easy way to extract a single column from a csv file into a variable is:
myVar = pd.read_csv('YourPath', sep = ",")['ColumnName']
A few things to consider:
The snippet above will produce a pandas Series and not dataframe.
The suggestion from ayhan with usecols will also be faster if speed is an issue.
Testing the two different approaches using %timeit on a 2122 KB sized csv file yields 22.8 ms for the usecols approach and 53 ms for my suggested approach.
And don't forget import pandas as pd

If you need to process the columns separately, I like to destructure the columns with the zip(*iterable) pattern (effectively "unzip"). So for your example:
ids, names, zips, phones = zip(*(
(row[1], row[2], row[6], row[7])
for row in reader
))

import pandas as pd
dataset = pd.read_csv('Train.csv')
X = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values
X is a a bunch of columns, use it if you want to read more that one column
y is single column, use it to read one column
[:, 1:-1] are [row_index : to_row_index, column_index : to_column_index]

SAMPLE.CSV
a, 1, +
b, 2, -
c, 3, *
d, 4, /
column_names = ["Letter", "Number", "Symbol"]
df = pd.read_csv("sample.csv", names=column_names)
print(df)
OUTPUT
Letter Number Symbol
0 a 1 +
1 b 2 -
2 c 3 *
3 d 4 /
letters = df.Letter.to_list()
print(letters)
OUTPUT
['a', 'b', 'c', 'd']

import csv
with open('input.csv', encoding='utf-8-sig') as csv_file:
# the below statement will skip the first row
next(csv_file)
reader= csv.DictReader(csv_file)
Time_col ={'Time' : []}
#print(Time_col)
for record in reader :
Time_col['Time'].append(record['Time'])
print(Time_col)

From CSV File Reading and Writing you can import csv and use this code:
with open('names.csv', newline='') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
print(row['first_name'], row['last_name'])

To fetch column name, instead of using readlines() better use readline() to avoid loop & reading the complete file & storing it in the array.
with open(csv_file, 'rb') as csvfile:
# get number of columns
line = csvfile.readline()
first_item = line.split(',')