[python] 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.

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(',')

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 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)
    reader.next()
    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=" ")

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

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.

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']

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')

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

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')