Read specific columns from a csv file with csv mod

问题:

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.

回答1:

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!

回答2:

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=\" \")

回答3:

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\')

回答4:

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\']

A bit more memory-friendly solution, if you really need those bytes (throws away 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.

回答5:

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

回答6:

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\')

回答7:

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(\',\')

回答8:

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