我试图通过csv文件进行解析，并仅从特定列中提取数据。
例csv:
ID | Name | Address | City | State | Zip | Phone | OPEID | IPEDS |
10 | C... | 130 W.. | Mo.. | AL... | 3.. | 334.. | 01023 | 10063 |
我试图只捕获特定的列，比如ID、Name、Zip和Phone。
我看过的代码让我相信我可以通过对应的数字调用特定的列，因此ie: Name将对应于2，并且使用行[2]遍历每一行将产生列2中的所有项。但事实并非如此。
以下是我目前所做的:
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
我期望它只打印出每行我想要的特定列，但它没有，我只打印出最后一列。
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'])
像这样的文件
name,phone,street
Bob,0893,32 Silly
James,000,400 McHilly
Smithers,4442,23 Looped St.
将输出
>>>
['Bob', 'James', 'Smithers']
['0893', '000', '4442']
['32 Silly', '400 McHilly', '23 Looped St.']
或者如果你想对列进行数字索引:
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']
要更改分隔符，请将delimiter=" "添加到适当的实例化，即reader = csv。读者(f,分隔符= " ")
对于pandas，你可以使用read_csv和usecols参数:
df = pd.read_csv(filename, usecols=['col1', 'col3', 'col7'])
例子:
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
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']
