使用python将每个列存储在单独的字典中 [英] Storing each column in a separate dictionary using python
问题描述
一个示例输入文件:(实际输入文件包含了一个有效的方法,可以使用python存储一个单独的字典中的制表符分隔文件的每一列?数百行和数百列。列数不是固定的,它会频繁更改。)
ABC
1 4 7
2 5 8
3 6 9
我需要打印值在mydict中的单元格中的 A
:
:
打印单元
并打印同一行中的值:
for i in range(1,numrows):
for key in keysOfMydict:
print mydict [key] [i]
最简单的方法是使用 DictReader
从 csv
模块:
with open('somefile.txt','r')as f:
reader = csv.DictReader(f,delimiter ='\t')
rows = list ader)#如果你的文件不大,你可以
#完全使用
#如果你的文件很大,你可能要
#读者中的行
#print(row ['A'])
行中的行
print(row ['A'])
@Marius提出了一个好点 - 您可能希望通过标题分开收集所有列。 / p>
如果是这样,你必须调整阅读逻辑:
code>从集合导入defaultdict
by_column = defaultdict(list)
行中的行:
for k,v in row.iteritems():
by_column [k] .append(v)
另一个选项是 pandas
:
>>>将大熊猫导入为pd
>>> i = pd.read_csv('foo.csv',sep ='')
>>> i
A B C
0 1 4 7
1 2 5 8
2 3 6 9
>>>我['A']
0 1
1 2
2 3
名称:A,dtype:int64
Is there an efficient way to store each column of a tab-delimited file in a separate dictionary using python?
A sample input file: (Real input file contains thousands of lines and hundreds of columns. Number of columns is not fixed, it changes frequently.)
A B C
1 4 7
2 5 8
3 6 9
I need to print values in column A
:
for cell in mydict["A"]:
print cell
and to print values in the same row:
for i in range(1, numrows):
for key in keysOfMydict:
print mydict[key][i]
The simplest way is to use DictReader
from the csv
module:
with open('somefile.txt', 'r') as f:
reader = csv.DictReader(f, delimiter='\t')
rows = list(reader) # If your file is not large, you can
# consume it entirely
# If your file is large, you might want to
# step over each row:
#for row in reader:
# print(row['A'])
for row in rows:
print(row['A'])
@Marius made a good point - that you might be looking to collect all columns separately by their header.
If that's the case, you'll have to adjust your reading logic a bit:
from collections import defaultdict
by_column = defaultdict(list)
for row in rows:
for k,v in row.iteritems():
by_column[k].append(v)
Another option is pandas
:
>>> import pandas as pd
>>> i = pd.read_csv('foo.csv', sep=' ')
>>> i
A B C
0 1 4 7
1 2 5 8
2 3 6 9
>>> i['A']
0 1
1 2
2 3
Name: A, dtype: int64
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