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下面的 pandas 脚本会在不应该的时候不断修改我导出到 CSV 的数据。如果您将原始文件与修改后的文件进行比较，testing2.csv您会看到如下数字：0.357从第一行变成：0.35700000000000004但在第 2 行，数字0.1128根本没有改变......

它不应该修改这些数字，它们都应该保持原样。

测试.py

import re
import pandas
# each block in the text file will be one element of this list
matchers = [[]]
i = 0 
with open('testing.txt') as infile:
    for line in infile:
        line = line.strip()
        # Blocks are seperated by blank lines
        if len(line) == 0:
            i += 1
            matchers.append([])
            # assume there are always two blank lines between items 
            # and just skip to the lext line
            infile.next()
            continue
        matchers[i].append(line)


# This regular expression matches the variable number of students in each block
studentlike = re.compile('(\d+) (.+) (\d+/\d+)')
# These are the names of the fields we expect at the end of each block
datanames = ['Data', 'misc2', 'bla3']
# We will build a table containing a list of elements for each student
table = []
for matcher in matchers:
    # We use an iterator over the block lines to make indexing simpler
    it = iter(matcher)
    # The first two elements are match values
    m1, m2 = it.next(), it.next()
    # then there are a number of students
    students = []
    for possiblestudent in it:
        m = studentlike.match(possiblestudent)
        if m:
            students.append(list(m.groups()))
        else:
            break
    # After the students come the data elements, which we read into a dictionary
    # We also add in the last possible student line as that didn't match the student re
    dataitems = dict(item.split() for item in [possiblestudent] + list(it))
    # Finally we construct the table
    for student in students:
        # We use the dictionary .get() method to return blanks for the missing fields
        table.append([m1, m2] + student + [dataitems.get(d, '') for d in datanames])

textcols = ['MATCH2', 'MATCH1', 'TITLE01', 'MATCH3', 'TITLE02', 'Data', 'misc2', 'bla3']
csvdata = pandas.read_csv('testing.csv')
textdata = pandas.DataFrame(table, columns=textcols)

# Add any new columns
newCols = textdata.columns - csvdata.columns
for c in newCols:
    csvdata[c] = None

mergecols = ['MATCH2', 'MATCH1', 'MATCH3']
csvdata.set_index(mergecols, inplace=True, drop=False)
textdata.set_index(mergecols, inplace=True,drop=False)
csvdata.update(textdata)
csvdata.to_csv('testing2.csv', index=False)

测试.csv

测试.txt

MData (N/A)
DMATCH1
3 Tommy 144512/23332
1 Jim 90000/222311
1 Elz M 90000/222311
1 Ben 90000/222311
Data $50.90
misc2 $10.40
bla3 $20.20


MData (B/B) 
DMATCH2
4 James Smith 2333/114441
4 Mike 90000/222311
4 Jessica Long 2333/114441
Data $50.90
bla3 $5.44

任何人有任何想法如何解决这个问题？

（上面的例子100%完美地重现了这个问题。我花了很长时间才找出导致这个问题的原因。）

4

3 回答 3

2

这看起来像是一个精度问题。

尝试更改您的to_csv行以包含float_format='%.4f'将四舍五入到小数点后 2 位的参数。

于 2013-11-11T18:35:22.863 回答
1

Pandas 支持两种基本的数字类型，Int64 和 Float64。Float64 不会精确地表示十进制值，因为它是浮点类型。你的选择是

  1. float_format按照@TomAugspurger 的建议指定（这可以按列或整个数据框完成
  2. 将您的列 dtype 转换为对象

选项 2 可以这样完成：

df['col_name'] = df['col_name'].astype(object)
于 2013-11-11T18:54:14.203 回答
0

试试这个 ：）

csvdata = pandas.read_csv('testing.csv', dtype={'TITLE5' : 'object', 'TITLE5.1' : 'object', 'TITLE5.2' : 'object', 'TITLE5.3' : 'object'})
于 2013-11-11T19:19:39.210 回答