尝试将 csv 文件的内容插入到不同的列表中答案

【问题标题】：Trying to insert the contents of a csv file into different lists尝试将 csv 文件的内容插入到不同的列表中
【发布时间】：2020-05-02 15:40:52
【问题描述】：

我正在尝试将 csv 文件的内容插入到多个列表中。为此，我剥离了 csv 的字符串。不幸的是，如果我尝试插入索引为 1 或更高的行的内容，我总是会收到错误“列表索引超出范围”。对于索引 0，它以某种方式起作用。我检查了 print(akt) 函数的输出。在此基础上，它应该可以工作。我正在使用 iris 数据集。编辑：也许是因为数据集的最后一行不是吗？最后有一个空列表，我一开始没看到。

import csv

x1 = []
x2 = []
colors = []

with open("iris.csv" ,"r") as csvfile:
    for line in csvfile:
        akt = line.strip().split(',')
        x1.append(akt[0])
        x2.append(akt[1])
        colors.append(akt[4])
        #print(akt)
    csvfile.close()

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-15-247b0992263a> in <module>
      9         akt = line.strip().split(',')
     10         x1.append(akt[0])
---> 11         x2.append(akt[1])
     12         colors.append(akt[4].split('-'))
     13         #print(akt)

IndexError: list index out of range

['5.1', '3.5', '1.4', '0.2', 'Iris-setosa']
['4.9', '3.0', '1.4', '0.2', 'Iris-setosa']
['4.7', '3.2', '1.3', '0.2', 'Iris-setosa']
['4.6', '3.1', '1.5', '0.2', 'Iris-setosa']
['5.0', '3.6', '1.4', '0.2', 'Iris-setosa']
['5.4', '3.9', '1.7', '0.4', 'Iris-setosa']
['4.6', '3.4', '1.4', '0.3', 'Iris-setosa']
['5.0', '3.4', '1.5', '0.2', 'Iris-setosa']
['4.4', '2.9', '1.4', '0.2', 'Iris-setosa']
['4.9', '3.1', '1.5', '0.1', 'Iris-setosa']
['5.4', '3.7', '1.5', '0.2', 'Iris-setosa']
['4.8', '3.4', '1.6', '0.2', 'Iris-setosa']
['4.8', '3.0', '1.4', '0.1', 'Iris-setosa']
['4.3', '3.0', '1.1', '0.1', 'Iris-setosa']
['5.8', '4.0', '1.2', '0.2', 'Iris-setosa']
['5.7', '4.4', '1.5', '0.4', 'Iris-setosa']
['5.4', '3.9', '1.3', '0.4', 'Iris-setosa']
['5.1', '3.5', '1.4', '0.3', 'Iris-setosa']
['5.7', '3.8', '1.7', '0.3', 'Iris-setosa']
['5.1', '3.8', '1.5', '0.3', 'Iris-setosa']
['5.4', '3.4', '1.7', '0.2', 'Iris-setosa']
['5.1', '3.7', '1.5', '0.4', 'Iris-setosa']
['4.6', '3.6', '1.0', '0.2', 'Iris-setosa']
['5.1', '3.3', '1.7', '0.5', 'Iris-setosa']
['4.8', '3.4', '1.9', '0.2', 'Iris-setosa']
['5.0', '3.0', '1.6', '0.2', 'Iris-setosa']
['5.0', '3.4', '1.6', '0.4', 'Iris-setosa']
['5.2', '3.5', '1.5', '0.2', 'Iris-setosa']
['5.2', '3.4', '1.4', '0.2', 'Iris-setosa']
['4.7', '3.2', '1.6', '0.2', 'Iris-setosa']
['4.8', '3.1', '1.6', '0.2', 'Iris-setosa']
['5.4', '3.4', '1.5', '0.4', 'Iris-setosa']
['5.2', '4.1', '1.5', '0.1', 'Iris-setosa']
['5.5', '4.2', '1.4', '0.2', 'Iris-setosa']
['4.9', '3.1', '1.5', '0.1', 'Iris-setosa']
['5.0', '3.2', '1.2', '0.2', 'Iris-setosa']
['5.5', '3.5', '1.3', '0.2', 'Iris-setosa']
['4.9', '3.1', '1.5', '0.1', 'Iris-setosa']
['4.4', '3.0', '1.3', '0.2', 'Iris-setosa']
['5.1', '3.4', '1.5', '0.2', 'Iris-setosa']
['5.0', '3.5', '1.3', '0.3', 'Iris-setosa']
['4.5', '2.3', '1.3', '0.3', 'Iris-setosa']
['4.4', '3.2', '1.3', '0.2', 'Iris-setosa']
['5.0', '3.5', '1.6', '0.6', 'Iris-setosa']
['5.1', '3.8', '1.9', '0.4', 'Iris-setosa']
['4.8', '3.0', '1.4', '0.3', 'Iris-setosa']
['5.1', '3.8', '1.6', '0.2', 'Iris-setosa']
['4.6', '3.2', '1.4', '0.2', 'Iris-setosa']
['5.3', '3.7', '1.5', '0.2', 'Iris-setosa']
['5.0', '3.3', '1.4', '0.2', 'Iris-setosa']
['7.0', '3.2', '4.7', '1.4', 'Iris-versicolor']
['6.4', '3.2', '4.5', '1.5', 'Iris-versicolor']
['6.9', '3.1', '4.9', '1.5', 'Iris-versicolor']
['5.5', '2.3', '4.0', '1.3', 'Iris-versicolor']
['6.5', '2.8', '4.6', '1.5', 'Iris-versicolor']
['5.7', '2.8', '4.5', '1.3', 'Iris-versicolor']
['6.3', '3.3', '4.7', '1.6', 'Iris-versicolor']
['4.9', '2.4', '3.3', '1.0', 'Iris-versicolor']
['6.6', '2.9', '4.6', '1.3', 'Iris-versicolor']
['5.2', '2.7', '3.9', '1.4', 'Iris-versicolor']
['5.0', '2.0', '3.5', '1.0', 'Iris-versicolor']
['5.9', '3.0', '4.2', '1.5', 'Iris-versicolor']
['6.0', '2.2', '4.0', '1.0', 'Iris-versicolor']
['6.1', '2.9', '4.7', '1.4', 'Iris-versicolor']
['5.6', '2.9', '3.6', '1.3', 'Iris-versicolor']
['6.7', '3.1', '4.4', '1.4', 'Iris-versicolor']
['5.6', '3.0', '4.5', '1.5', 'Iris-versicolor']
['5.8', '2.7', '4.1', '1.0', 'Iris-versicolor']
['6.2', '2.2', '4.5', '1.5', 'Iris-versicolor']
['5.6', '2.5', '3.9', '1.1', 'Iris-versicolor']
['5.9', '3.2', '4.8', '1.8', 'Iris-versicolor']
['6.1', '2.8', '4.0', '1.3', 'Iris-versicolor']
['6.3', '2.5', '4.9', '1.5', 'Iris-versicolor']
['6.1', '2.8', '4.7', '1.2', 'Iris-versicolor']
['6.4', '2.9', '4.3', '1.3', 'Iris-versicolor']
['6.6', '3.0', '4.4', '1.4', 'Iris-versicolor']
['6.8', '2.8', '4.8', '1.4', 'Iris-versicolor']
['6.7', '3.0', '5.0', '1.7', 'Iris-versicolor']
['6.0', '2.9', '4.5', '1.5', 'Iris-versicolor']
['5.7', '2.6', '3.5', '1.0', 'Iris-versicolor']
['5.5', '2.4', '3.8', '1.1', 'Iris-versicolor']
['5.5', '2.4', '3.7', '1.0', 'Iris-versicolor']
['5.8', '2.7', '3.9', '1.2', 'Iris-versicolor']
['6.0', '2.7', '5.1', '1.6', 'Iris-versicolor']
['5.4', '3.0', '4.5', '1.5', 'Iris-versicolor']
['6.0', '3.4', '4.5', '1.6', 'Iris-versicolor']
['6.7', '3.1', '4.7', '1.5', 'Iris-versicolor']
['6.3', '2.3', '4.4', '1.3', 'Iris-versicolor']
['5.6', '3.0', '4.1', '1.3', 'Iris-versicolor']
['5.5', '2.5', '4.0', '1.3', 'Iris-versicolor']
['5.5', '2.6', '4.4', '1.2', 'Iris-versicolor']
['6.1', '3.0', '4.6', '1.4', 'Iris-versicolor']
['5.8', '2.6', '4.0', '1.2', 'Iris-versicolor']
['5.0', '2.3', '3.3', '1.0', 'Iris-versicolor']
['5.6', '2.7', '4.2', '1.3', 'Iris-versicolor']
['5.7', '3.0', '4.2', '1.2', 'Iris-versicolor']
['5.7', '2.9', '4.2', '1.3', 'Iris-versicolor']
['6.2', '2.9', '4.3', '1.3', 'Iris-versicolor']
['5.1', '2.5', '3.0', '1.1', 'Iris-versicolor']
['5.7', '2.8', '4.1', '1.3', 'Iris-versicolor']
['6.3', '3.3', '6.0', '2.5', 'Iris-virginica']
['5.8', '2.7', '5.1', '1.9', 'Iris-virginica']
['7.1', '3.0', '5.9', '2.1', 'Iris-virginica']
['6.3', '2.9', '5.6', '1.8', 'Iris-virginica']
['6.5', '3.0', '5.8', '2.2', 'Iris-virginica']
['7.6', '3.0', '6.6', '2.1', 'Iris-virginica']
['4.9', '2.5', '4.5', '1.7', 'Iris-virginica']
['7.3', '2.9', '6.3', '1.8', 'Iris-virginica']
['6.7', '2.5', '5.8', '1.8', 'Iris-virginica']
['7.2', '3.6', '6.1', '2.5', 'Iris-virginica']
['6.5', '3.2', '5.1', '2.0', 'Iris-virginica']
['6.4', '2.7', '5.3', '1.9', 'Iris-virginica']
['6.8', '3.0', '5.5', '2.1', 'Iris-virginica']
['5.7', '2.5', '5.0', '2.0', 'Iris-virginica']
['5.8', '2.8', '5.1', '2.4', 'Iris-virginica']
['6.4', '3.2', '5.3', '2.3', 'Iris-virginica']
['6.5', '3.0', '5.5', '1.8', 'Iris-virginica']
['7.7', '3.8', '6.7', '2.2', 'Iris-virginica']
['7.7', '2.6', '6.9', '2.3', 'Iris-virginica']
['6.0', '2.2', '5.0', '1.5', 'Iris-virginica']
['6.9', '3.2', '5.7', '2.3', 'Iris-virginica']
['5.6', '2.8', '4.9', '2.0', 'Iris-virginica']
['7.7', '2.8', '6.7', '2.0', 'Iris-virginica']
['6.3', '2.7', '4.9', '1.8', 'Iris-virginica']
['6.7', '3.3', '5.7', '2.1', 'Iris-virginica']
['7.2', '3.2', '6.0', '1.8', 'Iris-virginica']
['6.2', '2.8', '4.8', '1.8', 'Iris-virginica']
['6.1', '3.0', '4.9', '1.8', 'Iris-virginica']
['6.4', '2.8', '5.6', '2.1', 'Iris-virginica']
['7.2', '3.0', '5.8', '1.6', 'Iris-virginica']
['7.4', '2.8', '6.1', '1.9', 'Iris-virginica']
['7.9', '3.8', '6.4', '2.0', 'Iris-virginica']
['6.4', '2.8', '5.6', '2.2', 'Iris-virginica']
['6.3', '2.8', '5.1', '1.5', 'Iris-virginica']
['6.1', '2.6', '5.6', '1.4', 'Iris-virginica']
['7.7', '3.0', '6.1', '2.3', 'Iris-virginica']
['6.3', '3.4', '5.6', '2.4', 'Iris-virginica']
['6.4', '3.1', '5.5', '1.8', 'Iris-virginica']
['6.0', '3.0', '4.8', '1.8', 'Iris-virginica']
['6.9', '3.1', '5.4', '2.1', 'Iris-virginica']
['6.7', '3.1', '5.6', '2.4', 'Iris-virginica']
['6.9', '3.1', '5.1', '2.3', 'Iris-virginica']
['5.8', '2.7', '5.1', '1.9', 'Iris-virginica']
['6.8', '3.2', '5.9', '2.3', 'Iris-virginica']
['6.7', '3.3', '5.7', '2.5', 'Iris-virginica']
['6.7', '3.0', '5.2', '2.3', 'Iris-virginica']
['6.3', '2.5', '5.0', '1.9', 'Iris-virginica']
['6.5', '3.0', '5.2', '2.0', 'Iris-virginica']
['6.2', '3.4', '5.4', '2.3', 'Iris-virginica']
['5.9', '3.0', '5.1', '1.8', 'Iris-virginica']
['']

【问题讨论】：

你说你试过print(akt) - 你能再做一次并发布结果吗？在拆分后立即打印，而不是在最后打印。我们希望在错误发生时看到正确的值。我注意到您没有使用 csv 模块。我认为它不能解释您的问题，但是使用它而不是 split 可以解决其他问题，例如嵌入在列中的逗号。

标签： python database csv neural-network

【解决方案1】：

问题是一行中只有一列。您没有使用 print(akt) 捕获它，因为您只在异常之后打印当前行，因此永远不会看到失败的行。

您需要检查输入并制定错误处理策略。这是一个示例，我已更新为使用 csv 模块并在尝试使用之前检查该行。我不知道你的情况，所以我添加了一些案例来忽略一种错误，但在另一种错误上引发异常。

import csv

x1 = []
x2 = []
colors = []

with open("iris.csv" ,"r") as csvfile:
    reader = csv.reader(csvfile):
        for i, akt in enumerate(csvfile, 1):
            # debug
            # print(akt)
            # Your error policy here. As an example, I'm going to allow
            # empty lines but not miscounted columns
            if not akt:
                continue
            if len(akt) != 5:
                raise ValueError("Invalid column in iris.csv line {}".format(i))
            x1.append(akt[0])
            x2.append(akt[1])
            colors.append(akt[4])

【讨论】：

是的，非常感谢。我很确定每个列表里面都有一些内容，但最后有一个空列表。
@znked - csv 末尾的空行很常见。碰到它并不奇怪。

【解决方案2】：

每当我处理 csv 文件时，我宁愿使用 pandas

import pandas as pd

df = pd.read_csv("iris.csv")
columns = df.columns

x1 = df[columns[0]].tolist()
x2 = df[columns[1]].tolist()
colors  = df[columns[4]].tolist()

【讨论】：