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python - pd.cut：缓冲区的维数错误（预期为 1，得到 2）

问题描述

我有以下两段 Python 代码：

import pandas
ratio = [0.01, 0.2, 0.45, 0.7, 0.9, 1.01, 1.05, 1.07, 1.23, 1.78, 2.56, 3.12, 5.01, 6.21]
our_bins = [0, 0.2, 0.5, 0.8334, 1.199, 1.999, 4.999, 1000],
our_labels = ['Very Negative', 'Negative', 'Slightly Negative', 'Neutral',
              'Slightly Positive', 'Positive', 'Very Positive']
pd.cut(ratio, 
       bins = our_bins,
       right = False,
       labels = our_labels)


import pandas
ratio = [0.01, 0.2, 0.45, 0.7, 0.9, 1.01, 1.05, 1.07, 1.23, 1.78, 2.56, 3.12, 5.01, 6.21]
pd.cut(ratio, 
       bins = [0, 0.2, 0.5, 0.8334, 1.199, 1.999, 4.999, 1000],
       right = False,
       labels = ['Very Negative', 'Negative', 'Slightly Negative', 'Neutral',
                 'Slightly Positive', 'Positive', 'Very Positive'])

为什么后者输出具有正确类别的数组而前者输出此错误？

ValueError: Buffer has wrong number of dimensions (expected 1, got 2)

标签： pythonpandaserror-handling

解决方案

our_bins不是列表，而是列表的元组，因为您在行尾添加了一个逗号

our_bins = [0, 0.2, 0.5, 0.8334, 1.199, 1.999, 4.999, 1000],  # <- HERE

所以：

import pandas
ratio = [0.01, 0.2, 0.45, 0.7, 0.9, 1.01, 1.05, 1.07, 1.23, 1.78, 2.56, 3.12, 5.01, 6.21]
our_bins = [0, 0.2, 0.5, 0.8334, 1.199, 1.999, 4.999, 1000]
our_labels = ['Very Negative', 'Negative', 'Slightly Negative', 'Neutral',
              'Slightly Positive', 'Positive', 'Very Positive']
pd.cut(ratio, 
       bins = our_bins,
       right = False,
       labels = our_labels)

输出：

['Very Negative', 'Negative', 'Negative', 'Slightly Negative', 'Neutral', ..., 'Slightly Positive', 'Positive', 'Positive', 'Very Positive', 'Very Positive']
Length: 14
Categories (7, object): ['Very Negative' < 'Negative' < 'Slightly Negative' < 'Neutral' <
                         'Slightly Positive' < 'Positive' < 'Very Positive']

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