数据集如下:
运行如下代码时报错:
TypeError: unsupported operand type(s) for -: 'str' and 'str'
all_DF = pd.read_csv('/Users/49996/Desktop/coding_vib/prices-split-adjusted.csv', header = None, delimiter="\t", encoding='utf-8', error_bad_lines=False)
#归一化 用于housing和prices
for i in list(all_DF.columns):
# 获取各个指标的最大值和最小值
Max = np.max(all_DF[i])
Min = np.min(all_DF[i])
all_DF[i] = (all_DF[i] - Min) / (Max - Min)
# data = all_DF.iloc[:, 0:7].values #softsensor
data = all_DF.iloc[:, 2:6].values #prices
# data = all_DF.iloc[:, 0:8].values #housing
问题出现在第三行,由于数据集0到2列中含有string类型的文本(分别是时间与文字),不能直接对文本类型做归一化
all_DF = pd.read_csv('/Users/49996/Desktop/coding_vib/prices-split-adjusted.csv', header = None, delimiter="\t", encoding='utf-8', error_bad_lines=False)
#归一化 用于housing和prices
# data = all_DF.iloc[:, 0:7].values #softsensor
data = all_DF.iloc[:, 2:6].values #prices
for i in list(data.columns):
# 获取各个指标的最大值和最小值
Max = np.max(all_DF[i])
Min = np.min(all_DF[i])
all_DF[i] = (all_DF[i] - Min) / (Max - Min)
# data = all_DF.iloc[:, 0:8].values #housing
解决方案:跳过前两列数据,然后再做归一化