Pandas:ValueError:无法将浮点 NaN 转换为整数 [英] Pandas: ValueError: cannot convert float NaN to integer

问题描述

我得到 ValueError: cannot convert float NaN to integer 以下内容:

df = pandas.read_csv('zoom11.csv')
df[['x']] = df[['x']].astype(int)

• x"显然是 csv 文件中的一列，但我无法在文件中发现任何 float NaN，并且不明白这是什么意思.
• 当我将列读取为字符串时，它的值类似于 -1,0,1,...2000，对我来说，所有的整数都非常漂亮.
• 当我将列读取为浮点数时，就可以加载它了.然后它显示值为 -1.0,0.0 等，仍然没有任何 NaN-s
• 我尝试在 read_csv 中使用 error_bad_lines = False 和 dtype 参数但无济于事.它只是以相同的异常取消加载.
• 文件不小(10+ M 行)，因此无法手动检查它，当我提取一个小的标题部分时，然后没有错误，但它发生在完整的文件中.所以它在文件中，但无法检测到什么.
• 从逻辑上讲，csv 不应该有缺失值，但即使有一些垃圾，我也可以跳过这些行.或者至少识别它们，但我看不到扫描文件和报告转换错误的方法.
• The "x" is obviously a column in the csv file, but I cannot spot any float NaN in the file, and dont get what does it mean by this.
• When I read the column as String, then it has values like -1,0,1,...2000, all look very nice int numbers to me.
• When I read the column as float, then this can be loaded. Then it shows values as -1.0,0.0 etc, still there are no any NaN-s
• I tried with error_bad_lines = False and dtype parameter in read_csv to no avail. It just cancels loading with same exception.
• The file is not small (10+ M rows), so cannot inspect it manually, when I extract a small header part, then there is no error, but it happens with full file. So it is something in the file, but cannot detect what.
• Logically the csv should not have missing values, but even if there is some garbage then I would be ok to skip the rows. Or at least identify them, but I do not see way to scan through file and report conversion errors.

更新:使用评论/答案中的提示，我用这个清理了我的数据:

Update: Using the hints in comments/answers I got my data clean with this:

# x contained NaN
df = df[~df['x'].isnull()]

# Y contained some other garbage, so null check was not enough
df = df[df['y'].str.isnumeric()]

# final conversion now worked
df[['x']] = df[['x']].astype(int)
df[['y']] = df[['y']].astype(int)

推荐答案

为了识别 NaN 值，请使用 布尔索引:

For identifying NaN values use boolean indexing:

print(df[df['x'].isnull()])

然后删除所有非数字值使用 to_numeric 带有参数 errors='coerce' - 将非数字值替换为 NaNs:

Then for removing all non-numeric values use to_numeric with parameter errors='coerce' - to replace non-numeric values to NaNs:

df['x'] = pd.to_numeric(df['x'], errors='coerce')

并且要删除 x 列中带有 NaN 的所有行，请使用 dropna:

And for remove all rows with NaNs in column x use dropna:

df = df.dropna(subset=['x'])

最后将值转换为 ints:

df['x'] = df['x'].astype(int)

这篇关于Pandas:ValueError:无法将浮点 NaN 转换为整数的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！