pandas read_csv 使用 dtypes 但列中有 na 值
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read_csv using dtypes but there is na value in columns
提问by
I used the following code to read csv, by specifying the types for each col:
我使用以下代码通过指定每个列的类型来读取 csv：
clean_pdf_type=pd.read_csv('table_updated.csv',usecols=col_names,dtype =col_types)
But it has the error:
但它有错误：
ValueError: Integer column has NA values in column 298
Not sure how to skip the NA?
不确定如何跳过 NA？
采纳答案by jpp
Pandas v0.24+
Pandas v0.24+
See NumPy or Pandas: Keeping array type as integer while having a NaN value
请参阅NumPy 或 Pandas：在具有 NaN 值的同时保持数组类型为整数
Pandas pre-v0.24
Pandas v0.24 之前的版本
You cannot have NaNvalues in an intdtype series. This is non-avoidable, because NaNvalues are considered float:
dtype 系列中不能有NaN值int。这是不可避免的，因为NaN值被考虑float：
import numpy as np
type(np.nan) # float
Your best bet is to read in these columns as floatinstead. If you are then able to replace NaNvalues by a filler value such as 0or -1, you can process accordingly and convert to int:
最好的办法是阅读这些专栏float。如果您随后能够用NaN填充值（例如0或 ）替换值-1，则可以进行相应处理并转换为int：
int_cols = ['col1', 'col2', 'col3']
df[int_cols] = df[int_cols].fillna(-1)
df[int_cols] = df[int_cols].apply(pd.to_numeric, downcast='integer')
The alternative of having mixed intand floatvalues will result in a series of dtype object. It is not recommended.
混合int和float值的替代方法将导致一系列 dtype object。不推荐。
回答by Frayal
clean_pdf_type=pd.read_csv('table_updated.csv',usecols=col_names)
clean_pdf_type = (clean_pdf_type.fillna(0)).astype(col_types)
As said in the comments, don't specify the type, remove the NA and then cast to a certain type
正如评论中所说，不要指定类型，删除 NA 然后转换为某种类型
