Method 1 : Convert float type column to int using astype() method Here we are going to convert the float type column in DataFrame to integer type using astype() method. we just need to pass int keyword inside this method. Syntax: python dataframe['column'].astype(int) where, dataframe ...
在对 dataframe 数据框中某列进行时间戳转换，或其他变换时，出现 ValueError: cannot convert float NaN to integer 这是因为这列中存在空值，无法转换，所以首先找出空值所在的行，然后将其删除；即可。
To convert a string column to an integer in a Pandas DataFrame, you can use theastype()method. To convert String to Int (Integer) from Pandas DataFrame or Series useSeries.astype(int)orpandas.to_numeric()functions. In this article, I will explain theastype()function, its syntax, parameters...
当出现ValueError: cannot convert float NaN to integer错误时,通常是因为我们尝试将一个包含NaN的浮点数转换为整数类型,这是不允许的。因为在Python中,NaN是不能转换为整数的。 解决方法 解决这个问题的方法通常有两种: 1. 检查NaN值 首先,我们需要检查数据中是否存在NaN值。如果我们知道出现错误的...
Python中的“cannot convert non-finite values (na or inf) to integer”错误 在Python中,尤其是使用pandas进行数据处理时,可能会遇到“cannot convert non-finite values (na or inf) to integer”这样的错误。这个错误通常发生在尝试将包含非有限值(如NaN或inf)的数据列转换为整数类型时。下面我将从三个方面详...
Suppose we are given a DataFrame with some integer in the date format, we know how to convert these values in datetime format (usingpandas.to_datetime()method), but here, we need to convert the datetime type value to integer timestamp value. ...
If you'd rather set values that cannot be converted to numeric toNaN, set theerrorsargument to"coerce"when callingDataFrame.apply(). main.py importpandasaspd df=pd.DataFrame({'id':['1','2','3','4'],'name':['Alice','Bobby','Carl','Dan'],'experience':['1','1','5','7']...
有时会遇到类似于ValueError: cannot convert float NaN to integer的错误。
Use pandas DataFrame.astype(int) and DataFrame.apply() methods to cast float column to integer(int/int64) type. I believe you would know float is bigger
Convert Column to Int (Integer) You can use pandasDataFrame.astype()function to convert column to int(integer). You can apply this to a specific column or to an entire DataFrame. To cast the data type to a 64-bit signed integer, you can use numpy.int64, numpy.int_, int64, or int ...