我试图将大DF中的稀疏列的类型转换(从float到int).我的问题是NaN值.即使将errors参数设置为’ignore’,使用列的字典时也不会忽略它们...这是一个玩具示例: t=pd.DataFrame([[1.01,2],[3.01, 10], [np.NaN,20]]) t.astype({0: int}, errors=’ignore’) ValueError...: Cannot convert non-finite...
{0: int}, errors=’ignore’) ValueError...: Cannot convert non-finite values (NA or inf) to integer 解决方法: 您可以在pandas 0.24.0中使用新的nullable integer...__version__ Out[1]: ‘0.24.2’ In [2]: t = pd.DataFrame([[1.01, 2],[3.01, 10], [np.NaN, 20]]) In [3...
convert_axes=None, convert_dates=True, keep_default_dates=True, precise_float=False, date_unit=None, encoding=None, encoding_errors='strict', lines=False, chunksize=None, compression='infer', nrows=None, storage_options=None, dtype_backend=_NoDefault.no_default, engine='ujson') ...
这是panda（支持整数NA）中的一个“陷阱”，其中带有NaN的整数列被转换为浮点数。这种折衷主要是出于内...
to_sql(name, con, *[, schema, if_exists, ...]) 将存储在DataFrame中的记录写入SQL数据库。 to_stata(path, *[, convert_dates, ...]) 将DataFrame对象导出为Stata dta格式。 to_string([buf, columns, col_space, header, ...]) 将DataFrame渲染为控制台友好的表格输出。 to_timestamp([freq, ...
This method is used to reshape the given DataFrame according to index and column values. It is used when we have multiple items in a column, we can reshape the DataFrame in such a way that all the multiple values fall under one single index or row, similarly, we can convert these multip...
By using replace() or fillna() methods you can replace NaN values with Blank/Empty string in… 0 Comments August 27, 2021 Pandas Pandas Convert Column to Float in DataFrame By using pandas DataFrame.astype() and pandas.to_numeric() methods you can convert a column from string/int type… ...
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{'Discount':'int'}) # Example 5: Converting multiple columns to int df = pd.DataFrame(technologies) df = df.astype({"Fee":"int","Discount":"int"}) # Example 6: Convert "Fee" from float # To int and replace NaN values df['Fee'] = df['Fee'].fillna(0).astype(int) print(df...