实践:
修改列值
分组、排序使用同一字段:整数
--》区间名称 字符串
FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[320, 439)' has dtype incompatible with int64, please explicitly cast to a compatible dtype first. Cast a pandas object to a specified dtype ``dtype``.
pandas.DataFrame.astype — pandas 2.2.2 documentation https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.astype.html
pandas.DataFrame.astype
- DataFrame.astype(dtype, copy=None, errors='raise')[source]
-
Cast a pandas object to a specified dtype
dtype
.- Parameters:
- dtypestr, data type, Series or Mapping of column name -> data type
-
Use a str, numpy.dtype, pandas.ExtensionDtype or Python type to cast entire pandas object to the same type. Alternatively, use a mapping, e.g. {col: dtype, …}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame’s columns to column-specific types.
- copybool, default True
-
Return a copy when
copy=True
(be very careful settingcopy=False
as changes to values then may propagate to other pandas objects).Note
The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.
You can already get the future behavior and improvements through enabling copy on write
pd.options.mode.copy_on_write = True
- errors{‘raise’, ‘ignore’}, default ‘raise’
-
Control raising of exceptions on invalid data for provided dtype.
-
raise
: allow exceptions to be raised -
ignore
: suppress exceptions. On error return original object.
-
- Returns:
- same type as caller
See also
to_datetime
-
Convert argument to datetime.
to_timedelta
-
Convert argument to timedelta.
to_numeric
-
Convert argument to a numeric type.
numpy.ndarray.astype
-
Cast a numpy array to a specified type.
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标签:类型转换,dtype,object,specified,copy,type,pandas From: https://www.cnblogs.com/papering/p/18423035