复制 In [116]: tdi / np.timedelta64(1, "s") Out[116]: Index([86400.0, nan, 172800.0], dtype='float64') In [117]: tdi.astype("timedelta64[s]") Out[117]: TimedeltaIndex(['1 days', NaT, '2 days'], dtype='timedelta64[s]', freq=None) 标量类型操作也有效。这些可能返回一个...
修复了在以二进制模式而不是文本模式打开codecs.StreamWriter并忽略用户提供的mode时的回归to_csv()(GH 39247) 当将np.int32传递给 dtype 参数时,修复了将Categorical.astype()转换为不正确 dtype 的回归(GH 39402) 修复了在追加 (mode="a") 到现有文件时创建损坏文件的to_excel()中的回归(GH 39576) 修复...
或者使用astype进行转换: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 In [4]: s = pd.Series(['a', 'b', 'c']) In [5]: s Out[5]: 0 a 1 b 2 c dtype: object In [6]: s.astype("string") Out[6]: 0 a 1 b 2 c dtype: string String 的方法 String可以转换成大写,小...
astype(int) # 把浮点型变成int >>df Group Value rk 0 A 2 2 1 A 2 2 2 A 1 1 3 B 7 3 4 B 5 2 5 B 5 2 6 B 1 1 # rank data = {'Group': ['A', 'A','A', 'B','B', 'B', 'B'], 'Value': [2, 2, 1, 7,5, 5,1]} df = pd.DataFrame(data) df['rk'...
pandas 使用 64 位整数以纳秒分辨率表示Timedeltas。因此,64 位整数限制确定了Timedelta的限制。 In [22]: pd.Timedelta.minOut[22]: Timedelta('-106752 days +00:12:43.145224193') In [23]: pd.Timedelta.maxOut[23]: Timedelta('106751 days 23:47:16.854775807') ...
float_format: 'FloatFormatType | None' = None, sparsify: 'bool | None' = None, index_names: 'bool' = True, justify: 'str | None' = None, max_rows: 'int | None' = None, max_cols: 'int | None' = None, show_dimensions: 'bool | str' = False, decimal: 'str' = '.', ...
In [116]: tdi / np.timedelta64(1, "s")Out[116]: Index([86400.0, nan, 172800.0], dtype='float64')In [117]: tdi.astype("timedelta64[s]")Out[117]: TimedeltaIndex(['1 days', NaT, '2 days'], dtype='timedelta64[s]', freq=None) ...
Pandas provide theastype()methodto convert a column to a specific type. We passfloatto the method and set the parametererrorsas'raise', which means it will raise exceptions for invalid values. Syntax: DataFrame.astype(dtype,copy=True,errors="raise") ...
>>> dtypes = {'POP': 'float64', 'AREA': 'float64', 'GDP': 'float64', ... 'IND_DAY': 'datetime64'} >>> df = pd.DataFrame(data=data).T.astype(dtype=dtypes) >>> df.to_pickle('data.pickle') Like you did with databases, it can be convenient first to specify the data...
df[cols]=df[cols].replace(',','.',regex=True).astype(float) OR An alternative approach involves defining a function that incorporates theconvertersparameter and passing it to theread_excel()method. def typecast_float(value): try: return float(value.replace(',', '.')) ...