复制 In [12]: df.loc[:, ['B', 'A']] = df[['A', 'B']].to_numpy() In [13]: df[['A', 'B']] Out[13]: A B 2000-01-01 0.469112 -0.282863 2000-01-02 1.212112 -0.173215 2000-01-03 -0.861849 -2.104569 2000-01-04 0.721555 -0.706771 2000-01-05 -0.424972 0.567020 2000-...
to_XXX()有以下种类: to_numeric() #转化为数字型,根据情况转化为int或float to_string() #转化为字符型 to_dict() #转化为字典,不能处理单列数据 to_timestamp() #转化为时间戳 to_datetime() #转化为datetime64[ns] DataFrame 的每一行数据都可以看成一个 Series 结构,只不过,DataFrame 为这些行中每...
# we have automagically already created an index (in the first section) In [531]: i = store.root.df.table.cols.index.index In [532]: i.optlevel, i.kind Out[532]: (6, 'medium') # change an index by passing new parameters In [533]: store.create_table_index("df", optlevel=9...
字符串别名"string[pyarrow]"映射到pd.StringDtype("pyarrow"),这与指定dtype=pd.ArrowDtype(pa.string())不等效。通常,对数据的操作行为会类似,除了pd.StringDtype("pyarrow")可以返回基于 NumPy 的可空类型,而pd.ArrowDtype(pa.string())将返回ArrowDtype。 In [7]:importpyarrowaspa In [8]: data =l...
pandas will attempt to infer the `dtype`from the data.Note that when `data` is a NumPy array, ``data.dtype`` is*not* used for inferring the array type. This is becauseNumPy cannot represent all the types of data that can beheld in extension arrays.Currently, pandas will infer an exte...
当多个series对象之间进行运算的时候,如果不同series之间具有不 同的索引值,那么运算会自动对齐相同索引值的数据,如果某个 series没有某个索引值,那么最终结果会赋值为NaN。 importpandas as pdimportnumpy as np ser01= pd.Series(np.random.randint(1,5,5),index = list('abcde'))print(ser01) ...
pct_change([periods, fill_method, limit, freq]) 当前元素与先前元素之间的百分比变化。pipe(func, *args, **kwargs) 应用func(self,* args,** kwargs)。pivot([index, columns, values]) 返回按给定的索引/列值组织的重整型DataFrame。pivot_table([values, index, columns, …]) 创建电子表格样式的...
# we have automagically already created an index (in the first section)In [531]: i = store.root.df.table.cols.index.indexIn [532]: i.optlevel, i.kindOut[532]: (6, 'medium')# change an index by passing new parametersIn [533]: store.create_table_index("df", optlevel=9, kind...
This section demonstrates how to change a boolean True/False indicator to different words. Once again, we can use the map function: data_new2=data.copy()# Create copy of pandas DataFramedata_new2['x1']=data_new2['x1'].map({True:'yes',False:'no'})# Replace boolean by stringprint(...
Yields below output. Note that when a key is not found for some dicts and it exists on other dicts, it creates a DataFrame withNaNfor non-existing keys. In case you would like to change the NaN values refer toHow to replace NaN/None values with empty String. ...