pandas调整列的顺序以及添加列的实现
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2022-09-07 09:54:24
在对excel的操作中,调整列的顺序以及添加一些列也是经常用到的,下面我们用pandas实现这一功能。1、调整列的顺序>>> df = pd.read_excel(r'd:/myex...
在对excel的操作中,调整列的顺序以及添加一些列也是经常用到的,下面我们用pandas实现这一功能。
1、调整列的顺序
>>> df = pd.read_excel(r'd:/myexcel/1.xlsx') >>> df a b c d 0 bob 12 78 87 1 millor 15 92 21 >>> df.columns index(['a', 'b', 'c', 'd'], dtype='object') # 这是最简单常用的一种方法,相当于指定列名让pandas # 从df中获取 >>> df[['a', 'd', 'c', 'b']] a d c b 0 bob 87 78 12 1 millor 21 92 15 # 这也是可以的 >>> df[['a', 'a', 'a', 'a']] a a a a 0 bob bob bob bob 1 millor millor millor millor
2、添加某一列或者某几列
(1)直接添加
>>> df['e']=[1, 2] >>> df a b c d e 0 bob 12 78 87 1 1 millor 15 92 21 2
(2)调用assign方法。该方法善于根据已有的列添加新的列,通过基本运算,或者调用函数
>>> df a b c d 0 bob 12 78 87 1 millor 15 92 21 # 其中e是列名,根据b列-c列的值得到 >>> df.assign(e=df['b'] - df['c']) a b c d e 0 bob 12 78 87 -66 1 millor 15 92 21 -77 # 添加两列也可以 >>> df.assign(e=df['b'] - df['c'], f=df['b'] * df['c']) a b c d e f 0 bob 12 78 87 -66 936 1 millor 15 92 21 -77 1380
哈哈,以上就是pandas关于调整列的顺序以及新增列的用法
补充:pandas修改dataframe中的列名&调整列的顺序
修改列名:
直接调用接口:
df.rename()
看一下接口中的定义:
def rename(self, *args, **kwargs): """ alter axes labels. function / dict values must be unique (1-to-1). labels not contained in a dict / series will be left as-is. extra labels listed don't throw an error. see the :ref:`user guide <basics.rename>` for more. parameters ---------- mapper, index, columns : dict-like or function, optional dict-like or functions transformations to apply to that axis' values. use either ``mapper`` and ``axis`` to specify the axis to target with ``mapper``, or ``index`` and ``columns``. axis : int or str, optional axis to target with ``mapper``. can be either the axis name ('index', 'columns') or number (0, 1). the default is 'index'. copy : boolean, default true also copy underlying data inplace : boolean, default false whether to return a new dataframe. if true then value of copy is ignored. level : int or level name, default none in case of a multiindex, only rename labels in the specified level. returns ------- renamed : dataframe see also -------- pandas.dataframe.rename_axis examples -------- ``dataframe.rename`` supports two calling conventions * ``(index=index_mapper, columns=columns_mapper, ...)`` * ``(mapper, axis={'index', 'columns'}, ...)`` we *highly* recommend using keyword arguments to clarify your intent. >>> df = pd.dataframe({"a": [1, 2, 3], "b": [4, 5, 6]}) >>> df.rename(index=str, columns={"a": "a", "b": "c"}) a c 0 1 4 1 2 5 2 3 6 >>> df.rename(index=str, columns={"a": "a", "c": "c"}) a b 0 1 4 1 2 5 2 3 6 using axis-style parameters >>> df.rename(str.lower, axis='columns') a b 0 1 4 1 2 5 2 3 6 >>> df.rename({1: 2, 2: 4}, axis='index') a b 0 1 4 2 2 5 4 3 6 """ axes = validate_axis_style_args(self, args, kwargs, 'mapper', 'rename') kwargs.update(axes) # pop these, since the values are in `kwargs` under different names kwargs.pop('axis', none) kwargs.pop('mapper', none) return super(dataframe, self).rename(**kwargs)
注意:
一个*,输入可以是数组、元组,会把输入的数组或元组拆分成一个个元素。
两个*,输入必须是字典格式
示例:
>>>import pandas as pd >>>a = pd.dataframe({'a':[1,2,3], 'b':[4,5,6], 'c':[7,8,9]}) >>> a a b c 0 1 4 7 1 2 5 8 2 3 6 9 #将列名a替换为列名a,b改为b,c改为c >>>a.rename(columns={'a':'a', 'b':'b', 'c':'c'}, inplace = true) >>>a a b c 0 1 4 7 1 2 5 8 2 3 6 9
调整列的顺序:
如:
>>> import pandas >>> dict_a = {'user_id':['webbang','webbang','webbang'],'book_id':['3713327','4074636','26873486'],'rating':['4','4','4'], 'mark_date':['2017-03-07','2017-03-07','2017-03-07']} >>> df = pandas.dataframe(dict_a) # 从字典创建dataframe >>> df # 创建好的df列名默认按首字母顺序排序,和字典中的先后顺序并不一样,字典中'user_id','book_id','rating','mark_date' book_id mark_date rating user_id 0 3713327 2017-03-07 4 webbang 1 4074636 2017-03-07 4 webbang 2 26873486 2017-03-07 4 webbang
直接修改列名:
>>> df = df[['user_id','book_id','rating','mark_date']] # 调整列顺序为'user_id','book_id','rating','mark_date' >>> df user_id book_id rating mark_date 0 webbang 3713327 4 2017-03-07 1 webbang 4074636 4 2017-03-07 2 webbang 26873486 4 2017-03-07
就可以了。
以上为个人经验,希望能给大家一个参考,也希望大家多多支持。如有错误或未考虑完全的地方,望不吝赐教。