pandas調整列的順序以及添加列的實現
在對excel的操作中,調整列的順序以及添加一些列也是經常用到的,下面我們用pandas實現這一功能。
1、調整列的順序
>>> 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') # 這是最簡單常用的一種方法,相當於指定列名讓pandas # 從df中獲取 >>> df[['A', 'D', 'C', 'B']] A D C B 0 bob 87 78 12 1 millor 21 92 15 # 這也是可以的 >>> df[['A', 'A', 'A', 'A']] A A A A 0 bob bob bob bob 1 millor millor millor millor
2、添加某一列或者某幾列
(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)調用assign方法。該方法善於根據已有的列添加新的列,通過基本運算,或者調用函數
>>> df A B C D 0 bob 12 78 87 1 millor 15 92 21 # 其中E是列名,根據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 # 添加兩列也可以 >>> 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關於調整列的順序以及新增列的用法
補充:pandas修改DataFrame中的列名&調整列的順序
修改列名:
直接調用接口:
df.rename()
看一下接口中的定義:
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)
註意:
一個*,輸入可以是數組、元組,會把輸入的數組或元組拆分成一個個元素。
兩個*,輸入必須是字典格式
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 #將列名A替換為列名a,B改為b,C改為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
調整列的順序:
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) # 從字典創建DataFrame >>> df # 創建好的df列名默認按首字母順序排序,和字典中的先後順序並不一樣,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']] # 調整列順序為'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
就可以瞭。
以上為個人經驗,希望能給大傢一個參考,也希望大傢多多支持WalkonNet。如有錯誤或未考慮完全的地方,望不吝賜教。
推薦閱讀:
- pandas中df.rename()的具體使用
- 詳解pandas映射與數據轉換
- Python數據分析之 Pandas Dataframe修改和刪除及查詢操作
- Pandas reindex重置索引的使用
- Python pandas索引的設置和修改方法