pandas 增加、插入、删除

df.dropna

dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)
主要参数说明：
Parameters
    ----------
    axis : {0 or 'index', 1 or 'columns'}, or tuple/list thereof
        Pass tuple or list to drop on multiple axes
    how : {'any', 'all'}
        * any : if any NA values are present, drop that label
        * all : if all values are NA, drop that label
    thresh : int, default None
        int value : require that many non-NA values
    subset : array-like
        Labels along other axis to consider, e.g. if you are dropping rows
        these would be a list of columns to include
    inplace : boolean, default False
    If True, do operation inplace and return None

>>>df = pd.DataFrame([[np.nan, 2, np.nan, 0], [3, 4, np.nan, 1],
[np.nan, np.nan, np.nan, np.nan], [1,2,3,4]],
columns=list('ABCD'))
>>>df.dropna()
     A    B    C    D
3  1.0  2.0  3.0  4.0
>>>df.dropna(how='any')
     A    B    C    D
3  1.0  2.0  3.0  4.0
>>>df.dropna(how='all')
     A    B    C    D
0  NaN  2.0  NaN  0.0
1  3.0  4.0  NaN  1.0
3  1.0  2.0  3.0  4.0

df.insert

insert(loc, column, value, allow_duplicates=False) 
参数：
    loc:  int型，表示第几列；若在第一列插入数据，则 loc=0
    column: 给插入的列取名，如 column='新的一列'
    value：数字，array，series等都可（可自己尝试）
allow_duplicates: 是否允许列名重复，选择Ture表示允许新的列名与已存在的列名重复。

>>> data=pd.DataFrame(np.arange(16).reshape(4,4), columns=list('abcd'))
>>> data.insert(loc=0,column='A',value=[1,2,3,4])
>>> data.insert(loc=0,column='B',value=12)

 df.append

append(other, ignore_index=False, verify_integrity=False, sort=False)
Parameters
    ----------
    other : DataFrame or Series/dict-like object, or list of these
        The data to append.
    ignore_index : bool, default False
        If True, do not use the index labels.
    verify_integrity : bool, default False
        If True, raise ValueError on creating index with duplicates.
sort : bool, default False

>>> df = pd.DataFrame(data = {'name':['Tom', 'Jack', 'Steve', 'Ricky'],'age':[28,34,29,42]})
>>> df3=df.append(pd.DataFrame({'name':['Rose'],'age':[18]}),ignore_index=True)
>>> df3.loc[4]=['Jack',32]