pandas 14 - 重新索引reindex,reindex_like,rename( tcy)

重新索引  2018/12/14

1.函数： 

df.reindex(labels=None, index=None, columns=None, axis=None, method=None,
copy=True, level=None, fill_value=nan, limit=None, tolerance=None)
# 重新索引会更改行标签和列标签。数据匹配标签。新标签位置插入缺失值(NA) fill or pad向前填充；bfill 向后填充

df.reindex_like(other, method=None, copy=True, limit=None, tolerance=None)
# 返回匹配索引的对象
# 参数：pad/ffill -向前填充bfill/backfill - 向后填充nearest - 最近索引值填充

df.rename(mapper=None, index=None, columns=None, axis=None,
copy=True, inplace=False, level=None)#重命名
# 允许基于一些映射(字典或者系列)或任意函数来重新标记一个轴
# 参数：mapper, index, columns : dict-like or function
# inplace默认为False并复制底层数据；inplace=True将数据重命名  

2.实例

df = pd.DataFrame({ 'A': ['Tom','Jim','May'],'B': [1,2,3]},index=[0,2,4])

df.reindex(index=[0,1,2,3,4])
df.reindex(index=[0,1,2,3,4],method='ffill')
df.reindex(index=[0,1,2,3,4],method='bfill')

#     A B        A B      A B
# 0 Tom 1.0  0 Tom 1  0 Tom 1
# 1 NaN NaN  1 Tom 1  1 Jim 2
# 2 Jim 2.0  2 Jim 2  2 Jim 2
# 3 NaN NaN  3 Jim 2  3 May 3
# 4 May 3.0  4 May 3  4 May 3   

# 实例2：Padding NAN's 

df1 = pd.DataFrame(np.arange(18).reshape(6,3),columns=['col1','col2','col3'])
df2 = pd.DataFrame(-np.arange(6).reshape(2,3),columns=['col1','col2','col3'])

df2.reindex_like(df1,method='ffill')#向前填充 or pad

#   col1 col2 col3
# 0  0 -1 -2
# 1 -3 -4 -5
# 2 -3 -4 -5
# 3 -3 -4 -5
# 4 -3 -4 -5
# 5 -3 -4 -5

df2.reindex_like(df1,method='bfill')#向后填充 or backfill

#    col1 col2 col3
# 0  0.0 -1.0 -2.0
# 1 -3.0 -4.0 -5.0
# 2 NaN NaN NaN
# 3 NaN NaN NaN
# 4 NaN NaN NaN
# 5 NaN NaN NaN

# 实例：填充限制- 指定连续匹配的最大计数。
df2.reindex_like(df1)
#   col1 col2 col3
# 0  0.0 -1.0 -2.0
# 1 -3.0 -4.0 -5.0
# 2 NaN NaN NaN
# 3 NaN NaN NaN
# 4 NaN NaN NaN
# 5 NaN NaN NaN

df2.reindex_like(df1,method='ffill',limit=1)

#   col1 col2 col3
# 0  0.0 -1.0 -2.0
# 1 -3.0 -4.0 -5.0
# 2 -3.0 -4.0 -5.0
# 3 NaN NaN NaN
# 4 NaN NaN NaN
# 5 NaN NaN NaN

df2.reindex_like(df1,method='ffill',limit=2)

#   col1 col2 col3
# 0  0.0 -1.0 -2.0
# 1 -3.0 -4.0 -5.0
# 2 -3.0 -4.0 -5.0
# 3 -3.0 -4.0 -5.0
# 4 NaN NaN NaN
# 5 NaN NaN NaN  

# 实例3：重命名

df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})

df.rename(index='行{}'.format, columns={"A": "a", "B": "c"})# 更改行名列名
df.rename(str.lower, axis='columns')  # 列名变小写
df.rename({1: 2, 2: 4}, axis='index') # 更改行名 等价axis=0

#     a c #   a b #   A B
# 行0 1 4 # 0 1 4 # 0 1 4
# 行1 2 5 # 1 2 5 # 2 2 5
# 行2 3 6 # 2 3 6 # 4 3 6 

# 实例4：reindex的坑  

df = pd.DataFrame(np.arange(12).reshape(6, 2), columns=['A', 'B'],index=list('abcdef'))

df.reindex(['b', 'c', 'e']) #应该这样用 等价df.iloc[[1, 2, 4]]
df.reindex([1, 2, 4])       #出现异常值

#   A B #     A B
# b 2 3 # 1 NaN NaN
# c 4 5 # 2 NaN NaN
# e 8 9 # 4 NaN NaN