pandas 基本功能 （一）

pandas 的基本功能

• 重新索引 reindex(), ix():作用是创建新索引的新对象

• 丢弃指定轴上的项

• 索引、选取和过滤

import pandas as pd
import numpy as np
from pandas import Series, DataFrame

Series 的 reindex 使用方法

obj =  Series  ([ 4.5 ,  7.2 , - 5.3 ,  3.6 ],  index= ['d','b','c','a'] )

obj

d    4.5
b    7.2
c   -5.3
a    3.6
dtype: float64

obj.reindex(['a','b','Y','Z'])# 取索引的交集，没有值的用缺失值补充

a    3.6
b    7.2
Y    NaN
Z    NaN
dtype: float64

obj.reindex(['a','b','Y','Z'], fill_value=0) 

a    3.6
b    7.2
Y    0.0
Z    0.0
dtype: float64

# obj.reindex(['a','b','Y','Z'], method='ffill')

DataFrame 的 reindex的使用方法：可以修改行索引 ，列索引，也可以哦同时修改。

frame = DataFrame(np.arange(9).reshape((3,3)), index=['a','b','d'], columns=['cal','china','cannada'])

frame

	cal	china	cannada
a	0	1	2
b	3	4	5
d	6	7	8

frame1 = frame.reindex(['a','b','c','d']) # 只穿入一个数组，则只改变index行索引

frame1

	cal	china	cannada
a	0.0	1.0	2.0
b	3.0	4.0	5.0
c	NaN	NaN	NaN
d	6.0	7.0	8.0

frame2 = frame.reindex(columns=['cal','y','china','w']) # 通过 columns关键字改变列索引

frame2

	cal	y	china	w
a	0	NaN	1	NaN
b	3	NaN	4	NaN
d	6	NaN	7	NaN

frame3 = frame.reindex(index=['a','b','c','d'],columns=['cal','y','china','w'])# 同时改变行列索引

frame3

	cal	y	china	w
a	0.0	NaN	1.0	NaN
b	3.0	NaN	4.0	NaN
c	NaN	NaN	NaN	NaN
d	6.0	NaN	7.0	NaN

丢弃指定轴上的项：drop()函数，需要有索引数组或列表即可

frame

	cal	china	cannada
a	0	1	2
b	3	4	5
d	6	7	8

frame.drop('a')

	cal	china	cannada
b	3	4	5
d	6	7	8

frame.drop('china',axis=1)# s删除列则需要 axis关键字为1

	cal	cannada
a	0	2
b	3	5
d	6	8

索引、选取和过滤

# 索引使用类似于切片，但是包含最后一个索引
se = Series(np.arange(6), index=['a','b','c','d','e','f'])

se

a    0
b    1
c    2
d    3
e    4
f    5
dtype: int32

se['b':'c']# 切片全部包含

b    1
c    2
dtype: int32

se[['c','e','d']]# 指定Series对象的索引

c    2
e    4
d    3
dtype: int32

se[se<3]

a    0
b    1
c    2
dtype: int32

对DataFrame对象索引 其实是获取一个或者多个列

frame

	cal	china	cannada
a	0	1	2
b	3	4	5
d	6	7	8

frame['cal']

a    0
b    3
d    6
Name: cal, dtype: int32

frame[['cal','china']] # 获取两列

	cal	china
a	0	1
b	3	4
d	6	7

frame[:2] #选取行，

	cal	china	cannada
a	0	1	2
b	3	4	5

frame[frame['china'] > 1]

	cal	china	cannada
b	3	4	5
d	6	7	8

frame.ix['d']

D:\anacoda\lib\site-packages\ipykernel_launcher.py:1: DeprecationWarning: 
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated
  """Entry point for launching an IPython kernel.





cal        6
china      7
cannada    8
Name: d, dtype: int32

算术运算和数据对齐

• pandas 很重要的一个功能就是可以对不同索引的对象进行算术运算