pandas学习笔记

from pandas import Series,DataFrame
import pandas as pd
'''
series 是类似一维数组的对象
数组和索引组成
可以看成是一个定长的有序字典
索引不会变，进行计算时会匹配索引
'''
obj=Series([4,7,-5,3])
obj

0 4 1 7 2 -5 3 3 dtype: int64

obj.values

array([ 4, 7, -5, 3], dtype=int64)

obj.index

Index([‘a’, ‘s’, ‘d’, ‘f’], dtype=’object’)

obj=Series([4,7,-5,3],index=('a','s','d','f'))
obj

a 4 s 7 d -5 f 3 dtype: int64

obj['d']

-5

obj[obj>0]

a 4 s 7 f 3 dtype: int64

'''
DataFrame是一个表格型的数据结构
包括行索引和列索引
++++可以指定列序列
'''
data={'a':obj,'b':obj,'c':obj}
objframe=DataFrame(data)
type(data)

dict

objframe

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	a	b	c
a	4	4	4
s	7	7	7
d	-5	-5	-5
f	3	3	3

objframe['a']   #返回一个Series结构对象，有name属性

a 4 s 7 d -5 f 3 Name: a, dtype: int64

import numpy as np
objframe['d']=np.random.randint(0,2,size=4)  #对列进行赋值
'''  Series会对索引配对'''
colval=Series([1,2,3,4],index=['a','d','f','g'])
objframe['e']=colval

objframe

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	a	b	c	d	e
a	4	4	4	0	1.0
s	7	7	7	1	NaN
d	-5	-5	-5	0	2.0
f	3	3	3	0	3.0

del objframe['a']

objframe

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	b	c	d	e
a	4	4	0	1.0
s	7	7	1	NaN
d	-5	-5	0	2.0
f	3	3	0	3.0

np.where(objframe>3,6,-1)    #np.where类似与if a 则 b，否则 c。

array([[ 6, 6, -1, -1], [ 6, 6, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1]])

objcopy=objframe>8  # 返回布尔值，也可以利用布尔值进行判断，0为false

objcopy

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	b	c	d	e
a	False	False	False	False
s	False	False	False	False
d	False	False	False	False
f	False	False	False	False

frame= DataFrame(np.arange(9).reshape(3,3),index=['a','b','c'],columns=['ohin','leis','cal'])

frame.T

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	a	b	c
ohin	0	3	6
leis	1	4	7
cal	2	5	8

frame['leis'][:2]   #访问

a 1 b 4 Name: leis, dtype: int32

'''
+++++++pd的基本功能++++++++
1，reindex重新索引
2，drop 丢弃指定值
3，索引，选取，过滤  
    Series索引类似数组，除了标签切片不同。
    DataFrame索引 obj.ix[]是行，obj[]是列
4，算术运算和数据对齐（add，sub，div，mul）
    在算术运算中加入填充值，fill_value=0等，并进行传播
    DataFrame和Series之间进行运算，Series会自动广播
5，函数应用与映射
'''

frame2=frame.reindex(['a','b','c','d'],fill_value=0)  #++++++++重新索引,没有值的索引填充。method=('ffill)
frame3=frame.reindex(list('abcde'),fill_value=0)
print(frame)
frame3

ohin leis cal a 0 1 2 b 3 4 5 c 6 7 8

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	ohin	leis	cal
a	0	1	2
b	3	4	5
c	6	7	8
d	0	0	0
e	0	0	0

sates =['ohin','leis','ca']
frame2.reindex(columns=sates)  #这是视图，没有对frame2做修改

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	ohin	leis	ca
a	0	1	NaN
b	3	4	NaN
c	6	7	NaN
d	0	0	NaN

frame3 =frame2.drop('a')
frame3

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	ohin	leis	cal
b	3	4	5
c	6	7	8
d	0	0	0

frame2.drop('leis',axis=1)  #这个轴axis

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	ohin	cal
a	0	2
b	3	5
c	6	8
d	0	0

frame2.mean(axis=0)  #axis=0是列

ohin 2.25 leis 3.00 cal 3.75 dtype: float64

frame2.mean(axis=1)  #axis=1是行

a 1.0 b 4.0 c 7.0 d 0.0 dtype: float64

frame2.drop('d',axis=0)

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	ohin	leis	cal
a	0	1	2
b	3	4	5
c	6	7	8

frame2.drop('cal',axis=1)

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	ohin	leis
a	0	1
b	3	4
c	6	7
d	0	0

frame2.ix['a']

ohin 0 leis 1 cal 2 Name: a, dtype: int32

frame2[:1]

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	ohin	leis	cal
a	0	1	2

frame2[frame2['cal']>2]

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	ohin	leis	cal
b	3	4	5
c	6	7	8

print(frame2.ix[:1,:1])

ohin a 0

frame3=DataFrame(np.arange(15).reshape((3,5)),index=list('abc'),columns=list('hello'))
frame3

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	h	e	l	l	o
a	0	1	2	3	4
b	5	6	7	8	9
c	10	11	12	13	14

serie=Series(frame3.ix['a'])
serie

h 0 e 1 l 2 l 3 o 4 Name: a, dtype: int32

frame3 + serie              #匹配行进行广播

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	h	e	l	l	o
a	0	2	4	6	8
b	5	7	9	11	13
c	10	12	14	16	18

serie2=Series(frame3['h'])
serie2

a 0 b 5 c 10 Name: h, dtype: int32

frame3.add(serie2 ,axis=0)   #沿着列进行广播

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	h	e	l	l	o
a	0	1	2	3	4
b	10	11	12	13	14
c	20	21	22	23	24