[Panads数据分析-02]Pandas数据结构之DataFrame

# DataFrame是一个表格型的数据结构，它包含一组有序的列，每列可以是不同的值类型（数值，字符串，布尔值等）。
# DataFrame即有行索引下标又有列索引下标，它可以看做是有Series组成的字典（共用同一个索引）
# 构建DataFrame的方法有很多，最常用的一种是直接传入一个由等长列表或Numpy数组组成的字典：

import pandas as pd

data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'],
       'year': [2000, 2001, 2002, 2001, 2001],
       'pop': [1.5, 1.7, 3.6, 2.4, 2.9]}
frame = pd.DataFrame(data)

frame

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	pop	state	year
0	1.5	Ohio	2000
1	1.7	Ohio	2001
2	3.6	Ohio	2002
3	2.4	Nevada	2001
4	2.9	Nevada	2001

# 如果指定了列序列，则DataFrame的列就会按照指定顺序进行排列

pd.DataFrame(data, columns=['year', 'state', 'pop'])

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	year	state	pop
0	2000	Ohio	1.5
1	2001	Ohio	1.7
2	2002	Ohio	3.6
3	2001	Nevada	2.4
4	2001	Nevada	2.9

# 跟Series一样，如果传入的列在数据中找不到，就会产生NA值

frame2 = pd.DataFrame(data, columns=['year', 'state', 'pop', 'debt'],
                 index=['one', 'two', 'three', 'four', 'five'])

frame2

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	year	state	pop	debt
one	2000	Ohio	1.5	NaN
two	2001	Ohio	1.7	NaN
three	2002	Ohio	3.6	NaN
four	2001	Nevada	2.4	NaN
five	2001	Nevada	2.9	NaN

frame2.columns

Index([‘year’, ‘state’, ‘pop’, ‘debt’], dtype=’object’)

# 通过类似字典的标记的方式或属性的方式，可以将DataFrame的列获取为一个Series

frame2['state']

one Ohio two Ohio three Ohio four Nevada five Nevada Name: state, dtype: object

frame2.year

one 2000 two 2001 three 2002 four 2001 five 2001 Name: year, dtype: int64

# 返回的Series拥有原来DataFrame相同的索引下标，且其name属性也已经相应地设置好了。行也可以通过位置或名称的方式进行获取，比如用索引下标ix

frame2.ix['three']

year 2002 state Ohio pop 3.6 debt NaN Name: three, dtype: object

# 列可以通过赋值的方式进行修改。如：我们可以给那个空的"debt"列赋上一个标量值或一组值

frame2['debt'] = 16.5

frame2

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	year	state	pop	debt
one	2000	Ohio	1.5	16.5
two	2001	Ohio	1.7	16.5
three	2002	Ohio	3.6	16.5
four	2001	Nevada	2.4	16.5
five	2001	Nevada	2.9	16.5

import numpy as np
frame2['debt'] = np.arange(5.)

frame2

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	year	state	pop	debt
one	2000	Ohio	1.5	0.0
two	2001	Ohio	1.7	1.0
three	2002	Ohio	3.6	2.0
four	2001	Nevada	2.4	3.0
five	2001	Nevada	2.9	4.0

# 将列表或数组赋给某个列时，其长度必须跟DataFrame的长度相匹配。
# 如果赋值的是一个Series，就会精确匹配到DataFrame的索引，所有的空位都将被填上缺失值：

val = pd.Series([-1.2, -1.5, -1.7], index = ['two', 'four', 'five'])

frame2['debt'] = val
frame2

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	year	state	pop	debt
one	2000	Ohio	1.5	NaN
two	2001	Ohio	1.7	-1.2
three	2002	Ohio	3.6	NaN
four	2001	Nevada	2.4	-1.5
five	2001	Nevada	2.9	-1.7

# 为不存在的列赋值会创建出一个新列。关键字del用于删除列：

frame2['eastern'] = frame2.state == 'Ohio'

frame2

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	year	state	pop	debt	eastern
one	2000	Ohio	1.5	NaN	True
two	2001	Ohio	1.7	-1.2	True
three	2002	Ohio	3.6	NaN	True
four	2001	Nevada	2.4	-1.5	False
five	2001	Nevada	2.9	-1.7	False

del frame2['eastern']

frame2

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	year	state	pop	debt
one	2000	Ohio	1.5	NaN
two	2001	Ohio	1.7	-1.2
three	2002	Ohio	3.6	NaN
four	2001	Nevada	2.4	-1.5
five	2001	Nevada	2.9	-1.7

frame2.columns

Index([‘year’, ‘state’, ‘pop’, ‘debt’], dtype=’object’)

# 另外一种数据形式是嵌套字典（也就是字典的字典）

pop = {'Nevada': {2001: 2.4, 2002: 2.9},
       'Ohio': {2000: 1.5, 2001: 1.7, 2002: 3.6}}

frame3 = pd.DataFrame(pop)

frame3

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	Nevada	Ohio
2000	NaN	1.5
2001	2.4	1.7
2002	2.9	3.6

# 可以对该结果进行装置

frame3.T

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	2000	2001	2002
Nevada	NaN	2.4	2.9
Ohio	1.5	1.7	3.6

# 内层字典的键会被合并、排序以形成最终的索引
# 如果显示的指定索引，则不会这样

pd.DataFrame(pop, index=[2001, 2002, 2003])

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	Nevada	Ohio
2001	2.4	1.7
2002	2.9	3.6
2003	NaN	NaN

# 由Series组成的字典差不多也是一样的用法：

pdata = {'Ohio': frame3['Ohio'][:-1],
        'Nevadd': frame3['Nevada'][:2]}

pd.DataFrame(pdata)

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	Nevadd	Ohio
2000	NaN	1.5
2001	2.4	1.7

# 如果设置了DataFrame的index和columns的name属性，则这些信息也会被显示出来：

frame3.index.name = 'year'
frame3.columns.name = 'state'

frame3

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state	Nevada	Ohio
year
2000	NaN	1.5
2001	2.4	1.7
2002	2.9	3.6

# 跟Series一样，values属性也会以二维ndarry的形式返回DataFrame中的数据：

frame3.values

array([[ nan, 1.5], [ 2.4, 1.7], [ 2.9, 3.6]])

# 如果DateFrame各列的数据类型不同，则值数组的数据类型就会被选用能兼容所有列数据类型：

frame2

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	year	state	pop	debt
one	2000	Ohio	1.5	NaN
two	2001	Ohio	1.7	-1.2
three	2002	Ohio	3.6	NaN
four	2001	Nevada	2.4	-1.5
five	2001	Nevada	2.9	-1.7

frame2.values

array([[2000, 'Ohio', 1.5, nan],
       [2001, 'Ohio', 1.7, -1.2],
       [2002, 'Ohio', 3.6, nan],
       [2001, 'Nevada', 2.4, -1.5],
       [2001, 'Nevada', 2.9, -1.7]], dtype=object)

[Panads数据分析-02]Pandas数据结构之DataFrame

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