DataFrame

2024-04-07 08:14:56

DataFrame

DataFrame 是一个表格型的数据结构，它含有一组有序的列，每列可以是不同的值类型(数值，字符串，布尔值等)
DataFrame 即有行索引也有列索引，它可以被看作由Series组成的字典(共用一个索引)

创建 DataFrame

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

	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	2002

DataFrame的列按照指定顺序进行排序

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

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

索引重命名

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

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

创建空列

传入的列在数据中找不到，会产生NaN值

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

	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	2002	Nevada	2.9	NaN

列之间进行对比，创建布尔值列

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

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

通过字典嵌套(字典的字典) 进行创建

外层字典的键作为列，内层键作为行

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

	Nevada	Ohio
2000	NaN	1.5
2001	2.4	1.7
2002	2.9	3.6

pop = {'Nevada':{2001:2.4,2002:2.9},
      'Ohio':{2000:1.5,2001:1.7,2002:3.6}}
frame = DataFrame(pop,index=[2001,2002,2003])
frame

	Nevada	Ohio
2001	2.4	1.7
2002	2.9	3.6
2003	NaN	NaN

pop = {'Nevada':{2001:2.4,2002:2.9},
      'Ohio':{2000:1.5,2001:1.7,2002:3.6}}
frame = DataFrame(pop)
pdata = {'Ohio':frame['Ohio'][:-1],
         'Nevada':frame['Nevada'][:2]}
DataFrame(pdata)

	Nevada	Ohio
2000	NaN	1.5
2001	2.4	1.7

给索引赋名

pop = {'Nevada':{2001:2.4,2002:2.9},
      'Ohio':{2000:1.5,2001:1.7,2002:3.6}}
frame = DataFrame(pop)
frame.index.name = 'year'
frame

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

给列赋名

frame.columns.name = 'state'
frame

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

转置

pop = {'Nevada':{2001:2.4,2002:2.9},
      'Ohio':{2000:1.5,2001:1.7,2002:3.6}}
frame = DataFrame(pop)
frame.T

	2000	2001	2002
Nevada	NaN	2.4	2.9
Ohio	1.5	1.7	3.6

.values 属性以二维ndarray形式返回DataFrame中数据

pop = {'Nevada':{2001:2.4,2002:2.9},
      'Ohio':{2000:1.5,2001:1.7,2002:3.6}}
frame = DataFrame(pop)
frame.values

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

删除列值 del

pop = {'Nevada':{2001:2.4,2002:2.9},
      'Ohio':{2000:1.5,2001:1.7,2002:3.6}}
frame = DataFrame(pop)
del frame['Ohio']
frame

	Nevada
2000	NaN
2001	2.4
2002	2.9

索取

获取列值

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

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

frame.year

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

获取所有列名 .columns

frame.columns

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

获取所有索引名 .index

frame.index

Index(['one', 'two', 'three', 'four', 'five'], dtype='object')

获取行值

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

/Users/*hong/anaconda2/envs/python35/lib/python3.5/site-packages/ipykernel/__main__.py:2: 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
  from ipykernel import kernelapp as app





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

赋值

frame = DataFrame(data,columns=['year','state','pop','debt'],index=['one','two','three','four','five'])
frame['debt'] = 16.5
frame

	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	2002	Nevada	2.9	16.5

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

	year	state	pop	debt
one	2000	Ohio	1.5	0
two	2001	Ohio	1.7	1
three	2002	Ohio	3.6	2
four	2001	Nevada	2.4	3
five	2002	Nevada	2.9	4

将 Series 赋值给 DataFrame

赋值的是一个Series，会精确匹配DataFrame的索引，所有的空位都将被填上缺失值

from pandas import Series 
val = Series([-1.2,-1.5,-1.7],index=['two','four','five'])
frame['debt'] = val
frame

	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	2002	Nevada	2.9	-1.7

索引对象

Index 对象是不可修改的,这样才能使Index对象在多个数据结构之间安全共享

from pandas import Seriesies
obj = Series(range(3),index=['a','b','c'])
obj

a    0
b    1
c    2
dtype: int64

index = obj.index 
index

Index(['a', 'b', 'c'], dtype='object')

index[1:]

Index(['b', 'c'], dtype='object')

pd.Index()

最泛化的Index对象，将轴标签表示为一个由python对象组成的NumPy数组

import numpy as np
import pandas as pd
pd.Index(np.arange(3))

Int64Index([0, 1, 2], dtype='int64')

index = pd.Index(np.arange(3))
obj = Series([1.5,-2.5,0],index=index)
obj

0    1.5
1   -2.5
2    0.0
dtype: float64

obj.index is index

True

用逻辑变量返回索引所包含的数据

from pandas import DataFrame,Series
pop = {'Nevada':{2001:2.4,2002:2.9},
      'Ohio':{2000:1.5,2001:1.7,2002:3.6}}
frame = DataFrame(pop)
frame.index.name = 'year'
frame.columns.name = 'state'
frame

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

'Ohio' in frame.columns

True

2003 in frame.index

False

基本功能

.reindex

其作用是创建一个适应新索引的新对象
参数

index	用作索引的新序列。即可以是index实例，也可以是其他序列型的python数据结构。 index会被完全使用，就像没有任何复制一样
method	插值(填充)方法
fill_value	在重索引的过程中，需要引入缺失值时使用的代替值
limit	前向或后向填充时的最大填充量
level	在MultiIndex的指定级别上匹配简答索引，否则选取其子集
copy	默认为True，无论如何都复制；如果为False，则新旧相等就不复制

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

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

obj = obj.reindex(['a','b','c','d','e'])
obj

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

fill_value 参数

obj = Series([4.5,7.2,-5.3,3.6],index=['d','b','a','c'])
obj.reindex(['a','b','c','d','e'],fill_value=0)

a   -5.3
b    7.2
c    3.6
d    4.5
e    0.0
dtype: float64

method方法

ffill或pad 前向填充(或搬运)值

bfill或backfill 后向填充(或搬运)值

obj = Series(['blue','purple','yellow'],index=[0,2,4])
obj

0      blue
2    purple
4    yellow
dtype: object

obj.reindex(range(6),method='ffill')

0      blue
1      blue
2    purple
3    purple
4    yellow
5    yellow
dtype: object

frame = DataFrame(np.arange(9).reshape((3,3)),
                  columns=['Ohio','Texas','California'],index=['a','c','d'])
frame

	Ohio	Texas	California
a	0	1	2
c	3	4	5
d	6	7	8

frame.reindex(['a','b','c','d'])

	Ohio	Texas	California
a	0.0	1.0	2.0
b	NaN	NaN	NaN
c	3.0	4.0	5.0
d	6.0	7.0	8.0

states = ['Texas','Utah','California']
frame.reindex(columns=states)

	Texas	Utah	California
a	1	NaN	2
c	4	NaN	5
d	7	NaN	8

frame = DataFrame(np.arange(9).reshape((3,3)),
                  columns=['Ohio','Texas','California'],index=['a','c','d'])
states = ['Texas','Utah','California']
frame = frame.reindex(columns=states)
frame.reindex(index=['a','b','c','d'],method='ffill',columns=states)

	Texas	Utah	California
a	1	NaN	2
b	1	NaN	2
c	4	NaN	5
d	7	NaN	8

.ix

frame = DataFrame(np.arange(9).reshape((3,3)),
                  columns=['Ohio','Texas','California'],index=['a','c','d'])
states = ['Texas','Utah','California']
frame = frame.reindex(columns=states)
frame.reindex(index=['a','b','c','d'],columns=states)

	Texas	Utah	California
a	1.0	NaN	2.0
b	NaN	NaN	NaN
c	4.0	NaN	5.0
d	7.0	NaN	8.0

丢弃指定轴上的项

drop 方法返回的是一个在指定轴上删除了指定值的新对象

obj = Series(np.arange(5),index=['a','b','c','d','e'])
obj

a    0
b    1
c    2
d    3
e    4
dtype: int64

obj.drop('c')

a    0
b    1
d    3
e    4
dtype: int64

obj.drop(['d','c'])

a    0
b    1
e    4
dtype: int64

data = DataFrame(np.arange(16).reshape((4,4)),
                index=['Ohio','Colorado','Utah','New York'],columns=['one','two','three','four'])
data

	one	two	three	four
Ohio	0	1	2	3
Colorado	4	5	6	7
Utah	8	9	10	11
New York	12	13	14	15

删除行

data.drop(['Colorado','Ohio'])

	one	two	three	four
Utah	8	9	10	11
New York	12	13	14	15

删除列

data.drop('two',axis=1)

	one	three	four
Ohio	0	2	3
Colorado	4	6	7
Utah	8	10	11
New York	12	14	15

data.drop(['two','four'],axis=1)

	one	three
Ohio	0	2
Colorado	4	6
Utah	8	10
New York	12	14

码农公寓

DataFrame

创建 DataFrame

DataFrame的列按照指定顺序进行排序

索引重命名

创建空列

列之间进行对比，创建 布尔值列

通过字典嵌套(字典的字典) 进行创建

给索引 赋名

给列 赋名

转置

.values 属性以二维ndarray形式返回DataFrame中数据

删除列值 del

索取

获取列值

获取所有列名 .columns

获取所有索引名 .index

获取行值

赋值

将 Series 赋值给 DataFrame

索引对象

pd.Index()

用逻辑变量 返回索引所包含的数据

基本功能

.reindex

fill_value 参数

method方法

.ix

丢弃指定轴上的项

drop 方法返回的是一个在指定轴上删除了指定值的新对象

删除行

删除列

相关文章

列之间进行对比，创建布尔值列

给索引赋名

给列赋名

用逻辑变量返回索引所包含的数据