Pandas快速上手(一):基本操作

本文包含一些 Pandas 的基本操作,旨在快速上手 Pandas 的基本操作。

读者最好有 NumPy 的基础,如果你还不熟悉 NumPy,建议您阅读NumPy基本操作快速熟悉

Pandas 数据结构

Pandas 有两个核心的数据结构:SeriesDataFrame

Series

Series一维的类数组对象,包含一个值序列以及对应的索引

1 obj = pd.Series([6, 66, 666, 6666])
2 obj
0       6
1      66
2     666
3    6666
dtype: int64

此时索引默认为 0 到 N。我们可以分别访问 Series 的值和索引:

1 obj.values
2 obj.index  # 类似于 range(4)
array([   6,   66,  666, 6666])
RangeIndex(start=0, stop=4, step=1)

索引可以用标签来指定:

1 obj2 = pd.Series([6, 66, 666, 6666], index=['d', 'b', 'a', 'c'])
2 obj2
3 obj2.index
d       6
b      66
a     666
c    6666
dtype: int64
Index(['d', 'b', 'a', 'c'], dtype='object')

可以使用标签索引来访问 Series 的值,这有点像 NumPy 和字典的结合。

1 obj2['a']
2 obj2['d'] = 66666
3 obj2[['c', 'a', 'd']]
666
c     6666
a      666
d    66666
dtype: int64

Series 可以使用很多类似于 NumPy 的操作:

1 obj2[obj2 > 100]
2 obj2 / 2
3 np.sqrt(obj2)
d    66666
a      666
c     6666
dtype: int64
d    33333.0
b       33.0
a      333.0
c     3333.0
dtype: float64
d    258.197599
b      8.124038
a     25.806976
c     81.645576
dtype: float64

判断某索引是否存在:

1 'b' in obj2
2 'e' in obj2
True
False

可以直接将字典传入 Series 来创建 Series 对象:

1 sdata = {'Ohio': 35000, 'Texas': 71000, 'Oregon': 16000, 'Utah': 5000}
2 obj3 = pd.Series(sdata)
3 obj3
4 states = ['Texas', 'California', 'Ohio', 'Oregon']
5 obj4 = pd.Series(sdata, index=states)  # 指定了索引及其顺序
6 obj4
Ohio      35000
Texas     71000
Oregon    16000
Utah       5000
dtype: int64
Texas         71000.0
California        NaN
Ohio          35000.0
Oregon        16000.0
dtype: float64

通过 isnullnotnull 可以检测是否有空值,既可以使用 Pandas 函数也可以使用 Series 方法

1 pd.isnull(obj4)
2 pd.notnull(obj4)
3 obj4.isnull()
4 obj4.notnull()
Texas         False
California     True
Ohio          False
Oregon        False
dtype: bool
Texas          True
California    False
Ohio           True
Oregon         True
dtype: bool
Texas         False
California     True
Ohio          False
Oregon        False
dtype: bool
Texas          True
California    False
Ohio           True
Oregon         True
dtype: bool

Series 的数据对齐,类似于数据库的连接操作:

1 obj3
2 obj4
3 obj3 + obj4
Ohio      35000
Texas     71000
Oregon    16000
Utah       5000
dtype: int64
Texas         71000.0
California        NaN
Ohio          35000.0
Oregon        16000.0
dtype: float64
California         NaN
Ohio           70000.0
Oregon         32000.0
Texas         142000.0
Utah               NaN
dtype: float64

Series 及其索引都有一个 name 属性:

1 obj4.name = 'population'
2 obj4.index.name = 'state'
3 obj4
state
Texas         71000.0
California        NaN
Ohio          35000.0
Oregon        16000.0
Name: population, dtype: float64

Series 的索引可以原地(in-place)修改:

1 obj
2 obj.index = ['Bob', 'Steve', 'Jeff', 'Ryan']
3 obj
0       6
1      66
2     666
3    6666
dtype: int64
Bob         6
Steve      66
Jeff      666
Ryan     6666
dtype: int64

DataFrame

DataFrame 表示一张矩阵数据表,其中包含有序的列集合,每列都可以表示不同的数据类型。

DataFrame 有行索引和列索引,可以把它当做是 Series 的字典,该字典共享同一套行索引。

通过等长列表(或 NumPy 数组)的字典是常用的创建方式:

1 # 每个值都是长度为 6 的列表
2 data = {
3     'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada', 'Nevada'],
4     'year': [2000, 2001, 2002, 2001, 2002, 2003],
5     'pop': [2.5, 1.7, 3.6, 2.4, 2.9, 3.2],
6 }
7 frame = pd.DataFrame(data)
8 data
	state	year	pop
0	Ohio	2000	2.5
1	Ohio	2001	1.7
2	Ohio	2002	3.6
3	Nevada	2001	2.4
4	Nevada	2002	2.9
5	Nevada	2003	3.2

通过 head 方法可以选择前几行:

1 frame.head()
2 frame.head(2)
	state	year	pop
0	Ohio	2000	2.5
1	Ohio	2001	1.7
2	Ohio	2002	3.6
3	Nevada	2001	2.4
4	Nevada	2002	2.9
state year pop 0 Ohio 2000 2.5 1 Ohio 2001 1.7

指定列的序列,可以按照响应顺序来展示:

1 pd.DataFrame(data, columns=['year', 'state', 'pop'])
	year	state	pop
0	2000	Ohio	2.5
1	2001	Ohio	1.7
2	2002	Ohio	3.6
3	2001	Nevada	2.4
4	2002	Nevada	2.9
5	2003	Nevada	3.2

同样也可以指定索引:

1 frame2 = pd.DataFrame(data, columns=['year', 'state', 'pop', 'debt'],
2                      index=['one', 'two', 'three', 'four', 'five', 'six'])
3 frame2
4 frame2.columns
5 frame2.index
	year	state	pop	debt
one	2000	Ohio	2.5	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
six	2003	Nevada	3.2	NaN
Index(['year', 'state', 'pop', 'debt'], dtype='object')
Index(['one', 'two', 'three', 'four', 'five', 'six'], dtype='object')

通过键或者属性提取 DataFrame 的一列,得到的是 Series:

1 frame2['state']
2 frame2.year
3 type(frame2.year)
one        Ohio
two        Ohio
three      Ohio
four     Nevada
five     Nevada
six      Nevada
Name: state, dtype: object
one      2000
two      2001
three    2002
four     2001
five     2002
six      2003
Name: year, dtype: int64
pandas.core.series.Series

通过 loc 属性可以指定标签,检索行数据:

1 frame2.loc['three']
2 type(frame2.loc['three'])
year     2002
state    Ohio
pop       3.6
debt      NaN
Name: three, dtype: object
pandas.core.series.Series

对 'debt' 列进行赋值:

1 frame2['debt'] = 16.5
2 frame2
3 frame2['debt'] = np.arange(6.)
4 frame2
     year	state	pop	debt
one	2000	Ohio	2.5	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
six	2003	Nevada	3.2	16.5
     year	state	pop	debt
one	2000	Ohio	2.5	0.0
two	2001	Ohio	1.7	1.0
three	2002	Ohio	3.6	2.0
four	2001	Nevada	2.4	3.0
five	2002	Nevada	2.9	4.0
six	2003	Nevada	3.2	5.0

可以用 Series 来赋值 DataFrame 列:

1 val = pd.Series([-1.2, -1.5, -1.7], index=['two', 'four', 'five'])
2 frame2['debt'] = val
3 frame2
	year	state	pop	debt
one	2000	Ohio	2.5	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
six	2003	Nevada	3.2	NaN

给不存在的列赋值会创建新列:

1 frame2['eastern'] = frame2.state == 'Ohio'
2 frame2
	year	state	pop	debt	eastern
one	2000	Ohio	2.5	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	2002	Nevada	2.9	-1.7	False
six	2003	Nevada	3.2	NaN	False

删除某列:

del frame2['eastern']
frame2.columns
Index(['year', 'state', 'pop', 'debt'], dtype='object')

DataFrame 取出来的列是底层数据的视图,不是拷贝。所做的修改会反映到底层数据中,如果要拷贝必须使用显式的 copy 方法。

传入嵌套字典的情况:

 1 pop = {
 2     'Nevada': {
 3         2001: 2.4,
 4         2002: 2.9,
 5     },
 6     'Ohio': {
 7         2000: 1.5,
 8         2001: 1.7,
 9         2002: 3.6,
10     }
11 }
12 frame3 =  pd.DataFrame(pop)
13 frame3
	Nevada	Ohio
2000	NaN	1.5
2001	2.4	1.7
2002	2.9	3.6

DataFrame 的转置:

1 frame3.T
	2000	2001	2002
Nevada	NaN	2.4	2.9
Ohio	1.5	1.7	3.6

values 属性是包含 DataFrame 值的 ndarray:

1 frame3.values
array([[nan, 1.5],
       [2.4, 1.7],
       [2.9, 3.6]])

索引对象

Pandas 的索引对象用于存储轴标签元数据(轴名称等)。

1 obj = pd.Series(range(3), index=['a', 'b', 'c'])
2 index = obj.index
3 index
4 index.name = 'alpha'
5 index[1:]
Index(['a', 'b', 'c'], dtype='object')
Index(['b', 'c'], dtype='object', name='alpha')

索引对象是不可变对象,因此不可修改:

1 index[1] = 'd'  # 报错

索引类似于固定长度的集合:

1 frame3
2 frame3.columns
3 frame3.index
4 # 类似于集合
5 'Nevada' in frame3.columns
6 2000 in frame3.index
Nevada	Ohio
2000	NaN	1.5
2001	2.4	1.7
2002	2.9	3.6
Index(['Nevada', 'Ohio'], dtype='object')
Int64Index([2000, 2001, 2002], dtype='int64')
True
True

但是索引和集合的不同之处是可以包含重复的标签:

1 dup_labels = pd.Index(['foo', 'foo', 'bar', 'bar'])
2 dup_labels
3 set_labels = set(['foo', 'foo', 'bar', 'bar'])
4 set_labels
Index(['foo', 'foo', 'bar', 'bar'], dtype='object')
{'bar', 'foo'}

索引对象的一些常用方法:

  • append
  • difference
  • intersection
  • union
  • isin
  • delete
  • drop
  • insert
  • is_monotonic
  • is_unique
  • unique

Pandas 基本功能

重新索引

reindex 用于创建一个新对象,其索引进行了重新编排。

1 obj = pd.Series([4.5, 7.2, -5.3, 3.6], index=['d', 'b', 'a', 'c'])
2 obj
3 obj2 = obj.reindex(['a', 'b', 'c', 'd', 'e'])
4 obj2
d    4.5
b    7.2
a   -5.3
c    3.6
dtype: float64
a   -5.3
b    7.2
c    3.6
d    4.5
e    NaN
dtype: float64

ffill 用于向前填充值,在重新索引时会进行插值:

1 obj3 = pd.Series(['blue', 'purple', 'yellow'], index=[0, 2, 4])
2 obj3
3 obj3.reindex(range(6), method='ffill')
0      blue
2    purple
4    yellow
dtype: object
0      blue
1      blue
2    purple
3    purple
4    yellow
5    yellow
dtype: object

无论行索引还是列索引都可以重新编排:

1 frame = pd.DataFrame(np.arange(9).reshape((3, 3)),
2                      index=['a', 'c', 'd'],
3                      columns=['Ohio', 'Texas', 'California'])
4 frame
5 frame.reindex(['a', 'b', 'c', 'd'])
6 frame.reindex(columns=['Texas', 'Utah', 'California'])
	Ohio	Texas	California
a	0	1	2
c	3	4	5
d	6	7	8
      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
     Texas	Utah	California
a	1	NaN	2
c	4	NaN	5
d	7	NaN	8

根据轴删除数据

使用 drop 方法对数据行或列进行删除:

1 obj = pd.Series(np.arange(5.), index=['a', 'b', 'c', 'd', 'e'])
2 obj
3 new_obj = obj.drop('c')
4 new_obj
5 obj.drop(['d', 'c'])
a    0.0
b    1.0
c    2.0
d    3.0
e    4.0
dtype: float64
a    0.0
b    1.0
d    3.0
e    4.0
dtype: float64
a    0.0
b    1.0
e    4.0
dtype: float64
1 data = pd.DataFrame(np.arange(16).reshape((4, 4)),
2                     index=['Ohio', 'Colorado', 'Utah', 'New York'],
3                     columns=['one', 'two', 'three', 'four'])
4 data
5 data.drop(['Colorado', 'Ohio'])
6 data.drop('two', axis=1)
7 data.drop(['two', 'four'], axis='columns')
	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
     one	two	three	four
Utah	8	9	10	11
New York	12	13	14	15
     one	three	four
Ohio	0	2	3
Colorado	4	6	7
Utah	8	10	11
New York	12	14	15
     one	three
Ohio	0	2
Colorado	4	6
Utah	8	10
New York	12	14

原地删除:

1 obj
2 obj.drop('c', inplace=True)
3 obj
a    0.0
b    1.0
c    2.0
d    3.0
e    4.0
dtype: float64
a    0.0
b    1.0
d    3.0
e    4.0
dtype: float64

索引、选择、过滤

Series 的索引类似于 NumPy,只不过 Series 还可以用索引标签,不一定是整型索引。

1 obj = pd.Series(np.arange(4.), index=['a', 'b', 'c', 'd'])
2 obj
3 obj['b']
4 obj[1]
5 obj[2:4]
6 obj[['b', 'a', 'd']]
7 obj[[1, 3]]
8 obj[obj < 2]
a    0.0
b    1.0
c    2.0
d    3.0
dtype: float64
1.0
1.0
c    2.0
d    3.0
dtype: float64
b    1.0
a    0.0
d    3.0
dtype: float64
b    1.0
d    3.0
dtype: float64
a    0.0
b    1.0
dtype: float64

Series 的切片和原生 Python 有所不同,是闭区间(Python 是左闭右开区间)。

1 obj['b':'c']
b    1.0
c    2.0
dtype: float64

使用切片进行赋值:

1 obj['b':'c'] = 5
2 obj
a    0.0
b    5.0
c    5.0
d    3.0
dtype: float64

选择 DataFrame 的若干列:

1 data = pd.DataFrame(np.arange(16).reshape((4, 4)),
2                     index=['Ohio', 'Colorado', 'Utah', 'New York'],
3                     columns=['one', 'two', 'three', 'four'])
4 data
5 data['two']
6 type(data['two'])
7 data[['three', 'one']]
8 type(data[['three', 'one']])
     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
Ohio         1
Colorado     5
Utah         9
New York    13
Name: two, dtype: int64
pandas.core.series.Series
     three	one
Ohio	2	0
Colorado	6	4
Utah	10	8
New York	14	12
pandas.core.frame.DataFrame

使用切片和布尔数组选择 DataFrame:

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

DataFrame 语法上很像二维的 NumPy 数组,使用布尔 DataFrame:

1 data < 5
2 data[data < 5] = 0
3 data
	one	two	three	four
Ohio	True	True	True	True
Colorado	True	False	False	False
Utah	False	False	False	False
New York	False	False	False	False
        one	two	three	four
Ohio	0	0	0	0
Colorado	0	5	6	7
Utah	8	9	10	11
New York	12	13	14	15

使用 loc 和 iloc 进行选择

lociloc 可以用于选择 DataFrame 的行和列。

1 data.loc['Colorado', ['two', 'three']]
two      5
three    6
Name: Colorado, dtype: int64
1 data.iloc[2, [3, 0, 1]]
2 data.iloc[2]
3 data.iloc[[1, 2], [3, 0, 1]]
four    11
one      8
two      9
Name: Utah, dtype: int64
one       8
two       9
three    10
four     11
Name: Utah, dtype: int64
        four	one	two
Colorado	7	0	5
Utah	11	8	9

同样可以使用切片:

1 data.loc[:'Utah', 'two']
2 data.iloc[:, :3][data.three > 5]
Ohio        0
Colorado    5
Utah        9
Name: two, dtype: int64
        one	two	three
Colorado	0	5	6
Utah	8	9	10
New York	12	13	14

DataFrame 常用索引方法:

  • df[val]:选列
  • df.loc[val]:选行
  • df.loc[:, val]:选列
  • df.loc[val1, val2]:选列和行
  • df.iloc[where]:选行
  • df.iloc[:, where]:选列
  • df.iloc[where_i, where_j]:选列和行
  • df.at[label_i, label_j]:选某一标量
  • df.iat[i, j]:选某一标量
  • reindex:选列和行
  • get_value, set_value:选某一标量

算术和数据对齐

前面我们介绍了 Series 的算术对齐,接下来是 DataFrame 的:

1 df1 = pd.DataFrame(np.arange(9.).reshape((3, 3)), columns=list('bcd'),
2                    index=['Ohio', 'Texas', 'Colorado'])
3 df2 = pd.DataFrame(np.arange(12.).reshape((4, 3)), columns=list('bde'),
4                    index=['Utah', 'Ohio', 'Texas', 'Oregon'])
5 df1
6 df2
7 df1 + df2
	b	c	d
Ohio	0.0	1.0	2.0
Texas	3.0	4.0	5.0
Colorado6.0	7.0	8.0
        b	d	e
Utah	0.0	1.0	2.0
Ohio	3.0	4.0	5.0
Texas	6.0	7.0	8.0
Oregon	9.0	10.0	11.0
        b	c	d	e
ColoradoNaN	NaN	NaN	NaN
Ohio	3.0	NaN	6.0	NaN
Oregon	NaN	NaN	NaN	NaN
Texas	9.0	NaN	12.0	NaN
Utah	NaN	NaN	NaN	NaN

没有相同行索引和列索引的情况:

1 df1 = pd.DataFrame({'A': [1, 2]})
2 df2 = pd.DataFrame({'B': [3, 4]})
3 df1
4 df2
5 df1 - df2
A
0	1
1	2
B
0	3
1	4
A	B
0	NaN	NaN
1	NaN	NaN

填补值的算术方法

对于算术运算后产生空值的情况:

1 df1 = pd.DataFrame(np.arange(12.).reshape((3, 4)),
2                    columns=list('abcd'))
3 df2 = pd.DataFrame(np.arange(20.).reshape((4, 5)),
4                    columns=list('abcde'))
5 df2.loc[1, 'b'] = np.nan
6 df1
7 df2
8 df1 + df2
	a	b	c	d
0	0.0	1.0	2.0	3.0
1	4.0	5.0	6.0	7.0
2	8.0	9.0	10.0	11.0
        a	b	c	d	e
0	0.0	1.0	2.0	3.0	4.0
1	5.0	NaN	7.0	8.0	9.0
2	10.0	11.0	12.0	13.0	14.0
3	15.0	16.0	17.0	18.0	19.0
        a	b	c	d	e
0	0.0	2.0	4.0	6.0	NaN
1	9.0	NaN	13.0	15.0	NaN
2	18.0	20.0	22.0	24.0	NaN
3	NaN	NaN	NaN	NaN	NaN

可以使用 fill_value 属性来自动填空值:

1 df1.add(df2, fill_value=0)
        a	b	c	d	e
0	0.0	2.0	4.0	6.0	4.0
1	9.0	5.0	13.0	15.0	9.0
2	18.0	20.0	22.0	24.0	14.0
3	15.0	16.0	17.0	18.0	19.0

常用的算术方法(可用于补空值):

  • add, radd
  • sub, rsub
  • div, rdiv
  • floordiv, rfloordiv
  • mul, rmul
  • pow, rpow

DataFrame 和 Series 之间的运算

首先,看看一维数组和二维数组的减法的情况。

1 arr = np.arange(12.).reshape((3, 4))
2 arr
3 arr[0]
4 arr - arr[0]
array([[ 0.,  1.,  2.,  3.],
       [ 4.,  5.,  6.,  7.],
       [ 8.,  9., 10., 11.]])
array([0., 1., 2., 3.])
array([[0., 0., 0., 0.],
       [4., 4., 4., 4.],
       [8., 8., 8., 8.]])

默认情况下,DataFrame 和 Series 会匹配索引进行计算。

1 frame = pd.DataFrame(np.arange(12.).reshape((4, 3)),
2                      columns=list('bde'),
3                      index=['Utah', 'Ohio', 'Texas', 'Oregon'])
4 series = frame.iloc[0]
5 frame
6 series
7 frame - series
        b	d	e
Utah	0.0	1.0	2.0
Ohio	3.0	4.0	5.0
Texas	6.0	7.0	8.0
Oregon	9.0	10.0	11.0
b    0.0
d    1.0
e    2.0
Name: Utah, dtype: float64
        b	d	e
Utah	0.0	0.0	0.0
Ohio	3.0	3.0	3.0
Texas	6.0	6.0	6.0
Oregon	9.0	9.0	9.0

如果索引不匹配,会用外连接的方式重组索引:

1 series2 = pd.Series(range(3), index=['b', 'e', 'f'])
2 frame + series2
        b	d	e	f
Utah	0.0	NaN	3.0	NaN
Ohio	3.0	NaN	6.0	NaN
Texas	6.0	NaN	9.0	NaN
Oregon	9.0	NaN	12.0	NaN

如果希望对于所有列进行算术计算(broadcast 机制),必须使用前面介绍的算术方法。

1 series3 = frame['d']
2 frame
3 series3
4 frame.sub(series3, axis=0)
	b	d	e
Utah	0.0	1.0	2.0
Ohio	3.0	4.0	5.0
Texas	6.0	7.0	8.0
Oregon	9.0	10.0	11.0
Utah       1.0
Ohio       4.0
Texas      7.0
Oregon    10.0
Name: d, dtype: float64
        b	d	e
Utah	-1.0	0.0	1.0
Ohio	-1.0	0.0	1.0
Texas	-1.0	0.0	1.0
Oregon	-1.0	0.0	1.0

apply 和 map

NumPy 的通用函数(按元素的数组方法)可以用于 Pandas 对象:

1 frame = pd.DataFrame(np.random.randn(4, 3), columns=list('bde'),
2                      index=['Utah', 'Ohio', 'Texas', 'Oregon'])
3 frame
4 np.abs(frame)
	b	d	e
Utah	-0.590458	-0.352861	0.820549
Ohio	-1.708280	0.174739	-1.081811
Texas	0.857712	0.246972	0.532208
Oregon	0.812756	1.260538	0.818304
        b	d	e
Utah	0.590458	0.352861	0.820549
Ohio	1.708280	0.174739	1.081811
Texas	0.857712	0.246972	0.532208
Oregon	0.812756	1.260538	0.818304

一个常用的操作是按或者调用某一个函数,apply 可以达到该功能:

1 f = lambda x: x.max() - x.min()
2 frame.apply(f)
b    2.565993
d    1.613398
e    1.902360
dtype: float64
1 frame.apply(f, axis=1)
Utah      1.411007
Ohio      1.883019
Texas     0.610741
Oregon    0.447782
dtype: float64

apply 传入的函数不一定非得返回一个标量值,可以返回 Series:

1 def f(x):
2     return pd.Series([x.min(), x.max()], index=['min', 'max'])
3 frame.apply(f)
                b	        d	        e
min	-1.708280	-0.352861	-1.081811
max	0.857712	1.260538	0.820549

还可以传入按元素计算的函数:

1 frame
2 format = lambda x: '%.2f' % x
3 frame.applymap(format)
        b	        d	        e
Utah	-0.590458	-0.352861	0.820549
Ohio	-1.708280	0.174739	-1.081811
Texas	0.857712	0.246972	0.532208
Oregon	0.812756	1.260538	0.818304
        b	d	e
Utah	-0.59	-0.35	0.82
Ohio	-1.71	0.17	-1.08
Texas	0.86	0.25	0.53
Oregon	0.81	1.26	0.82

按元素应用某函数必须使用 applymap。取这个名字的原因是 Series 有一个 map 方法就是用来按元素调用函数的。

1 frame['e'].map(format)
Utah       0.82
Ohio      -1.08
Texas      0.53
Oregon     0.82
Name: e, dtype: object

排序和排名

对行索引进行排列:

1 obj = pd.Series(range(4), index=['d', 'a', 'b', 'c'])
2 obj
3 obj.sort_index()
d    0
a    1
b    2
c    3
dtype: int64
a    1
b    2
c    3
d    0
dtype: int64

对列索引进行排列:

1 frame = pd.DataFrame(np.arange(8).reshape((2, 4)),
2                      index=['three', 'one'],
3                      columns=['d', 'a', 'b', 'c'])
4 frame
5 frame.sort_index()
6 frame.sort_index(axis=1)
	d	a	b	c
three	0	1	2	3
one	4	5	6	7
        d	a	b	c
one	4	5	6	7
three	0	1	2	3
        a	b	c	d
three	1	2	3	0
one	5	6	7	4

按降序排列:

1 frame.sort_index(axis=1, ascending=False)
        d	c	b	a
three	0	3	2	1
one	4	7	6	5

对于 Series 和 DataFrame,如果需要根据值来进行排列,使用 sort_values 方法:

1 obj = pd.Series([4, 7, -3, 2])
2 obj.sort_values()
3 obj = pd.Series([4, np.nan, 7, np.nan, -3, 2])
4 obj.sort_values()
2   -3
3    2
0    4
1    7
dtype: int64
4   -3.0
5    2.0
0    4.0
2    7.0
1    NaN
3    NaN
dtype: float64
1 frame = pd.DataFrame({'b': [4, 7, -3, 2], 'a': [0, 1, 0, 1]})
2 frame
3 frame.sort_values(by='b')
4 frame.sort_values(by=['a', 'b'])
	b	a
0	4	0
1	7	1
2	-3	0
3	2	1
        b	a
2	-3	0
3	2	1
0	4	0
1	7	1
        b	a
2	-3	0
0	4	0
3	2	1
1	7	1

排名(ranking)用于计算出排名值。

1 obj = pd.Series([7, -5, 7, 4, 2, 4, 0, 4, 4])
2 obj.rank()
3 obj.rank(method='first')  # 不使用平均值的排名方法
0    8.5
1    1.0
2    8.5
3    5.5
4    3.0
5    5.5
6    2.0
7    5.5
8    5.5
dtype: float64
0    8.0
1    1.0
2    9.0
3    4.0
4    3.0
5    5.0
6    2.0
7    6.0
8    7.0
dtype: float64

降序排列,并且按最大值指定名次:

1 obj.rank(ascending=False, method='max')
0    2.0
1    9.0
2    2.0
3    6.0
4    7.0
5    6.0
6    8.0
7    6.0
8    6.0
dtype: float64

按列进行排名:

1 frame = pd.DataFrame({'b': [4.3, 7, -3, 2], 'a': [0, 1, 0, 1],'c': [-2, 5, 8, -2.5]})
2 frame
3 frame.rank(axis=1)
        b	a	c
0	4.3	0	-2.0
1	7.0	1	5.0
2	-3.0	0	8.0
3	2.0	1	-2.5
        b	a	c
0	3.0	2.0	1.0
1	3.0	1.0	2.0
2	1.0	2.0	3.0
3	3.0	2.0	1.0

带有重复标签的索引

Series 的重复索引:

1 obj = pd.Series(range(5), index=['a', 'a', 'b', 'b', 'c'])
2 obj
3 obj.index.is_unique
4 obj['a']
5 obj['c']
a    0
a    1
b    2
b    3
c    4
dtype: int64
False
a    0
a    1
dtype: int64
4

Pandas 的重复索引:

1 df = pd.DataFrame(np.random.randn(4, 3), index=['a', 'a', 'b', 'b'])
2 df
3 df.loc['b']
        0	        1	        2
a	-0.975030	2.041130	1.022168
a	0.321428	2.124496	0.037530
b	0.343309	-0.386692	-0.577290
b	0.002090	-0.890841	1.759072
        0	        1	        2
b	0.343309	-0.386692	-0.577290
b	0.002090	-0.890841	1.759072

求和与计算描述性统计量

sum 求和:

1 df = pd.DataFrame([[1.4, np.nan], [7.1, -4.5],
2                    [np.nan, np.nan], [0.75, -1.3]],
3                   index=['a', 'b', 'c', 'd'],
4                   columns=['one', 'two'])
5 df
6 df.sum()
7 df.sum(axis=1)
8 df.mean(axis=1, skipna=False)  # 不跳过 NAN
	one	two
a	1.40	NaN
b	7.10	-4.5
c	NaN	NaN
d	0.75	-1.3
one    9.25
two   -5.80
dtype: float64
a    1.40
b    2.60
c    0.00
d   -0.55
dtype: float64
a      NaN
b    1.300
c      NaN
d   -0.275
dtype: float64

返回最大值或最小值对应的索引:

1 df.idxmax()
2 df.idxmin(axis=1)
one    b
two    d
dtype: object
a    one
b    two
c    NaN
d    two
dtype: object

累加:

1 df.cumsum()
	one	two
a	1.40	NaN
b	8.50	-4.5
c	NaN	NaN
d	9.25	-5.8

describe 方法生成一些统计信息:

1 df.describe()
        one	        two
count	3.000000	2.000000
mean	3.083333	-2.900000
std	3.493685	2.262742
min	0.750000	-4.500000
25%	1.075000	-3.700000
50%	1.400000	-2.900000
75%	4.250000	-2.100000
max	7.100000	-1.300000
1 # 非数值数据
2 obj = pd.Series(['a', 'a', 'b', 'c'] * 4)
3 obj
4 obj.describe()
0     a
1     a
2     b
3     c
4     a
5     a
6     b
7     c
8     a
9     a
10    b
11    c
12    a
13    a
14    b
15    c
dtype: object
count     16
unique     3
top        a
freq       8
dtype: object

一些常用的统计方法:

  • count
  • describe
  • min, max
  • argmin, argmax
  • idxmin, idxmax
  • quantile
  • sum
  • mean
  • median
  • mad
  • prod
  • var
  • std
  • skew
  • kurt
  • cumsum
  • cummin, cummax
  • cumprod
  • diff
  • pct_change

自相关和协方差

首先安装一个读取数据集的模块:

pip install pandas-datareader -i https://pypi.douban.com/simple

下载一个股票行情的数据集:

1 import pandas_datareader.data as web
2 all_data = {ticker: web.get_data_yahoo(ticker)
3             for ticker in ['AAPL', 'IBM', 'MSFT', 'GOOG']}
4 price = pd.DataFrame({ticker: data['Adj Close']                     
5                       for ticker, data in all_data.items()})
6 volume = pd.DataFrame({ticker: data['Volume']                      
7                        for ticker, data in all_data.items()})

计算价格的百分比变化:

1 returns = price.pct_change()
2 returns.tail()
                AAPL	        IBM	        MSFT	        GOOG
Date				
2019-08-06	0.018930	-0.000213	0.018758	0.015300
2019-08-07	0.010355	-0.011511	0.004380	0.003453
2019-08-08	0.022056	0.018983	0.026685	0.026244
2019-08-09	-0.008240	-0.028337	-0.008496	-0.013936
2019-08-12	-0.002537	-0.014765	-0.013942	-0.011195

Series 的 corr 方法会计算两个 Series 的相关性,而 cov 方法计算协方差。

1 returns['MSFT'].corr(returns['IBM'])
2 returns.MSFT.cov(returns['IBM'])
0.48863990166304594
8.714318020797283e-05

DataFrame 的 corr 方法和 cov 会计算相关性和协方差的矩阵:

1 returns.corr()
2 returns.cov()
	AAPL	        IBM	        MSFT	        GOOG
AAPL	1.000000	0.381659	0.453727	0.459663
IBM	0.381659	1.000000	0.488640	0.402751
MSFT	0.453727	0.488640	1.000000	0.535898
GOOG	0.459663	0.402751	0.535898	1.000000
        AAPL	        IBM	        MSFT	        GOOG
AAPL	0.000266	0.000077	0.000107	0.000117
IBM	0.000077	0.000152	0.000087	0.000077
MSFT	0.000107	0.000087	0.000209	0.000120
GOOG	0.000117	0.000077	0.000120	0.000242

DataFrame 的 corrwith 方法可以计算和其他 Series 或 DataFrame 的相关性:

1 returns.corrwith(returns.IBM)
2 returns.corrwith(volume)
AAPL    0.381659
IBM     1.000000
MSFT    0.488640
GOOG    0.402751
dtype: float64
AAPL   -0.061924
IBM    -0.151708
MSFT   -0.089946
GOOG   -0.018591
dtype: float64

唯一值、值的计数、关系

unique 方法唯一值数组:

1 obj = pd.Series(['c', 'a', 'd', 'a', 'a', 'b', 'b', 'c', 'c'])
2 obj.unique()
array(['c', 'a', 'd', 'b'], dtype=object)

value_counts 方法返回值的计数:

1 obj.value_counts()
2 pd.value_counts(obj.values, sort=False)  # 等价的写法
a    3
c    3
b    2
d    1
dtype: int64
c    3
b    2
d    1
a    3
dtype: int64

isin 检查 Series 中的数是否属于某个集合:

1 obj
2 mask = obj.isin(['b', 'c'])
3 mask
4 obj[mask]
0    c
1    a
2    d
3    a
4    a
5    b
6    b
7    c
8    c
dtype: object
0     True
1    False
2    False
3    False
4    False
5     True
6     True
7     True
8     True
dtype: bool
0    c
5    b
6    b
7    c
8    c
dtype: object

get_indexer 将唯一值转换为索引(对于标签转换为数值很管用):

1 to_match = pd.Series(['c', 'a', 'b', 'b', 'c', 'a'])
2 unique_vals = pd.Series(['c', 'b', 'a'])
3 pd.Index(unique_vals).get_indexer(to_match)
array([0, 2, 1, 1, 0, 2])

计算多个列的直方图:

1 data = pd.DataFrame({'Qu1': [1, 3, 4, 3, 4],
2                      'Qu2': [2, 3, 1, 2, 3],
3                      'Qu3': [1, 5, 2, 4, 4]})
4 data
5 data.apply(pd.value_counts).fillna(0)
        Qu1	Qu2	Qu3
0	1	2	1
1	3	3	5
2	4	1	2
3	3	2	4
4	4	3	4
        Qu1	Qu2	Qu3
1	1.0	1.0	1.0
2	0.0	2.0	1.0
3	2.0	2.0	0.0
4	2.0	0.0	2.0
5	0.0	0.0	1.0

 

参考

  • 《Python for Data Analysis, 2nd Edition》by Wes McKinney

 

上一篇:lesson3 运算符与表达式


下一篇:Attribute-based Signature