(这篇文章很长，建议收藏起来，用到时再检索)
pandas简介
- Pandas是python的一个数据分析包
- 官网链接：http://pandas.pydata.org/
- 具备按轴自动或显式数据对齐功能的数据结构
- 集成时间序列功能
- 既能处理时间序列数据也能处理非时间序列数据的数据结构
- 数学运算和约简（比如对某个轴求和）可以根据不同的元数据（轴编号）执行
- 灵活处理缺失数据
- 合并及其他出现在常见数据库（例如基于SQL的）中的关系型运算
1. 数据结构
1.1 Series
- Series是一种类似于一维数组的对象，它由一组数据（各种NumPy数据类型） 以及一组与之相关的数据标签（即索引）组成。
- Series的字符串表现形式为：索引在左边，值在右边。
>>> from pandas import Series
>>> print ('用数组生成Series')
用数组生成Series
>>> obj = Series([4, 7, -5, 3])
>>> print (obj)
0 4
1 7
2 -5
3 3
dtype: int64
>>> print (obj.values)
[ 4 7 -5 3]
>>> print (obj.index)
RangeIndex(start=0, stop=4, step=1)
>>> print ('指定Series的index')
指定Series的index
>>> obj2 = Series([4, 7, -5, 3], index = ['d', 'b', 'a', 'c'])
>>> print (obj2)
d 4
b 7
a -5
c 3
dtype: int64
>>> print (obj2.index)
Index(['d', 'b', 'a', 'c'], dtype='object')
>>> print (obj2['a'])
-5
>>> obj2['d'] = 6
>>> print (obj2[['c', 'a', 'd']])
c 3
a -5
d 6
dtype: int64
>>> print (obj2[obj2 > 0]) # 找出大于0的元素
d 6
b 7
c 3
dtype: int64
>>> print ('b' in obj2) # 判断索引是否存在
True
>>> print ('e' in obj2)
False
>>> print ('替换index')
替换index
>>> obj.index = ['Bob', 'Steve', 'Jeff', 'Ryan']
>>> print (obj)
Bob 4
Steve 7
Jeff -5
Ryan 3
dtype: int64
>>> print ('使用字典生成Series')
使用字典生成Series
>>> sdata = {'Ohio':45000, 'Texas':71000, 'Oregon':16000, 'Utah':5000}
>>> obj3 = Series(sdata)
>>> print (obj3)
Ohio 45000
Oregon 16000
Texas 71000
Utah 5000
dtype: int64
>>> print ('使用字典生成Series，并额外指定index，不匹配部分为NaN。')
使用字典生成Series，并额外指定index，不匹配部分为NaN。
>>> states = ['California', 'Ohio', 'Oregon', 'Texas']
>>> obj4 = Series(sdata, index = states)
>>> print (obj4)
California NaN
Ohio 45000.0
Oregon 16000.0
Texas 71000.0
dtype: float64
>>> print ('Series相加，相同索引部分相加。')
Series相加，相同索引部分相加。
>>> print (obj3 + obj4)
California NaN
Ohio 90000.0
Oregon 32000.0
Texas 142000.0
Utah NaN
dtype: float64
>>> print ('指定Series及其索引的名字')
指定Series及其索引的名字
>>> obj4.name = 'population'
>>> obj4.index.name = 'state'
>>> print (obj4)
state
California NaN
Ohio 45000.0
Oregon 16000.0
Texas 71000.0
Name: population, dtype: float64
1.2 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既有行索引也有列索引，它可以被看做由Series组成的字典（共用 同一个索引）
image.png
>>> import numpy as np
>>> from pandas import Series, DataFrame
>>>
>>> print ('用字典生成DataFrame，key为列的名字。')
用字典生成DataFrame，key为列的名字。
>>> data = {'state':['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'],
... 'year':[2000, 2001, 2002, 2001, 2002],
... 'pop':[1.5, 1.7, 3.6, 2.4, 2.9]}
>>> print (DataFrame(data))
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
>>> print (DataFrame(data, columns = ['year', 'state', 'pop'])) # 指定列 顺序
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
>>> print ('指定索引，在列中指定不存在的列，默认数据用NaN。')
指定索引，在列中指定不存在的列，默认数据用NaN。
>>> frame2 = DataFrame(data,
... columns = ['year', 'state', 'pop', 'debt'],
... index = ['one', 'two', 'three', 'four', 'five'])
>>> print (frame2)
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
>>> print (frame2['state'])
one Ohio
two Ohio
three Ohio
four Nevada
five Nevada
Name: state, dtype: object
>>> print (frame2.year)
one 2000
two 2001
three 2002
four 2001
five 2002
Name: year, dtype: int64
>>> print (frame2.ix['three'])
year 2002
state Ohio
pop 3.6
debt NaN
Name: three, dtype: object
>>> frame2['debt'] = 16.5 # 修改一整列
>>> print (frame2)
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
>>> frame2.debt = np.arange(5) # 用numpy数组修改元素
>>> print (frame2)
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
>>> print ('用Series指定要修改的索引及其对应的值，没有指定的默认数据用NaN。')
用Series指定要修改的索引及其对应的值，没有指定的默认数据用NaN。
>>> val = Series([-1.2, -1.5, -1.7], index = ['two', 'four', 'five'])
>>> frame2['debt'] = val
>>> print (frame2)
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
>>> print ('赋值给新列')
赋值给新列
>>> frame2['eastern'] = (frame2.state == 'Ohio') # 如果state等于Ohio为True
>>> print (frame2)
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 2002 Nevada 2.9 -1.7 False
>>> print (frame2.columns)
Index(['year', 'state', 'pop', 'debt', 'eastern'], dtype='object')
>>> print ('DataFrame转置')
DataFrame转置
>>> pop = {'Nevada':{2001:2.4, 2002:2.9},
... 'Ohio':{2000:1.5, 2001:1.7, 2002:3.6}}
>>> frame3 = DataFrame(pop)
>>> print (frame3)
Nevada Ohio
2000 NaN 1.5
2001 2.4 1.7
2002 2.9 3.6
>>> print (frame3.T)
2000 2001 2002
Nevada NaN 2.4 2.9
Ohio 1.5 1.7 3.6
>>> print ('指定索引顺序，以及使用切片初始化数据。')
指定索引顺序，以及使用切片初始化数据。
>>> print (DataFrame(pop, index = [2001, 2002, 2003]))
Nevada Ohio
2001 2.4 1.7
2002 2.9 3.6
2003 NaN NaN
>>> pdata = {'Ohio':frame3['Ohio'][:-1], 'Nevada':frame3['Nevada'][:2]}
>>> print (DataFrame(pdata))
Nevada Ohio
2000 NaN 1.5
2001 2.4 1.7
>>> print ('指定索引和列的名称')
指定索引和列的名称
>>> frame3.index.name = 'year'
>>> frame3.columns.name = 'state'
>>> print (frame3)
state Nevada Ohio
year
2000 NaN 1.5
2001 2.4 1.7
2002 2.9 3.6
>>> print (frame3.values)
[[ nan 1.5]
[ 2.4 1.7]
[ 2.9 3.6]]
>>> print (frame2.values)
[[2000 'Ohio' 1.5 nan True]
[2001 'Ohio' 1.7 -1.2 True]
[2002 'Ohio' 3.6 nan True]
[2001 'Nevada' 2.4 -1.5 False]
[2002 'Nevada' 2.9 -1.7 False]]
1.3 索引对象
- pandas中主要的index对象
image.png
- Index的方法和属性
image.png
image.png
>>> import numpy as np
>>> import pandas as pd
>>> import sys
>>> from pandas import Series, DataFrame, Index
>>>
>>> print ('获取index')
获取index
>>> obj = Series(range(3), index = ['a', 'b', 'c'])
>>> index = obj.index
>>> print (index[1:])
Index(['b', 'c'], dtype='object')
>>> try:
... index[1] = 'd' # index对象不可修改，所以会报错
... except:
... print (sys.exc_info()[0])
...
<class 'TypeError'>
>>> print ('使用Index对象')
使用Index对象
>>> index = Index(np.arange(3))
>>> obj2 = Series([1.5, -2.5, 0], index = index)
>>> print (obj2)
0 1.5
1 -2.5
2 0.0
dtype: float64
>>> print (obj2.index is index)
True
>>> print ('判断列和索引是否存在')
判断列和索引是否存在
>>> pop = {'Nevada':{20001:2.4, 2002:2.9},
... 'Ohio':{2000:1.5, 2001:1.7, 2002:3.6}}
>>> frame3 = DataFrame(pop)
>>> print ('Ohio' in frame3.columns)
True
>>> print ('2003' in frame3.index)
False
2. 基本功能
2.1 重新索引
- 创建一个适应新索引的新对象，该Series的reindex将会根据新索引进行重排。 如果某个索引值当前不存在，就引入缺失值
- 对于时间序列这样的有序数据，重新索引时可能需要做一些插值处理。 method选项即可达到此目的
image.png
>>> import numpy as np
>>> from pandas import DataFrame, Series
>>>
>>> print ('重新指定索引及顺序')
重新指定索引及顺序
>>> obj = Series([4.5, 7.2, -5.3, 3.6], index = ['d', 'b', 'a', 'c'])
>>> print (obj)
d 4.5
b 7.2
a -5.3
c 3.6
dtype: float64
>>> obj2 = obj.reindex(['a', 'b', 'd', 'c', 'e'])
>>> print (obj2)
a -5.3
b 7.2
d 4.5
c 3.6
e NaN
dtype: float64
>>> print (obj.reindex(['a', 'b', 'd', 'c', 'e'], fill_value = 0)) # 指 定不存在元素的默认值
a -5.3
b 7.2
d 4.5
c 3.6
e 0.0
dtype: float64
>>> print ('重新指定索引并指定填元素充方法')
重新指定索引并指定填元素充方法
>>> obj3 = Series(['blue', 'purple', 'yellow'], index = [0, 2, 4])
>>> print (obj3)
0 blue
2 purple
4 yellow
dtype: object
>>> print (obj3.reindex(range(6), method = 'ffill'))
0 blue
1 blue
2 purple
3 purple
4 yellow
5 yellow
dtype: object
>>> print ('对DataFrame重新指定索引')
对DataFrame重新指定索引
>>> frame = DataFrame(np.arange(9).reshape(3, 3),
... index = ['a', 'c', 'd'],
... columns = ['Ohio', 'Texas', 'California'])
>>> print (frame)
Ohio Texas California
a 0 1 2
c 3 4 5
d 6 7 8
>>> frame2 = frame.reindex(['a', 'b', 'c', 'd'])
>>> print (frame2)
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
>>> print ('重新指定column')
重新指定column
>>> states = ['Texas', 'Utah', 'California']
>>> print (frame.reindex(columns = states))
Texas Utah California
a 1 NaN 2
c 4 NaN 5
d 7 NaN 8
>>> print ('对DataFrame重新指定索引并指定填元素充方法')
对DataFrame重新指定索引并指定填元素充方法
>>> print (frame.reindex(index = ['a', 'b', 'c', 'd'],
... method = 'ffill').reindex(columns = states))
Texas Utah California
a 1 NaN 2
b 1 NaN 2
c 4 NaN 5
d 7 NaN 8
>>> print (frame.ix[['a', 'b', 'd', 'c'], states])
Texas Utah California
a 1.0 NaN 2.0
b NaN NaN NaN
d 7.0 NaN 8.0
c 4.0 NaN 5.0
2.2 丢弃指定轴上的项
- 丢弃某条轴上的一个或多个项很简单，只要有一个索引数组或列表即可。由于 需要执行一些数据整理和集合逻辑，所以drop方法返回的是一个在指定轴上删 除了指定值的新对象
>>> import numpy as np
>>> from pandas import Series, DataFrame
>>>
>>> print ('Series根据索引删除元素')
Series根据索引删除元素
>>> obj = Series(np.arange(5.), index = ['a', 'b', 'c', 'd', 'e'])
>>> new_obj = obj.drop('c')
>>> print (new_obj)
a 0.0
b 1.0
d 3.0
e 4.0
dtype: float64
>>> print (obj.drop(['d', 'c']))
a 0.0
b 1.0
e 4.0
dtype: float64
>>> print ('DataFrame删除元素，可指定索引或列。')
DataFrame删除元素，可指定索引或列。
>>> data = DataFrame(np.arange(16).reshape((4, 4)),
... index = ['Ohio', 'Colorado', 'Utah', 'New York'],
... columns = ['one', 'two', 'three', 'four'])
>>> print (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
>>> print (data.drop(['Colorado', 'Ohio']))
one two three four
Utah 8 9 10 11
New York 12 13 14 15
>>> print (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
>>> print (data.drop(['two', 'four'], axis = 1))
one three
Ohio 0 2
Colorado 4 6
Utah 8 10
New York 12 14
2.3 索引、选取和过滤
- Series索引（obj[...]）的工作方式类似于NumPy数组的索引，只不过Series的 索引值不只是整数。
- 利用标签的切片运算与普通的Python切片运算不同，其末端是包含的 （inclusive）。
- 对DataFrame进行索引其实就是获取一个或多个列
- 为了在DataFrame的行上进行标签索引，引入了专门的索引字段ix
DataFrame的索引选项
image.png
>>> import numpy as np
>>> from pandas import Series, DataFrame
>>>
>>> print ('Series的索引，默认数字索引可以工作。')
Series的索引，默认数字索引可以工作。
>>> obj = Series(np.arange(4.), index = ['a', 'b', 'c', 'd'])
>>> print (obj['b'])
1.0
>>> print (obj[3])
3.0
>>> print (obj[[1, 3]])
b 1.0
d 3.0
dtype: float64
>>> print (obj[obj < 2])
a 0.0
b 1.0
dtype: float64
>>> print ('Series的数组切片')
Series的数组切片
>>> print (obj['b':'c']) # 闭区间
b 1.0
c 2.0
dtype: float64
>>> obj['b':'c'] = 5
>>> print (obj)
a 0.0
b 5.0
c 5.0
d 3.0
dtype: float64
>>> print ('DataFrame的索引')
DataFrame的索引
>>> data = DataFrame(np.arange(16).reshape((4, 4)),
... index = ['Ohio', 'Colorado', 'Utah', 'New York'],
... columns = ['one', 'two', 'three', 'four'])
>>> print (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
>>> print (data['two']) # 打印列
Ohio 1
Colorado 5
Utah 9
New York 13
Name: two, dtype: int32
>>> print (data[['three', 'one']])
three one
Ohio 2 0
Colorado 6 4
Utah 10 8
New York 14 12
>>> print (data[:2])
one two three four
Ohio 0 1 2 3
Colorado 4 5 6 7
>>> print (data.ix['Colorado', ['two', 'three']]) # 指定索引和列
two 5
three 6
Name: Colorado, dtype: int32
>>> print (data.ix[['Colorado', 'Utah'], [3, 0, 1]])
four one two
Colorado 7 4 5
Utah 11 8 9
>>> print (data.ix[2]) # 打印第2行（从0开始）
one 8
two 9
three 10
four 11
Name: Utah, dtype: int32
>>> print (data.ix[:'Utah', 'two']) # 从开始到Utah，第2列。
Ohio 1
Colorado 5
Utah 9
Name: two, dtype: int32
>>> print ('根据条件选择')
根据条件选择
>>> print (data[data.three > 5])
one two three four
Colorado 4 5 6 7
Utah 8 9 10 11
New York 12 13 14 15
>>> print (data < 5) # 打印True或者False
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
>>> data[data < 5] = 0
>>> print (data)
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
2.4 算术运算和数据对齐
>>> import numpy as np
>>> from pandas import Series, DataFrame
>>>
>>> print ('加法')
加法
>>> s1 = Series([7.3, -2.5, 3.4, 1.5], index = ['a', 'c', 'd', 'e'])
>>> s2 = Series([-2.1, 3.6, -1.5, 4, 3.1], index = ['a', 'c', 'e', 'f', 'g'])
>>> print (s1)
a 7.3
c -2.5
d 3.4
e 1.5
dtype: float64
>>> print (s2)
a -2.1
c 3.6
e -1.5
f 4.0
g 3.1
dtype: float64
>>> print (s1 + s2)
a 5.2
c 1.1
d NaN
e 0.0
f NaN
g NaN
dtype: float64
>>> print ('DataFrame加法，索引和列都必须匹配。')
DataFrame加法，索引和列都必须匹配。
>>> df1 = DataFrame(np.arange(9.).reshape((3, 3)),
... columns = list('bcd'),
... index = ['Ohio', 'Texas', 'Colorado'])
>>> df2 = DataFrame(np.arange(12).reshape((4, 3)),
... columns = list('bde'),
... index = ['Utah', 'Ohio', 'Texas', 'Oregon'])
>>> print (df1)
b c d
Ohio 0.0 1.0 2.0
Texas 3.0 4.0 5.0
Colorado 6.0 7.0 8.0
>>> print (df2)
b d e
Utah 0 1 2
Ohio 3 4 5
Texas 6 7 8
Oregon 9 10 11
>>> print (df1 + df2)
b c d e
Colorado NaN 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
>>> print ('数据填充')
数据填充
>>> df1 = DataFrame(np.arange(12.).reshape((3, 4)), columns = list('abcd'))
>>> df2 = DataFrame(np.arange(20.).reshape((4, 5)), columns = list('abcde'))
>>> print (df1)
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
>>> print (df2)
a b c d e
0 0.0 1.0 2.0 3.0 4.0
1 5.0 6.0 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
>>> print (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 11.0 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
>>> print (df1.reindex(columns = df2.columns, fill_value = 0))
a b c d e
0 0.0 1.0 2.0 3.0 0
1 4.0 5.0 6.0 7.0 0
2 8.0 9.0 10.0 11.0 0
>>> print ('DataFrame与Series之间的操作')
DataFrame与Series之间的操作
>>> arr = np.arange(12.).reshape((3, 4))
>>> print (arr)
[[ 0. 1. 2. 3.]
[ 4. 5. 6. 7.]
[ 8. 9. 10. 11.]]
>>> print (arr[0])
[ 0. 1. 2. 3.]
>>> print (arr - arr[0])
[[ 0. 0. 0. 0.]
[ 4. 4. 4. 4.]
[ 8. 8. 8. 8.]]
>>> frame = DataFrame(np.arange(12).reshape((4, 3)),
... columns = list('bde'),
... index = ['Utah', 'Ohio', 'Texas', 'Oregon'])
>>> series = frame.ix[0]
>>> print (frame)
b d e
Utah 0 1 2
Ohio 3 4 5
Texas 6 7 8
Oregon 9 10 11
>>> print (series)
b 0
d 1
e 2
Name: Utah, dtype: int32
>>> print (frame - series)
b d e
Utah 0 0 0
Ohio 3 3 3
Texas 6 6 6
Oregon 9 9 9
>>> series2 = Series(range(3), index = list('bef'))
>>> print (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
>>> series3 = frame['d']
>>> print (frame.sub(series3, axis = 0)) # 按列减
b d e
Utah -1 0 1
Ohio -1 0 1
Texas -1 0 1
Oregon -1 0 1
2.5 函数应用和映射
>>> import numpy as np
>>> from pandas import Series, DataFrame
>>>
>>> print ('函数')
函数
>>> frame = DataFrame(np.random.randn(4, 3),
... columns = list('bde'),
... index = ['Utah', 'Ohio', 'Texas', 'Oregon'])
>>> print (frame)
b d e
Utah 0.490747 -0.001959 -0.160986
Ohio 0.743594 -1.157825 1.067688
Texas -0.607442 0.785016 -0.496046
Oregon 1.720088 0.149294 -1.645419
>>> print (np.abs(frame))
b d e
Utah 0.490747 0.001959 0.160986
Ohio 0.743594 1.157825 1.067688
Texas 0.607442 0.785016 0.496046
Oregon 1.720088 0.149294 1.645419
>>> print ('lambda以及应用')
lambda以及应用
>>> f = lambda x: x.max() - x.min()
>>> print (frame.apply(f))
b 2.327530
d 1.942841
e 2.713107
dtype: float64
>>> print (frame.apply(f, axis = 1))
Utah 0.651733
Ohio 2.225513
Texas 1.392458
Oregon 3.365508
dtype: float64
>>> _format = lambda x: '%.2f' % x
>>> print (frame.applymap(_format))
b d e
Utah 0.49 -0.00 -0.16
Ohio 0.74 -1.16 1.07
Texas -0.61 0.79 -0.50
Oregon 1.72 0.15 -1.65
>>> print (frame['e'].map(_format))
Utah -0.16
Ohio 1.07
Texas -0.50
Oregon -1.65
Name: e, dtype: object
2.6 排序和排名
>>> import numpy as np
>>> from pandas import Series, DataFrame
>>>
>>> print ('根据索引排序，对于DataFrame可以指定轴。')
根据索引排序，对于DataFrame可以指定轴。
>>> obj = Series(range(4), index = ['d', 'a', 'b', 'c'])
>>> print (obj.sort_index())
a 1
b 2
c 3
d 0
dtype: int32
>>> frame = DataFrame(np.arange(8).reshape((2, 4)),
... index = ['three', 'one'],
... columns = list('dabc'))
>>> print (frame.sort_index())
d a b c
one 4 5 6 7
three 0 1 2 3
>>> print (frame.sort_index(axis = 1))
a b c d
three 1 2 3 0
one 5 6 7 4
>>> print (frame.sort_index(axis = 1, ascending = False)) # 降序
d c b a
three 0 3 2 1
one 4 7 6 5
>>> print ('根据值排序')
根据值排序
>>> obj = Series([4, 7, -3, 2])
>>> print (obj.sort_values()) # order已淘汰
2 -3
3 2
0 4
1 7
dtype: int64
>>> print ('DataFrame指定列排序')
DataFrame指定列排序
>>> frame = DataFrame({'b':[4, 7, -3, 2], 'a':[0, 1, 0, 1]})
>>> print (frame)
a b
0 0 4
1 1 7
2 0 -3
3 1 2
>>> print (frame.sort_values(by = 'b')) # sort_index(by = ...)已淘汰
a b
2 0 -3
3 1 2
0 0 4
1 1 7
>>> print (frame.sort_values(by = ['a', 'b']))
a b
2 0 -3
0 0 4
3 1 2
1 1 7
>>> print ('rank，求排名的平均位置(从1开始)')
rank，求排名的平均位置(从1开始)
>>> obj = Series([7, -5, 7, 4, 2, 0, 4])
>>> # 对应排名：-5(1), 0(2), 2(3), 4(4), 4(5), 7(6), 7(7)
... print (obj.rank())
0 6.5
1 1.0
2 6.5
3 4.5
4 3.0
5 2.0
6 4.5
dtype: float64
>>> print (obj.rank(method = 'first')) # 去第一次出现，不求平均值。
0 6.0
1 1.0
2 7.0
3 4.0
4 3.0
5 2.0
6 5.0
dtype: float64
>>> print (obj.rank(ascending = False, method = 'max')) # 逆序，并取最大 值。所以-5的rank是7.
0 2.0
1 7.0
2 2.0
3 4.0
4 5.0
5 6.0
6 4.0
dtype: float64
>>> frame = DataFrame({'b':[4.3, 7, -3, 2],
... 'a':[0, 1, 0, 1],
... 'c':[-2, 5, 8, -2.5]})
>>> print (frame)
a b c
0 0 4.3 -2.0
1 1 7.0 5.0
2 0 -3.0 8.0
3 1 2.0 -2.5
>>> print (frame.rank(axis = 1))
a b c
0 2.0 3.0 1.0
1 1.0 3.0 2.0
2 2.0 1.0 3.0
3 2.0 3.0 1.0
2.7 带有重复值的索引
>>> print ('重复的索引')
重复的索引
>>> obj = Series(range(5), index = ['a', 'a', 'b', 'b', 'c'])
>>> print (obj.index.is_unique) # 判断是非有重复索引
False
>>> df = DataFrame(np.random.randn(4, 3), index = ['a', 'a', 'b', 'b'])
>>> print (df)
0 1 2
a -0.819338 0.518905 -0.861562
a 0.573467 1.027358 -0.902153
b 0.990493 -1.257021 -0.687425
b 1.199946 -0.240610 -0.898948
>>> print (df.ix['b'].ix[0])
0 0.990493
1 -1.257021
2 -0.687425
Name: b, dtype: float64
>>> print (df.ix['b'].ix[1])
0 1.199946
1 -0.240610
2 -0.898948
Name: b, dtype: float64
汇总和计算描述统计
- 常用方法选项
image.png
- 常用描述和汇总统计函数 I
image.png
image.png
>>> import numpy as np
>>> from pandas import Series, DataFrame
>>>
>>> print ('求和')
求和
>>> df = DataFrame([[1.4, np.nan], [7.1, -4.5], [np.nan, np.nan], [0.75, -1.3]],
... index = ['a', 'b', 'c', 'd'],
... columns = ['one', 'two'])
>>> print (df)
one two
a 1.40 NaN
b 7.10 -4.5
c NaN NaN
d 0.75 -1.3
>>> print (df.sum()) # 按列求和
one 9.25
two -5.80
dtype: float64
>>> print (df.sum(axis = 1)) # 按行求和
a 1.40
b 2.60
c NaN
d -0.55
dtype: float64
>>> print ('平均数')
平均数
>>> print (df.mean(axis = 1, skipna = False))
a NaN
b 1.300
c NaN
d -0.275
dtype: float64
>>> print (df.mean(axis = 1))
a 1.400
b 1.300
c NaN
d -0.275
dtype: float64
>>> print ('其它')
其它
>>> print (df.idxmax())
one b
two d
dtype: object
>>> print (df.cumsum())
one two
a 1.40 NaN
b 8.50 -4.5
c NaN NaN
d 9.25 -5.8
>>> print (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
>>> obj = Series(['a', 'a', 'b', 'c'] * 4)
>>> print (obj.describe())
count 16
unique 3
top a
freq 8
dtype: object
3.1 相关系数与协方差
- 相关系数：相关系数是用以反映变量之间相关关系密切程度的统计指标
- 协方差：从直观上来看，协方差表示的是两个变量总体误差的期望。如果两个 变量的变化趋势一致，也就是说如果其中一个大于自身的期望值时另外一个也 大于自身的期望值，那么两个变量之间的协方差就是正值；如果两个变量的变 化趋势相反，即其中一个变量大于自身的期望值时另外一个却小于自身的期望 值，那么两个变量之间的协方差就是负值
# 这一段代码不能翻墙的话读不了
import numpy as np
import pandas_datareader.data as web
from pandas import DataFrame
print ('相关性与协方差') # 协方差：https://zh.wikipedia.org/wiki/%E5%8D%8F%E6%96%B9%E5%B7%AE)
all_data = {}
for ticker in ['AAPL', 'IBM', 'MSFT', 'GOOG']:
all_data[ticker] = web.get_data_yahoo(ticker, '4/1/2016', '7/15/2015')
price = DataFrame({tic: data['Adj Close'] for tic, data in all_data.iteritems()})
volume = DataFrame({tic: data['Volume'] for tic, data in all_data.iteritems()})
returns = price.pct_change()
print (returns.tail())
print (returns.MSFT.corr(returns.IBM))
print (returns.corr()) # 相关性，自己和自己的相关性总是1
print (returns.cov()) # 协方差
print (returns.corrwith(returns.IBM))
print (returns.corrwith(returns.volume))
3.2 唯一值以及成员资格
- 常用方法
image.png
>>> print ('去重')
去重
>>> obj = Series(['c', 'a', 'd', 'a', 'a', 'b', 'b', 'c', 'c'])
>>> print (obj.unique())
['c' 'a' 'd' 'b']
>>> print (obj.value_counts())
c 3
a 3
b 2
d 1
dtype: int64
>>> print ('判断元素存在')
判断元素存在
>>> mask = obj.isin(['b', 'c'])
>>> print (mask)
0 True
1 False
2 False
3 False
4 False
5 True
6 True
7 True
8 True
dtype: bool
>>> print (obj[mask]) #只打印元素b和c
0 c
5 b
6 b
7 c
8 c
dtype: object
>>> data = DataFrame({'Qu1':[1, 3, 4, 3, 4],
... 'Qu2':[2, 3, 1, 2, 3],
... 'Qu3':[1, 5, 2, 4, 4]})
>>> print (data)
Qu1 Qu2 Qu3
0 1 2 1
1 3 3 5
2 4 1 2
3 3 2 4
4 4 3 4
>>> print (data.apply(pd.value_counts).fillna(0))
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
>>> print (data.apply(pd.value_counts, axis = 1).fillna(0))
1 2 3 4 5
0 2.0 1.0 0.0 0.0 0.0
1 0.0 0.0 2.0 0.0 1.0
2 1.0 1.0 0.0 1.0 0.0
3 0.0 1.0 1.0 1.0 0.0
4 0.0 0.0 1.0 2.0 0.0
4. 处理缺失数据
4.1 NA处理方法
- NaN（Not a Number）表示浮点数和非浮点数组中的缺失数据
-
None也被当作NA处理
image.png
>>> import numpy as np
>>> from pandas import Series
>>>
>>> print ('作为null处理的值')
作为null处理的值
>>> string_data = Series(['aardvark', 'artichoke', np.nan, 'avocado'])
>>> print (string_data)
0 aardvark
1 artichoke
2 NaN
3 avocado
dtype: object
>>> print (string_data.isnull())
0 False
1 False
2 True
3 False
dtype: bool
>>> string_data[0] = None
>>> print (string_data.isnull())
0 True
1 False
2 True
3 False
dtype: bool
4.2 滤除缺失数据
>>> import numpy as np
>>> from numpy import nan as NA
>>> from pandas import Series, DataFrame
>>>
>>> print ('丢弃NA')
丢弃NA
>>> data = Series([1, NA, 3.5, NA, 7])
>>> print (data.dropna())
0 1.0
2 3.5
4 7.0
dtype: float64
>>> print (data[data.notnull()])
0 1.0
2 3.5
4 7.0
dtype: float64
>>> print ('DataFrame对丢弃NA的处理')
DataFrame对丢弃NA的处理
>>> data = DataFrame([[1., 6.5, 3.], [1., NA, NA],
... [NA, NA, NA], [NA, 6.5, 3.]])
>>> print (data.dropna()) # 默认只要某行有NA就全部删除
0 1 2
0 1.0 6.5 3.0
>>> print (data.dropna(how = 'all')) # 全部为NA才删除
0 1 2
0 1.0 6.5 3.0
1 1.0 NaN NaN
3 NaN 6.5 3.0
>>> data[4] = NA # 新增一列
>>> print (data.dropna(axis = 1, how = 'all'))
0 1 2
0 1.0 6.5 3.0
1 1.0 NaN NaN
2 NaN NaN NaN
3 NaN 6.5 3.0
>>> data = DataFrame(np.random.randn(7, 3))
>>> data.ix[:4, 1] = NA
>>> data.ix[:2, 2] = NA
>>> print (data)
0 1 2
0 -0.286526 NaN NaN
1 0.739128 NaN NaN
2 -1.813093 NaN NaN
3 -0.030338 NaN -1.822855
4 -0.580589 NaN 1.189702
5 -1.292924 0.109001 1.036571
6 -0.510740 0.857497 -0.126522
>>> print (data.dropna(thresh = 2)) # 每行至少要有2个非NA元素
0 1 2
3 -0.030338 NaN -1.822855
4 -0.580589 NaN 1.189702
5 -1.292924 0.109001 1.036571
6 -0.510740 0.857497 -0.126522
4.3 填充缺失数据
>>> import numpy as np
>>> from numpy import nan as NA
>>> import pandas as pd
>>> from pandas import Series, DataFrame, Index
>>>
>>> print ('填充0')
填充0
>>> df = DataFrame(np.random.randn(7, 3))
>>> df.ix[:4, 1] = NA
>>> df.ix[:2, 2] = NA
>>> print (df.fillna(0))
0 1 2
0 -0.906578 0.000000 0.000000
1 0.071408 0.000000 0.000000
2 0.026809 0.000000 0.000000
3 -1.800546 0.000000 0.432918
4 -1.568870 0.000000 0.409149
5 1.313441 0.948818 -0.035420
6 0.829020 1.023328 0.274207
>>> df.fillna(0, inplace = True)
>>> print (df)
0 1 2
0 -0.906578 0.000000 0.000000
1 0.071408 0.000000 0.000000
2 0.026809 0.000000 0.000000
3 -1.800546 0.000000 0.432918
4 -1.568870 0.000000 0.409149
5 1.313441 0.948818 -0.035420
6 0.829020 1.023328 0.274207
>>> print ('不同行列填充不同的值')
不同行列填充不同的值
>>> print (df.fillna({1:0.5, 3:-1})) # 第3列不存在
0 1 2
0 -0.906578 0.000000 0.000000
1 0.071408 0.000000 0.000000
2 0.026809 0.000000 0.000000
3 -1.800546 0.000000 0.432918
4 -1.568870 0.000000 0.409149
5 1.313441 0.948818 -0.035420
6 0.829020 1.023328 0.274207
>>> print ('不同的填充方式')
不同的填充方式
>>> df = DataFrame(np.random.randn(6, 3))
>>> df.ix[2:, 1] = NA
>>> df.ix[4:, 2] = NA
>>> print (df)
0 1 2
0 1.260661 -0.195192 -1.566377
1 -0.280009 -0.926795 -1.772026
2 -0.269668 NaN -1.223589
3 -1.829935 NaN -0.860152
4 -0.562236 NaN NaN
5 2.009366 NaN NaN
>>> print (df.fillna(method = 'ffill'))
0 1 2
0 1.260661 -0.195192 -1.566377
1 -0.280009 -0.926795 -1.772026
2 -0.269668 -0.926795 -1.223589
3 -1.829935 -0.926795 -0.860152
4 -0.562236 -0.926795 -0.860152
5 2.009366 -0.926795 -0.860152
>>> print (df.fillna(method = 'ffill', limit = 2))
0 1 2
0 1.260661 -0.195192 -1.566377
1 -0.280009 -0.926795 -1.772026
2 -0.269668 -0.926795 -1.223589
3 -1.829935 -0.926795 -0.860152
4 -0.562236 NaN -0.860152
5 2.009366 NaN -0.860152
>>> print ('用统计数据填充')
用统计数据填充
>>> data = Series([1., NA, 3.5, NA, 7])
>>> print (data.fillna(data.mean()))
0 1.000000
1 3.833333
2 3.500000
3 3.833333
4 7.000000
dtype: float64
5. 层次化索引
- 使你能在一个轴上拥有多个（两个以上）索引级别。抽象的说，它使你能以低 纬度形式处理高维度数据。
>>> import numpy as np
>>> from pandas import Series, DataFrame, MultiIndex
>>>
>>> print ('Series的层次索引')
Series的层次索引
>>> data = Series(np.random.randn(10),
... index = [['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'd', 'd'],
... [1, 2, 3, 1, 2, 3, 1, 2, 2, 3]])
>>> print (data)
a 1 -0.120844
2 -0.165075
3 0.819057
b 1 -1.853474
2 -1.412147
3 1.399640
c 1 1.398124
2 -0.129620
d 2 1.042479
3 -1.025431
dtype: float64
>>> print (data.index)
MultiIndex(levels=[['a', 'b', 'c', 'd'], [1, 2, 3]],
labels=[[0, 0, 0, 1, 1, 1, 2, 2, 3, 3], [0, 1, 2, 0, 1, 2, 0, 1, 1, 2]])
>>> print (data.b)
1 -1.853474
2 -1.412147
3 1.399640
dtype: float64
>>> print (data['b':'c'])
b 1 -1.853474
2 -1.412147
3 1.399640
c 1 1.398124
2 -0.129620
dtype: float64
>>> print (data[:2])
a 1 -0.120844
2 -0.165075
dtype: float64
>>> print (data.unstack())
1 2 3
a -0.120844 -0.165075 0.819057
b -1.853474 -1.412147 1.399640
c 1.398124 -0.129620 NaN
d NaN 1.042479 -1.025431
>>> print (data.unstack().stack())
a 1 -0.120844
2 -0.165075
3 0.819057
b 1 -1.853474
2 -1.412147
3 1.399640
c 1 1.398124
2 -0.129620
d 2 1.042479
3 -1.025431
dtype: float64
>>> print ('DataFrame的层次索引')
DataFrame的层次索引
>>> frame = DataFrame(np.arange(12).reshape((4, 3)),
... index = [['a', 'a', 'b', 'b'], [1, 2, 1, 2]],
... columns = [['Ohio', 'Ohio', 'Colorado'], ['Green', 'Red', 'Green']])
>>> print (frame)
Ohio Colorado
Green Red Green
a 1 0 1 2
2 3 4 5
b 1 6 7 8
2 9 10 11
>>> frame.index.names = ['key1', 'key2']
>>> frame.columns.names = ['state', 'color']
>>> print (frame)
state Ohio Colorado
color Green Red Green
key1 key2
a 1 0 1 2
2 3 4 5
b 1 6 7 8
2 9 10 11
>>> print (frame.ix['a', 1])
state color
Ohio Green 0
Red 1
Colorado Green 2
Name: (a, 1), dtype: int32
>>> print (frame.ix['a', 2]['Colorado'])
color
Green 5
Name: (a, 2), dtype: int32
>>> print (frame.ix['a', 2]['Ohio']['Red'])
4
>>> print ('直接用MultiIndex创建层次索引结构')
直接用MultiIndex创建层次索引结构
>>> print (MultiIndex.from_arrays([['Ohio', 'Ohio', 'Colorado'], ['Gree', 'Red', 'Green']],
... names = ['state', 'color']))
MultiIndex(levels=[['Colorado', 'Ohio'], ['Gree', 'Green', 'Red']],
labels=[[1, 1, 0], [0, 2, 1]],
names=['state', 'color'])
5.1 重新分级顺序
>>> import numpy as np
>>> from pandas import Series, DataFrame
>>>
>>> print ('索引层级交换')
索引层级交换
>>> frame = DataFrame(np.arange(12).reshape((4, 3)),
... index = [['a', 'a', 'b', 'b'], [1, 2, 1, 2]],
... columns = [['Ohio', 'Ohio', 'Colorado'], ['Green', 'Red', 'Green']])
>>> frame.index.names = ['key1', 'key2']
>>> frame_swapped = frame.swaplevel('key1', 'key2')
>>> print (frame_swapped)
Ohio Colorado
Green Red Green
key2 key1
1 a 0 1 2
2 a 3 4 5
1 b 6 7 8
2 b 9 10 11
>>> print (frame_swapped.swaplevel(0, 1))
Ohio Colorado
Green Red Green
key1 key2
a 1 0 1 2
2 3 4 5
b 1 6 7 8
2 9 10 11
>>> print ('根据索引排序')
根据索引排序
>>> print (frame.sortlevel('key2'))
__main__:1: FutureWarning: sortlevel is deprecated, use sort_index(level= ...)
Ohio Colorado
Green Red Green
key1 key2
a 1 0 1 2
b 1 6 7 8
a 2 3 4 5
b 2 9 10 11
>>> print (frame.swaplevel(0, 1).sortlevel(0))
Ohio Colorado
Green Red Green
key2 key1
1 a 0 1 2
b 6 7 8
2 a 3 4 5
b 9 10 11
5.2 根据级别汇总统计
>>> import numpy as np
>>> from pandas import DataFrame
>>>
>>> print ('根据指定的key计算统计信息')
根据指定的key计算统计信息
>>> frame = DataFrame(np.arange(12).reshape((4, 3)),
... index = [['a', 'a', 'b', 'b'], [1, 2, 1, 2]],
... columns = [['Ohio', 'Ohio', 'Colorado'], ['Green', 'Red', 'Green']])
>>> frame.index.names = ['key1', 'key2']
>>> print (frame)
Ohio Colorado
Green Red Green
key1 key2
a 1 0 1 2
2 3 4 5
b 1 6 7 8
2 9 10 11
>>> print (frame.sum(level = 'key2'))
Ohio Colorado
Green Red Green
key2
1 6 8 10
2 12 14 16
5.3 使用DataFrame的列
>>> import numpy as np
>>> from pandas import DataFrame
>>>
>>> print ('使用列生成层次索引')
使用列生成层次索引
>>> frame = DataFrame({'a':range(7),
... 'b':range(7, 0, -1),
... 'c':['one', 'one', 'one', 'two', 'two', 'two', 'two'],
... 'd':[0, 1, 2, 0, 1, 2, 3]})
>>> print (frame)
a b c d
0 0 7 one 0
1 1 6 one 1
2 2 5 one 2
3 3 4 two 0
4 4 3 two 1
5 5 2 two 2
6 6 1 two 3
>>> print (frame.set_index(['c', 'd'])) # 把c/d列变成索引
a b
c d
one 0 0 7
1 1 6
2 2 5
two 0 3 4
1 4 3
2 5 2
3 6 1
>>> print (frame.set_index(['c', 'd'], drop = False)) # 列依然保留
a b c d
c d
one 0 0 7 one 0
1 1 6 one 1
2 2 5 one 2
two 0 3 4 two 0
1 4 3 two 1
2 5 2 two 2
3 6 1 two 3
>>> frame2 = frame.set_index(['c', 'd'])
>>> print (frame2.reset_index())
c d a b
0 one 0 0 7
1 one 1 1 6
2 one 2 2 5
3 two 0 3 4
4 two 1 4 3
5 two 2 5 2
6 two 3 6 1
6. 其他话题
6.1 整数索引
>>> import numpy as np
>>> import sys
>>> from pandas import Series, DataFrame
>>>
>>> print ('整数索引')
整数索引
>>> ser = Series(np.arange(3.))
>>> print (ser)
0 0.0
1 1.0
2 2.0
dtype: float64
>>> try:
... print (ser[-1]) # 这里会有歧义
... except:
... print (sys.exc_info()[0])
... ser2 = Series(np.arange(3.), index = ['a', 'b', 'c'])
File "<stdin>", line 5
ser2 = Series(np.arange(3.), index = ['a', 'b', 'c'])
^
SyntaxError: invalid syntax
>>> print (ser2[-1])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'ser2' is not defined
>>> ser3 = Series(range(3), index = [-5, 1, 3])
>>> print (ser3.iloc[2]) # 避免直接用[2]产生的歧义
2
>>> print ('对DataFrame使用整数索引')
对DataFrame使用整数索引
>>> frame = DataFrame(np.arange(6).reshape((3, 2)), index = [2, 0, 1])
>>> print (frame)
0 1
2 0 1
0 2 3
1 4 5
>>> print (frame.iloc[0])
0 0
1 1
Name: 2, dtype: int32
>>> print (frame.iloc[:, 1])
2 1
0 3
1 5
Name: 1, dtype: int32
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