Pandas 10分钟入门(官方文档注释版二)

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     本文接续注释版1,前文重点讲述了如何创建一个panads对象,本文重点讲述如何查看这些已经创建的对象。

 【查看数据】

  •      See the top & bottom rows of the frame(查看frame头部和尾部的行)
>>> import pandas as pd
>>> long_series = pd.Series(np.random.randn(1000))
>>> import numpy as np
>>> long_series = pd.Series(np.random.randn(1000))
>>> long_series
0      0.526507
1     -0.085210
2      1.292113
3     -1.948114
4     -1.386582
5     -2.596821
6      0.268965
7     -0.635905
8     -1.839953
9     -1.240820
10     0.122215
.......

   上面为完成的series,可以看到定义了一个10000个值,现在我们只取头部和尾部,因此可以使用head()和tail()两个方法,两个方法默认取的数据都是5个,当然你可以自己定义取几个,具体如下:

>>> long_series.head()
0    0.526507
1   -0.085210
2    1.292113
3   -1.948114
4   -1.386582
dtype: float64
>>> long_series.tail(6)  ----lst: 取最后6个值
994   -1.300574
995    0.659815
996   -0.340045
997    0.685664
998   -0.972145
999    0.410191
dtype: float64
  • 显示索引、列和底层numpy数据

         pandas获取这些比较简单,直接采用属性的方式即可。如下:

>>> df = pd.DataFrame(np.random.randn(6,4), index=dates,columns=list(‘ABCD‘))
>>> df
                   A         B         C         D
2017-01-01  0.906245  1.815924  0.123356 -1.798571
2017-01-02 -0.459646  0.520100  0.511138  0.183975
2017-01-03  0.463326 -0.970487 -1.120780 -0.614481
2017-01-04  1.505464 -1.743313  1.020903 -1.049047
2017-01-05 -0.709366  1.378030  1.874955 -1.017548
2017-01-06  1.113554 -0.951963 -1.266802 -0.586571
>>> df.index    获取行索引
DatetimeIndex([‘2017-01-01‘, ‘2017-01-02‘, ‘2017-01-03‘, ‘2017-01-04‘,
               ‘2017-01-05‘, ‘2017-01-06‘],
              dtype=‘datetime64[ns]‘, freq=‘D‘)
>>> df.columns   获取列索引
Index([u‘A‘, u‘B‘, u‘C‘, u‘D‘], dtype=‘object‘)
>>> df.values    获取值
array([[ 0.90624543,  1.81592368,  0.12335647, -1.79857091],
       [-0.45964616,  0.52009988,  0.51113763,  0.1839755 ],
       [ 0.46332631, -0.97048662, -1.12078016, -0.61448135],
       [ 1.50546445, -1.74331294,  1.02090281, -1.04904748],
       [-0.70936561,  1.37802983,  1.87495471, -1.01754786],
       [ 1.11355431, -0.95196258, -1.2668023 , -0.58657136]])
  •  对数据的一些快速基本统计
>>> df.describe()
              A         B         C         D
count  6.000000  6.000000  6.000000  6.000000
mean   0.469930  0.008049  0.190462 -0.813707
std    0.886775  1.439019  1.222903  0.656284
min   -0.709366 -1.743313 -1.266802 -1.798571
25%   -0.228903 -0.965856 -0.809746 -1.041173
50%    0.684786 -0.215931  0.317247 -0.816015
75%    1.061727  1.163547  0.893462 -0.593549
max    1.505464  1.815924  1.874955  0.183975

   注意上述的统计,是按照不同维度(也就是列)进行统计。

  • 数据的行列转换
>>> df.T
   2017-01-01  2017-01-02  2017-01-03  2017-01-04  2017-01-05  2017-01-06
A    0.906245   -0.4596 46    0.463326    1.505464   -0.709366    1.113554
B    1.815924    0.520100   -0.970487   -1.743313    1.378030   -0.951963
C    0.123356    0.511138   -1.120780    1.020903    1.874955   -1.266802
D   -1.798571    0.183975   -0.614481   -1.049047   -1.017548   -0.586571
  • 按照某一个轴axis进行排序
>>> df.sort_index(axis=1,ascending=False)
                   D         C         B         A
2017-01-01 -1.798571  0.123356  1.815924  0.906245
2017-01-02  0.183975  0.511138  0.520100 -0.459646
2017-01-03 -0.614481 -1.120780 -0.970487  0.463326
2017-01-04 -1.049047  1.020903 -1.743313  1.505464
2017-01-05 -1.017548  1.874955  1.378030 -0.709366
2017-01-06 -0.586571 -1.266802 -0.951963  1.113554
  • 按值进行排序   (lst:以前的版本是sort(columns=xxx),该方法将被废止,现在官方已经开始使用sort_values)
>>> df.sort_values(by=B)
                   A         B         C         D
2017-01-04  1.505464 -1.743313  1.020903 -1.049047
2017-01-03  0.463326 -0.970487 -1.120780 -0.614481
2017-01-06  1.113554 -0.951963 -1.266802 -0.586571
2017-01-02 -0.459646  0.520100  0.511138  0.183975
2017-01-05 -0.709366  1.378030  1.874955 -1.017548
2017-01-01  0.906245  1.815924  0.123356 -1.798571

【选择数据】

 注意:虽然标准的Python/Numpy表达式是直观且可用的,但是我们推荐使用优化后的pandas方法,例如:.at,.iat,.loc,.iloc以及.ix 详情请查看: Indexing and Selecting Data 和 MultiIndex / Advanced Indexing

  •    获取

      获取一个单独的列   

>>> df[A]
2017-01-01    0.906245
2017-01-02   -0.459646
2017-01-03    0.463326
2017-01-04    1.505464
2017-01-05   -0.709366
2017-01-06    1.113554
Freq: D, Name: A, dtype: float64   

      通过切片获取数据  

>>> df[1:3]
                   A         B         C         D
2017-01-02 -0.459646  0.520100  0.511138  0.183975
2017-01-03  0.463326 -0.970487 -1.120780 -0.614481

    通过标签获取数据  (获取时间为2017-01-01的数据)

>> df.loc[dates[0]]
A    0.906245
B    1.815924
C    0.123356
D   -1.798571
Name: 2017-01-01 00:00:00, dtype: float64

    通过标签获取多轴数据

>>> df.loc[:,[A,C]]
                   A         C
2017-01-01  0.906245  0.123356
2017-01-02 -0.459646  0.511138
2017-01-03  0.463326 -1.120780
2017-01-04  1.505464  1.020903
2017-01-05 -0.709366  1.874955
2017-01-06  1.113554 -1.266802

    标签切片(Showing label slicing, both endpoints are included

>>> df.loc[20170101:20170103,[A,B]]
                   A         B
2017-01-01  0.906245  1.815924
2017-01-02 -0.459646  0.520100
2017-01-03  0.463326 -0.970487
  •     对返回的对象进行维度缩减
>>> df.loc[20170103,[A,B]]
A    0.463326
B   -0.970487
Name: 2017-01-03 00:00:00, dtype: float64

    获取单个值

>>> df.loc[dates[0],A]
0.90624542800545049

   快速访问单个值(与上相同,区别还不明白) 

>>> df.at[dates[0],A]
0.90624542800545049

    以上获取数据,大部分都是采用loc的方式获取的数据,下面将主要采用iloc的方式获取数据。两者主要的区别是:loc主要是通过行标签的方式获取,仔细观察上面的代码,可以发现我们变换的主要都是第一个参数,也就是行的标签,而下面获取的iloc主要变换的是行号。

  •      位置式选择获取

       数值选择获取

>>> df
                   A         B         C         D
2017-01-01  0.906245  1.815924  0.123356 -1.798571
2017-01-02 -0.459646  0.520100  0.511138  0.183975
2017-01-03  0.463326 -0.970487 -1.120780 -0.614481
2017-01-04  1.505464 -1.743313  1.020903 -1.049047
2017-01-05 -0.709366  1.378030  1.874955 -1.017548
2017-01-06  1.113554 -0.951963 -1.266802 -0.586571
>>> df.iloc[3]
A    1.505464
B   -1.743313
C    1.020903
D   -1.049047
Name: 2017-01-04 00:00:00, dtype: float64

      数值切片

>>> df.iloc[3:5,0:2]  注意切片是左闭环
                   A         B
2017-01-04  1.505464 -1.743313
2017-01-05 -0.709366  1.378030

    获取指定列表位置数据

>>> df.iloc[[1,2,4],[0,2]]
                   A         C
2017-01-02 -0.459646  0.511138
2017-01-03  0.463326 -1.120780
2017-01-05 -0.709366  1.874955
>>>

    行、列切片

>>> df.iloc[1:3,:]
                   A         B         C         D
2017-01-02 -0.459646  0.520100  0.511138  0.183975
2017-01-03  0.463326 -0.970487 -1.120780 -0.614481
>>> df.iloc[:,1:3]
                   B         C
2017-01-01  1.815924  0.123356
2017-01-02  0.520100  0.511138
2017-01-03 -0.970487 -1.120780
2017-01-04 -1.743313  1.020903
2017-01-05  1.378030  1.874955
2017-01-06 -0.951963 -1.266802

   获取特定值

>>> df.iloc[1,1]
0.52009988180243594
>>> df.iat[1,1]
0.52009988180243594
  • 布尔索引(通过增加条件判断的结果来获取数据)

        使用一个单独列的值来选择数据     

>>> df
                   A         B         C         D
2017-01-01  0.906245  1.815924  0.123356 -1.798571
2017-01-02 -0.459646  0.520100  0.511138  0.183975
2017-01-03  0.463326 -0.970487 -1.120780 -0.614481
2017-01-04  1.505464 -1.743313  1.020903 -1.049047
2017-01-05 -0.709366  1.378030  1.874955 -1.017548
2017-01-06  1.113554 -0.951963 -1.266802 -0.586571
>>> df[df.A>0]
                   A         B         C         D
2017-01-01  0.906245  1.815924  0.123356 -1.798571
2017-01-03  0.463326 -0.970487 -1.120780 -0.614481
2017-01-04  1.505464 -1.743313  1.020903 -1.049047
2017-01-06  1.113554 -0.951963 -1.266802 -0.586571

    Selecting values from a DataFrame where a boolean condition is met.

    (获取所有DataFrame中满足条件的数据)

>>> df[df>0]
                   A         B         C         D
2017-01-01  0.906245  1.815924  0.123356       NaN
2017-01-02       NaN  0.520100  0.511138  0.183975
2017-01-03  0.463326       NaN       NaN       NaN
2017-01-04  1.505464       NaN  1.020903       NaN
2017-01-05       NaN  1.378030  1.874955       NaN
2017-01-06  1.113554       NaN       NaN       NaN

    通过isin()过滤数据

>>> df2 = df.copy()
>>> df2[E] =[one,one,two,three,four,three]
>>> df2
                   A         B         C         D      E
2017-01-01  0.906245  1.815924  0.123356 -1.798571    one
2017-01-02 -0.459646  0.520100  0.511138  0.183975    one
2017-01-03  0.463326 -0.970487 -1.120780 -0.614481    two
2017-01-04  1.505464 -1.743313  1.020903 -1.049047  three
2017-01-05 -0.709366  1.378030  1.874955 -1.017548   four
2017-01-06  1.113554 -0.951963 -1.266802 -0.586571  three
>>> df2[df2[E].isin([two,four])]
                   A         B         C         D     E
2017-01-03  0.463326 -0.970487 -1.120780 -0.614481   two
2017-01-05 -0.709366  1.378030  1.874955 -1.017548  four

      lst:此处官方的例子有点复杂。在Series的isin的方法中,其应该是返回一个包含布尔类型的Series对象,用以表示源对象是否包含传入的参数值才对(DataFrame也类似)。isin的官方定义如下:

技术分享

>>> df3 = pd.DataFrame({A:[1,2,3],B:[a,b,c]})
>>> df3
   A  B
0  1  a
1  2  b
2  3  c
>>> df3.isin([1,3])
       A      B
0   True  False
1  False  False
2   True  False
>>> df

       但在官方的例子中,返回的是一个DataFrame,主要原因是判断完毕two和four是否在df2中以后,如果为TRUE将判断结果传入df2,并返回符合的结果。

  • 设置数据

      通过索引新增一列数据

>>> s3 = pd.Series([1,2,3,4,5,6],index=pd.date_range(20170101,periods=6))
>>> s3
2017-01-01    1
2017-01-02    2
2017-01-03    3
2017-01-04    4
2017-01-05    5
2017-01-06    6
Freq: D, dtype: int64
>>> df[F]= s3
>>> df
                   A         B         C         D   E  F
2017-01-01  0.906245  1.815924  0.123356 -1.798571 NaN  1
2017-01-02 -0.459646  0.520100  0.511138  0.183975 NaN  2
2017-01-03  0.463326 -0.970487 -1.120780 -0.614481 NaN  3
2017-01-04  1.505464 -1.743313  1.020903 -1.049047 NaN  4
2017-01-05 -0.709366  1.378030  1.874955 -1.017548 NaN  5
2017-01-06  1.113554 -0.951963 -1.266802 -0.586571 NaN  6

   通过标签更新值

>>> df.at[dates[0],A] =1.5
>>> df.at[dates[0],A]
1.5

通过位置更新值

>>> df.iat[0,1]=2.5
>>> df.iat[0,1]
2.5
>>> df
                   A         B         C         D   E  F
2017-01-01  1.500000  2.500000  0.123356 -1.798571 NaN  1
2017-01-02 -0.459646  0.520100  0.511138  0.183975 NaN  2
2017-01-03  0.463326 -0.970487 -1.120780 -0.614481 NaN  3
2017-01-04  1.505464 -1.743313  1.020903 -1.049047 NaN  4
2017-01-05 -0.709366  1.378030  1.874955 -1.017548 NaN  5
2017-01-06  1.113554 -0.951963 -1.266802 -0.586571 NaN  6

通过数组更新

>>> df.loc[:,E] =np.array([5]*len(df))
>>> df
                   A         B         C         D  E  F
2017-01-01  1.500000  2.500000  0.123356 -1.798571  5  1
2017-01-02 -0.459646  0.520100  0.511138  0.183975  5  2
2017-01-03  0.463326 -0.970487 -1.120780 -0.614481  5  3
2017-01-04  1.505464 -1.743313  1.020903 -1.049047  5  4
2017-01-05 -0.709366  1.378030  1.874955 -1.017548  5  5
2017-01-06  1.113554 -0.951963 -1.266802 -0.586571  5  6

通过where条件更新值

>>> df4= df.copy()
>>> df4[df4<0] = 3.6
>>> df4
                   A        B         C         D  E  F
2017-01-01  1.500000  2.50000  0.123356  3.600000  5  1
2017-01-02  3.600000  0.52010  0.511138  0.183975  5  2
2017-01-03  0.463326  3.60000  3.600000  3.600000  5  3
2017-01-04  1.505464  3.60000  1.020903  3.600000  5  4
2017-01-05  3.600000  1.37803  1.874955  3.600000  5  5
2017-01-06  1.113554  3.60000  3.600000  3.600000  5  6

 

       

 

   

          

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