主要内容：

创建数据表
查看数据表
数据表索引、选取部分数据
- 通过标签选取.loc
- 多重索引选取
- 位置选取.iloc
- 布尔索引

Object Creation 新建数据

用list建series序列

In [73]: s = pd.Series([1,3,5,np.nan,6,8])

In [74]: s
Out[74]:
0    1.0
1    3.0
2    5.0
3    NaN
4    6.0
5    8.0
dtype: float64

用numpy array建dataframe

In [75]: dates = pd.date_range('20130101', periods=6)

In [76]: df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))

In [77]: df
Out[77]:
                   A         B         C         D
2013-01-01 -0.411674  0.273549  0.629843  1.881497
2013-01-02  1.240512  0.970725  0.033099  1.553420
2013-01-03 -0.544326  0.545738 -1.325810  0.130738
2013-01-04  1.044803 -0.117151  0.874583  2.278227
2013-01-05 -2.194728 -2.536257  0.478644  0.057728
2013-01-06 -1.092031  1.249952  1.598761 -0.153423

#---pd.date_range?---
In [115]: pd.date_range(start='12/31/2011', end='12/31/2013', freq='A')
Out[115]: DatetimeIndex(['2011-12-31', '2012-12-31', '2013-12-31'], dtype='datetime64[ns]', freq='A-DEC')

用dictionary

In [78]: df2 = pd.DataFrame({ 'A' : 1.,
    ...:                      'B' : pd.Timestamp('20130102'),
    ...:                      'C' : pd.Series(1,index=list(range(4)),dtype='float32'),
    ...:                      'D' : np.array([3] * 4,dtype='int32'),
    ...:                      'E' : pd.Categorical(["test","train","test","train"]),
    ...:                      'F' : 'foo' })
    ...: df2
    ...:
Out[78]:
     A          B    C  D      E    F
0  1.0 2013-01-02  1.0  3   test  foo
1  1.0 2013-01-02  1.0  3  train  foo
2  1.0 2013-01-02  1.0  3   test  foo
3  1.0 2013-01-02  1.0  3  train  foo

In [80]: df2.dtypes
Out[80]:
A           float64
B    datetime64[ns]
C           float32
D             int32
E          category
F            object
dtype: object

在ipython中可以使用<tab>键进行自动补充，它会列出数据对象可以执行的操作。

查看数据

df.head()
df.tail(3)
df.index
df.columns              #返回一个这样的东西：pandas.indexes.numeric.Int64Index
df.values               #提取出数据框的数值，返回一个array

数据选取

建议使用pandas的数据选取方法：.at, .iat, .loc, .iloc, .ix. 这些更高效。

df['A']       # 选取某一列，返回一个Series，== df.A，【只能选某一列，不能用":"多选。】

df[0:3]       # 选行
df['20130102':'20130104']

通过标签label选取，.loc

用.loc[]选取数据时，方括号里对应的是：[行,列]（逗号分隔），如果只有一个值，默认是行。可以用“:”。

In [82]: df
Out[82]:
                   A         B         C         D
2013-01-01 -0.411674  0.273549  0.629843  1.881497
2013-01-02  1.240512  0.970725  0.033099  1.553420
2013-01-03 -0.544326  0.545738 -1.325810  0.130738
2013-01-04  1.044803 -0.117151  0.874583  2.278227
2013-01-05 -2.194728 -2.536257  0.478644  0.057728
2013-01-06 -1.092031  1.249952  1.598761 -0.153423

In [83]: df.loc[dates[0]]   # 作为index的日期列叫dates
Out[83]:
A   -0.411674
B    0.273549
C    0.629843
D    1.881497
Name: 2013-01-01 00:00:00, dtype: float64

#---对多个维度轴axis进行选取---
In [84]: df.loc['20130102':'20130104',['A','B']]
Out[84]:
                   A         B
2013-01-02  1.240512  0.970725
2013-01-03 -0.544326  0.545738
2013-01-04  1.044803 -0.117151

#---选取某个数值---
In [85]: df.loc[dates[0],'A']
Out[85]: -0.41167416696608039

In [86]: df.at[dates[0],'A']     # 更高效的做法
Out[86]: -0.41167416696608039

多重索引的选取

index有多个维度

#这里有一个多重索引
MultiIndex(levels=[[1, 2, 3], ['count', 'mean', 'std', 'min', '5%', '10%', '15.0%', '20%', '25%', 
                            '30.0%', '35%', '40%', '45%', '50%', '55.0%', '60.0%', '65%', '70%', 
                            '75%', '80%', '85.0%', '90%', '95%', 'max']],
           labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
                    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                    2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], 
                    [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
                     21, 22, 23, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 
                     18, 19, 20, 21, 22, 23, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 
                     15, 16, 17, 18, 19, 20, 21, 22, 23]],
           names=['label_1', None])
           
df[columnName]                      #选某一列，或多列（":",[,,,]）
df.loc[:,columnName]                #选某一列，或多列（":",[,,,]）
df.loc[1,columnName]             #可以直接用最外层的索引
df.loc[(1,'std'),columnName]     #多层索引要用tuple。选多行用":"连接tuple。
df.loc[[(1,'std'),(2,"count")],'feature_001']

用位置选取：.iloc

.lic[]，位置索引，方括号里是整数值。同样的用“,”隔开行列。

In [93]: df.iloc[3]
Out[93]:
A    1.044803
B   -0.117151
C    0.874583
D    2.278227
Name: 2013-01-04 00:00:00, dtype: float64

In [94]: df.iloc[3:5,0:2]
Out[94]:
                   A         B
2013-01-04  1.044803 -0.117151
2013-01-05 -2.194728 -2.536257

In [95]: df.iat[1,1]
Out[95]: 0.97072539301549565

**布尔索引 **Boolean Indexing

某一列大于0的数据

In [96]: df[df.A > 0]
Out[96]:
                   A         B         C         D
2013-01-02  1.240512  0.970725  0.033099  1.553420
2013-01-04  1.044803 -0.117151  0.874583  2.278227

整体大于零的数据。小于0的为NaN

In [97]: df[df > 0]     
Out[97]:
                   A         B         C         D
2013-01-01       NaN  0.273549  0.629843  1.881497
2013-01-02  1.240512  0.970725  0.033099  1.553420
2013-01-03       NaN  0.545738       NaN  0.130738
2013-01-04  1.044803       NaN  0.874583  2.278227
2013-01-05       NaN       NaN  0.478644  0.057728
2013-01-06       NaN  1.249952  1.598761       NaN

对字符型数据选取

#---isin ---
In [98]: df2 = df.copy()
    ...: df2['E'] = ['one', 'one','two','three','four','three']
    ...: df2
    ...:
Out[98]:
                   A         B         C         D      E
2013-01-01 -0.411674  0.273549  0.629843  1.881497    one
2013-01-02  1.240512  0.970725  0.033099  1.553420    one
2013-01-03 -0.544326  0.545738 -1.325810  0.130738    two
2013-01-04  1.044803 -0.117151  0.874583  2.278227  three
2013-01-05 -2.194728 -2.536257  0.478644  0.057728   four
2013-01-06 -1.092031  1.249952  1.598761 -0.153423  three

In [99]: df2[df2['E'].isin(['two','four'])]
Out[99]:
                   A         B         C         D     E
2013-01-03 -0.544326  0.545738 -1.325810  0.130738   two
2013-01-05 -2.194728 -2.536257  0.478644  0.057728  four

使用布尔面具

In [107]: mask = df2["A"] >0

In [108]: df3 = df2[mask]

In [109]: df3
Out[109]:
                   A         B         C         D      E
2013-01-02  1.240512  0.970725  0.033099  1.553420    ONE
2013-01-04  1.044803 -0.117151  0.874583  2.278227  THREE

# 查看无重复的值：.unique()
In [101]: df2.loc[:,"E"].unique()
Out[101]: array(['one', 'two', 'three', 'four'], dtype=object)

posted on 2018-06-25 22:12 J'KYO 阅读(7964) 评论(0) 编辑收藏举报