原文出处: pandas.pydata.org 译文出处:石卓林
这是关于pandas的简短介绍,主要面向新用户。可以参阅Cookbook了解更复杂的使用方法。
习惯上,我们做以下导入
In[1]:import pandasas pd
In [2]:import numpyas np
In[3]:import matplotlib.pyplotas plt
创建对象
使用传递的值列表序列创建序列, 让pandas创建默认整数索引
In[4]:s =pd.Series([1,3,5,np.nan,6,8])
In [5]:s
Out[5]:
0 1
1 3
2 5
3 NaN
4 6
5 8
dtype:float64
使用传递的numpy数组创建数据帧,并使用日期索引和标记列.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
In[6]:dates =pd.date_range('20130101',periods=6)
In [7]:dates
Out[7]:
<class'pandas.tseries.index.DatetimeIndex'>
[2013-01-01,...,2013-01-06]
Length:6,Freq:D,Timezone:None
In [8]:df =pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))
In[9]:df
Out[9]:
A B C D
2013-01-01 0.469112-0.282863-1.509059-1.135632
2013-01-02 1.212112-0.173215 0.119209-1.044236
2013-01-03-0.861849-2.104569-0.494929 1.071804
2013-01-04 0.721555-0.706771-1.039575 0.271860
2013-01-05-0.424972 0.567020 0.276232-1.087401
2013-01-06-0.673690 0.113648-1.478427 0.524988
使用传递的可转换序列的字典对象创建数据帧.
In[10]: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'})
....:
In [11]:df2
Out[11]:
A B C D E F
0 12013-01-02 1 3 test foo
1 12013-01-02 1 3 train foo
2 12013-01-02 1 3 test foo
3 12013-01-02 1 3 train foo
所有明确类型
In[12]:df2.dtypes
Out[12]:
A float64
B datetime64[ns]
C float32
D int32
E category
F object
dtype:object
如果你这个正在使用IPython,标签补全列名(以及公共属性)将自动启用。这里是将要完成的属性的子集:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
In[13]:df2.<TAB>
df2.A df2.boxplot
df2.abs df2.C
df2.add df2.clip
df2.add_prefix df2.clip_lower
df2.add_suffix df2.clip_upper
df2.align df2.columns
df2.all df2.combine
df2.any df2.combineAdd
df2.append df2.combine_first
df2.apply df2.combineMult
df2.applymap df2.compound
df2.as_blocks df2.consolidate
df2.asfreq df2.convert_objects
df2.as_matrix df2.copy
df2.astype df2.corr
df2.at df2.corrwith
df2.at_time df2.count
df2.axes df2.cov
df2.B df2.cummax
df2.between_time df2.cummin
df2.bfill df2.cumprod
df2.blocks df2.cumsum
df2.bool df2.D
如你所见, 列 A, B, C, 和 D 也是自动完成标签. E 也是可用的; 为了简便起见,后面的属性显示被截断.
查看数据
参阅基础部分
查看帧顶部和底部行
In[14]:df.head()
Out[14]:
A B C D
2013-01-01 0.469112-0.282863-1.509059-1.135632
2013-01-02 1.212112-0.173215 0.119209-1.044236
2013-01-03-0.861849-2.104569-0.494929 1.071804
2013-01-04 0.721555-0.706771-1.039575 0.271860
2013-01-05-0.424972 0.567020 0.276232-1.087401
In [15]:df.tail(3)
Out[15]:
A B C D
2013-01-04 0.721555-0.706771-1.039575 0.271860
2013-01-05-0.424972 0.567020 0.276232-1.087401
2013-01-06-0.673690 0.113648-1.478427 0.524988
显示索引,列,和底层numpy数据
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
In[16]:df.index
Out[16]:
<class'pandas.tseries.index.DatetimeIndex'>
[2013-01-01,...,2013-01-06]
Length:6,Freq:D,Timezone:None
In[17]:df.columns
Out[17]:Index([u'A',u'B',u'C',u'D'],dtype='object')
In [18]:df.values
Out[18]:
array([[0.4691,-0.2829,-1.5091,-1.1356],
[ 1.2121,-0.1732, 0.1192,-1.0442],
[-0.8618,-2.1046,-0.4949, 1.0718],
[ 0.7216,-0.7068,-1.0396, 0.2719],
[-0.425, 0.567, 0.2762,-1.0874],
[-0.6737, 0.1136,-1.4784, 0.525]])
描述显示数据快速统计摘要
In[19]:df.describe()
Out[19]:
A B C D
count 6.000000 6.000000 6.000000 6.000000
mean 0.073711 -0.431125-0.687758-0.233103
std 0.843157 0.922818 0.779887 0.973118
min -0.861849-2.104569-1.509059-1.135632
25% -0.611510-0.600794-1.368714-1.076610
50% 0.022070-0.228039-0.767252-0.386188
75% 0.658444 0.041933-0.034326 0.461706
max 1.212112 0.567020 0.276232 1.071804
转置数据
In[20]:df.T
Out[20]:
2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06
A 0.469112 1.212112 -0.861849 0.721555 -0.424972 -0.673690
B -0.282863 -0.173215 -2.104569 -0.706771 0.567020 0.113648
C -1.509059 0.119209 -0.494929 -1.039575 0.276232 -1.478427
D -1.135632 -1.044236 1.071804 0.271860 -1.087401 0.524988
按轴排序
In[21]:df.sort_index(axis=1,ascending=False)
Out[21]:
D C B A
2013-01-01-1.135632-1.509059-0.282863 0.469112
2013-01-02-1.044236 0.119209-0.173215 1.212112
2013-01-03 1.071804-0.494929-2.104569-0.861849
2013-01-04 0.271860-1.039575-0.706771 0.721555
2013-01-05-1.087401 0.276232 0.567020-0.424972
2013-01-06 0.524988-1.478427 0.113648-0.673690
按值排序
In[22]:df.sort(columns='B')
Out[22]:
A B C D
2013-01-03-0.861849-2.104569-0.494929 1.071804
2013-01-04 0.721555-0.706771-1.039575 0.271860
2013-01-01 0.469112-0.282863-1.509059-1.135632
2013-01-02 1.212112-0.173215 0.119209-1.044236
2013-01-06-0.673690 0.113648-1.478427 0.524988
2013-01-05-0.424972 0.567020 0.276232-1.087401
选择器
注释: 标准Python / Numpy表达式可以完成这些互动工作, 但在生产代码中, 我们推荐使用优化的pandas数据访问方法, .at, .iat, .loc, .iloc 和 .ix.
参阅索引文档 索引和选择数据 and 多索引/高级索引
读取
选择单列, 这会产生一个序列, 等价df.A
In[23]:df['A']
Out[23]:
2013-01-01 0.469112
2013-01-02 1.212112
2013-01-03 -0.861849
2013-01-04 0.721555
2013-01-05 -0.424972
2013-01-06 -0.673690
Freq:D,Name:A,dtype:float64
使用[]选择行片断
In[24]:df[0:3]
Out[24]:
A B C D
2013-01-01 0.469112-0.282863-1.509059-1.135632
2013-01-02 1.212112-0.173215 0.119209-1.044236
2013-01-03-0.861849-2.104569-0.494929 1.071804
In [25]:df['20130102':'20130104']
Out[25]:
A B C D
2013-01-02 1.212112-0.173215 0.119209-1.044236
2013-01-03-0.861849-2.104569-0.494929 1.071804
2013-01-04 0.721555-0.706771-1.039575 0.271860
使用标签选择
更多信息请参阅按标签选择
使用标签获取横截面
In[26]:df.loc[dates[0]]
Out[26]:
A 0.469112
B -0.282863
C -1.509059
D -1.135632
Name:2013-01-0100:00:00,dtype:float64
使用标签选择多轴
In[27]:df.loc[:,['A','B']]
Out[27]:
A B
2013-01-01 0.469112-0.282863
2013-01-02 1.212112-0.173215
2013-01-03-0.861849-2.104569
2013-01-04 0.721555-0.706771
2013-01-05-0.424972 0.567020
2013-01-06-0.673690 0.113648
显示标签切片, 包含两个端点
In[28]:df.loc['20130102':'20130104',['A','B']]
Out[28]:
A B
2013-01-02 1.212112-0.173215
2013-01-03-0.861849-2.104569
2013-01-04 0.721555-0.706771
降低返回对象维度
In[29]:df.loc['20130102',['A','B']]
Out[29]:
A 1.212112
B -0.173215
Name:2013-01-0200:00:00,dtype:float64
获取标量值
In[30]:df.loc[dates[0],'A']
Out[30]:0.46911229990718628
快速访问并获取标量数据 (等价上面的方法)
In[31]:df.at[dates[0],'A']
Out[31]:0.46911229990718628
按位置选择
更多信息请参阅按位置参阅
传递整数选择位置
In[32]:df.iloc[3]
Out[32]:
A 0.721555
B -0.706771
C -1.039575
D 0.271860
Name:2013-01-0400:00:00,dtype:float64
使用整数片断,效果类似numpy/python
In[33]:df.iloc[3:5,0:2]
Out[33]:
A B
2013-01-04 0.721555-0.706771
2013-01-05-0.424972 0.567020
使用整数偏移定位列表,效果类似 numpy/python 样式
In[34]:df.iloc[[1,2,4],[0,2]]
Out[34]:
A C
2013-01-02 1.212112 0.119209
2013-01-03-0.861849-0.494929
2013-01-05-0.424972 0.276232
显式行切片
In[35]:df.iloc[1:3,:]
Out[35]:
A B C D
2013-01-02 1.212112-0.173215 0.119209-1.044236
2013-01-03-0.861849-2.104569-0.494929 1.071804
显式列切片
In[36]:df.iloc[:,1:3]
Out[36]:
B C
2013-01-01-0.282863-1.509059
2013-01-02-0.173215 0.119209
2013-01-03-2.104569-0.494929
2013-01-04-0.706771-1.039575
2013-01-05 0.567020 0.276232
2013-01-06 0.113648-1.478427
显式获取一个值
In[37]:df.iloc[1,1]
Out[37]:-0.17321464905330861
快速访问一个标量(等同上个方法)
In[38]:df.iat[1,1]
Out[38]:-0.17321464905330861
布尔索引
使用单个列的值选择数据.
In[39]:df[df.A> 0]
Out[39]:
A B C D
2013-01-01 0.469112-0.282863-1.509059-1.135632
2013-01-02 1.212112-0.173215 0.119209-1.044236
2013-01-04 0.721555-0.706771-1.039575 0.271860
where 操作.
In[40]:df[df> 0]
Out[40]:
A B C D
2013-01-01 0.469112 NaN NaN NaN
2013-01-02 1.212112 NaN 0.119209 NaN
2013-01-03 NaN NaN NaN 1.071804
2013-01-04 0.721555 NaN NaN 0.271860
2013-01-05 NaN 0.567020 0.276232 NaN
2013-01-06 NaN 0.113648 NaN 0.524988
使用 isin() 筛选:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
In[41]:df2 =df.copy()
In [42]:df2['E']=['one','one','two','three','four','three']
In [43]:df2
Out[43]:
A B C D E
2013-01-01 0.469112-0.282863-1.509059-1.135632 one
2013-01-02 1.212112-0.173215 0.119209-1.044236 one
2013-01-03-0.861849-2.104569-0.494929 1.071804 two
2013-01-04 0.721555-0.706771-1.039575 0.271860 three
2013-01-05-0.424972 0.567020 0.276232-1.087401 four
2013-01-06-0.673690 0.113648-1.478427 0.524988 three
In [44]:df2[df2['E'].isin(['two','four'])]
Out[44]:
A B C D E
2013-01-03-0.861849-2.104569-0.494929 1.071804 two
2013-01-05-0.424972 0.567020 0.276232-1.087401 four
赋值
赋值一个新列,通过索引自动对齐数据
In[45]:s1 =pd.Series([1,2,3,4,5,6],index=pd.date_range('20130102',periods=6))
In [46]:s1
Out[46]:
2013-01-02 1
2013-01-03 2
2013-01-04 3
2013-01-05 4
2013-01-06 5
2013-01-07 6
Freq:D,dtype:int64
In [47]:df['F']= s1
按标签赋值
In[48]:df.at[dates[0],'A']= 0
按位置赋值
通过numpy数组分配赋值
In[50]:df.loc[:,'D']= np.array([5]* len(df))
之前的操作结果
In[51]:df
Out[51]:
A B C D F
2013-01-01 0.000000 0.000000-1.509059 5NaN
2013-01-02 1.212112-0.173215 0.119209 5 1
2013-01-03-0.861849-2.104569-0.494929 5 2
2013-01-04 0.721555-0.706771-1.039575 5 3
2013-01-05-0.424972 0.567020 0.276232 5 4
2013-01-06-0.673690 0.113648-1.478427 5 5
where 操作赋值.
In[52]:df2 =df.copy()
In [53]:df2[df2> 0]= -df2
In[54]:df2
Out[54]:
A B C D F
2013-01-01 0.000000 0.000000-1.509059-5NaN
2013-01-02-1.212112-0.173215-0.119209-5 -1
2013-01-03-0.861849-2.104569-0.494929-5 -2
2013-01-04-0.721555-0.706771-1.039575-5 -3
2013-01-05-0.424972-0.567020-0.276232-5 -4
2013-01-06-0.673690-0.113648-1.478427-5 -5
丢失的数据
pandas主要使用np.nan替换丢失的数据. 默认情况下它并不包含在计算中. 请参阅 Missing Data section
重建索引允许更改/添加/删除指定轴索引,并返回数据副本.
In[55]:df1 =df.reindex(index=dates[0:4],columns=list(df.columns)+ ['E'])
In [56]:df1.loc[dates[0]:dates[1],'E']= 1
In[57]:df1
Out[57]:
A B C D F E
2013-01-01 0.000000 0.000000-1.509059 5NaN 1
2013-01-02 1.212112-0.173215 0.119209 5 1 1
2013-01-03-0.861849-2.104569-0.494929 5 2 NaN
2013-01-04 0.721555-0.706771-1.039575 5 3 NaN
删除任何有丢失数据的行.
In[58]:df1.dropna(how='any')
Out[58]:
A B C D F E
2013-01-02 1.212112-0.173215 0.119209 5 1 1
填充丢失数据
In[59]:df1.fillna(value=5)
Out[59]:
A B C D F E
2013-01-01 0.000000 0.000000-1.509059 5 5 1
2013-01-02 1.212112-0.173215 0.119209 5 1 1
2013-01-03-0.861849-2.104569-0.494929 5 2 5
2013-01-04 0.721555-0.706771-1.039575 5 3 5
获取值是否nan的布尔标记
In[60]:pd.isnull(df1)
Out[60]:
A B C D F E
2013-01-01 False False False False True False
2013-01-02 False False False False False False
2013-01-03 False False False False False True
2013-01-04 False False False False False True
运算
参阅二元运算基础
统计
计算时一般不包括丢失的数据
执行描述性统计
In[61]:df.mean()
Out[61]:
A -0.004474
B -0.383981
C -0.687758
D 5.000000
F 3.000000
dtype:float64
在其他轴做相同的运算
In[62]:df.mean(1)
Out[62]:
2013-01-01 0.872735
2013-01-02 1.431621
2013-01-03 0.707731
2013-01-04 1.395042
2013-01-05 1.883656
2013-01-06 1.592306
Freq:D,dtype:float64
用于运算的对象有不同的维度并需要对齐.除此之外,pandas会自动沿着指定维度计算.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
In[63]:s =pd.Series([1,3,5,np.nan,6,8],index=dates).shift(2)
In [64]:s
Out[64]:
2013-01-01 NaN
2013-01-02 NaN
2013-01-03 1
2013-01-04 3
2013-01-05 5
2013-01-06 NaN
Freq:D,dtype:float64
In [65]:df.sub(s,axis='index')
Out[65]:
A B C D F
2013-01-01 NaN NaN NaN NaN NaN
2013-01-02 NaN NaN NaN NaN NaN
2013-01-03-1.861849-3.104569-1.494929 4 1
2013-01-04-2.278445-3.706771-4.039575 2 0
2013-01-05-5.424972-4.432980-4.723768 0 -1
2013-01-06 NaN NaN NaN NaN NaN
Apply
在数据上使用函数
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
In[66]:df.apply(np.cumsum)
Out[66]:
A B C D F
2013-01-01 0.000000 0.000000-1.509059 5 NaN
2013-01-02 1.212112-0.173215-1.389850 10 1
2013-01-03 0.350263-2.277784-1.884779 15 3
2013-01-04 1.071818-2.984555-2.924354 20 6
2013-01-05 0.646846-2.417535-2.648122 25 10
2013-01-06-0.026844-2.303886-4.126549 30 15
In[67]:df.apply(lambdax:x.max()- x.min())
Out[67]:
A 2.073961
B 2.671590
C 1.785291
D 0.000000
F 4.000000
dtype:float64
直方图
请参阅 直方图和离散化
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
In[68]:s =pd.Series(np.random.randint(0,7,size=10))
In [69]:s
Out[69]:
0 4
1 2
2 1
3 2
4 6
5 4
6 4
7 6
8 4
9 4
dtype:int32
In [70]:s.value_counts()
Out[70]:
4 5
6 2
2 2
1 1
dtype:int64
字符串方法
序列可以使用一些字符串处理方法很轻易操作数据组中的每个元素,比如以下代码片断。 注意字符匹配方法默认情况下通常使用正则表达式(并且大多数时候都如此). 更多信息请参阅字符串向量方法.
In[71]:s =pd.Series(['A','B','C','Aaba','Baca',np.nan,'CABA','dog','cat'])
In [72]:s.str.lower()
Out[72]:
0 a
1 b
2 c
3 aaba
4 baca
5 NaN
6 caba
7 dog
8 cat
dtype:object
合并
连接
pandas提供各种工具以简便合并序列,数据桢,和组合对象, 在连接/合并类型操作中使用多种类型索引和相关数学函数.
请参阅合并部分
把pandas对象连接到一起
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
In[73]:df =pd.DataFrame(np.random.randn(10,4))
In [74]:df
Out[74]:
0 1 2 3
0-0.548702 1.467327-1.015962-0.483075
1 1.637550-1.217659-0.291519-1.745505
2-0.263952 0.991460-0.919069 0.266046
3 -0.709661 1.669052 1.037882-1.705775
4-0.919854-0.042379 1.247642-0.009920
5 0.290213 0.495767 0.362949 1.548106
6-1.131345-0.089329 0.337863-0.945867
7 -0.932132 1.956030 0.017587-0.016692
8-0.575247 0.254161-1.143704 0.215897
9 1.193555-0.077118-0.408530-0.862495
# break it into pieces
In[75]:pieces =[df[:3],df[3:7],df[7:]]
In [76]:pd.concat(pieces)
Out[76]:
0 1 2 3
0-0.548702 1.467327-1.015962-0.483075
1 1.637550-1.217659-0.291519-1.745505
2-0.263952 0.991460-0.919069 0.266046
3 -0.709661 1.669052 1.037882-1.705775
4-0.919854-0.042379 1.247642-0.009920
5 0.290213 0.495767 0.362949 1.548106
6-1.131345-0.089329 0.337863-0.945867
7 -0.932132 1.956030 0.017587-0.016692
8-0.575247 0.254161-1.143704 0.215897
9 1.193555-0.077118-0.408530-0.862495
连接
SQL样式合并. 请参阅 数据库style联接
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
In[77]:left =pd.DataFrame({'key':['foo','foo'],'lval':[1,2]})
In [78]:right =pd.DataFrame({'key':['foo','foo'],'rval':[4,5]})
In[79]:left
Out[79]:
key lval
0 foo 1
1 foo 2
In[80]:right
Out[80]:
key rval
0 foo 4
1 foo 5
In[81]:pd.merge(left,right,on='key')
Out[81]:
key lval rval
0 foo 1 4
1 foo 1 5
2 foo 2 4
3 foo 2 5
添加
添加行到数据增. 参阅 添加
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
In[82]:df =pd.DataFrame(np.random.randn(8,4),columns=['A','B','C','D'])
In [83]:df
Out[83]:
A B C D
0 1.346061 1.511763 1.627081-0.990582
1 -0.441652 1.211526 0.268520 0.024580
2-1.577585 0.396823-0.105381-0.532532
3 1.453749 1.208843-0.080952-0.264610
4-0.727965-0.589346 0.339969-0.693205
5 -0.339355 0.593616 0.884345 1.591431
6 0.141809 0.220390 0.435589 0.192451
7 -0.096701 0.803351 1.715071-0.708758
In [84]:s =df.iloc[3]
In[85]:df.append(s,ignore_index=True)
Out[85]:
A B C D
0 1.346061 1.511763 1.627081-0.990582
1-0.441652 1.211526 0.268520 0.024580
2 -1.577585 0.396823-0.105381-0.532532
3 1.453749 1.208843-0.080952-0.264610
4 -0.727965-0.589346 0.339969-0.693205
5-0.339355 0.593616 0.884345 1.591431
6 0.141809 0.220390 0.435589 0.192451
7-0.096701 0.803351 1.715071-0.708758
8 1.453749 1.208843-0.080952-0.264610
分组
对于“group by”指的是以下一个或多个处理
- 将数据按某些标准分割为不同的组
- 在每个独立组上应用函数
- 组合结果为一个数据结构
请参阅 分组部分
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
In[86]:df =pd.DataFrame({'A': ['foo','bar','foo','bar',
....: 'foo','bar','foo','foo'],
....: 'B': ['one','one','two','three',
....: 'two','two','one','three'],
....: 'C': np.random.randn(8),
....: 'D': np.random.randn(8)})
....:
In [87]:df
Out[87]:
A B C D
0 foo one-1.202872-0.055224
1 bar one-1.814470 2.395985
2 foo two 1.018601 1.552825
3 bar three-0.595447 0.166599
4 foo two 1.395433 0.047609
5 bar two-0.392670-0.136473
6 foo one 0.007207-0.561757
7 foo three 1.928123-1.623033
分组然后应用函数统计总和存放到结果组
In[88]:df.groupby('A').sum()
Out[88]:
C D
A
bar-2.802588 2.42611
foo 3.146492-0.63958
按多列分组为层次索引,然后应用函数
In[89]:df.groupby(['A','B']).sum()
Out[89]:
C D
A B
barone -1.814470 2.395985
three-0.595447 0.166599
two -0.392670-0.136473
foo one -1.195665-0.616981
three 1.928123-1.623033
two 2.414034 1.600434
重塑
请参阅章节 分层索引 和 重塑.
堆叠
In[90]:tuples =list(zip(*[['bar','bar','baz','baz',
....: 'foo','foo','qux','qux'],
....: ['one','two','one','two',
....: 'one','two','one','two']]))
....:
In [91]:index =pd.MultiIndex.from_tuples(tuples,names=['first','second'])
In[92]:df =pd.DataFrame(np.random.randn(8,2),index=index,columns=['A','B'])
In [93]:df2 =df[:4]
In[94]:df2
Out[94]:
A B
first second
bar one 0.029399-0.542108
two 0.282696 -0.087302
baz one -1.575170 1.771208
two 0.816482 1.100230
堆叠 函数 “压缩” 数据桢的列一个级别.
In[95]:stacked =df2.stack()
In [96]:stacked
Out[96]:
first second
bar one A 0.029399
B -0.542108
two A 0.282696
B -0.087302
baz one A -1.575170
B 1.771208
two A 0.816482
B 1.100230
dtype:float64
被“堆叠”数据桢或序列(有多个索引作为索引), 其堆叠的反向操作是未堆栈, 上面的数据默认反堆叠到上一级别:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
In[97]:stacked.unstack()
Out[97]:
A B
first second
bar one 0.029399-0.542108
two 0.282696 -0.087302
baz one -1.575170 1.771208
two 0.816482 1.100230
In [98]:stacked.unstack(1)
Out[98]:
second one two
first
bar A 0.029399 0.282696
B-0.542108-0.087302
baz A-1.575170 0.816482
B 1.771208 1.100230
In[99]:stacked.unstack(0)
Out[99]:
first bar baz
second
one A 0.029399-1.575170
B-0.542108 1.771208
two A 0.282696 0.816482
B-0.087302 1.100230
数据透视表
查看数据透视表.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
In[100]:df =pd.DataFrame({'A': ['one','one','two','three']* 3,
.....: 'B': ['A','B','C']* 4,
.....: 'C': ['foo','foo','foo','bar','bar','bar']* 2,
.....: 'D': np.random.randn(12),
.....: 'E': np.random.randn(12)})
.....:
In[101]:df
Out[101]:
A B C D E
0 one A foo 1.418757-0.179666
1 one B foo-1.879024 1.291836
2 two C foo 0.536826-0.009614
3 three A bar 1.006160 0.392149
4 one B bar-0.029716 0.264599
5 one C bar-1.146178-0.057409
6 two A foo 0.100900-1.425638
7 three B foo-1.035018 1.024098
8 one C foo 0.314665-0.106062
9 one A bar-0.773723 1.824375
10 two B bar-1.170653 0.595974
11 three C bar 0.648740 1.167115
我们可以从此数据非常容易的产生数据透视表:
In[102]:pd.pivot_table(df,values='D',index=['A','B'],columns=['C'])
Out[102]:
C bar foo
A B
one A -0.773723 1.418757
B-0.029716-1.879024
C-1.146178 0.314665
three A 1.006160 NaN
B NaN -1.035018
C 0.648740 NaN
two A NaN 0.100900
B-1.170653 NaN
C NaN 0.536826
时间序列
pandas有易用,强大且高效的函数用于高频数据重采样转换操作(例如,转换秒数据到5分钟数据), 这是很普遍的情况,但并不局限于金融应用, 请参阅时间序列章节
In[103]:rng =pd.date_range('1/1/2012',periods=100,freq='S')
In [104]:ts =pd.Series(np.random.randint(0,500,len(rng)),index=rng)
In[105]:ts.resample('5Min',how='sum')
Out[105]:
2012-01-01 25083
Freq:5T,dtype:int32
时区表示
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
In[106]:rng =pd.date_range('3/6/2012 00:00',periods=5,freq='D')
In [107]:ts =pd.Series(np.random.randn(len(rng)),rng)
In[108]:ts
Out[108]:
2012-03-06 0.464000
2012-03-07 0.227371
2012-03-08 -0.496922
2012-03-09 0.306389
2012-03-10 -2.290613
Freq:D,dtype:float64
In [109]:ts_utc =ts.tz_localize('UTC')
In[110]:ts_utc
Out[110]:
2012-03-0600:00:00+00:00 0.464000
2012-03-0700:00:00+00:00 0.227371
2012-03-0800:00:00+00:00 -0.496922
2012-03-0900:00:00+00:00 0.306389
2012-03-1000:00:00+00:00 -2.290613
Freq:D,dtype:float64
转换到其它时区
In[111]:ts_utc.tz_convert('US/Eastern')
Out[111]:
2012-03-0519:00:00-05:00 0.464000
2012-03-0619:00:00-05:00 0.227371
2012-03-0719:00:00-05:00 -0.496922
2012-03-0819:00:00-05:00 0.306389
2012-03-0919:00:00-05:00 -2.290613
Freq:D,dtype:float64
转换不同的时间跨度
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
In[112]:rng =pd.date_range('1/1/2012',periods=5,freq='M')
In [113]:ts =pd.Series(np.random.randn(len(rng)),index=rng)
In[114]:ts
Out[114]:
2012-01-31 -1.134623
2012-02-29 -1.561819
2012-03-31 -0.260838
2012-04-30 0.281957
2012-05-31 1.523962
Freq:M,dtype:float64
In [115]:ps =ts.to_period()
In[116]:ps
Out[116]:
2012-01 -1.134623
2012-02 -1.561819
2012-03 -0.260838
2012-04 0.281957
2012-05 1.523962
Freq:M,dtype:float64
In [117]:ps.to_timestamp()
Out[117]:
2012-01-01 -1.134623
2012-02-01 -1.561819
2012-03-01 -0.260838
2012-04-01 0.281957
2012-05-01 1.523962
Freq:MS,dtype:float64
转换时段并且使用一些运算函数, 下例中, 我们转换年报11月到季度结束每日上午9点数据
In[118]:prng =pd.period_range('1990Q1','2000Q4',freq='Q-NOV')
In [119]:ts =pd.Series(np.random.randn(len(prng)),prng)
In[120]:ts.index= (prng.asfreq('M','e')+ 1).asfreq('H','s')+ 9
In [121]:ts.head()
Out[121]:
1990-03-0109:00 -0.902937
1990-06-0109:00 0.068159
1990-09-0109:00 -0.057873
1990-12-0109:00 -0.368204
1991-03-0109:00 -1.144073
Freq:H,dtype:float64
分类
自版本0.15起, pandas可以在数据桢中包含分类. 完整的文档, 请查看分类介绍 and theAPI文档.
In[122]:df =pd.DataFrame({"id":[1,2,3,4,5,6],"raw_grade":['a','b','b','a','a','e']})
转换原始类别为分类数据类型.
In[123]:df["grade"]= df["raw_grade"].astype("category")
In [124]:df["grade"]
Out[124]:
0 a
1 b
2 b
3 a
4 a
5 e
Name:grade,dtype:category
Categories(3,object):[a,b,e]
重命令分类为更有意义的名称 (分配到Series.cat.categories对应位置!)
In[125]:df["grade"].cat.categories= ["very good","good","very bad"]
重排顺分类,同时添加缺少的分类(序列 .cat方法下返回新默认序列)
In[126]:df["grade"]= df["grade"].cat.set_categories(["very bad", "bad","medium","good","very good"])
In [127]:df["grade"]
Out[127]:
0 verygood
1 good
2 good
3 verygood
4 verygood
5 very bad
Name:grade,dtype:category
Categories(5,object):[very bad,bad,medium,good,very good]
排列分类中的顺序,不是按词汇排列.
In[128]:df.sort("grade")
Out[128]:
id raw_grade grade
5 6 e verybad
1 2 b good
2 3 b good
0 1 a verygood
3 4 a verygood
4 5 a verygood
类别列分组,并且也显示空类别.
In[129]:df.groupby("grade").size()
Out[129]:
grade
very bad 1
bad NaN
medium NaN
good 2
very good 3
dtype:float64
绘图
绘图文档.
In[130]:ts =pd.Series(np.random.randn(1000),index=pd.date_range('1/1/2000',periods=1000))
In [131]:ts =ts.cumsum()
In[132]:ts.plot()
Out[132]:<matplotlib.axes._subplots.AxesSubplotat 0xb02091ac>
在数据桢中,可以很方便的绘制带标签列:
In[133]:df =pd.DataFrame(np.random.randn(1000,4),index=ts.index,
.....: columns=['A','B','C','D'])
.....:
In [134]:df =df.cumsum()
In[135]:plt.figure();df.plot();plt.legend(loc='best')
Out[135]:<matplotlib.legend.Legendat 0xb01c9cac>
获取数据输入/输出
CSV
写入csv文件
In[136]:df.to_csv('foo.csv')
读取csv文件
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
In[137]:pd.read_csv('foo.csv')
Out[137]:
Unnamed:0 A B C D
0 2000-01-01 0.266457 -0.399641-0.219582 1.186860
1 2000-01-02 -1.170732 -0.345873 1.653061 -0.282953
2 2000-01-03 -1.734933 0.530468 2.060811 -0.515536
3 2000-01-04 -1.555121 1.452620 0.239859 -1.156896
4 2000-01-05 0.578117 0.511371 0.103552 -2.428202
5 2000-01-06 0.478344 0.449933-0.741620 -1.962409
6 2000-01-07 1.235339 -0.091757-1.543861 -1.084753
.. ... ... ... ... ...
993 2002-09-20-10.628548 -9.153563-7.883146 28.313940
994 2002-09-21-10.390377 -8.727491-6.399645 30.914107
995 2002-09-22 -8.985362 -8.485624-4.669462 31.367740
996 2002-09-23 -9.558560 -8.781216-4.499815 30.518439
997 2002-09-24 -9.902058 -9.340490-4.386639 30.105593
998 2002-09-25-10.216020 -9.480682-3.933802 29.758560
999 2002-09-26-11.856774-10.671012-3.216025 29.369368
[1000rows x5 columns]
HDF5
读写HDF存储
写入HDF5存储
In[138]:df.to_hdf('foo.h5','df')
读取HDF5存储
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
In[139]:pd.read_hdf('foo.h5','df')
Out[139]:
A B C D
2000-01-01 0.266457 -0.399641-0.219582 1.186860
2000-01-02 -1.170732 -0.345873 1.653061 -0.282953
2000-01-03 -1.734933 0.530468 2.060811 -0.515536
2000-01-04 -1.555121 1.452620 0.239859 -1.156896
2000-01-05 0.578117 0.511371 0.103552 -2.428202
2000-01-06 0.478344 0.449933-0.741620 -1.962409
2000-01-07 1.235339 -0.091757-1.543861 -1.084753
... ... ... ... ...
2002-09-20-10.628548 -9.153563-7.883146 28.313940
2002-09-21-10.390377 -8.727491-6.399645 30.914107
2002-09-22 -8.985362 -8.485624-4.669462 31.367740
2002-09-23 -9.558560 -8.781216-4.499815 30.518439
2002-09-24 -9.902058 -9.340490-4.386639 30.105593
2002-09-25-10.216020 -9.480682-3.933802 29.758560
2002-09-26-11.856774-10.671012-3.216025 29.369368
[1000rows x4 columns]
Excel
读写MS Excel
写入excel文件
In[140]:df.to_excel('foo.xlsx',sheet_name='Sheet1')
读取excel文件
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
In[141]:pd.read_excel('foo.xlsx','Sheet1',index_col=None,na_values=['NA'])
Out[141]:
A B C D
2000-01-01 0.266457 -0.399641-0.219582 1.186860
2000-01-02 -1.170732 -0.345873 1.653061 -0.282953
2000-01-03 -1.734933 0.530468 2.060811 -0.515536
2000-01-04 -1.555121 1.452620 0.239859 -1.156896
2000-01-05 0.578117 0.511371 0.103552 -2.428202
2000-01-06 0.478344 0.449933-0.741620 -1.962409
2000-01-07 1.235339 -0.091757-1.543861 -1.084753
... ... ... ... ...
2002-09-20-10.628548 -9.153563-7.883146 28.313940
2002-09-21-10.390377 -8.727491-6.399645 30.914107
2002-09-22 -8.985362 -8.485624-4.669462 31.367740
2002-09-23 -9.558560 -8.781216-4.499815 30.518439
2002-09-24 -9.902058 -9.340490-4.386639 30.105593
2002-09-25-10.216020 -9.480682-3.933802 29.758560
2002-09-26-11.856774-10.671012-3.216025 29.369368
[1000rows x4 columns]
陷阱
如果尝试这样操作可能会看到像这样的异常:
>>>if pd.Series([False,True,False]):
print("I was true")
Traceback
...
ValueError:The truth valueof an arrayis ambiguous.Use a.empty,a.any()or a.all().
查看对照获取解释和怎么做的帮助
也可以查看陷阱.