In [95]: tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',
....: 'foo', 'foo', 'qux', 'qux'],
....: ['one', 'two', 'one', 'two',
....: 'one', 'two', 'one', 'two']]))
....:
In [96]: index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
In [97]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])
In [98]: df2 = df[:4]
In [99]: df2
Out[99]:
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
stack()方法压缩DataFrame的列：
In [100]: stacked = df2.stack()
In [101]: stacked
Out[101]:
first second
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
In [102]: stacked.unstack()
Out[102]:
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 [103]: stacked.unstack(1)
Out[103]:
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 [104]: stacked.unstack(0)
Out[104]:
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
二、 数据透视表(PivotTables)
In [105]: 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 [106]: df
Out[106]:
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
我们可以非常轻松地从这些数据生成数据透视表：
In [107]: pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])
Out[107]:
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
In [128]: df["grade"] = df["raw_grade"].astype("category")
In [129]: df["grade"]
Out[129]:
0 a
1 b
2 b
3 a
4 a
5 e
Name: grade, dtype: category
Categories (3, object): [a, b, e]
将类别重命名为更有意义的名称(通过调用Series.cat.categories来替换！)。
In [130]: df[“grade”].cat.categories = [“very good”, “good”, “very bad”]
In [131]: df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium",
.....: "good", "very good"])
.....:
In [132]: df["grade"]
Out[132]:
0 very good
1 good
2 good
3 very good
4 very good
5 very bad
Name: grade, dtype: category
Categories (5, object): [very bad, bad, medium, good, very good]
排序是按categories中的顺序排序，而不是词汇顺序：
In [133]: df.sort_values(by="grade")
Out[133]:
id raw_grade grade
5 6 e very bad
1 2 b good
2 3 b good
0 1 a very good
3 4 a very good
4 5 a very good
按分好类的列分组(groupby)可以显示空categories：
In [134]: df.groupby("grade").size()
Out[134]:
grade
very bad 1
bad 0
medium 0
good 2
very good 3
dtype: int64
五、绘图
In [135]: ts = pd.Series(np.random.randn(1000),
.....: index=pd.date_range('1/1/2000', periods=1000))
.....:
In [136]: ts = ts.cumsum()
In [137]: ts.plot()
Out[137]:
plotting
在一个DataFrame中, plot() 方法可以方便地绘制带有label的所有列：
In [138]: df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,
.....: columns=['A', 'B', 'C', 'D'])
.....:
In [139]: df = df.cumsum()
In [140]: plt.figure()
Out[140]:
In [141]: df.plot()
Out[141]:
In [142]: plt.legend(loc='best')
Out[142]:
plotting
>>> if pd.Series([False, True, False]):
... print("I was true")
Traceback
...
ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().
public class Power {
/**
*Q71-数值的整数次方
*实现函数double Power(double base, int exponent)，求base的exponent次方。不需要考虑溢出。
*/
private static boolean InvalidInput=false;
public static void main(
实现两个WEB之间通过session 共享数据
查看tomcat 关于 HTTP Connector 中有个emptySessionPath 其解释如下：
If set to true, all paths for session cookies will be set to /. This can be useful for portlet specification impleme
Parses a raw HTTP request using yii\helpers\Json::decode()
To enable parsing for JSON requests you can configure yii\web\Request::$parsers using this class:
'request' =&g
Sort a linked list in O(n log n) time using constant space complexity.
====analysis=======
mergeSort for singly-linked list
====code======= /**
* Definition for sin
我使用的是ubuntu13.04系统，在安装nginx的时候遇到如下几个问题，然后找思路解决的，nginx 的下载与安装
wget http://nginx.org/download/nginx-1.0.11.tar.gz
tar zxvf nginx-1.0.11.tar.gz
./configure
make
make install
安装的时候出现
在系统开发过程中，总少不免要自己处理一些异常信息，然后将异常信息变成友好的提示返回到客户端的这样一个过程，之前都是new一个自定义的异常，当然这个所谓的自定义异常也是继承RuntimeException的，但这样往往会造成异常信息说明不一致的情况，所以就想到了用枚举来解决的办法。
1，先创建一个接口，里面有两个方法，一个是getCode, 一个是getMessage
public
