Ни один из приведенных выше фрагментов кода не работал для меня. После 1-дневного расхода на Google и источника tomcat следующий код работал хорошо, чтобы найти группы пользователей.
import java.util.Hashtable;
import javax.naming.CompositeName;
import javax.naming.Context;
import javax.naming.Name;
import javax.naming.NameParser;
import javax.naming.NamingEnumeration;
import javax.naming.NamingException;
import javax.naming.directory.Attribute;
import javax.naming.directory.Attributes;
import javax.naming.directory.InitialDirContext;
import javax.naming.directory.SearchControls;
import javax.naming.directory.SearchResult;
public class MemberOfTest{
private static final String contextFactory = "com.sun.jndi.ldap.LdapCtxFactory";
private static final String connectionURL = "ldap://HOST:PORT";
private static final String connectionName = "CN=Query,CN=Users,DC=XXX,DC=XX";
private static final String connectionPassword = "XXX";
// Optioanl
private static final String authentication = null;
private static final String protocol = null;
private static String username = "XXXX";
private static final String MEMBER_OF = "memberOf";
private static final String[] attrIdsToSearch = new String[] { MEMBER_OF };
public static final String SEARCH_BY_SAM_ACCOUNT_NAME = "(sAMAccountName=%s)";
public static final String SEARCH_GROUP_BY_GROUP_CN = "(&(objectCategory=group)(cn={0}))";
private static String userBase = "DC=XXX,DC=XXX";
public static void main(String[] args) throws NamingException {
Hashtable<String, String> env = new Hashtable<String, String>();
// Configure our directory context environment.
env.put(Context.INITIAL_CONTEXT_FACTORY, contextFactory);
env.put(Context.PROVIDER_URL, connectionURL);
env.put(Context.SECURITY_PRINCIPAL, connectionName);
env.put(Context.SECURITY_CREDENTIALS, connectionPassword);
if (authentication != null)
env.put(Context.SECURITY_AUTHENTICATION, authentication);
if (protocol != null)
env.put(Context.SECURITY_PROTOCOL, protocol);
InitialDirContext context = new InitialDirContext(env);
String filter = String.format(SEARCH_BY_SAM_ACCOUNT_NAME, username);
SearchControls constraints = new SearchControls();
constraints.setSearchScope(SearchControls.SUBTREE_SCOPE);
constraints.setReturningAttributes(attrIdsToSearch);
NamingEnumeration results = context.search(userBase, filter,constraints);
// Fail if no entries found
if (results == null || !results.hasMore()) {
System.out.println("No result found");
return;
}
// Get result for the first entry found
SearchResult result = (SearchResult) results.next();
// Get the entry's distinguished name
NameParser parser = context.getNameParser("");
Name contextName = parser.parse(context.getNameInNamespace());
Name baseName = parser.parse(userBase);
Name entryName = parser.parse(new CompositeName(result.getName())
.get(0));
// Get the entry's attributes
Attributes attrs = result.getAttributes();
Attribute attr = attrs.get(attrIdsToSearch[0]);
NamingEnumeration e = attr.getAll();
System.out.println("Member of");
while (e.hasMore()) {
String value = (String) e.next();
System.out.println(value);
}
}
}
Я думаю, вы и @jezrael неправильно поняли пример из pandas docs:
df.set_index(['A', 'B'])
A
и B
- это имена столбцов / метки в этом примере:
In [55]: df = pd.DataFrame(np.random.randint(0, 10, (5,4)), columns=list('ABCD'))
In [56]: df
Out[56]:
A B C D
0 6 9 7 4
1 5 1 3 4
2 4 4 0 5
3 9 0 9 8
4 6 4 5 7
In [57]: df.set_index(['A','B'])
Out[57]:
C D
A B
6 9 7 4
5 1 3 4
4 4 0 5
9 0 9 8
6 4 5 7
Документация говорит, что это должен быть список меток / массивов столбцов .
, чтобы вы искали:
In [58]: df.set_index([['A','B','C','D','E']])
Out[58]:
A B C D
A 6 9 7 4
B 5 1 3 4
C 4 4 0 5
D 9 0 9 8
E 6 4 5 7
, но как @jezrael предложил df.index = ['A','B',...]
быстрее и более идиоматический метод ...
Вам нужно назначить list
на summaryDF.index
, если length
из list
совпадает с length
в DataFrame
:
summaryDF.index = ['A','B','C', 'D','E','F','G','H','I','J','K','L']
print (summaryDF)
accuracy f1 precision recall
A 0.494 0.722433 0.722433 0.722433
B 0.290 0.826087 0.826087 0.826087
C 0.274 0.629630 0.629630 0.629630
D 0.278 0.628571 0.628571 0.628571
E 0.288 0.718750 0.718750 0.718750
F 0.740 0.740000 0.740000 0.740000
G 0.698 0.765133 0.765133 0.765133
H 0.582 0.778547 0.778547 0.778547
I 0.682 0.748235 0.748235 0.748235
J 0.574 0.767918 0.767918 0.767918
K 0.398 0.711656 0.711656 0.711656
L 0.530 0.780083 0.780083 0.780083
print (summaryDF.index)
Index(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L'], dtype='object')
Сроки:
In [117]: %timeit summaryDF.index = ['A','B','C', 'D','E','F','G','H','I','J','K','L']
The slowest run took 6.86 times longer than the fastest. This could mean that an intermediate result is being cached.
10000 loops, best of 3: 76.2 µs per loop
In [118]: %timeit summaryDF.set_index(pd.Index(['A','B','C', 'D','E','F','G','H','I','J','K','L']))
The slowest run took 6.77 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 227 µs per loop
Другим решением является преобразование list
в numpy array
:
summaryDF.set_index(np.array(['A','B','C', 'D','E','F','G','H','I','J','K','L']), inplace=True)
print (summaryDF)
accuracy f1 precision recall
A 0.494 0.722433 0.722433 0.722433
B 0.290 0.826087 0.826087 0.826087
C 0.274 0.629630 0.629630 0.629630
D 0.278 0.628571 0.628571 0.628571
E 0.288 0.718750 0.718750 0.718750
F 0.740 0.740000 0.740000 0.740000
G 0.698 0.765133 0.765133 0.765133
H 0.582 0.778547 0.778547 0.778547
I 0.682 0.748235 0.748235 0.748235
J 0.574 0.767918 0.767918 0.767918
K 0.398 0.711656 0.711656 0.711656
L 0.530 0.780083 0.780083 0.780083