. . . . . . 18 3. 3. 1 - E n s u r e t h at ad m i n i s t r at or p r i v i l e ge s ar e on l y gr an t e d t o t h os e w h o r e q u i r e t h e m . . . . . . . . . . . . . . . . . . . 18 3. 4 - i z e d r e ad s or w r i t e s . I t s h ou l d n ot b e r e ad ab l e or w r i t ab l e b y an y gr ou p m e m b e r s or t h e w or l d . A u d i t O n t h e e t c d s e r v e r n od e , ge t t h : E n ab l i n g t h e AlwaysPullImages ad m i s s i on c on t r ol p l u gi n c an c au s e d e - gr ad e d p e r f or m an c e d u e t o ov e r h e ad of al w ay s p u l l i n g i m age s . E n ab l

Recueillir des informations de débogage . . . . . . . . . . . . . . . . . 221 12.1.3 Le débogueur de VM intégré . . . . . . . . . . . . . . . . . . . . . . . . 222 12.1.4 Format du cœur d’une VM . . . . . . . iSCSI, page 98. 14 1 Premiers pas – Amorçage par le réseau PXE. Les cartes réseaux virtuelles intégrées de VirtualBox supportent complètement l’amorçage à distance via Preboot Execution Environment (PXE) xvf - À partir de VirtualBox 3.1, le paquet noyau de VirtualBox n’est plus distinct et il a été intégré au paquet principal. Installez le paquet VirtualBox en utilisant : pkgadd -d VirtualBox-4.3.13-SunOS

f971f1f3-5112-402f-9d1e-48f1d091ff04 (GR_ip-10-0-145-205.ec2.internal) 69c992d8-a4cf-429e-81a3-5361209ffe44 (GR_ip-10-0-147-219.ec2.internal) 7d164271-af9e-4283-b84a-48f2a44851cd (GR_ip-10-0-163-212.ec2.internal) 111052e3-c395-408b-97b2-8dd0a20a29a5 (GR_ip-10-0-165-9.ec2.internal) ed50ce33-df5d-48e8-8862-2df6a59169a0 (GR_ip-10-0-209-170.ec2.internal) f44e2a96-8d1e-4a4d-abae-ed8728ac6851 (GR_ip-10-0-242-240.ec2.internal) Port_Binding etor-GR_ip-10-0-156-16.ec2.internal Port_Binding jtor-GR_ip-10-0-156-16.ec2.internal Port_Binding openshift-ingress-canary_ingress-canary-hsblx Port_Binding rtoj-GR_ip-10-0-156-16

far as we know. type Candidate struct { Name string `lang_es:"nombre" lang_gr:"όνομα"` Age int `lang_es:"edad" lang_gr:"ηλικία"` } gopher := &Candidate{ Name: "Gopher Cleveland", Age: 40, } asBytes

252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 gr, err := gzip.NewReader(r) if err != nil { return nil, fmt.Errorf("failed to parse layer as tar.gz: wasmPluginFileName = "plugin.wasm" // Search for the file walking through the archive. tr := tar.NewReader(gr) for { h, err := tr.Next() if err == io.EOF { break } else if err != nil { return nil, err }

decoded to unicode in the result: In [77]: data = (b'word,length\n' ....: b'Tr\xc3\xa4umen,7\n' ....: b'Gr\xc3\xbc\xc3\x9fe,5') ....: In [78]: data = data.decode('utf8').encode('latin-1') In [79]: df = pd read_csv(BytesIO(data), encoding='latin-1') In [80]: df Out[80]: word length 0 Träumen 7 1 GrüSSe 5 In [81]: df['word'][1] Out[81]: 'GrüSSe' Some formats which encode all characters as multiple bytes, like UTF-16 'UK', 'GR', 'JP']) In [109]: key = countries[np.random.randint(0, 4, 1000)] In [110]: grouped = data_df.groupby(key) # Non-NA count in each group In [111]: grouped.count() Out[111]: A B C GR 244 256

non-null values C 820 non-null values dtypes: float64(3) In [69]: countries = np.array([’US’, ’UK’, ’GR’, ’JP’]) In [70]: key = countries[np.random.randint(0, 4, 1000)] In [71]: grouped = data_df.groupby(key) data analysis toolkit, Release 0.12.0 # Non-NA count in each group In [72]: grouped.count() A B C GR 219 223 194 JP 238 250 211 UK 228 239 213 US 223 241 202 In [73]: f = lambda x: x.fillna(x.mean()) [75]: grouped_trans = transformed.groupby(key) In [76]: grouped.mean() # original group means A B C GR 0.093655 -0.004978 -0.049883 JP -0.067605 0.025828 0.006752 UK -0.054246 0.031742 0.068974 US 0

pedantic compiler warnings about unused variables (GR-P) - 2.05: Use nanosleep() instead of usleep(), which is deprecated (GR-P). - 2.06: check for integer overflow (Glenn R-P) -- Added "void(argc);" statement to quiet pedantic compiler warnings about unused variable (GR-P) - 2.03: check for integer overflow (Glenn R-P) ---------------------------------------- 2006-2007 Keith Packard Copyright © 2008 Red Hat, Inc Copyright © 2008 George Sapountzis gr> Permission to use, copy, modify, distribute, and sell this software and its documentation for any

to unicode in the result: In [77]: data = (b'word,length\n' ....: b'Tr\xc3\xa4umen,7\n' ....: b'Gr\xc3\xbc\xc3\x9fe,5') ....: In [78]: data = data.decode('utf8').encode('latin-1') In [79]: df = pd [80]: df Out[80]: word length 0 Träumen 7 1 Grüße 5 In [81]: df['word'][1] \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[81]: 'Grüße' Some formats which encode all characters 'UK', 'GR', 'JP']) In [104]: key = countries[np.random.randint(0, 4, 1000)] In [105]: grouped = data_df.groupby(key) # Non-NA count in each group In [106]: grouped.count() Out[106]: A B C GR 209 217

4 9 In [12]: gr = df.groupby(df[’jim’] < 2) previous behavior (excludes 1st column from output): In [4]: gr.apply(sum) Out[4]: joe jim False 24 True 11 current behavior: In [13]: gr.apply(sum) Out[13]: ’UK’, ’GR’, ’JP’]) In [76]: key = countries[np.random.randint(0, 4, 1000)] In [77]: grouped = data_df.groupby(key) # Non-NA count in each group In [78]: grouped.count() Out[78]: A B C GR 209 217 grouped_trans = transformed.groupby(key) In [82]: grouped.mean() # original group means Out[82]: A B C GR -0.098371 -0.015420 0.068053 JP 0.069025 0.023100 -0.077324 UK 0.034069 -0.052580 -0.116525 US 0