量轴‐1长度 的总和（4 + 4）。 X = torch.arange(12, dtype=torch.float32).reshape((3,4)) Y = torch.tensor([[2.0, 1, 4, 3], [1, 2, 3, 4], [4, 3, 2, 1]]) torch.cat((X, Y), dim=0), torch.cat((X, Y), dim=1) (tensor([[ pandas as pd data = pd.read_csv(data_file) print(data) NumRooms Alley Price 0 NaN Pave 127500 1 2.0 NaN 106000 2 4.0 NaN 178100 3 NaN NaN 140000 2.2.2 处理缺失值 注意，“NaN”项代表缺失值。为了处理缺失的数据，典型的方法包括插值法和删除法，其中插值法用一个替 next page) 48 2. 预备知识 (continued from previous page) print(inputs) NumRooms Alley 0 3.0 Pave 1 2.0 NaN 2 4.0 NaN 3 3.0 NaN 对于inputs中的类别值或离散值，我们将“NaN”视为一个类别。由于“巷子类型”（“Alley”）列只接受两 种类型的类别值“Pave”和

org et qui offre les fonc- tionnalités supplémentaires suivantes : a) Périphériques virtuels USB 2.0 (EHCI) ; voir le chapitre 3.10.1, Paramètres USB, page 61. b) Support de VirtualBox Remote Desktop installées et que le système exécute le noyau le plus récent proposé par la distribution. Les numéros de version des paquets des fichiers d’en-tête doivent être les mêmes que celui du noyau que vous utilisez standards par la VESA BIOS Extensions (VBE). 2Le support des invités 64 bits a été ajouté avec VirtualBox 2.0 ; le support des invités 64 bits sur des hôtes 32 bits a été ajouté avec VirtualBox 2.1. 50 3 Configurer

their domain knowledge by adopting OAM ROS-OAM Operator OpenKruise Monitoring Logging Scaling Rollout Ingress Elastisearch Operator Prometheus Operator ROS (Alibaba Cloud Resources) Stateless Component

astype(float) \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[12]: 0 1.0 1 2.0 2 NaN Name: A, Length: 3, dtype: float64 Reduction and groupby operations such as sum work. In floating-point array, since NaN is a float. In [29]: pd.array([1, 2, np.nan]) Out[29]: [1.0, 2.0, nan] Length: 3, dtype: float64 1.1.4 Storing Interval and Period Data in Series and DataFrame Interval (GH21767). In the future this will raise a ValueError. Also changes the behavior of .from_codes([1. 1, 2.0]). • Bug in Categorical.sort_values() where NaN values were always positioned in front regardless

\\\\\Out[7]: ˓→ height weight kind cat -0.4 18.6 -2.0 17.8 dog -28.0 40.0 -190.5 205.5 [2 rows x 4 columns] Previously, these raised a SpecificationError 126404 5 NaN 2013-01-02 0.275165 0.804503 0.446934 5 1.0 2013-01-03 -0.918029 -1.032644 1.599718 5 2.0 2013-01-04 -1.236791 -0.438204 0.101452 5 3.0 2013-01-05 -1.632181 -0.992838 0.741029 5 4.0 2013-01-06 NaN 2013-01-02 -0.275165 -0.804503 -0.446934 -5 -1.0 2013-01-03 -0.918029 -1.032644 -1.599718 -5 -2.0 2013-01-04 -1.236791 -0.438204 -0.101452 -5 -3.0 2013-01-05 -1.632181 -0.992838 -0.741029 -5 -4.0 2013-01-06

\\\\\Out[7]: ˓→ height weight kind cat -0.4 18.6 -2.0 17.8 dog -28.0 40.0 -190.5 205.5 [2 rows x 4 columns] Previously, these raised a SpecificationError 612266 5 NaN 2013-01-02 -0.348338 1.733068 1.226064 5 1.0 2013-01-03 -0.136964 -0.276600 -0.614256 5 2.0 2013-01-04 0.066430 0.886690 1.544564 5 3.0 2013-01-05 0.996132 0.368752 1.232876 5 4.0 2013-01-06 NaN 2013-01-02 -0.348338 -1.733068 -1.226064 -5 -1.0 2013-01-03 -0.136964 -0.276600 -0.614256 -5 -2.0 2013-01-04 -0.066430 -0.886690 -1.544564 -5 -3.0 2013-01-05 -0.996132 -0.368752 -1.232876 -5 -4.0 2013-01-06

Timothy J ˓→ male 54.0 0 0 17463 51.8625 E46 S 7 8 0 3 Palsson, Master. Gosta Leonard ˓→ male 2.0 3 1 349909 21.0750 NaN S To see the first N rows of a DataFrame, use the head() method with the required Moran, Mr. James male NaN 0 ˓→ 0 330877 8.4583 NaN Q 7 8 0 3 Palsson, Master. Gosta Leonard male 2.0 3 ˓→ 1 349909 21.0750 NaN S Similar to the conditional expression, the isin() conditional function Moran, Mr. James male NaN 0 ˓→ 0 330877 8.4583 NaN Q 7 8 0 3 Palsson, Master. Gosta Leonard male 2.0 3 ˓→ 1 349909 21.0750 NaN S Note: When combining multiple conditional statements, each condition

Moran, Mr. James male NaN 0 ˓→ 0 330877 8.4583 NaN Q 7 8 0 3 Palsson, Master. Gosta Leonard male 2.0 3 ˓→ 1 349909 21.0750 NaN S Similar to the conditional expression, the isin() conditional function Moran, Mr. James male NaN 0 ˓→ 0 330877 8.4583 NaN Q 7 8 0 3 Palsson, Master. Gosta Leonard male 2.0 3 ˓→ 1 349909 21.0750 NaN S Note: When combining multiple conditional statements, each condition DataFrame([tuple(list(x) + [val]) for x, val in np.ndenumerate(a)]) Out[29]: 0 1 2 3 0 0 0 0 1.0 1 0 0 1 2.0 2 0 0 2 3.0 3 0 0 3 4.0 4 0 1 0 5.0 .. .. .. .. ... 19 1 1 3 20.0 20 1 2 0 21.0 21 1 2 1 22

nan], ....: 'z': [True, False, True]}) ....: In [19]: df Out[19]: x y z 0 abc 1.0 True 1 None 2.0 False 2 def NaN True [3 rows x 3 columns] In [20]: df.dtypes Out[20]: x object y float64 z bool 495508 5 NaN 2013-01-02 -1.217227 -0.814532 1.419296 5 1.0 2013-01-03 1.789156 0.984494 1.794371 5 2.0 2013-01-04 0.743967 -0.470009 -1.308438 5 3.0 2013-01-05 0.969829 -0.538649 -0.384829 5 4.0 2013-01-06 NaN 2013-01-02 -1.217227 -0.814532 -1.419296 -5 -1.0 2013-01-03 -1.789156 -0.984494 -1.794371 -5 -2.0 2013-01-04 -0.743967 -0.470009 -1.308438 -5 -3.0 2013-01-05 -0.969829 -0.538649 -0.384829 -5 -4.0 2013-01-06

Timothy J ␣ ˓→male 54.0 0 0 17463 51.8625 E46 S 7 8 0 3 Palsson, Master. Gosta Leonard ␣ ˓→male 2.0 3 1 349909 21.0750 NaN S To see the first N rows of a DataFrame, use the head() method with the required Moran, Mr. James male NaN 0 ␣ ˓→ 0 330877 8.4583 NaN Q 7 8 0 3 Palsson, Master. Gosta Leonard male 2.0 3 ␣ ˓→ 1 349909 21.0750 NaN S Similar to the conditional expression, the isin() conditional function Moran, Mr. James male NaN 0 ␣ ˓→ 0 330877 8.4583 NaN Q 7 8 0 3 Palsson, Master. Gosta Leonard male 2.0 3 ␣ ˓→ 1 349909 21.0750 NaN S 1.4. Tutorials 25 pandas: powerful Python data analysis toolkit, Release