复制 In [1]: arr = np.random.randn(10) In [2]: arr[2:-2] = np.nan In [3]: ts = pd.Series(pd.arrays.SparseArray(arr)) In [4]: ts Out[4]: 0 0.469112 1 -0.282863 2 NaN 3 NaN 4 NaN 5 NaN 6 NaN 7 NaN 8 -0.861849 9 -2.104569 dtype: Sparse[float64, nan] 注意dt...
random_state: If int, array-like, or BitGenerator, seed for random number generator. If np.random.RandomState or np.random.Generator, use as given. axis: {0 or ‘index’, 1 or ‘columns’, None}, default is stat axis for given data type. ignore_index: bool, deciding if the resultin...
from numpy.randomimportrand from numpy.randomimportseed from scipy.statsimportspearmanr # seed random number generatorseed(1)# prepare data data1=data['x']data2=data['price']# calculate spearman's correlation coef,p=spearmanr(data1,data2)print('Spearmans correlation coefficient: %.3f'%coef)#...
...: n =len(index) ...: state = np.random.RandomState(seed) ...: columns = { ...:"name": state.choice(["Alice","Bob","Charlie"], size=n), ...:"id": state.poisson(1000, size=n), ...:"x": state.rand(n) *2-1, ...:"y": state.rand(n) *2-1, ...: } .....
random_state Seed for the random number generator (if int), or numpy RandomState object. int or numpy.random.RandomState, Optional axis Axis to sample. Accepts axis number or name. Default is stat axis for given data type (0 for Series and DataFrames). int or string OptionalReturns...
EXAMPLE 2: Create a reproducible example with random_state Here, we’re going to use therandom_stateparameter to set a “seed” value for the random number generator. When we do this, the output is still “random” in the sense that it will be selected in a way that’s not predictable...
# 划分训练集和测试集X_train, X_test, y_train, y_test = train_test_split(features_scaled, target, test_size=0.2, random_state=42) 2.训练线性回归模型 from sklearn.linear_model import LinearRegressionfrom sklearn.metrics import mean_squared_error, r2_score ...
Note: random.seed() is use to seed, or initialize, the underlying pseudorandom number generator (PRNG) used by random. It may sound like an oxymoron, but this is a way of making random data reproducible and deterministic. That is, if you copy the code here as is, you should get ...
从版本 2.1.0 开始弃用:传递字典已被弃用,将在 pandas 的将来版本中引发错误。请传递一个聚合列表。 *args 传递给 func 的位置参数。 engine字符串,默认为 None 'cython':通过 cython 的 C 扩展运行函数。 'numba':通过 numba 的 JIT 编译代码运行函数。
相比之下,Polars 能够同时执行 Eager 和惰性执行,查询优化器将对所有必需运算求值并制定最有效的代码执行方式。,这可能包括重写运算的执行顺序或删除冗余计算。 例如,我们要基于列 Category 对列 Number 进行聚合求平均值,然后将 Category 中值 A 和 B 的记录筛选出来。