y = np.array([1,5,6,8,1,7,3,6,9])# Where y is greater than 5, returns index positionnp.where(y>5)array([2, 3, 5, 7, 8], dtype=int64),)# First will replace the values that match the condition,# second will replace the values t...
af,b,c,1#replace nan value with 0#注意如果dtype不为float的像字符串这样就会被转为nanworld_alcohol = numpy.genfromtxt("test.txt", delimiter=",",dtype=float)print(world_alcohol)#这里is_value_empty 返回的是一个布尔列表is_value_empty = numpy.isnan(world_alcohol[:,3])print(is_value_empty...
另外,我无法更改数组的类型,因为整数类型数组不支持“nan”。我该怎么做呢? 2 回答 汪汪一只猫 TA贡献1898条经验 获得超8个赞 问题是,这些应该转换成什么值? 假设这个数组是 names x,那么: x[np.isnan(x)] = 0 # value you want to replace NaN with 现在,您可以按照自己的方式将数据转换为整数。 反...
1.先替换为? 2.然后删除 data = data.replace(to_replace = "?", value = np.nan) data.dropna(inplace = True) 1. 2. 替换空值? 为nan 然后删除nan值 data.isnull().any() 1. 检查结果 出现全部为false的话为删除成功
使用pandas/numpy替换值的更好方法是使用相关的函数和方法来实现。以下是一些常用的方法: 1. 使用pandas的replace()函数:该函数可以用来替换DataFrame或Series中...
array([2, 3, 5, 7, 8], dtype=int64),)# First will replace the values that matchthe condition, # second will replace the values that does not np.where(y>5, "Hit", "Miss") array(['Miss', 'Miss', 'Hit', 'Hit', 'Miss', 'Hit', 'Miss', 'Hit','Hit'],dtype=' ...
y = np.array([1,5,6,8,1,7,3,6,9])# Where y is greater than 5, returns index positionnp.where(y>5)array([2, 3, 5, 7, 8], dtype=int64),)# First will replace the values that match the condition, # second will replace the values that does notnp.where(y>5, "Hit", "...
Replace NaN values with the mean of another array. Write a NumPy program to replace all the nan (missing values) of a given array with the mean of another array. Sample Solution: Python Code: # Importing the NumPy library import numpy as np ...
array([2, 3, 5, 7, 8], dtype=int64) # First will replace the values that match the condition, # second will replace the values that does not >>> np.where(y>5, "Hit", "Miss") array(['Miss', 'Miss', 'Hit', 'Hit', 'Miss', ...
numpy as npoutfile = r'.\data1.csv'x = np.genfromtxt(outfile, delimiter=',', names=True)print(x)# [(1., 123., 1.4, 23.) (2., 110., nan, 18.) (3., nan, 2.1, 19.)]print(type(x)) # <class 'numpy.ndarray'>print(x.dtype)# [('id', '<f8'), ('value1'...