熊猫: 使用范围内的随机整数在 df 中创建新列

One solution is to use numpy.random.randint:

import numpy as np
df1['randNumCol'] = np.random.randint(1, 6, df1.shape[0])

Or if the numbers are non-consecutive (albeit slower), you can use this:

df1['randNumCol'] = np.random.choice([1, 9, 20], df1.shape[0])

In order to make the results reproducible you can set the seed with numpy.random.seed (e.g. np.random.seed(42))

To add a column of random integers, use randint(low, high, size). There's no need to waste memory allocating range(low, high); that could be a lot of memory if high is large.

df1['randNumCol'] = np.random.randint(0,5, size=len(df1))

Notes:

• when we're just adding a single column, size is just an integer. In general if we want to generate an array/dataframe of randint()s, size can be a tuple, as in Pandas: How to create a data frame of random integers?)
• in Python 3.x range(low, high) no longer allocates a list (potentially using lots of memory), it produces a range() object
• use random.seed(...) for determinism and reproducibility

An option that doesn't require an additional import for numpy:

df1['randNumCol'] = pd.Series(range(1,6)).sample(int(5e4), replace=True).array