在pandas数据帧中创建一组随机化的列名

Question

我正在尝试创建一组列(在panda数据帧中(，其中列名是随机化的。这是因为我想以随机化的方式从较大的数据集生成过滤器数据。

如何根据以下内容生成一组N(=4(*3的列名？

    car_speed   state_8 state_17    state_19    state_16    wd_8    wd_17   wd_19   wd_16   wu_8    wu_17   wu_19   wu_16

下面是我的潜在代码，但实际上不起作用。我需要先用"state_"，然后用"wd_"，再用"wd_'"。下面的代码按连续顺序分别生成"state_"、"wd_"one_answers"wu_"。我还有问题，当它按这个顺序时，从更大的数据集中填充数据

def iteration1(data, classes = 50, sigNum = 4):
    dataNN = pd.DataFrame(index = [0])
    dataNN['car_speed'] = np.zeros(1)
    while len(dataNN.columns) < sigNum + 1:
        state = np.int(np.random.uniform(0, 50))
        dataNN['state_'+str(state)] = np.zeros(1) # this is the state value set-up
        dataNN['wd_' + str(state)] = np.zeros(1) # this is the weight direction
        dataNN['wu_' + str(state)] = np.zeros(1) # this is the weight magnitude
    count = 0 # initialize count row as zero
    while count < classes :
        dataNN.loc[count] = np.zeros(len(dataNN.columns))
        for state in dataNN.columns[1:10]:
            dataNN[state].loc[count] = data[state].loc[count]
        count = count + 1
        if count > classes : break
    return dataNN

Answer 1

假设问题是缺少对"state_*"、"wd_*"和"wu_*"的分组，我建议您首先选择sigNum / 3随机整数，然后使用它们来标记列。如下所示：

states = [np.int(np.random.uniform(0, 50)) for _ in range (sigNum/3)]
i = 0
while len(dataNN.columns) <= sigNum:
    state = states[i]
    i += 1
    dataNN['state_'+str(state)] = np.zeros(1) # this is the state value set-up
    dataNN['wd_' + str(state)] = np.zeros(1) # this is the weight direction
    dataNN['wu_' + str(state)] = np.zeros(1) # this is the weight magnitude

Answer 2

import random
import pandas as pd
def iteration1(data, classes = 5, subNum = 15):
    dataNN = pd.DataFrame(index = [0])
    dataNN['car_speed'] = np.zeros(1)
    states = random.sample(range(50), sub_sig)
    for i in range(0, sub_sig, 1):
        dataNN['state_'+str(states[i])] = np.zeros(1) # this is the state value set-up
    for i in range(0, subNum, 1):
        dataNN['wd_' + str(states[i])] = np.zeros(1) # this is the weight direction
    for i in range(0, subNum, 1):
        dataNN['wu_' + str(states[i])] = np.zeros(1) # this is the weight magnitude
    return dataNN