小编sup*_*ask的帖子

我的数据文件如下所示：

data.txt
user,activity,timestamp,x-axis,y-axis,z-axis
0,33,Jogging,49105962326000,-0.6946376999999999,12.680544,0.50395286;
1,33,Jogging,49106062271000,5.012288,11.264028,0.95342433;
2,33,Jogging,49106112167000,4.903325,10.882658000000001,-0.08172209;
3,33,Jogging,49106222305000,-0.61291564,18.496431,3.0237172;

可以看出，最后一列以分号结尾，所以当我读入熊猫时，该列被推断为类型对象（以分号结尾。

df = pd.read_csv('data.txt')
df
    user    activity    timestamp   x-axis  y-axis  z-axis
0   33  Jogging     49105962326000  -0.694638   12.680544   0.50395286;
1   33  Jogging     49106062271000  5.012288    11.264028   0.95342433;
2   33  Jogging     49106112167000  4.903325    10.882658   -0.08172209;
3   33  Jogging     49106222305000  -0.612916   18.496431   3.0237172;

我如何让熊猫忽略那个分号？

我正在努力将我的数据帧转换成一组固定大小的片段，我应该将这些片段提供给卷积神经网络。具体来说，我想将每个包含段 sizeddf的m数组列表转换为(1,5,4)。所以最后，我会有一个(m,1,5,4)数组。

为了澄清我的问题，我使用 this 进行解释MWE。假设这是我的df：

df = {
    'id': [1,1,1,1,1,1,1,1,1,1,1,1],
'speed': [17.63,17.63,0.17,1.41,0.61,0.32,0.18,0.43,0.30,0.46,0.75,0.37],
'acc': [0.00,-0.09,1.24,-0.80,-0.29,-0.14,0.25,-0.13,0.16,0.29,-0.38,0.27],
'jerk': [0.00,0.01,-2.04,0.51,0.15,0.39,-0.38,0.29,0.13,-0.67,0.65,0.52],
'bearing': [29.03,56.12,18.49,11.85,36.75,27.52,81.08,51.06,19.85,10.76,14.51,24.27],
'label' : [3,3,3,3,3,3,3,3,3,3,3,3] }

df = pd.DataFrame.from_dict(df)

为此，我使用此功能：

def df_transformer(dataframe, chunk_size=5):
    
    grouped = dataframe.groupby('id')

    # initialize accumulators
    X, y = np.zeros([0, 1, chunk_size, 4]), np.zeros([0,])

    # loop over segments (id)
    for _, group in grouped:

        inputs = group.loc[:, 'speed':'bearing'].values
        label = group.loc[:, 'label'].values[0]

        # calculate number of splits
        N = …