从for循环输出连接一个大数据

Question

我正试图从切片结果中为每个for循环输出构建一个大数据集。

我所制定的代码如下：

for n in range(4): 
    script_dir = os.path.dirname(directory)
    rel_path = files[n]
    abs_file_path = os.path.join(script_dir, rel_path)
    to_open = pd.read_csv(abs_file_path, header=0)
    to_open["Geographic Address"] = to_open["Geographic Address"].astype(str)
    to_open["Geographic Address"] = to_open["Geographic Address"].map(lambda x: x[3:-1])
    to_open = to_open[to_open["Geographic Address"] == ld_up[n]]
    to_open.index = range(len(to_open))
    ind = np.searchsorted(to_open["Time"], time[n])
    ind = np.asscalar(np.array(ind))
    UpperBound = ind - 30
    data = to_open.iloc[UpperBound:ind,:]

因此，从data列中可以看到，如果我切片输出，只显示来自case 3的输出，我希望有一个大文件，同时包含案例0、1、2、3。

Answer 1

看起来您正在尝试堆叠这些不同的情况，在这种情况下，您应该做的是将它们附加到列表中，然后将列表连接起来。

df_list = []
for n in range(4): 
    script_dir = os.path.dirname(directory)
    rel_path = files[n]
    abs_file_path = os.path.join(script_dir, rel_path)
    to_open = pd.read_csv(abs_file_path, header=0)
    to_open["Geographic Address"] = to_open["Geographic Address"].astype(str)
    to_open["Geographic Address"] = to_open["Geographic Address"].map(lambda x: x[3:-1])
    to_open = to_open[to_open["Geographic Address"] == ld_up[n]]
    to_open.index = range(len(to_open))
    ind = np.searchsorted(to_open["Time"], time[n])
    ind = np.asscalar(np.array(ind))
    UpperBound = ind - 30
    data = to_open.iloc[UpperBound:ind,:]
    df_list.append(data)

df = pd.concat(df_list)