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我有很多 .csv 来自nyc.gov的纽约出租车，一个 .csv = 年月。在那里我抓取了 cca 15 的 csv 并从中制作 HDF5：

import h5py
import pandas as pd
import os 
import glob
import numpy as np

import vaex
from tqdm import  tqdm_notebook as tqdm

#hdf = pd.HDFStore('c:/Projekty/H5Edu/NYCTaxi/NYCTaxi.hp')
#df1 = pd.read_csv('path nejake csvcko')
#hdf.put('DF1', df1, format = 'table', data_columns = True)


csv_list = np.sort(np.array(glob.glob('G:\\NYCTaxi\\*.csv')))[::-1]

csv_list = csv_list[20:39]

output_dir = 'c:\\Datasety\\YelowTaxi\\DataH5\\'

for file in tqdm(csv_list, leave=False, desc='Converting to hdf5...'):
    # Setting up the files, and directories
    #zip_file = ZipFile(file)
    output_file = file.split('\\')[-1][:-3]+'hdf5'
    output = output_dir + output_file
    #output = output_file
    
    # Check if a converted file already exists: if it does skip it, otherwise read in the raw csv and convert it
    if (os.path.exists(output) and os.path.isfile(output)):
        pass
    else:
        # Importing the data into pandas 
        #pandas_df = [pd.read_csv(file, index_col=None, header=0)][0]
        pandas_df = [pd.read_csv(file, index_col=None, header=0, low_memory=False)][0]
        # Rename some columns to match the more well known dataset from 
        # http://stat-computing.org/dataexpo/2009/the-data.html
        

        # Importing the data from pandas to vaex
        vaex_df = vaex.from_pandas(pandas_df, copy_index=False)
        
        # Export the data with vaex to hdf5
        vaex_df.export_hdf5(path=output, progress=False)

接下来我制作一个大的 HDF5：

import re
import glob
import vaex
import numpy as np

def tryint(s):
    try:
        return int(s)
    except:
        return s

def alphanum_key(s):
    """ Turn a string into a list of string and number chunks.
        "z23a" -> ["z", 23, "a"]
    """
    return [ tryint(c) for c in re.split('([0-9]+)', s) ]

hdf5_list = glob.glob('c:\\Datasety\\YelowTaxi\\DataH5\\*.hdf5')
hdf5_list.sort(key=alphanum_key)
hdf5_list = np.array(hdf5_list)

#assert len(hdf5_list) == 3, "Incorrect number of files"

# This is an important step
master_df = vaex.open_many(hdf5_list)

# exporting
#master_df.export_hdf5(path='c:\\Datasety\\YelowTaxi\\DataH5\\Spojene.hd5', progress=True)
master_df.export_hdf5(path='c:\\Datasety\\YelowTaxi\\DataH5\\Spojene.hdf5', progress=True)

到目前为止，一切正常，我可以打开输出文件 Spojene.hdf5。

接下来，我将新的 .csv 附加到 Spojene.hdf5：

for file in csv_list:
#file = csv_list[0]
    df2 = pd.read_csv(file, index_col=None, header=0, low_memory=False)
    filename = 'c:\\Datasety\\YelowTaxi\\DataH5\\Spojene.hdf5'
    df2.to_hdf(filename, 'data', append=True)

但是，当我将新的 .csv 附加到 Spojene.hdf5 时，我无法打开它：

df = vaex.open('c:\\Datasety\\YelowTaxi\\DataH5\\Spojene.hdf5')

ValueError：第一列的长度为 289184484，而列表的长度为 60107988

错误信息

请问，我能做什么？

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1 回答 1

2

我认为这与熊猫如何创建 hdf5 文件有关。根据 vaex 的文档to_hdf，如果它是通过pandas 方法创建的，则无法使用 vaex 打开 HDF5 文件。如果您附加到现有的 HDF5 文件，我认为它是相同的。

为避免此错误，您可以重用将 pandas 数据帧转换为 vaex 数据帧的逻辑，将其导出到 HDF5，然后使用open_many. 像这样的东西应该工作：

main_hdf5_file_path = "c:\\Datasety\\YelowTaxi\\DataH5\\Spojene.hdf5"

hdf5_files_created = []
for file in csv_list:
   hdf5_file = file.replace(".csv", ".hdf5")
   # from_csv can take additional parameters to forward to pd.read_csv
   # You can also use convert=True to convert it automatically to hdf5 without the export_hdf5
   # Refer to https://vaex.readthedocs.io/en/docs/api.html#vaex.from_csv
   df = vaex.from_csv(file) 
   df.export_hdf5(hdf5_file)
   hdf5_files_created.append(hdf5_file)

hdf5_to_read = hdf5_files_created + [main_hdf5_file_path]

final_df = vaex.open_many(hdf5_to_read)
final_df.export_hdf5(main_hdf5_file_path)
于 2021-04-12T15:16:57.097 回答