如何在Python中过滤和清理多个Dask框架？

Question

将多个 .csv 文件作为 Dask 数据帧进行读取/附加，我试图通过排除不必要的内容来清理框架行。 但这会引发数据类型不匹配的错误，尽管下面的代码能够正确识别数据类型。 它既无法显示前 5 行 [dfb.head()]，也无法通过 dfb = dfb.compute() 转换为 pandas 数据帧。

#########Reading and appending multiple .csv files###############
import pandas as pd[enter image description here][1]
import numpy as np
import glob
import os
import warnings
import seaborn as sns
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
from dask import dataframe as dd

path = 'C:\\Nitin Folder\\PYTHON\\Py4\\1800\\AS32\\300\\Input'
files = glob.glob(os.path.join(path +"/*.csv"))

data = [] 
for csv in files:
frame = dd.read_csv(csv)
frame['filename'] = os.path.basename(csv)
data.append(frame)

dfb = dd.concat(data, ignore_index=True)
dfb = dfb.repartition(npartitions=1) 
dfb.dtypes

output:
id                 int64
Timestamp         object
student_name      object
country           object
Distance(mts)      int64
cellpower        float64
filename          object
dtype: object

############# filtering daskframe to remove non integer rows ###################
dfb = dfb[~(dfb.id == 'id')]
dfb.head()

dfb['Distance(mts)'] = dfb['Distance(mts)'].astype(int)
dfb['cellpower'] = dfb['cellpower'].astype(int)
dfb['id'] = dfb['id'].astype(int)

据我了解，此过滤器并未应用于整个 Daskframe。我什至尝试手动转换数据类型，但仍然存在相同的错误。 寻求支持以获得解决方案。:)

output error message:
ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.

+---------------+--------+----------+
| Column        | Found  | Expected |
+---------------+--------+----------+
| Distance(mts) | object | int64    |
| cellpower     | object | float64  |
| id            | object | int64    |
+---------------+--------+----------+

The following columns also raised exceptions on conversion:

- Distance(mts)
  ValueError("invalid literal for int() with base 10: 'Distance(mts)'")
- cellpower
  ValueError("could not convert string to float: 'cellpower'")
- id
  ValueError("invalid literal for int() with base 10: 'id'")

Usually this is due to dask's dtype inference failing, and
*may* be fixed by specifying dtypes manually by adding:

dtype={'Distance(mts)': 'object',
   'cellpower': 'object',
   'id': 'object'}

to the call to `read_csv`/`read_table`.