pandas read_csv删除空白行 [英] pandas read_csv remove blank rows

问题描述

在定义每一列的数据类型时，我正在读取CSV文件作为DataFrame.如果CSV文件中包含空白行，则此代码会产生错误.如何读取没有空白行的CSV?

I am reading in a CSV file as a DataFrame while defining each column's data type. This code gives an error if the CSV file has a blank row in it. How do I read the CSV without blank rows?

dtype = {'material_id': object, 'location_id' : object, 'time_period_id' : int, 'demand' : int, 'sales_branch' : object, 'demand_type' : object }

df = pd.read_csv('./demand.csv', dtype = dtype)

我想到了一种解决方法，但是不确定这种方法是否有效:

I thought of one workaround of doing something like this but not sure if this is the efficient way:

df=pd.read_csv('demand.csv')
df=df.dropna()

，然后在df中重新定义列数据类型.

and then redefining the column data types in the df.

代码-

import pandas as pd
dtype1 = {'material_id': object, 'location_id' : object, 'time_period_id' : int, 'demand' : int, 'sales_branch' : object, 'demand_type' : object }
df = pd.read_csv('./demand.csv', dtype = dtype1)
df

错误-ValueError: Integer column has NA values in column 2

我的CSV文件的快照-

My CSV file's snapshot -

推荐答案

这对我有用.

def delete_empty_rows(file_path, new_file_path):
    data = pd.read_csv(file_path, skip_blank_lines=True)
    data.dropna(how="all", inplace=True)
    data.to_csv(new_file_path, header=True)

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