pandas read_csv 删除空白行
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pandas read_csv remove blank rows
提问by Karvy1
I am reading in a CSV file as a DataFramewhile 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?
我正在读取 CSV 文件，DataFrame同时定义每列的数据类型。如果 CSV 文件中有空行，则此代码会出错。如何在没有空白行的情况下读取 CSV？
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)
I thought of one workaround of doing something like this but not sure if this is the efficient way:
我想到了一种解决方法来做这样的事情，但不确定这是否是有效的方法：
df=pd.read_csv('demand.csv')
df=df.dropna()
and then redefining the column data types in the df.
然后重新定义df.
Edit : Code -
编辑：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
Error - ValueError: Integer column has NA values in column 2
错误 - ValueError: Integer column has NA values in column 2
My CSV file's snapshot - 
我的 CSV 文件的快照 -
回答by fuwiak
try smth like this:
像这样尝试：
data = pd.read_table(filenames,skip_blank_lines=True, a_filter=True)
回答by NadavWeisler
This worked for me.
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)
回答by Shivaansh Agarwal
try.csv
尝试.csv
s,v,h,h
1,2,3,4
4,5,6,7
9,10,1,2
Python Code
Python代码
df = pd.read_csv('try.csv', delimiter=',')
print(df)
Output
输出
s v h h.1
0 1 2 3 4
1 4 5 6 7
2 9 10 1 2
