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)
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()
and then redefining the column data types in the df
.
Edit : Code -
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
This worked for me.
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)
像这样尝试:
data = pd.read_table(filenames,skip_blank_lines=True, a_filter=True)
解决方案可能是:
data = pd.read_table(filenames,skip_blank_lines=True, na_filter=True)
try.csv
s,v,h,h
1,2,3,4
4,5,6,7
9,10,1,2
Python Code
df = pd.read_csv('try.csv', delimiter=',')
print(df)
Output
s v h h.1
0 1 2 3 4
1 4 5 6 7
2 9 10 1 2
I am not sure whether its efficient or not but it works. This code does not load nan values while reading a csv.
df=pd.read_csv('demand.csv').dropna()
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