Numpy：从CSV中读取数字，数字为字符串
[英]Numpy : read data from CSV having numerals as string
I'm reading a .csv file in python using command as:
我正在使用命令在python中读取.csv文件:
data = np.genfromtxt('home_data.csv', dtype=float, delimiter=',', names=True)
this csv has one column with zipcode which are numerals but in string format, for eg "85281". This column has values as nan:
这个csv有一个带有zipcode的列,它是数字但是字符串格式,例如“85281”。此列的值为nan:
data['zipcode']
Output : array([ nan, nan, nan, ..., nan, nan, nan])
How can I convert these values in string to integers so as to get an array of values and not of 'nan's.
如何将字符串中的这些值转换为整数,以便得到一个值数组而不是'nan'。
2 个解决方案
#1
1
you must help genfromtxt a little :
你必须帮助genfromtxt一点:
data = np.genfromtxt('home_data.csv',
dtype=[int,float],delimiter=',',names=True,
converters={0: lambda b:(b.decode().strip('"'))})
each field is collected as bytes. float(b'1\n') return 1.0 , but float(b'"8210"') give an error. the converters option allow to define for each field (here field 0) a function to do the proper conversion, here converting in string(decode) and removing (strip) the trailing ".
每个字段都以字节形式收集。 float(b'1 \ n')返回1.0,但float(b'“8210”')给出错误。转换器选项允许为每个字段(此处为字段0)定义一个执行正确转换的函数,此处转换为字符串(解码)和删除(剥离)尾随“。
If home_data.csv is :
如果home_data.csv是:
zipcode,val
"8210",1
"8320",2
"14",3
you will obtain :
你会得到:
data -> array([(8210, 1.0), (8320, 2.0), (14, 3.0)], dtype=[('zipcode', '<i4'), ('val', '<f8')])
data['zipcode'] -> array([8210, 8320, 14])
#2
1
Maybe not the most efficient solution, but read your data as string and convert it afterwards to float:
也许不是最有效的解决方案,但是将您的数据读取为字符串并将其转换为float:
data = np.genfromtxt('home_data.csv', dtype=float, delimiter=',', names=True)
zipcode = data['zipcode'].astype(np.float)
Btw., is there a reason you want to save a zipcode as a float?
顺便说一下,你有没有理由想把一个邮政编码保存为浮动?
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