python-使用numpy loadtxt读取每列具有不同数据类型的csv文件

Question

I created a csv file with two columns, the first column is time data, and the second one is some measured data values.

2015/1/1 0:00   5       
2015/1/1 0:15   10    
2015/1/1 0:30   10   
2015/1/1 0:45   15   
2015/1/1 1:00   5  
2015/1/1 1:15   20  
2015/1/1 1:30   20  
2015/1/1 1:45   40  
2015/1/1 2:00   30  
2015/1/1 2:15   20  
2015/1/1 2:30   25  
2015/1/1 2:45   10  
2015/1/1 3:00   
2015/1/1 3:15   
2015/1/1 3:30   
2015/1/1 3:45   
2015/1/1 4:00   
2015/1/1 4:15   
2015/1/1 4:30   30  
2015/1/1 4:45   50  
2015/1/1 5:00   70  

Now I want to use numpy.loadtxt function to read this two columns into two different numpy arrays with string data type for the date column and integer data type for the value column.

I tried different statements to do that, but none of them works.

time, data = np.loadtxt('TS.csv',dtype=str,delimiter=',',usecols=(0, 1),unpack=True)
time, data = np.loadtxt('TS.csv',dtype=(str,int),delimiter=',',usecols=(0, 1),unpack=True)
time, data = np.loadtxt('TS.csv',dtype=[str,int],delimiter=',',usecols=(0, 1),unpack=True)

Does anyone know how to realize the goal I just described? Thanks for your help!

Answer 1

You are very close to what you are looking for. Try this

data = np.loadtxt('TS.csv', dtype='str,int', delimiter=',', usecols=(0, 1), unpack=True)

Answer 2

I would generally suggest np.genfromtxt if you have something that np.loadtxt can't handle, but they both struggle with space delimited files if there is missing data. It'd be hard to define how many missing data points there are without a comma separator for instance.

A similar function that may work is pd.read_csv or pd.read_table (same thing mostly), which does take care of this issue. Just make sure to set the parameter delim_whitespace to True with this file formatting.

pd.read_table('TS.csv', delim_whitespace=True, header=None)