numpy reading a csv file to an numpy array

Question

I am new to python and using numpy to read a csv into an array .So I used two methods:

Approach 1

train = np.asarray(np.genfromtxt(open("/Users/mac/train.csv","rb"),delimiter=","))

Approach 2

with open('/Users/mac/train.csv') as csvfile:
        rows = csv.reader(csvfile)
        for row in rows:
            newrow = np.array(row).astype(np.int)
            train.append(newrow)

I am not sure what is the difference between these two approaches? What is recommended to use?

I am not concerned which is faster since my data size is small but instead concerned more about differences in the resulting data type.

Answer 1

You can use pandas also, it is better and simple to use.

import pandas as pd
import numpy as np

dataset = pd.read_csv('file.csv')
# get all headers in csv
values = list(dataset.columns.values)

# get the labels, assuming last row is labels in csv
y = dataset[values[-1:]]
y = np.array(y, dtype='float32')
X = dataset[values[0:-1]]
X = np.array(X, dtype='float32')

Answer 2

So what is the difference in the result?

genfromtxt is the numpy csv reader. It returns an array. No need for an extra asarray .

The second expression is incomplete, looks like would produce a list of arrays, one for each line of the file. It uses the generic python csv reader which doesn't do much other than read a line and split it into strings.