How do I read CSV data into a record array in NumPy?

Question

I wonder if there is a direct way to import the contents of a CSV file into a record array, much in the way that R\'s read.table(), read.delim(), a

Answer 1

Using numpy.loadtxt

A quite simple method. But it requires all the elements being float (int and so on)

import numpy as np 
data = np.loadtxt('c:\\1.csv',delimiter=',',skiprows=0)  

Answer 2

I tried this:

import pandas as p
import numpy as n

closingValue = p.read_csv("<FILENAME>", usecols=[4], dtype=float)
print(closingValue)

Answer 3

You can use Numpy's genfromtxt() method to do so, by setting the delimiter kwarg to a comma.

from numpy import genfromtxt
my_data = genfromtxt('my_file.csv', delimiter=',')

More information on the function can be found at its respective documentation.

Answer 4

I would recommend the read_csv function from the pandas library:

import pandas as pd
df=pd.read_csv('myfile.csv', sep=',',header=None)
df.values
array([[ 1. ,  2. ,  3. ],
       [ 4. ,  5.5,  6. ]])

This gives a pandas DataFrame - allowing many useful data manipulation functions which are not directly available with numpy record arrays.

DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table...

---

I would also recommend genfromtxt. However, since the question asks for a record array, as opposed to a normal array, the dtype=None parameter needs to be added to the genfromtxt call:

Given an input file, myfile.csv:

1.0, 2, 3
4, 5.5, 6

import numpy as np
np.genfromtxt('myfile.csv',delimiter=',')

gives an array:

array([[ 1. ,  2. ,  3. ],
       [ 4. ,  5.5,  6. ]])

and

np.genfromtxt('myfile.csv',delimiter=',',dtype=None)

gives a record array:

array([(1.0, 2.0, 3), (4.0, 5.5, 6)], 
      dtype=[('f0', '<f8'), ('f1', '<f8'), ('f2', '<i4')])

This has the advantage that file with multiple data types (including strings) can be easily imported.

Answer 5

You can also try recfromcsv() which can guess data types and return a properly formatted record array.

Answer 6

As I tried both ways using NumPy and Pandas, using pandas has a lot of advantages:

• Faster
• Less CPU usage
• 1/3 RAM usage compared to NumPy genfromtxt

This is my test code:

$ for f in test_pandas.py test_numpy_csv.py ; do  /usr/bin/time python $f; done
2.94user 0.41system 0:03.05elapsed 109%CPU (0avgtext+0avgdata 502068maxresident)k
0inputs+24outputs (0major+107147minor)pagefaults 0swaps

23.29user 0.72system 0:23.72elapsed 101%CPU (0avgtext+0avgdata 1680888maxresident)k
0inputs+0outputs (0major+416145minor)pagefaults 0swaps

test_numpy_csv.py

from numpy import genfromtxt
train = genfromtxt('/home/hvn/me/notebook/train.csv', delimiter=',')

test_pandas.py

from pandas import read_csv
df = read_csv('/home/hvn/me/notebook/train.csv')

Data file:

du -h ~/me/notebook/train.csv
 59M    /home/hvn/me/notebook/train.csv

With NumPy and pandas at versions:

$ pip freeze | egrep -i 'pandas|numpy'
numpy==1.13.3
pandas==0.20.2