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

Is there a direct way to import the contents of a CSV file into a record array, just like how R's read.table(), read.delim(), and read.csv() import data into R dataframes?

Or should I use csv.reader() and then apply numpy.core.records.fromrecords()?

13 Answers

Use numpy.genfromtxt() by setting the delimiter kwarg to a comma:

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

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.

I tried it :

from numpy import genfromtxt genfromtxt(fname = dest_file, dtype = (<whatever options>)) 

versus :

import csv import numpy as np with open(dest_file,'r') as dest_f: data_iter = csv.reader(dest_f, delimiter = delimiter, quotechar = '"') data = [data for data in data_iter] data_array = np.asarray(data, dtype = <whatever options>) 

on 4.6 million rows with about 70 columns and found that the NumPy path took 2 min 16 secs and the csv-list comprehension method took 13 seconds.

I would recommend the csv-list comprehension method as it is most likely relies on pre-compiled libraries and not the interpreter as much as NumPy. I suspect the pandas method would have similar interpreter overhead.

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

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 

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) 

You can use this code to send CSV file data into an array:

import numpy as np csv = np.genfromtxt('test.csv', delimiter=",") print(csv) 

I would suggest using tables (pip3 install tables). You can save your .csv file to .h5 using pandas (pip3 install pandas),

import pandas as pd data = pd.read_csv("dataset.csv") store = pd.HDFStore('dataset.h5') store['mydata'] = data store.close() 

You can then easily, and with less time even for huge amount of data, load your data in a NumPy array.

import pandas as pd store = pd.HDFStore('dataset.h5') data = store['mydata'] store.close() # Data in NumPy format data = data.values 

This work as a charm...

import csv with open("data.csv", 'r') as f: data = list(csv.reader(f, delimiter=";")) import numpy as np data = np.array(data, dtype=np.float) 

This is the easiest way:

import csv with open('testfile.csv', newline='') as csvfile: data = list(csv.reader(csvfile)) 

Now each entry in data is a record, represented as an array. So you have a 2D array. It saved me so much time.

Available on the newest pandas and numpy version.

import pandas as pd import numpy as np data = pd.read_csv('data.csv', header=None) # Discover, visualize, and preprocess data using pandas if needed. data = data.to_numpy() 

I tried this:

import pandas as p import numpy as n closingValue = p.read_csv("<FILENAME>", usecols=[4], dtype=float) print(closingValue) 

In [329]: %time my_data = genfromtxt('one.csv', delimiter=',') CPU times: user 19.8 s, sys: 4.58 s, total: 24.4 s Wall time: 24.4 s In [330]: %time df = pd.read_csv("one.csv", skiprows=20) CPU times: user 1.06 s, sys: 312 ms, total: 1.38 s Wall time: 1.38 s