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

Question

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()?

Answer 1

this is a very simple task, the best way to do this is as follows

import pandas as pd
import numpy as np


df = pd.read_csv(r'C:\Users\Ron\Desktop\Clients.csv')   #read the file (put 'r' before the path string to address any special characters in the file such as \). Don't forget to put the file name at the end of the path + ".csv"

print(df)`

y = np.array(df)

Answer 2

Use pandas.read_csv:

import pandas as pd
df = pd.read_csv('myfile.csv', sep=',', header=None)
print(df.values)

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

This gives a pandas DataFrame which provides 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 numpy.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:

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

For the following 'myfile.csv':

1.0, 2, 3
4, 5.5, 6

the code above 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 files with multiple data types (including strings) can be easily imported.

Answer 3

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

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

Answer 4

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.

Answer 5

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()

Answer 6

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

Answer 7

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

Answer 8

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.

Answer 9

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)

Answer 10

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

Answer 11

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

Answer 12

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)

Answer 13

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 14

I tried this:

import pandas as p
import numpy as n

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