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()
, and read.csv()
family imports data to R's data frame?
Or is the best way to use csv.reader() and then apply something like numpy.core.records.fromrecords()
?
This question is related to
python
numpy
scipy
genfromtxt
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
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.
As I tried both ways using NumPy and Pandas, using pandas has a lot of advantages:
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
from numpy import genfromtxt
train = genfromtxt('/home/hvn/me/notebook/train.csv', delimiter=',')
from pandas import read_csv
df = read_csv('/home/hvn/me/notebook/train.csv')
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
You can also try recfromcsv()
which can guess data types and return a properly formatted record array.
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)
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
I timed the
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.
I tried this:
import pandas as p
import numpy as n
closingValue = p.read_csv("<FILENAME>", usecols=[4], dtype=float)
print(closingValue)
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 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.
Source: Stackoverflow.com