Dump a NumPy array into a csv file

tofile is a convenient function to do this:

import numpy as np
a = np.asarray([ [1,2,3], [4,5,6], [7,8,9] ])
a.tofile('foo.csv',sep=',',format='%10.5f')

The man page has some useful notes:

This is a convenience function for quick storage of array data.
Information on endianness and precision is lost, so this method is not
a good choice for files intended to archive data or transport data
between machines with different endianness. Some of these problems can
be overcome by outputting the data as text files, at the expense of
speed and file size.

Note. This function does not produce multi-line csv files, it saves everything to one line.

If you want to save your numpy array (e.g. your_array = np.array([[1,2],[3,4]])) to one cell, you could convert it first with your_array.tolist().

Then save it the normal way to one cell, with delimiter=';'
and the cell in the csv-file will look like this [[1, 2], [2, 4]]

Then you could restore your array like this:
your_array = np.array(ast.literal_eval(cell_string))

Writing record arrays as CSV files with headers requires a bit more work.

This example reads from a CSV file (example.csv) and writes its contents to another CSV file (out.csv).

import numpy as np

# Write an example CSV file with headers on first line
with open('example.csv', 'w') as fp:
    fp.write('''
col1,col2,col3
1,100.1,string1
2,222.2,second string
''')

# Read it as a Numpy record array
ar = np.recfromcsv('example.csv', encoding='ascii')
print(repr(ar))
# rec.array([(1, 100.1, 'string1'), (2, 222.2, 'second string')], 
#           dtype=[('col1', '<i8'), ('col2', '<f8'), ('col3', '<U13')])

# Write as a CSV file with headers on first line
with open('out.csv', 'w') as fp:
    fp.write(','.join(ar.dtype.names) + 'n')
    np.savetxt(fp, ar, '%s', ',')

Note that the above example cannot handle values which are strings with commas. To always enclose non-numeric values within quotes, use the csv built-in module:

import csv

with open('out2.csv', 'w', newline='') as fp:
    writer = csv.writer(fp, quoting=csv.QUOTE_NONNUMERIC)
    writer.writerow(ar.dtype.names)
    writer.writerows(ar.tolist())

You can use pandas. It does take some extra memory so it’s not always possible, but it’s very fast and easy to use.

import pandas as pd 
pd.DataFrame(np_array).to_csv("path/to/file.csv")

if you don’t want a header or index, use to_csv("/path/to/file.csv", header=None, index=None)

if you want to write in column:

    for x in np.nditer(a.T, order='C'): 
            file.write(str(x))
            file.write("n")

Here ‘a’ is the name of numpy array and ‘file’ is the variable to write in a file.

If you want to write in row:

    writer= csv.writer(file, delimiter=',')
    for x in np.nditer(a.T, order='C'): 
            row.append(str(x))
    writer.writerow(row)

You can also do it with pure python without using any modules.

# format as a block of csv text to do whatever you want
csv_rows = ["{},{}".format(i, j) for i, j in array]
csv_text = "n".join(csv_rows)

# write it to a file
with open('file.csv', 'w') as f:
    f.write(csv_text)

As already discussed, the best way to dump the array into a CSV file is by using .savetxt(...)method. However, there are certain things we should know to do it properly.

For example, if you have a numpy array with dtype = np.int32 as

   narr = np.array([[1,2],
                 [3,4],
                 [5,6]], dtype=np.int32)

and want to save using savetxt as

np.savetxt('values.csv', narr, delimiter=",")

It will store the data in floating point exponential format as

1.000000000000000000e+00,2.000000000000000000e+00
3.000000000000000000e+00,4.000000000000000000e+00
5.000000000000000000e+00,6.000000000000000000e+00

You will have to change the formatting by using a parameter called fmt as

np.savetxt('values.csv', narr, fmt="%d", delimiter=",")

to store data in its original format

Saving Data in Compressed gz format

Also, savetxt can be used for storing data in .gz compressed format which might be useful while transferring data over network.

We just need to change the extension of the file as .gz and numpy will take care of everything automatically

np.savetxt('values.gz', narr, fmt="%d", delimiter=",")

Hope it helps

In Python we use csv.writer() module to write data into csv files. This module is similar to the csv.reader() module.

import csv

person = [['SN', 'Person', 'DOB'],
['1', 'John', '18/1/1997'],
['2', 'Marie','19/2/1998'],
['3', 'Simon','20/3/1999'],
['4', 'Erik', '21/4/2000'],
['5', 'Ana', '22/5/2001']]

csv.register_dialect('myDialect',
delimiter = '|',
quoting=csv.QUOTE_NONE,
skipinitialspace=True)

with open('dob.csv', 'w') as f:
    writer = csv.writer(f, dialect='myDialect')
    for row in person:
       writer.writerow(row)

f.close()

A delimiter is a string used to separate fields. The default value is comma(,).

I believe you can also accomplish this quite simply as follows:

1. Convert Numpy array into a Pandas dataframe
2. Save as CSV

e.g. #1:

    # Libraries to import
    import pandas as pd
    import nump as np

    #N x N numpy array (dimensions dont matter)
    corr_mat    #your numpy array
    my_df = pd.DataFrame(corr_mat)  #converting it to a pandas dataframe

e.g. #2:

    #save as csv 
    my_df.to_csv('foo.csv', index=False)   # "foo" is the name you want to give
                                           # to csv file. Make sure to add ".csv"
                                           # after whatever name like in the code

To store a NumPy array to a text file, import savetxt from the NumPy module

consider your Numpy array name is train_df:

import numpy as np
np.savetxt('train_df.txt', train_df, fmt='%s')

OR

from numpy import savetxt
savetxt('train_df.txt', train_df, fmt='%s')

numpy.savetxt() method is used to save a NumPy array into an output text file, however by default it will make use of scientific notation.

If you’d like to avoid this, then you need to specify an appropriate format using fmt argument. For example,

import numpy as np

np.savetxt('output.csv', arr, delimiter=',', fmt='%f')

As other answers mentioned, it’s important to pass the fmt= in order to save a "human-readable" file. In fact, if you pass a separate format for each column, you don’t need to pass a delimiter.

arr = np.arange(9).reshape(3, 3)
np.savetxt('out.csv', arr, fmt='%f,%.2f,%.1f')

It saves a file whose contents look like:

0.000000,1.00,2.0
3.000000,4.00,5.0
6.000000,7.00,8.0

Now to read the file from csv, use np.loadtxt():

np.loadtxt('out.csv', delimiter=',')

If you want to append to an existing file (as well as create a new file), use a context manager and open a file with mode='ab'.

with open('out.csv', 'ab') as f:
    np.savetxt(f, arr, delimiter=',', fmt='%.1f')