动态为多个数据集创建数组

Question

This is a quality of life query that I feel like there is an answer to, but can't find (maybe I'm using the wrong terms)

Essentially, I have multiple sets of large data files that I would like to perform analysis on. This involves reading each of these datafiles and storing them as an array (of variable length).

So far I have been doing

import numpy as np

input1 = np.genfromtxt('data1.dat')
input2 = np.genfromtxt('data2.dat')

etc. I was wondering if there is a method of dynamically assigning an array to each of these datasets. Since you can read these dynamically with a for loop,

for i in xrange(2):

input = np.genfromtxt('data%i.dat'%i)

I was hoping to combine the above to create a bunch of arrays; input1, input2, etc. without myself typing out genfromtxt multiple times. Surely there is a method if I had 100 datasets (aptly named data0, data1, etc) to import.

A solution I can think of is maybe creating a function,

import numpy as np

def input(a):
    return np.genfromtxt('data%i.dat'%a)

But obviously, I would prefer to store this in memory instead of constantly regenerate a list, and would be extremely grateful to know if this is possible in Python.

Answer 1

You can choose to store your arrays in either a dict or a list :

Option 1

Using a dict .

data = {}
for i in xrange(2):
    data['input{}'.format(i)] = np.genfromtxt('data{}.dat'.format(i))

You can access each array by key.

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Option 2

Using a list .

data = []
for i in xrange(2):
    data.append(np.genfromtxt('data{}.dat'.format(i)))

Alternatively, using a list comprehension:

data = [np.genfromtxt('data{}.dat'.format(i)) for i in xrange(2)]

You can also use a map , it returns a list:

data = map(lambda x: np.genfromtxt('data{}.dat'.format(x)), xrange(2))

Now you can access each array by index.