我是Python的新手，我最终得到了两个列表列表，每个列表都包含一个整数（在下面的y中浮动），如下所示：
>x
array([[11], [101], [1001], [10001], [100001]], dtype=object)
>y
array([[0.0], [0.0009751319885253906], [0.03459000587463379],
[3.7970290184020996], [498.934268951416]], dtype=object)
我想做的就是绘制x vs y，但这显然不会起作用，可能有很多原因，但至少因为每个“值”都在方括号中（即列表本身）。我怎么能阻止这些值（例如11,101,1001,10001）成为列表？
从Fortran背景来看，我在Python的列表，元组，数组，numpy数组等方面都在苦苦挣扎。我想做的是从一个文本文件中读取内容（例如）：
11 0.0
101 0.0009751319885253906
1001 0.03459000587463379
10001 3.7970290184020996
100001 498.934268951416
并将第一个'列'读为x，将第二个'列'读为y，目的是绘制此数据。
任何人都可以推荐一个在线课程，澄清列表，元组，数组等的用途吗？
非常感谢提前。
编辑：为了回应人们的意见和建议，我在运行结束时包括我使用的代码，输入文件内容和交互式窗口输出。
非常感谢所有回应我的人;我发现所有意见和建议都非常有帮助。我会对所有这些反应采取行动并尝试为自己解决问题，但如果有人能看一下我的代码，'输入文件'内容和交互式窗口'输出'，看看他们是否可以帮助我，我将不胜感激进一步。我再次非常感谢人们为此与我沟通的时间和精力。
以下是代码：
import re
import numpy as np
import time
import pandas as pd
def dict2mat(res1, res2):
#
# input 2 dictionaries and return the content of the first as x
# and the content of the second as y
#
s = pd.Series(res1)
x = s.values
s = pd.Series(res2)
y = s.values
return x, y
f = open('results.txt', 'r')
nnp = {}
tgen = {}
tconn = {}
tcalc = {}
tfill = {}
iline = 0
for i in range(1000):
line = f.readline()
if "Example" in line:
#
# first line of text having numerical values of interest contains
# the string 'Example'
#
iline = iline+1
#
# extract number of nodes (integer)
#
nnp[iline] = [int(s) for s in re.findall(r"\d+", line)]
line = f.readline()
#
# extract time taken to generate data set (float)
#
tgen[iline] = [float(s) for s in re.findall(r"\d+[\.]\d+", line)]
line = f.readline()
#
# extract time taken to generate connectivity data (float)
#
tconn[iline] = [float(s) for s in re.findall(r"\d+[\.]\d+", line)]
line = f.readline()
#
# extract time taken to calculate error (float) for corners
#
tcalc[iline] = [float(s) for s in re.findall(r"\d+[\.]\d+", line)]
line = f.readline()
#
# extract time taken to fill in stress results at midsides (float)
#
tfill[iline] = [float(s) for s in re.findall(r"\d+[\.]\d+", line)]
#
# use function dict2mat to replace the contents of 'number of nodes'
# and each of the 'times' in turn by vectors x and y
#
xgen, ygen = dict2mat(nnp, tgen)
xconn, yconn = dict2mat(nnp, tconn)
xcalc, ycalc = dict2mat(nnp, tcalc)
xfill, yfill = dict2mat(nnp, tfill)
# get x and y vectors
x = np.array(xgen)
y = np.array(ygen)
print('x: ')
print(x)
print('y: ')
print(y)
以下是代码的文件内容：
Random seed used to form data = 9001
Example has 11 generated global surface nodes
Time taken to generate the data: --- 0.002001047134399414 seconds ---
Time taken to find connectivity: --- 0.0 seconds ---
Time taken to calculate Stress Error for corner nodes only: --- 0.0004999637603759766 seconds ---
Time taken to fill-in midside node Stress Errors: --- 0.0 seconds ---
Random seed used to form data = 9001
Example has 101 generated global surface nodes
Time taken to generate the data: --- 0.01451420783996582 seconds ---
Time taken to find connectivity: --- 0.0 seconds ---
Time taken to calculate Stress Error for corner nodes only: --- 0.004984855651855469 seconds ---
Time taken to fill-in midside node Stress Errors: --- 0.0009751319885253906 seconds ---
Random seed used to form data = 9001
Example has 1001 generated global surface nodes
Time taken to generate the data: --- 0.10301804542541504 seconds ---
Time taken to find connectivity: --- 0.0 seconds ---
Time taken to calculate Stress Error for corner nodes only: --- 0.04008197784423828 seconds ---
Time taken to fill-in midside node Stress Errors: --- 0.03459000587463379 seconds ---
Random seed used to form data = 9001
Example has 10001 generated global surface nodes
Time taken to generate the data: --- 1.0397570133209229 seconds ---
Time taken to find connectivity: --- 0.0 seconds ---
Time taken to calculate Stress Error for corner nodes only: --- 0.41377687454223633 seconds ---
Time taken to fill-in midside node Stress Errors: --- 3.7970290184020996 seconds ---
Random seed used to form data = 9001
Example has 100001 generated global surface nodes
Time taken to generate the data: --- 10.153867959976196 seconds ---
Time taken to find connectivity: --- 0.0 seconds ---
Time taken to calculate Stress Error for corner nodes only: --- 3.938124895095825 seconds ---
Time taken to fill-in midside node Stress Errors: --- 498.934268951416 seconds ---
最后，这是执行后出现在交互式窗口中的内容：
x:
>>> print(x)
[[11] [101] [1001] [10001] [100001]]
>>> print('y: ')
y:
>>> print(y)
[[0.002001047134399414] [0.01451420783996582] [0.10301804542541504]
[1.0397570133209229] [10.153867959976196]]
>>>
我希望这一切都有所帮助，我提前感谢任何人提供的任何帮助。
西蒙。
答案 0 :(得分：1)
如果没有进入从文件中读取的代码，您首先要使用元组列表设置程序。
#Example empty list
points = []
#x,y = (1, 2) assigns 1 to x and 2 to y
x,y = (1, 2)
#this appends the tuple (x, y) into the points list
points.append((x, y))
如果你有一个想要拉出坐标的文件，可以试试这样的代码：
#Example empty list
points = []
filename = "myfile.txt"
file_with_points = open(filename, "r")
for line in file_with_points.readlines():
#assume the points are separated by a space
splitline = line.split(" ")
x, y = splitline[0], splitline[1]
points.append((x, y))
file_with_points.close()
print points
希望此解决方案可以帮助您处理列表。如果您需要有关基本python的更多信息，请查看https://www.codecademy.com/learn/python