小编Edu*_*ira的帖子

我正在尝试使用 pandas.read_csv 导入 csv 文件。文件内容如下：

    "COL_A","COL_B","COL_C"
    "ROW1COLA","ROW1COLB","ROW1COLC","ROW1COLD"
    "ROW2COLA","ROW2COLB","ROW2COLC","ROW2COLD"
    "ROW3COLA","ROW3COLB","ROW3COLC","ROW3COLD"
    "ROW4COLA","ROW4COLB","ROW4COLC","ROW4COLD"
    "ROW5COLA","ROW5COLB","ROW5COLC","ROW5COLD"
    "ROW6COLA","ROW6COLB","ROW6COLC","ROW6COLD"
    "ROW7COLA","ROW7COLB","ROW7COLC","ROW7COLD"

在第一次尝试中我跑了：

    data = pd.read_csv('broken.csv')

我得到了：

                 COL_A     COL_B     COL_C
    ROW1COLA  ROW1COLB  ROW1COLC  ROW1COLD
    ROW2COLA  ROW2COLB  ROW2COLC  ROW2COLD
    ROW3COLA  ROW3COLB  ROW3COLC  ROW3COLD
    ROW4COLA  ROW4COLB  ROW4COLC  ROW4COLD
    ROW5COLA  ROW5COLB  ROW5COLC  ROW5COLD
    ROW6COLA  ROW6COLB  ROW6COLC  ROW6COLD
    ROW7COLA  ROW7COLB  ROW7COLC  ROW7COLD

设置index_col=False

    data = pd.read_csv('broken.csv',index_col=False)

我有

          COL_A     COL_B     COL_C
    0  ROW1COLA  ROW1COLB  ROW1COLC
    1  ROW2COLA  ROW2COLB  ROW2COLC
    2  ROW3COLA  ROW3COLB  ROW3COLC
    3  ROW4COLA  ROW4COLB  ROW4COLC
    4  ROW5COLA  ROW5COLB  ROW5COLC
    5  ROW6COLA  ROW6COLB …

我正在编写一个处理 CSV 文件的程序。这些文件可以具有特定的编码。我正在尝试合并一个过程来尝试猜测用户想要使用 chardet 打开的文件的编码。

我正在尝试使用以下代码：

rawdata = open(\'file.csv\', "r").read()\nresult = chardet.detect(rawdata)\n

但我收到以下例外：

/usr/lib/python3.5/site-packages/chardet/__init__.py in detect(aBuf)\n     23     if ((version_info < (3, 0) and isinstance(aBuf, unicode)) or\n     24             (version_info >= (3, 0) and not isinstance(aBuf, bytes))):\n---> 25         raise ValueError(\'Expected a bytes object, not a unicode object\')\n     26 \n     27     from . import universaldetector\n\nValueError: Expected a bytes object, not a unicode object\n

我也尝试过：

result = chardet.detect(bytes(rawdata))\n

但得到：

TypeError                                 Traceback (most recent call last)\n<ipython-input-47-1137b0adb486> in …

我有以下功能

import numpy as np
import scipy.optimize as optimize

def x(theta1, theta2, w, h, L1, L2):
    sint1 = np.sin(theta1)
    cost1 = np.cos(theta1)
    sint2 = np.sin(theta2)
    cost2 = np.cos(theta2)

    i1 = L1 * (cost1 + cost2) + w
    j1 = L1 * (sint1 - sint2) - h
    D = np.sqrt((L1*(cost2-cost1)+w)**2+(L1*(sint2-sint1)+h)**2)
    a = (0.25)*np.sqrt((4*L2**2-D**2)*D**2)

    return i1/2 + 2*j1*a/(D**2)

def y(theta1, theta2, w, h, L1, L2):
    sint1 = np.sin(theta1)
    cost1 = np.cos(theta1)
    sint2 = np.sin(theta2)
    cost2 = np.cos(theta2)

    i2 = L1 * (sint1 …