python pandas报错TypeError

Question

重装系统之前没有这个错误，重装之后报这个错，我重装python，重新下载库，但不解决问题，别的电脑就能运行，求高手解决！

TypeError: int() argument must be a string, a bytes-like object or a number, not '_NoValueType'

Answer 1

你这代码我运行的没有问题。

应该是环境问题。

先重装pandas，

python运行其它没问题的话，就是pandas安装不全等问题

Answer 2

请问是到了哪一步出现这个问题的？你装的是什么版本？ 其他安装信息呢？ 

Answer 3

使用pandas的代码呢
错误信息要具体

Answer 4

输出dataframe对象的时候报错

Answer 5

回去我把代码发一下

Answer 6

 

报错信息：

Traceback (most recent call last):
  File "E:/Users/jjh/大坝图/dabatu11.py", line 1329, in <module>
    test(ls)
  File "E:/Users/jjh/大坝图/dabatu11.py", line 1268, in test
    print('1268:',df1)
  File "D:\Users\asus\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\core\frame.py", line 751, in __repr__
    show_dimensions=show_dimensions,
  File "D:\Users\asus\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\core\frame.py", line 883, in to_string
    return formatter.to_string(buf=buf, encoding=encoding)
  File "D:\Users\asus\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\io\formats\format.py", line 921, in to_string
    return self.get_result(buf=buf, encoding=encoding)
  File "D:\Users\asus\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\io\formats\format.py", line 520, in get_result
    self.write_result(buf=f)
  File "D:\Users\asus\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\io\formats\format.py", line 841, in write_result
    text = self._join_multiline(*strcols)
  File "D:\Users\asus\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\io\formats\format.py", line 883, in _join_multiline
    lwidth -= np.array([self.adj.len(x) for x in idx]).max() + adjoin_width
  File "D:\Users\asus\AppData\Local\Programs\Python\Python37\lib\site-packages\numpy\core\_methods.py", line 39, in _amax
    return umr_maximum(a, axis, None, out, keepdims, initial, where)
TypeError: int() argument must be a string, a bytes-like object or a number, not '_NoValueType'

Process finished with exit code 1

Answer 7

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# with open("./test.json", 'r') as load_f:
#     load_dict = eval(load_f.read())
#     print(load_dict)
# data = load_dict
# print(795,data)

arr1 = arr[1:]


import pandas as pd
import numpy as np

x_mean = np.mean([item[0] for item in arr1])
y_mean = np.mean([item[1] for item in arr1])
z_mean = np.mean([item[2] for item in arr1])
pd.set_option('display.max_rows', 60000)
pd.set_option('display.max_columns', 1000)


# pos_ls = [(pos[0], pos[1], pos[2]) for index, pos in arr[1:]]


def touying(a, b, c, d, x0, y0, z0):
    pingfanghe = a ** 2 + b ** 2 + c ** 2
    xp = ((b ** 2 + c ** 2) * x0 - a * (b * y0 + c * z0 + d)) / pingfanghe
    yp = ((a ** 2 + c ** 2) * y0 - b * (a * x0 + c * z0 + d)) / pingfanghe
    zp = ((a ** 2 + b ** 2) * z0 - c * (a * x0 + b * y0 + d)) / pingfanghe
    return xp, yp, zp


# print('touying:', touying(0, 0, 1, -1, 1, 1, 3.8))
# 投影到的平面
a1 = 0.001
b1 = 1
c1 = 0.001
d1 = 0
# a1 = data[0] - x_mean
# b1 = data[1] - y_mean
# c1=data[2]-z_mean
# a1x+b1y+c1z+d1=0
# 原点
x1 = 0.01
y1 = 0.01
if c1 == 0:
    c1 = 0.01
z1 = -(a1 * x1 + b1 * y1 + d1) / c1
print(837,z1)
# -1
# x轴上一点
x2 = 1
y2 = 0.01
z2 = -(a1 * x2 + b1 * y2 + d1) / c1
# -3
# print(z2)
# (x-x1)/(x2-x1)=(y-y1)/(y2-y1)=(z-z1)/(z2-z1)
# (x-x1)(x2-x1)+(y-y1)(y2-y1)+(z-z1)(z2-z1)=0
# x轴：(x-x1)/a2=(y-y1)/b2=(z-z1)/c2
a2 = x2 - x1
b2 = y2 - y1
c2 = z2 - z1
# print(a2,b2,c2)
# 1,1,-2
# y轴直线上一点
x0 = 0.01
# z0 = (x1 * x2 - x1 ** 2 - (y1 - y2) * d1 / b1 - y1 ** 2
#       - y1 * y2 + z1 * z2 - z1 ** 2) / z2 - z1 + (y1 - y2) * c1 / b1
# y0 = -(c1 * z1 + d1) / b1
# y轴方向
from sympy import Symbol, solve

# 定义变量
y = Symbol('y')
z = Symbol('z')
res = solve([a2 * (x1 - x0) + b2 * (y1 - y) + c2 * (z1 - z), a1 * x0 + b1 * y + c1 * z + d1], [y, z])
print(res, type(res))
y0 = res[y]
z0 = res[z]
del z, y
print(869,y0, z0)
a3 = x0 - x1
# a3 = b1 * c2 - b2 * c1
# b3 = a2 * c1 - a1 * c2
# c3 = a1 * b2 - a2 * b1
b3 = y0 - y1
c3 = z0 - z1
# y轴：(x-x0)/a3=(y-y0)/b3=(z-z0)/c3
# xzh = ((x1, y1, z1), (a2, b2, c2))
# yzh = ((x0, y0, z0), (a3, b3, c3))
xzh = ((0, 0, 0), (1, 0, 0))
yzh = ((0, 0, 0), (0, 0, 1))
print(xzh)
print(yzh)

def juli_pointtoline(x1, y1, z1, zh):
    (x2, y2, z2), (a, b, c) = zh
    x = Symbol('x')
    y = Symbol('y')
    z = Symbol('z')
    res = {}
    if a == 0:
        a = 0.00001
    if b == 0:
        b = 0.00001
    if c == 0:
        c = 0.00001
    res = solve([a * (x - x1) + b * (y -
                                     y1) + c * (z - z1), (x - x2) / a - (y - y2) / b,
                 (x - x2) / a - (z - z2) / c], [x, y, z])
    x_j = res[x]
    y_j = res[y]
    z_j = res[z]
    # x = (a * x1 + (b ** 2 + c ** 2) / a * x2 + b * y1
    #      - b * y2 + c * z1 - c * z2) / (a + (b ** 2 + c ** 2) / a)
    # y = (b * (x1 - x2)) / a + y2
    # z = (c * (x1 - x2)) / a + z2
    juli = round(((x_j - x1) ** 2 + (y_j - y1) **
                  2 + (z_j - z1) ** 2) ** 0.5, 3)
    return float(juli)


# print('juli_pointtoline', juli_pointtoline(0, 0, 0, ((1, 0, 0), (1, 0, 0))))


def IsTrangleOrArea(x1, y1, x2, y2, x3, y3):
    return round(abs((x1 * (y2 - y3) + x2 * (y3 - y1) + x3 * (y1 - y2)) / 2.0), 3)


def IsInside(x1, y1, x2, y2, x3, y3, x, y):
    # 三角形ABC的面积
    ABC = IsTrangleOrArea(x1, y1, x2, y2, x3, y3)
    if ABC:
        # 三角形PBC的面积
        PBC = IsTrangleOrArea(x, y, x2, y2, x3, y3)

        # 三角形ABC的面积
        PAC = IsTrangleOrArea(x1, y1, x, y, x3, y3)

        # 三角形ABC的面积
        PAB = IsTrangleOrArea(x1, y1, x2, y2, x, y)

        return (round(ABC, 3) == round(PBC + PAC + PAB, 3))
    else:
        return False


ls = []
# a, b, c, d = 0, 0, 1, 30
arr1=arr[1:]
arr1=[(item[0]+100,item[1],item[2],item[4])for item in arr1]
for point in arr1:
    x4, y4, z4 = point[0], point[1], point[2]
    x5, y5, z5 = touying(a1, b1, c1, d1, x4, y4, z4)
    juli_x = juli_pointtoline(x5, y5, z5, xzh)
    juli_y = juli_pointtoline(x5, y5, z5, yzh)
    ls.append([juli_x, juli_y,point[0], point[1], point[2],point[3]])

# with_goto = goto.with_goto
# print(IsInside(0,0,1,1,2,2.1,0,0))

print('945:',ls)
ls=[[item[0],item[1],item[2],item[3],item[4],item[5]]for item in ls]
import pandas as pd


def plot_triangle(x1, y1, x2, y2, x3, y3, x4, y4):
    import matplotlib.pyplot as plt
    import numpy as np
    fig = plt.figure()
    # 将字体修改为 支持雅黑 ---支持中文
    plt.rcParams['font.sans-serif'] = 'SimHei'
    # 修改编码 让其支持负号
    plt.rcParams['axes.unicode_minus'] = False
    x_ls = [x1, x2, x3, x4]
    y_ls = [y1, y2, y3, y4]
    global df, df1

    # plt.scatter(df1.iloc[:,0],df1.iloc[:,1], s=50,
    #                         c='r',
    #                         marker='*',
    #                         alpha=0.5)
    # plt.scatter(x_ls, y_ls, marker='*', alpha=0.5)
    x = x_ls[:-1] + [x_ls[0]]
    y = y_ls[:-1] + [y_ls[0]]
    plt.plot(x, y, linewidth=1, color='red')
    x_two = [x3, x1]
    y_two = [y3, y1]
    plt.scatter(x4, y4, s=50,
                c='black',
                marker='*',
                alpha=0.5)
    # plt.plot(x_two, y_two, linewidth=1, color='red')
    # plt.fill(x_ls, y_ls, facecolor='red', alpha=0.5)
    # fig.add_subplot(2, 1, 2)

    # fig.add_subplot(2, 2, 3)
    #
    # fig.add_subplot(2, 2, 4)

    # 图片保存
    # plt.savefig('./子图绘制.png')

    # 3、展示
    plt.show()


def waibaoluo(ls):
    global df1
    df = pd.DataFrame(ls)
    # print(df)
    # lenth = ls.__len__()
    df = df.sort_values(by=0)
    df = df.reset_index(drop=True)
    # print(df)
    df['x'] = df.iloc[:, 0]
    df['y'] = df.iloc[:, 1]
    df['rx'] = df.iloc[:, 2]
    df['ry'] = df.iloc[:, 3]
    df['rz'] = df.iloc[:, 4]
    del df[0], df[1],df[2],df[3],df[4]
    print('1010df:',df)

    xmin = df.iloc[0].x
    ymin = df.iloc[0].y
    ox=df.iloc[0].rx
    oy = df.iloc[0].ry
    oz = df.iloc[0].rz
    df = df.drop(index=0)
    df['xielv'] = (df['y'] - ymin) / (df['x'] - xmin)
    df['juli'] = (df['x'] - xmin) ** 2 + (df['y'] - ymin) ** 2
    df = df.sort_values(by=['xielv', 'juli'], ascending=[False, False]).reset_index(drop=True)
    # print(df)
    # df.to_csv('data/df.csv')
    # df1=pd.read_csv('data/df.csv')
    A = xmin, ymin
    B = df.iloc[0].x, df.iloc[0].y
    C = df.iloc[1].x, df.iloc[1].y

    ls_new = []
    ls_new.append(A)
    ls_new.append(B)

    for i in range(2, len(df)):
        condition = False
        D = df.iloc[i].x, df.iloc[i].y
        if not IsInside(A[0], A[1], B[0], B[1], D[0], D[1], C[0], C[1]):
            # plot_triangle(A[0], A[1], B[0], B[1], D[0], D[1], C[0], C[1])
            ls_new.append(tuple(C))
            C = df.iloc[i].x,df.iloc[i].y
            condition = True
        else:
            # plot_triangle(A[0], A[1], B[0], B[1], D[0], D[1], C[0], C[1])
            ls_it = []
            df.iloc[i - 1].x,df.iloc[i - 1].y = -999, -999
            if len(ls_new) > 2:
                ls_it = list(range(2, len(ls_new)))
                if ls_it != None:
                    ls_it.reverse()

            for j in ls_it:
                B = ls_new[j]
                print('B:', B)
                for k in range(j, len(ls_new)):
                    C = ls_new[k]
                    if IsInside(A[0], A[1], B[0], B[1], D[0], D[1], C[0], C[1]):
                        # plot_triangle(A[0], A[1], B[0], B[1], D[0], D[1], C[0], C[1])
                        # if C in ls_new:
                        # print(C)
                        # print(tuple(C))
                        ls_new.remove(tuple(C))
                        cond = (df.iloc[:]['x'] == C[0]) & (df.iloc[:]['y'] == C[1])
                        # print(cond)
                        df_con = df[cond]
                        # df_con.loc['x']=999
            if i < len(ls):
                C = df.iloc[i].x, df.iloc[i].y
            continue
        B = df.iloc[i - 1].x, df.iloc[i - 1].y
        if i + 1 < len(ls) and not condition:
            C = df.iloc[i + 1].x, df.iloc[i + 1].y

    print(df)
    is999 = df['x'] == -999
    cond999 = []

    for i in range(len(df)):
        if is999[i]:
            cond999.append(i)

    print(cond999)
    df = df.drop(index=cond999)
    df.index = [index for index in range(1, len(df) + 1)]
    bool_arr = df['x'] == df['x'].max()
    bool_arr = list(bool_arr)
    index = bool_arr.index(True)
    df_front = df.iloc[:index + 1]
    df_rear = df.iloc[index:]
    df2 = pd.DataFrame([[xmin, ymin, 0, 0,ox,oy,oz]],
    columns=['x', 'y', 'xielv', 'juli','rx','ry','rz'])
    print('line1089:',df2)
    print(df2.iloc[0])
    df = pd.concat([df2, df, df2]).reset_index(drop=True)
    print('line1092',df)
    return df


# def waibaoluo_reverse(ls):
#     global df1
#     df = pd.DataFrame(ls)
#     # print(df)
#     # lenth = ls.__len__()
#     df = df.sort_values(by=0)
#     df = df.reset_index(drop=True)
#     # print(df)
#     df['x'] = df.iloc[:, 0]
#     df['y'] = df.iloc[:, 1]
#     del df[0], df[1]
#     print(df)
#
#
#     xmin = df.iloc[0, 0]
#     ymin = df.iloc[0, 1]
#     df = df.drop(index=0)
#     df['xielv'] = (df['y'] - ymin)/(df['x'] - xmin)
#     df['juli'] = (df['x'] - xmin) ** 2 + (df['y'] - ymin) ** 2
#     df = df.sort_values(by=['xielv', 'juli'], ascending=[True, False]).reset_index(drop=True)
#     print(df)
#     df1=df.copy()
#     A = xmin, ymin
#     B = df.iloc[0][0], df.iloc[0][1]
#     C = df.iloc[1][0], df.iloc[1][1]
#
#     ls_new = []
#     ls_new.append(A)
#     ls_new.append(B)
#
#     for i in range(2, len(df)):
#         condition=False
#         D = df.iloc[i][0], df.iloc[i][1]
#         if not IsInside(A[0], A[1], B[0], B[1], D[0], D[1], C[0], C[1]):
#             plot_triangle(A[0], A[1], B[0], B[1], D[0], D[1], C[0], C[1])
#             ls_new.append(tuple(C))
#             C=df.iloc[i][:2]
#             condition=True
#         else:
#             xy=df[(df['x']==C[0])&(df['y']==C[1])]
#             xy['x'],xy['y']=(-999,-999)
#             if tuple(C) in ls:
#                 C=tuple(C)
#                 ls.remove(C)
#             plot_triangle(A[0], A[1], B[0], B[1], D[0], D[1], C[0], C[1])
#             ls_it = []
#             df.iloc[i-1][:2]=[-999,-999]
#             if len(ls_new) > 2:
#                 ls_it = list(range(2, len(ls_new)))
#                 if ls_it != None:
#                     ls_it.reverse()
#
#             for j in ls_it:
#                 B = ls_new[j]
#                 print('B:',B)
#                 for k in range(j, len(ls_new)):
#                     C = ls_new[k]
#                     if IsInside(A[0], A[1], B[0], B[1], D[0], D[1], C[0], C[1]):
#                         plot_triangle(A[0], A[1], B[0], B[1], D[0], D[1], C[0], C[1])
#                         # if C in ls_new:
#                         # print(C)
#                         # print(tuple(C))
#                         ls_new.remove(tuple(C))
#                         cond=(df.iloc[:]['x']==C[0])&(df.iloc[:]['y']==C[1])
#                         # print(cond)
#                         df_con=df[cond]
#                         # df_con.loc['x']=999
#             if i < len(ls):
#                 C = df.iloc[i][0], df.iloc[i][1]
#             continue
#         B = df.iloc[i - 1][0], df.iloc[i - 1][1]
#         if i + 1 < len(ls) and not condition:
#             C = df.iloc[i + 1][0], df.iloc[i + 1][1]
#             print(df)
#
#
#     print(df)
#     is999 = df['x'] == -999
#     cond999 = []
#
#     for i in range(len(df)):
#         if is999[i]:
#             cond999.append(i)
#
#     print(cond999)
#     df = df.drop(index=cond999)
#     df.index = [index for index in range(1, len(df) + 1)]
#     bool_arr = df['x'] == df['x'].max()
#     bool_arr = list(bool_arr)
#     index = bool_arr.index(True)
#     df_front = df.iloc[:index + 1]
#     df_rear = df.iloc[index:]
#     df2 = pd.DataFrame([[xmin, ymin, 0, 0]], columns=['x', 'y', '1', '2'])
#     print(df2)
#     print(df2.iloc[0])
#     df = pd.concat([df2, df, df2]).reset_index(drop=True)
#     return df
#
#
# # df=waibaoluo_reverse(ls)
# df = pd.DataFrame(ls)
# # print(df)
# # lenth = ls.__len__()
# df = df.sort_values(by=0)
# df = df.reset_index(drop=True)
# # print(df)
# df['x'] = df.iloc[:, 0]
# df['y'] = df.iloc[:, 1]
# del df[0], df[1]
# print(df)
#
#
# xmin = df.iloc[0, 0]
# ymin = df.iloc[0, 1]
# df = df.drop(index=0)
# df['xielv'] = (df['y'] - ymin)/(df['x'] - xmin)
# df['juli'] = (df['x'] - xmin) ** 2 + (df['y'] - ymin) ** 2
# df = df.sort_values(by=['xielv', 'juli'], ascending=[True, False]).reset_index(drop=True)
# print(df)
# df1=df.copy()
# print(df)
# def orientation(p,q,r):
#     val = (q[1] - p[1]) * (r[0] - q[0]) - (q[0] - p[0]) * (r[1] - q[1])
#
#     if (val == 0):
#         return 0 # colinear
#     if val > 0:
#         return 1
#     else:
#         return 2
#
#
# }
# x_ls=df['x']
# y_ls=df['y']
def duplicated(i, j, k, l):
    ls = i, j, k, l
    c1 = 0
    while (c1 < ls.__len__() - 1):
        c2 = c1 + 1
        while (c2 < ls.__len__()):
            if ls[c1] == ls[c2]:
                return True
            c2 += 1
        c1 += 1
    return False


# def zuhe(l):
#     ls = []
#     for i in range(l):
#         for j in range(l):
#             for k in range(l):
#                 for l in range(l):
#                     if duplicated(i, j, k, l):
#                         continue
#                     ls.append((i, j, k, l))


def test(ls):
    import matplotlib.pyplot as plt
    import numpy as np
    ijl_ls = []
    global df1
    df1 = pd.DataFrame(list(ls))
    # print(df)
    # lenth = ls.__len__()
    df1 = df1.sort_values(by=0)
    df1 = df1.reset_index(drop=True)
    # print(df)
    df1['x'] = df1.iloc[:, 0]
    df1['y'] = df1.iloc[:, 1]
    print('1268:',df1)
    df1['type'] = df1.iloc[:, 5]

    del df1[0], df1[1],df1[5]

    while True:
        b = False
        for i in range(len(ls)):
            if b == True: break
            for j in range(len(ls)):
                if b == True: break
                for k in range(len(ls)):
                    if b == True: break
                    for l in range(len(ls)):
                        # ijk = [i, j, k]
                        # ijk.sort()
                        # ijk.append(l)
                        # if ijk not in ijl_ls:
                        #     ijl_ls.append(ijk)
                        # else:
                        #     continue
                        x1, y1 = ls[i][0],ls[i][1]
                        x2, y2 = ls[j][0],ls[j][1]
                        x3, y3 = ls[k][0],ls[k][1]
                        x4, y4 = ls[l][0],ls[l][1]
                        # print(x1, y1, x2, y2, x3, y3, x4, y4)
                        if not duplicated(i, j, k, l):
                            if IsInside(x1, y1, x2, y2, x3, y3, x4, y4):
                                x_ls = [x1, x2, x3, x4]
                                y_ls = [y1, y2, y3, y4]
                                # plt.scatter(x_ls, y_ls,
                                #
                                #             marker='.',
                                #             alpha=0.5)
                                # x = x_ls[:-1] + [x_ls[0]]
                                # y = y_ls[:-1] + [y_ls[0]]
                                # plt.plot(x, y, linewidth=1, color='red')
                                # # plt.fill(x_ls, y_ls, facecolor='red', alpha=0.5)
                                # fig.add_subplot(2, 1, 2)
                                #
                                # # fig.add_subplot(2, 2, 3)
                                # #
                                # # fig.add_subplot(2, 2, 4)
                                #
                                # # 图片保存
                                # # plt.savefig('./子图绘制.png')
                                #
                                # # 3、展示
                                # plt.show()
                                # plot_triangle(x1, y1, x2, y2, x3, y3, x4, y4)
                                del ls[l]
                                # print(i,j,k,l)
                                b = True
                                break
        if b == False:
            break


print('1314ls::', ls)
# print(type(ls[0][0]))
ls = [(float(xy[0]), float(xy[1]),xy[2],xy[3],xy[4],xy[5]) for xy in ls]
test(ls)
print('1318ls:', ls)
df = waibaoluo(ls)
# x_ls = [xy[0] for xy in ls_new]
# y_ls = [xy[1] for xy in ls_new]
print(1332,df)
df['y']-=100
df['tmp']=df['x']
df['x']=df['y']
df['y']=df['tmp']
del df['tmp']
print('line1329',df)
df1['tmp']=df1['x']
df1['x']=df1['y']
df1['y']=df1['tmp']
del df1['tmp']
df1['x']-=100
print('line1343df1:',df1)
df.to_csv('./data/大坝图.csv')
df.to_json('data/大坝图xz.json')
# 散点图
# 直角坐标系 中---  点 ---横纵坐标  ---(x1,y1) (x2,y2) ....

import matplotlib.pyplot as plt
import numpy as np

# # 1、创建画布
# plt.figure()
#
# # 2、绘图
# # 需要准备两个长度相同的数组
# x = np.array([1, 2, 3, 4, 5])
# y = np.array([18, 20, 21, 15, 13])
#
# # 绘制散点图
# # s 表示点的大小--可以指定数组或者 具体的点的大小
# # c 表示点的颜色 --可以指定数组或者具体的一个颜色
# # marker  点的样式
# # alpha  透明度 --[0,1]
# plt.scatter(x, y, s=[30, 20, 10, 5, 50],
#             c=['r', 'g', 'b', 'black', 'c'],
#             marker='*',
#             alpha=0.5)
# # 3、展示
# plt.show()

# 散点图 ---应用场景：
#  可以用来查看 发展趋势
#  可以用来查看数据集中数据的分布状态

# 如何增加子图  ？？？
# 1、创建画布  ---返回画布对象
fig = plt.figure(figsize=(24,20))
# 将字体修改为 支持雅黑 ---支持中文
plt.rcParams['font.sans-serif'] = 'SimHei'
# 修改编码 让其支持负号
plt.rcParams['axes.unicode_minus'] = False
# 调整子图的间距
# wspace=None, 具体的数值 ---便是宽度间距为wspace倍的子图宽度
# hspace=None  具体的数值 ---表示的高度间距为 hspace倍的子图高度
# plt.subplots_adjust(hspace=0.3)
# 增加子图
# 参数1 绘制的子图的 行数
# 参数2 绘制的子图的 列数
# 参数3 绘制的子图的 编号
# fig.add_subplot(2, 1, 1)
# 2、绘图
# 需要准备两个长度相同的数组
# x = np.array([1, 2, 3, 4, 5])
# y = np.array([18, 20, 21, 15, 13])

# 绘制散点图
# s 表示点的大小--可以指定数组或者 具体的点的大小
# c 表示点的颜色 --可以指定数组或者具体的一个颜色
# marker  点的样式
# alpha  透明度 --[0,1]
# plt.scatter(x_ls, y_ls,
#
#             marker='.',
#             alpha=0.5)
# for i in range(len(x_ls)):
#     for j in range(len(y_ls)):
#         x = x_ls[i], x_ls[j]
#         y = y_ls[i], y_ls[j]
#         plt.plot(x, y, linewidth=1, color='red')
plt.plot(df['x'], df['y'], linewidth=1, color='red')
plt.fill(df['x'], df['y'], facecolor='red', alpha=0.5)
print(1398,df1)

plt.scatter(df1['x'], df1['y'], s=50,
            c='black',
            marker='*',
            alpha=0.5)
from matplotlib import pyplot as plt
import numpy as np
# fig = plt.figure()
ax = fig.add_subplot(111)
# A = -0.75, -0.25, 0, 0.25, 0.5, 0.75, 1.0
# B = 0.73, 0.97, 1.0, 0.97, 0.88, 0.73, 0.54
# plt.plot(A,B)# annotations at the side (ordered by B values)
#
x0,x1=ax.get_xlim()
y0,y1=ax.get_ylim()
for ii, ind in enumerate(np.argsort(df1['type'])):
    x = df1.iloc[ind].x
    y = df1.iloc[ind].y
    xPos = x1 + .02 * (x1 - x0)
    yPos = y0 + ii * (y1 - y0)/(len(df1['type']) - 1)
    ax.annotate('',#label,
   xy=(x, y),
   xycoords='data',
   xytext=(xPos, yPos),
   textcoords='data',
   arrowprops=dict(
   connectionstyle="arc3,rad=0.",
   shrinkA=0, shrinkB=10,
   arrowstyle= '-|>',
   ls= '-', linewidth=2
   ),
   va='bottom',
   ha='left',
   zorder=19
   )
    ax.text(xPos + .01 * (x1 - x0),
            yPos,
            '{}:({:.2f}, {:.2f})'.format(df1.iloc[ind].type,x,y),
            transform=ax.transData,
            va='center')
plt.grid()
plt.xlabel('坝横')
plt.ylabel('高程')
# fig.add_subplot(2, 1, 2)

# fig.add_subplot(2, 2, 3)
#
# fig.add_subplot(2, 2, 4)

# 图片保存
plt.savefig('./大坝图11.png')

# 3、展示
plt.show()
# input('按回车以继续')

Answer 8

soar，我用了你的方法

 

Answer 9

不管用啊·

Answer 10

重装一下anaconda或者python  然后安装pandas和numpy应该就可以

Answer 11

好的，我试试重装

 

Answer 12

还是不行，我重装了python，pycharm，numpy，pandas，还是不行

Answer 13

你安装一个anaconda呢 用anaconda的python试试

Answer 14

好的

 

Answer 15

运行的非常慢，但是只能出此下策了。

Answer 16

成功了，谢谢

 

Answer 17

你们的回答我都点赞了，不过积分不多

Answer 18

anaconda的方法的确管用