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python如何去除异常值和缺失值的插值

发布于：2022-01-28 10:40:02  栏目：技术文档

python如何去除异常值和缺失值的插值

1.使用箱型法去除异常值：

  1. import numpy as np
  2. import pandas as pd
  3. import matplotlib as plt
  4. import os
  5. data = pd.read_excel('try.xls', header=0)
  6. # print(data.shape)
  7. # print(data.head(10))
  8. # print(data.describe())
  9. neg_list = ['位移']
  10. print("（1）数据的行数为：")
  11. R = data.shape[0]
  12. print(R)
  13. print("（2）小于或大于阈值的数据提取：")
  14. for item in neg_list:
  15.     neg_item = data[item]<2000
  16.     print(item + '小于2000的有' + str(neg_item.sum()) + '个')
  17. print("（3）异常值的个数：")
  18. for item in neg_list:
  19.     iqr = data[item].quantile(0.75) - data[item].quantile(0.25)
  20.     q_abnormal_L = data[item] < data[item].quantile(0.25) - 1.5 * iqr
  21.     q_abnormal_U = data[item] > data[item].quantile(0.75) + 1.5 * iqr
  22.     print(item + '中有' + str(q_abnormal_L.sum() + q_abnormal_U.sum()) + '个异常值')
  23. print("（4）箱型图确定上下限：")
  24. for item in neg_list:
  25.     iqr = data[item].quantile(0.75) - data[item].quantile(0.25)
  26.     Too_small = data[item].quantile(0.25) - 1.5 * iqr
  27.     Too_big = data[item].quantile(0.25) + 1.5 * iqr
  28. print("下限是", Too_small)
  29. print("上限是", Too_big )
  30. print("（5）所有数据为：")
  31. a = []
  32. for i in neg_list:
  33.     a.append(data[i])
  34. print(a)
  35. print("（6）所有正常数据：")
  36. b = []
  37. j = 0
  38. while j < R:
  39.     if (a[0][j] > Too_small):
  40.         if (a[0][j] < Too_big):
  41.             b.append(a[0][j])
  42.     j += 1
  43. print(b)
  44. print("（7）所有异常数据：")
  45. c = []
  46. i = 0
  47. while i < R:
  48.     if (a[0][i] < Too_small or a[0][i] > Too_big):
  49.         c.append(a[0][i])
  50.         a[0][i] = None
  51.     i +=1
  52. print(c)
  53. print("（8）把所有异常数据删除后：")
  54. print(a)
  55. print("（9）所有数据处理后输出：")
  56. d = []
  57. k = 0
  58. while k < R:
  59.     d.append(a[0][k])
  60.     k +=1
  61. print(d)
  62. df = pd.DataFrame(d,columns= ['位移'])
  63. df.to_excel("try_result.xls")

2.拉格朗日插值：

  1. import os
  2. import pandas as pd
  3. import numpy as np
  4. from scipy.interpolate import lagrange
  5. import matplotlib.pyplot as plt
  7. plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
  9. plt.rcParams['axes.unicode_minus']=False #用来正常显示负号
  11. # 数据的读取
  12. data = pd.read_excel('try.xls', header=0)
  13. neg_list = ['位移']
  15. # 数据的行数
  16. R = data.shape[0]
  18. # 异常数据的个数
  19. for item in neg_list:
  20.     iqr = data[item].quantile(0.75) - data[item].quantile(0.25)
  21.     q_abnormal_L = data[item] < data[item].quantile(0.25) - 1.5 * iqr
  22.     q_abnormal_U = data[item] > data[item].quantile(0.75) + 1.5 * iqr
  23.     # print(item + '中有' + str(q_abnormal_L.sum() + q_abnormal_U.sum()) + '个异常值')
  25. # 确定数据上限和下限
  26. for item in neg_list:
  27.     iqr = data[item].quantile(0.75) - data[item].quantile(0.25)
  28.     Too_small = data[item].quantile(0.25) - 1.5 * iqr
  29.     Too_big = data[item].quantile(0.25) + 1.5 * iqr
  31. data[u'位移'][(data[u'位移']<Too_small) | (data[u'位移']>Too_big)] = None #过滤异常值，将其变为空值
  33. #s为列向量，n为被插值位置，k为取前后的数据个数
  34. def ployinter(s,n,k=5):
  35.     y = s[list(range(n-k,n)) + list(range(n+1,n+1+k))]
  36.     y = y[y.notnull()] #剔除空值
  37.     return lagrange(y.index,list(y))(n)
  39. #逐个元素判断是否需要插值
  40. for i in data.columns:
  41.     for j in range(len(data)):
  42.         if(data[i].isnull())[j]:
  43.             data[i][j] = ployinter(data[i],j)
  44. # print(data[u'位移'])
  46. # 输出拉格朗日插值后的数据
  47. data.to_excel("try_result.xls")
  49. # 把表格列数据调整为arr,arr为修改后的数据
  50. print("拉格朗日插值后的数据：")
  51. d = []
  52. k = 0
  53. while k < R:
  54.     d.append(data[u'位移'][k])
  55.     k +=1
  56. # print(d)
  57. arr = np.array(d)
  58. print(arr)
  60. # 输出图像
  61. x = np.arange(len(d))
  63. plt.plot(x,d,'b-',label="one", marker='*',markersize=4,linewidth=1) # b代表blue颜色  -代表直线
  65. plt.title('位移曲线')
  67. plt.legend(loc='upper left',bbox_to_anchor=(1.0,1.0))
  69. # 直接更改X轴坐标数
  70. # plt.xticks((0,1,2,3,4,5,6,7,8),('0', '1', '2', '3', '4', '5', '6', '7', '8'))
  72. plt.xlabel('时间/h')
  73. plt.ylabel('位移/mm')
  75. #plt.grid(x1)
  76. plt.show

import numpy as npimport pandas as pdimport matplotlib.pyplot as pltfrom scipy.optimize import leastsq

def Fun(p, x): # 定义拟合函数形式 a1, a2, a3 , a4 = p return a1 x ** 3 + a2 x * 2 + a3 x + a4

def error(p, x, y): # 拟合残差 return Fun(p, x) - y

def main(): x = np.linspace(1, 31, 31) # 创建时间序列 data = pd.read_excel(‘try.xls’, header=0) y = data[u’位移’] p0 = [0.1, -0.01, 100, 1000] # 拟合的初始参数设置 para = leastsq(error, p0, args=(x, y)) # 进行拟合 y_fitted = Fun(para[0], x) # 画出拟合后的曲线

  1. plt.figure
  2. plt.plot(x, y, 'r', label='Original curve')
  3. plt.plot(x, y_fitted, '-b', label='Fitted curve')
  4. plt.legend()
  5. plt.show()
  6. print(para[0])
  7. main()

import numpy as npimport pandas as pdimport matplotlib.pyplot as pltfrom scipy.optimize import leastsq

def Fun(p, x): # 定义拟合函数形式 a1, a2, a3 , a4 = p return a1 x ** 3 + a2 x * 2 + a3 x + a4

def error(p, x, y): # 拟合残差 return Fun(p, x) - y

def main(): x = np.linspace(1, 31, 31) # 创建时间序列 data = pd.read_excel(‘try.xls’, header=0) y = data[u’位移’] p0 = [0.1, -0.01, 100, 1000] # 拟合的初始参数设置 para = leastsq(error, p0, args=(x, y)) # 进行拟合 y_fitted = Fun(para[0], x) # 画出拟合后的曲线

  1. plt.figure
  2. plt.plot(x, y, 'r', label='Original curve')
  3. plt.plot(x, y_fitted, '-b', label='Fitted curve')
  4. plt.legend()
  5. plt.show()
  6. print(para[0])
  8. main()

3.数据拟合： import numpy as np import pandas as pd import matplotlib.pyplot as plt from scipy.optimize import leastsq

  1. def Fun(p, x):  # 定义拟合函数形式
  2.     a1, a2, a3 , a4 = p
  3.     return a1 * x ** 3 + a2 * x ** 2 + a3 * x + a4
  5. def error(p, x, y):  # 拟合残差
  6.     return Fun(p, x) - y
  8. def main():
  9.     x = np.linspace(1, 31, 31)  # 创建时间序列
  10.     data = pd.read_excel('try.xls', header=0)
  11.     y = data[u'位移']
  12.     p0 = [0.1, -0.01, 100, 1000]  # 拟合的初始参数设置
  13.     para = leastsq(error, p0, args=(x, y))  # 进行拟合
  14.     y_fitted = Fun(para[0], x)  # 画出拟合后的曲线
  16.     plt.figure
  17.     plt.plot(x, y, 'r', label='Original curve')
  18.     plt.plot(x, y_fitted, '-b', label='Fitted curve')
  19.     plt.legend()
  20.     plt.show()
  21.     print(para[0])
  23. if __name__ == '__main__':
  24.     main()

4.输出图像： import pandas as pd

  1. import numpy as np
  3. import matplotlib.pyplot as plt
  5. plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
  7. plt.rcParams['axes.unicode_minus']=False #用来正常显示负号
  9. jiaodu = ['0', '15', '30', '15', '60', '75', '90', '105', '120']
  11. x = range(len(jiaodu))
  13. y = [85.6801,   7.64586,    86.0956,    159.229,    179.534,    163.238,    96.4436,    10.1619,    90.9262,]
  15. #plt.figure(figsize=(10, 6))
  17. plt.plot(x,y,'b-',label="1", marker='*',markersize=7,linewidth=3) # b代表blue颜色  -代表直线
  19. plt.title('各个区域亮度变化')
  21. plt.legend(loc='upper left',bbox_to_anchor=(1.0,1.0))
  23. plt.xticks((0,1,2,3,4,5,6,7,8),('0', '15', '30', '15', '60', '75', '90', '105', '120'))
  25. plt.xlabel('角度')
  27. plt.ylabel('亮度')
  29. #plt.grid(x1)
  31. plt.show()

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