python怎么去除异常值和缺失值的插值

python怎么去除异常值和缺失值的插值，针对这个问题，这篇文章详细介绍了相对应的分析和解答，希望可以帮助更多想解决这个问题的小伙伴找到更简单易行的方法。

1.使用箱型法去除异常值：

import numpy as npimport pandas as pdimport matplotlib as pltimport osdata = pd.read_excel('try.xls', header=0)# print(data.shape)# print(data.head(10))# print(data.describe())neg_list = ['位移']print("（1）数据的行数为：")R = data.shape[0]print(R)print("（2）小于或大于阈值的数据提取：")for item in neg_list:    neg_item = data[item]<2000    print(item + '小于2000的有' + str(neg_item.sum()) + '个')print("（3）异常值的个数：")for item in neg_list:    iqr = data[item].quantile(0.75) - data[item].quantile(0.25)    q_abnormal_L = data[item] < data[item].quantile(0.25) - 1.5 * iqr    q_abnormal_U = data[item] > data[item].quantile(0.75) + 1.5 * iqr    print(item + '中有' + str(q_abnormal_L.sum() + q_abnormal_U.sum()) + '个异常值')print("（4）箱型图确定上下限：")for item in neg_list:    iqr = data[item].quantile(0.75) - data[item].quantile(0.25)    Too_small = data[item].quantile(0.25) - 1.5 * iqr    Too_big = data[item].quantile(0.25) + 1.5 * iqrprint("下限是", Too_small)print("上限是", Too_big )print("（5）所有数据为：")a = []for i in neg_list:    a.append(data[i])print(a)print("（6）所有正常数据：")b = []j = 0while j < R:    if (a[0][j] > Too_small):        if (a[0][j] < Too_big):            b.append(a[0][j])    j += 1print(b)print("（7）所有异常数据：")c = []i = 0while i < R:    if (a[0][i] < Too_small or a[0][i] > Too_big):        c.append(a[0][i])        a[0][i] = None    i +=1print(c)print("（8）把所有异常数据删除后：")print(a)print("（9）所有数据处理后输出：")d = []k = 0while k < R:    d.append(a[0][k])    k +=1print(d)df = pd.DataFrame(d,columns= ['位移'])df.to_excel("try_result.xls")

2.拉格朗日插值：

import osimport pandas as pdimport numpy as npfrom scipy.interpolate import lagrangeimport matplotlib.pyplot as pltplt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签plt.rcParams['axes.unicode_minus']=False #用来正常显示负号# 数据的读取data = pd.read_excel('try.xls', header=0)neg_list = ['位移']# 数据的行数R = data.shape[0]# 异常数据的个数for item in neg_list:    iqr = data[item].quantile(0.75) - data[item].quantile(0.25)    q_abnormal_L = data[item] < data[item].quantile(0.25) - 1.5 * iqr    q_abnormal_U = data[item] > data[item].quantile(0.75) + 1.5 * iqr    # print(item + '中有' + str(q_abnormal_L.sum() + q_abnormal_U.sum()) + '个异常值')# 确定数据上限和下限for item in neg_list:    iqr = data[item].quantile(0.75) - data[item].quantile(0.25)    Too_small = data[item].quantile(0.25) - 1.5 * iqr    Too_big = data[item].quantile(0.25) + 1.5 * iqrdata[u'位移'][(data[u'位移']Too_big)] = None #过滤异常值，将其变为空值#s为列向量，n为被插值位置，k为取前后的数据个数def ployinter(s,n,k=5):    y = s[list(range(n-k,n)) + list(range(n+1,n+1+k))]    y = y[y.notnull()] #剔除空值    return lagrange(y.index,list(y))(n)#逐个元素判断是否需要插值for i in data.columns:    for j in range(len(data)):        if(data[i].isnull())[j]:            data[i][j] = ployinter(data[i],j)# print(data[u'位移'])# 输出拉格朗日插值后的数据data.to_excel("try_result.xls")# 把表格列数据调整为arr,arr为修改后的数据print("拉格朗日插值后的数据：")d = []k = 0while k < R:    d.append(data[u'位移'][k])    k +=1# print(d)arr = np.array(d)print(arr)# 输出图像x = np.arange(len(d))plt.plot(x,d,'b-',label="one", marker='*',markersize=4,linewidth=1) # b代表blue颜色  -代表直线plt.title('位移曲线')plt.legend(loc='upper left',bbox_to_anchor=(1.0,1.0))# 直接更改X轴坐标数# plt.xticks((0,1,2,3,4,5,6,7,8),('0', '1', '2', '3', '4', '5', '6', '7', '8'))plt.xlabel('时间/h')plt.ylabel('位移/mm')#plt.grid(x1)plt.show()

3.数据拟合：

import numpy as npimport pandas as pdimport matplotlib.pyplot as pltfrom scipy.optimize import leastsqdef 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 + a4def error(p, x, y):  # 拟合残差    return Fun(p, x) - ydef 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    plt.figure    plt.plot(x, y, 'r', label='Original curve')    plt.plot(x, y_fitted, '-b', label='Fitted curve')    plt.legend()    plt.show()    print(para[0])if __name__ == '__main__':    main()

4.输出图像：

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

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