1.使用箱型法去除异常值：
import numpy as np
import pandas as pd
import matplotlib as plt
import os
data = 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 * iqr
print("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 = 0
while j < R:
if (a[0][j] > Too_small):
if (a[0][j] < Too_big):
b.append(a[0][j])
j += 1
print(b)
print("（7）所有异常数据：")
c = []
i = 0
while i < R:
if (a[0][i] < Too_small or a[0][i] > Too_big):
c.append(a[0][i])
a[0][i] = None
i +=1
print(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 = 0
while k < R:
d.append(a[0][k])
k +=1
print(d)
df = pd.DataFrame(d,columns= ['位移'])
df.to_excel("try_result.xls")
2.拉格朗日插值：
import os
import pandas as pd
import numpy as np
from scipy.interpolate import lagrange
import matplotlib.pyplot as plt
plt.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 * iqr
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
#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 = 0
while 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 np
import pandas as pd
import matplotlib.pyplot as plt
from 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
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 pd
import numpy as np
import matplotlib.pyplot as plt
plt.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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