根据在Python中创建新表用户titusarmah99 https://stackoverflow.com/users/8363478/titusarmah99，使用Python字典转换数据

import datetime
import numpy as np
import seaborn as sb 
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use("ggplot")

%matplotlib inline 

%config InlineBackend.figure_format='svg'}

正在阅读Sandvik.log和Iscar.log文件。

data=[]
        with open('Sandvik.log','r') as file:
            for row in file:
                data.append(row.rstrip('\n').split('|'))
        columns =['DateTime','Xload']

        data_dic = []
        for row in data:
            tmp ={}
            tmp['DateTime']=row[0]
            for i in range(1,len(row)-1):
                if row[i] in columns:
                    tmp[row[i]]=row[i+1]
            for c in columns:
                if c not in tmp:
                    tmp[c] = '' #for rows which donot have the property
            data_dic.append(tmp)

            dfs = pd.DataFrame(data_dic)
        print (dfs.dtypes)


    # Reading Iscar.log    

    data=[]
    with open('Iscar.log','r') as file:
        for row in file:
            data.append(row.rstrip('\n').split('|'))
    columns =['DateTime','Xload']

    data_dic = []
    for row in data:
        tmp ={}
        tmp['DateTime']=row[0]
        for i in range(1,len(row)-1):
            if row[i] in columns:
                tmp[row[i]]=row[i+1]
        for c in columns:
            if c not in tmp:
                tmp[c] = '' #for rows which donot have the property
        data_dic.append(tmp)

        dfi = pd.DataFrame(data_dic)
    print (dfi.dtypes) 

    # Converting the Xload and Datetime variables
    dfs['Xload']=pd.to_numeric(dfs.Xload)

    dfs['DateTime']= pd.to_datetime(dfs['DateTime']) 

    dfi['Xload']=pd.to_numeric(dfi.Xload)

    dfi['DateTime']= pd.to_datetime(dfi['DateTime']) 

# removing null data
dfs.dropna(inplace=True)
dfi.dropna(inplace=True)

# Reset the DataFrame
dfs.reset_index(drop=True, inplace=True)
dfi.reset_index(drop=True, inplace=True)

绘制山特维克 Dataframe 的Xload变量。

dfs.plot('DateTime', color = "red", figsize = (8, 6))

plt.ylim(0,100) # scale up to 100% for Y axis

# creating subtitles
plt.legend(['Sandvik'], loc='upper left') 
plt.title("Machining Time vs. Xload Power")
plt.xlabel("Machining Time")
plt.ylabel("% in Xload variable")

Dataframe Sandvik Chart

绘制Iscar DataFrame的Xload变量

dfi.plot('DateTime', color = "royalblue", figsize = (8, 6))

plt.ylim(0,100)

# creating subtitles
plt.legend(['Iscar'], loc='upper left') 
plt.title("Machining Time vs Xload Power")
plt.xlabel("Machining Time")
plt.ylabel("% in Xload variable")

Dataframe Iscar Chart

在连接两个图形后，我无法将小时、分钟和秒缩放到“X”轴。

plt.figure(figsize = (10, 6))

for frame in [dfs, dfi]:
    plt.plot(frame['Xload'])

#plt.xlim()
plt.ylim(0,100)

# Criando as legendas
plt.legend(['Sandvik', 'Iscar'], shadow=True, loc='upper left') 
plt.title("Machining Time vs Xload Power")
plt.xlabel("Machining Time")
plt.ylabel("% in Xload variable")

Grouped Charts
我将只使用以秒为单位的刻度dt.strftime（'% S'）。我需要覆盖图形（Sandvik和Iscar），并每5秒更改一次有序的X轴比例。

dfs['DateTime'] = dfs['DateTime'].dt.strftime('%S') 
dfi['DateTime'] = dfi['DateTime'].dt.strftime('%S')

# overlapping graphics
plt.figure(figsize = (10, 4))
for frame in [dfs, dfi]:
    plt.plot(frame['Xload'])
    plt.legend(['Sandvik', 'Iscar'], loc='upper left') #plot da legend

#plt.xlim()
plt.ylim(0,100)

# using seaborn
x1 = dfs['DateTime']
x2 = dfi['DateTime']
y1 = dfs['Xload']
y2 = dfi['Xload']

f, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, sharey=True, figsize=(10,4))
ax = sns.lineplot(x=x1, y=y1, ax=ax1, color='blue', label='Sardvik', ci=None)
ax = sns.lineplot(x=x2, y=y2, ax=ax2, color='red', label='Iscar', ci=None)

ax1.set_xlim(min(x1), max(x1))
ax2.set_xlim(min(x2), max(x2))
ax1.set_xlabel('Machine Time')
ax2.set_xlabel('Machine Time')
ax1.set_ylabel('% in Xload variable')
ax1.set_xticks(ax1.get_xticks()[::5])
ax2.set_xticks(ax2.get_xticks()[::5])
plt.setp( ax1.xaxis.get_majorticklabels(), rotation=90 )
plt.setp( ax2.xaxis.get_majorticklabels(), rotation=90 )