Use Matplotlib to draw a line chart

Use matplotlib Draw a line

The following is the use of Python,matplotlib An example of drawing a line chart , Including subgraphs , Axis adjustment , All samples including label position adjustment , And notes , For your reference .

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator

df_loss_arm = pd.read_csv("./result/loss_arm.csv", header=None)
df_loss_arm = df_loss_arm.reset_index()
df_loss_arm = df_loss_arm.rename(columns={
    "index": "epoch", 0: "loss"})
df_acc_arm = pd.read_csv("./result/acc_arm.csv", header=None)
df_acc_arm = df_acc_arm.reset_index()
df_acc_arm = df_acc_arm.rename(columns={
    "index": "epoch", 0: "accuracy"})
df_result_arm = pd.merge(df_loss_arm, df_acc_arm, "inner", "epoch")
""" epoch loss accuracy 0 0 1.598689 0.100060 1 1 1.022335 0.229167 2 2 0.745279 0.225962 3 3 0.771946 0.404247 4 4 0.627058 0.567007 """

df_loss_x86 = pd.read_csv("./result/loss_x86.csv", header=None)
df_loss_x86 = df_loss_x86.reset_index()
df_loss_x86 = df_loss_x86.rename(columns={
    "index": "epoch", 0: "loss"})
df_acc_x86 = pd.read_csv("./result/acc_x86.csv", header=None)
df_acc_x86 = df_acc_x86.reset_index()
df_acc_x86 = df_acc_x86.rename(columns={
    "index": "epoch", 0: "accuracy"})
df_result_x86 = pd.merge(df_loss_x86, df_acc_x86, "inner", "epoch")

#  Define the font style of the two drawing titles 
title_font = {
    'weight': 'bold', 'style': 'normal', 'size': 16}
#  Defining subgraphs ,2 That's ok 1 Column 
fig, axs = plt.subplots(nrows=2, ncols=1, figsize=(9, 12), dpi=300)

#  Setting parameters of Figure 1 
axs[0].set_title("ResNet Training Loss Curve", color="b", font=title_font)
axs[0].plot(df_result_arm["epoch"], df_result_arm["loss"], "r", label="Ascend910")
axs[0].plot(df_result_x86["epoch"], df_result_x86["loss"], "g", label="Tesla V100")
#  Set up x The scope of the shaft 0-100
axs[0].set_xlim(0, 100)
#  adjustment x Axis scale 
# axs[0].set_xticks(np.linspace(0, 100, 11))
#  Set the unit scale to 5
x_major_locator = MultipleLocator(5)
axs[0].xaxis.set_major_locator(x_major_locator)
axs[0].set_ylim(0., 2.)
axs[0].set_yticks(np.linspace(0, 2, 9))
axs[0].set_xlabel("Epoch")
axs[0].set_ylabel("Loss")
#  Set the curve description label to be placed in the upper right corner 
axs[0].legend(loc="upper left")
#  Set grid machine style 
axs[0].grid(True, linestyle='--', alpha=0.5)


axs[1].set_title("ResNet Training Accuracy Curve", color="b", font=title_font)
axs[1].plot(df_result_arm["epoch"], df_result_arm["accuracy"], "r", label="Ascend910")
axs[1].plot(df_result_x86["epoch"], df_result_x86["accuracy"], "g", label="Tesla V100")
axs[1].set_xlim(0, 100)
# axs[1].set_xticks(np.linspace(0, 100, 11))
x_major_locator = MultipleLocator(5)
axs[1].xaxis.set_major_locator(x_major_locator)
axs[1].set_ylim(0, 1)
axs[1].set_yticks(np.linspace(0, 1, 11))
axs[1].set_xlabel("Accuracy")
axs[1].set_ylabel("Loss")
axs[1].legend(loc="upper left")
axs[1].grid(True, linestyle='--', alpha=0.5)

plt.show()

Finally, the output effect .