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程序員用來(lái)做筆記的網(wǎng)站,搜索引擎是指什么,wordpress 數(shù)據(jù)庫(kù) 地址,凡客誠(chéng)品鞋子質(zhì)量怎么樣趨勢(shì)（一）利用python繪制折線圖折線圖（ Line Chart）簡(jiǎn)介折線圖用于在連續(xù)間隔或時(shí)間跨度上顯示定量數(shù)值，最常用來(lái)顯示趨勢(shì)和關(guān)系（與其他折線組合起來(lái)）。折線圖既能直觀地顯示數(shù)量隨時(shí)間的變化…

趨勢(shì)（一）利用python繪制折線圖

折線圖（ Line Chart）簡(jiǎn)介

折線圖用于在連續(xù)間隔或時(shí)間跨度上顯示定量數(shù)值，最常用來(lái)顯示趨勢(shì)和關(guān)系（與其他折線組合起來(lái)）。折線圖既能直觀地顯示數(shù)量隨時(shí)間的變化趨勢(shì)，也能展示兩個(gè)變量的關(guān)系。

快速繪制

基于matplotlib

import matplotlib.pyplot as plt
import numpy as np# 自定義數(shù)據(jù)
values = np.cumsum(np.random.randn(1000,1))# 繪制折線圖
plt.plot(values)
plt.show()

基于seaborn

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np# 自定義數(shù)據(jù)
values = np.cumsum(np.random.randn(1000,1))# 繪制折線圖
sns.lineplot(x=np.array(range(1, 1001)), y=values.ravel())  # 使用 ravel() 將 values 轉(zhuǎn)化為一維
plt.show()

基于plotly

import plotly.graph_objects as go
import numpy as np# 自定義數(shù)據(jù)
values = np.cumsum(np.random.randn(1000,1))# 繪制折線圖
fig = go.Figure(data=go.Scatter(x=list(range(1, 1001)), y=values.ravel(), mode='lines'))
fig.show()

基于pandas

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd# 自定義數(shù)據(jù)
values = np.cumsum(np.random.randn(1000,1))
df = pd.DataFrame(values, columns=['Values'])# 繪制折線圖
df.plot()
plt.show()

定制多樣化的折線圖

自定義折線圖一般是結(jié)合使用場(chǎng)景對(duì)相關(guān)參數(shù)進(jìn)行修改，并輔以其他的繪圖知識(shí)。參數(shù)信息可以通過(guò)官網(wǎng)進(jìn)行查看，其他的繪圖知識(shí)則更多來(lái)源于實(shí)戰(zhàn)經(jīng)驗(yàn)，大家不妨將接下來(lái)的繪圖作為一種學(xué)習(xí)經(jīng)驗(yàn)，以便于日后總結(jié)。

通過(guò)matplotlib繪制多樣化的折線圖

matplotlib主要利用plot繪制折線圖，可以通過(guò)matplotlib.pyplot.plot了解更多用法

修改參數(shù)

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pdplt.rcParams['font.sans-serif'] = ['SimHei'] # 用來(lái)正常顯示中文標(biāo)簽# 自定義數(shù)據(jù)
df=pd.DataFrame({'x_values': range(1,11), 'y_values': np.random.randn(10) })# 初始化布局
fig = plt.figure(figsize=(12,3))# 自定義顏色
plt.subplot(1, 3, 1) 
plt.plot( 'x_values', 'y_values', data=df, color='skyblue')
plt.title('自定義顏色')# 自定義透明度
plt.subplot(1, 3, 2) 
plt.plot( 'x_values', 'y_values', data=df, color='skyblue', alpha=0.3)
plt.title('自定義透明度')# 自定義線條
plt.subplot(1, 3, 3) 
plt.plot( 'x_values', 'y_values', data=df, linestyle='dashed', linewidth=5)
plt.title('自定義線條')plt.show()

帶注釋的折線圖

import matplotlib.pyplot as plt
import numpy as np
import pandas as pdplt.rcParams['font.sans-serif'] = ['SimHei'] # 用來(lái)正常顯示中文標(biāo)簽# 自定義數(shù)據(jù)
dates = []for date in range(1970,2023):dates.append(str(date))sample_size = 2023-1970
variable = np.random.normal(100,15,sample_size,)df = pd.DataFrame({'date': dates,'value': variable})# 初始化布局
fig = plt.figure(figsize=(12,8))# 1-基本折線圖
plt.subplot(2, 1, 1) 
plt.plot(df['date'], df['value'])# 軸標(biāo)簽
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('基本折線圖')# 刻度
plt.xticks(rotation=45)# 2-帶注釋的折線圖
plt.subplot(2, 1, 2) 
plt.plot(df['date'], df['value'])# 軸標(biāo)簽
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('帶注釋的折線圖')# 刻度
plt.xticks(rotation=45)# 添加文本注釋
plt.text(df['date'].iloc[38], # x位置df['value'].iloc[1], # y位置'What a nice chart!', # 文本注釋fontsize=13,color='red')# 找到最大值索引
highest_index = df['value'].idxmax()# 最高值標(biāo)記
plt.scatter(df['date'].iloc[highest_index],df['value'].iloc[highest_index],color='blue',marker='o', # 標(biāo)記特殊的店s=100,)# 計(jì)算均值
median_value = df['value'].median()# 添加均值線
plt.axhline(y=median_value, color='green', linestyle='--', label='Reference Line (Median)')fig.tight_layout() # 自動(dòng)調(diào)整間距
plt.show()

對(duì)數(shù)變換的折線圖

import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
import numpy as np
import pandas as pdplt.rcParams['font.sans-serif'] = ['SimHei'] # 用來(lái)正常顯示中文標(biāo)簽# 自定義數(shù)據(jù)
x = np.linspace(1, 10, 100)
y = np.exp(x)
df = pd.DataFrame({'x': x, 'y': y})# 初始化布局
fig = plt.figure(figsize=(12,4))# 1-基本折線圖
plt.subplot(1, 2, 1) 
plt.plot(df['x'], df['y'])# 軸標(biāo)簽
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('基本折線圖')# 添加網(wǎng)格線
plt.grid(True, linestyle='--', alpha=0.6)# 2-對(duì)數(shù)轉(zhuǎn)化折線圖
plt.subplot(1, 2, 2) 
plt.plot(df['x'], df['y'])# y軸對(duì)數(shù)化
plt.yscale('log')# 軸標(biāo)簽
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('對(duì)數(shù)化的折線圖')# 對(duì)數(shù)刻度的網(wǎng)格
y_major_locator = MultipleLocator(3000) 
plt.gca().yaxis.set_major_locator(y_major_locator)# 添加網(wǎng)格線
plt.grid(True, linestyle='--', alpha=0.6)plt.show()

雙軸折線圖

import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime, timedelta
from matplotlib import colorsplt.rcParams['font.sans-serif'] = ['SimHei'] # 用來(lái)正常顯示中文標(biāo)簽rng = np.random.default_rng(1234)date = [datetime(2019, 1, 1) + timedelta(i) for i in range(100)]
temperature = np.arange(100) ** 2.5 / 10000 + rng.uniform(size=100)
price = np.arange(120, 20, -1) ** 1.5 / 10 + rng.uniform(size=100)# 設(shè)置多子圖
fig, axarr = plt.subplots(2, 2, figsize=(12, 8))# 1-基礎(chǔ)的雙軸折線圖
ax1 = axarr[0, 0]
ax2 = ax1.twinx()
ax1.plot(date, temperature)
ax2.plot(date, price)
ax1.set_title('基礎(chǔ)的雙軸折線圖')# 2-自定義顏色雙軸
COLOR_TEMPERATURE = "#69b3a2"
COLOR_PRICE = "#3399e6"ax1 = axarr[0, 1]
ax2 = ax1.twinx()
ax1.plot(date, temperature, color=COLOR_TEMPERATURE, lw=3)
ax2.plot(date, price, color=COLOR_PRICE, lw=4)ax1.set_xlabel("Date")
ax1.set_ylabel("Temperature (Celsius °)", color=COLOR_TEMPERATURE, fontsize=14)
ax1.tick_params(axis="y", labelcolor=COLOR_TEMPERATURE)
ax2.set_ylabel("Price ($)", color=COLOR_PRICE, fontsize=14)
ax2.tick_params(axis="y", labelcolor=COLOR_PRICE)
ax1.set_title('自定義顏色雙軸')# 3-折線與條形圖組合
ax1 = axarr[1, 0]
ax2 = ax1.twinx()
ax1.bar(date, temperature, color=COLOR_TEMPERATURE, edgecolor="black", alpha=0.4, width=1.0)
ax2.plot(date, price, color=COLOR_PRICE, lw=4)ax1.set_xlabel("Date")
ax1.set_ylabel("Temperature (Celsius °)", color=COLOR_TEMPERATURE, fontsize=14)
ax1.tick_params(axis="y", labelcolor=COLOR_TEMPERATURE)ax2.set_ylabel("Price ($)", color=COLOR_PRICE, fontsize=14)
ax2.tick_params(axis="y", labelcolor=COLOR_PRICE)fig.autofmt_xdate()
ax1.set_title("折線與條形圖組合")# 4-自定義組合圖樣式
color = list(colors.to_rgba(COLOR_TEMPERATURE))
color[3] = 0.4ax1 = axarr[1, 1]
ax2 = ax1.twinx()
ax1.bar(date, temperature, color=color, edgecolor="black", width=1.0)
ax2.plot(date, price, color=COLOR_PRICE, lw=4)ax1.set_xlabel("Date")
ax1.set_ylabel("Temperature (Celsius °)", color=COLOR_TEMPERATURE, fontsize=14)
ax1.tick_params(axis="y", labelcolor=COLOR_TEMPERATURE)ax2.set_ylabel("Price ($)", color=COLOR_PRICE, fontsize=14)
ax2.tick_params(axis="y", labelcolor=COLOR_PRICE)fig.autofmt_xdate()
ax1.set_title("自定義組合圖樣式")fig.tight_layout() # 自動(dòng)調(diào)整間距
plt.show()

多個(gè)折線圖

import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime, timedelta
from matplotlib import colors
import matplotlib.gridspec as gridspecplt.rcParams['font.sans-serif'] = ['SimHei'] # 用來(lái)正常顯示中文標(biāo)簽# 創(chuàng)建 2x3 的大布局
fig = plt.figure(figsize=(18, 8))
gs = gridspec.GridSpec(2, 3, figure=fig)
# 獲得每個(gè)子圖的位置
ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1])
ax3 = fig.add_subplot(gs[0, 2])
# ax4 = fig.add_subplot(gs[1, 0])
# ax5 = fig.add_subplot(gs[1, 1])
ax6 = fig.add_subplot(gs[1, 2])# 1-基礎(chǔ)的多折線圖
df=pd.DataFrame({'x_values': range(1,11), 'y1_values': np.random.randn(10), 'y2_values': np.random.randn(10)+range(1,11), 'y3_values': np.random.randn(10)+range(11,21) })ax1.plot( 'x_values', 'y1_values', data=df, marker='o', markerfacecolor='blue', markersize=12, color='skyblue', linewidth=4)
ax1.plot( 'x_values', 'y2_values', data=df, marker='', color='olive', linewidth=2)
ax1.plot( 'x_values', 'y3_values', data=df, marker='', color='olive', linewidth=2, linestyle='dashed', label="toto")
ax1.legend()
ax1.set_title('基礎(chǔ)的多折線圖')# 2-高亮顯示一組
df=pd.DataFrame({'x': range(1,11), 'y1': np.random.randn(10), 'y2': np.random.randn(10)+range(1,11), 'y3': np.random.randn(10)+range(11,21), 'y4': np.random.randn(10)+range(6,16), 'y5': np.random.randn(10)+range(4,14)+(0,0,0,0,0,0,0,-3,-8,-6), 'y6': np.random.randn(10)+range(2,12), 'y7': np.random.randn(10)+range(5,15), 'y8': np.random.randn(10)+range(4,14) })for column in df.drop('x', axis=1):ax2.plot(df['x'], df[column], marker='', color='grey', linewidth=1, alpha=0.4)
ax2.plot(df['x'], df['y5'], marker='', color='orange', linewidth=4, alpha=0.7) # 高亮y5
ax2.set_xlim(0,12)
# 增加注釋
num=0
for i in df.values[9][1:]:num+=1name=list(df)[num]if name != 'y5':ax2.text(10.2, i, name, horizontalalignment='left', size='small', color='grey')else:ax2.text(10.2, i, 'Mr Orange', horizontalalignment='left', size='small', color='orange') # 高亮組的文本注釋
ax2.set_title("高亮顯示一組")# 3-多折線模式（Spaghetti Plot）
df=pd.DataFrame({'x': range(1,11), 'y1': np.random.randn(10), 'y2': np.random.randn(10)+range(1,11), 'y3': np.random.randn(10)+range(11,21), 'y4': np.random.randn(10)+range(6,16), 'y5': np.random.randn(10)+range(4,14)+(0,0,0,0,0,0,0,-3,-8,-6), 'y6': np.random.randn(10)+range(2,12), 'y7': np.random.randn(10)+range(5,15), 'y8': np.random.randn(10)+range(4,14), 'y9': np.random.randn(10)+range(4,14), 'y10': np.random.randn(10)+range(2,12) })palette = plt.get_cmap('Set1')
num=0
for column in df.drop('x', axis=1):num+=1ax3.plot(df['x'], df[column], marker='', color=palette(num), linewidth=1, alpha=0.9, label=column)ax3.legend(loc=2, ncol=2)
ax3.set_title("多折線模式（Spaghetti Plot）")# 4-多折線小圖
df=pd.DataFrame({'x': range(1,11), 'y1': np.random.randn(10), 'y2': np.random.randn(10)+range(1,11), 'y3': np.random.randn(10)+range(11,21), 'y4': np.random.randn(10)+range(6,16), 'y5': np.random.randn(10)+range(4,14)+(0,0,0,0,0,0,0,-3,-8,-6), 'y6': np.random.randn(10)+range(2,12), 'y7': np.random.randn(10)+range(5,15), 'y8': np.random.randn(10)+range(4,14), 'y9': np.random.randn(10)+range(4,14) })
# 在第一行第一列的位置創(chuàng)建3*3的子布局
ax4 = gridspec.GridSpecFromSubplotSpec(3, 3, subplot_spec=gs[1, 0])
# 在 3x3 的小布局中添加子圖
axes = []
for i in range(3):for j in range(3):ax = fig.add_subplot(ax4[i, j])axes.append(ax)  # 將子圖句柄添加到列表中
num = 0
for column in df.drop('x', axis=1):num += 1ax = axes[num - 1]ax.plot(df['x'], df[column], marker='', color=palette(num), linewidth=1.9, alpha=0.9, label=column)ax.set_xlim(0,10)ax.set_ylim(-2,22)# 如果當(dāng)前子圖不在最左邊，就不顯示y軸的刻度標(biāo)簽if num not in [1,4,7] :ax.tick_params(labelleft=False)# 如果當(dāng)前子圖不在最下邊，就不顯示x軸的刻度標(biāo)簽if num not in [7,8,9] :ax.tick_params(labelbottom=False)ax.annotate(column, xy=(0, 1), xycoords='axes fraction', fontsize=12, fontweight=0, color=palette(num),xytext=(5, -5), textcoords='offset points', ha='left', va='top')axes[1].set_title('多折線小圖') # 通過(guò)設(shè)置3*3圖的第二個(gè)子圖的標(biāo)題替代2*3圖中的第4個(gè)圖的子標(biāo)題# 5-多折線小圖細(xì)節(jié)處理
df=pd.DataFrame({'x': range(1,11), 'y1': np.random.randn(10), 'y2': np.random.randn(10)+range(1,11), 'y3': np.random.randn(10)+range(11,21), 'y4': np.random.randn(10)+range(6,16), 'y5': np.random.randn(10)+range(4,14)+(0,0,0,0,0,0,0,-3,-8,-6), 'y6': np.random.randn(10)+range(2,12), 'y7': np.random.randn(10)+range(5,15), 'y8': np.random.randn(10)+range(4,14), 'y9': np.random.randn(10)+range(4,14) })
# 在第一行第一列的位置創(chuàng)建3*3的子布局
ax5 = gridspec.GridSpecFromSubplotSpec(3, 3, subplot_spec=gs[1, 1])
# 在 3x3 的小布局中添加子圖
axes = []
for i in range(3):for j in range(3):ax = fig.add_subplot(ax5[i, j])axes.append(ax)  # 將子圖句柄添加到列表中
num=0
for column in df.drop('x', axis=1):num+=1ax = axes[num - 1]for v in df.drop('x', axis=1):ax.plot(df['x'], df[v], marker='', color='grey', linewidth=0.6, alpha=0.3)ax.plot(df['x'], df[column], marker='', color=palette(num), linewidth=2.4, alpha=0.9, label=column)ax.set_xlim(0,10)ax.set_ylim(-2,22)# 如果當(dāng)前子圖不在最左邊，就不顯示y軸的刻度標(biāo)簽if num not in [1,4,7] :ax.tick_params(labelleft=False)# 如果當(dāng)前子圖不在最下邊，就不顯示x軸的刻度標(biāo)簽if num not in [7,8,9] :ax.tick_params(labelbottom=False)ax.annotate(column, xy=(0, 1), xycoords='axes fraction', fontsize=12, fontweight=0, color=palette(num),xytext=(5, -5), textcoords='offset points', ha='left', va='top')axes[1].set_title('多折線小圖細(xì)節(jié)處理') # 通過(guò)設(shè)置3*3圖的第二個(gè)子圖的標(biāo)題替代2*3圖中的第5個(gè)圖的子標(biāo)題# 6-帶區(qū)域填充的多折線圖
time = np.arange(12)
income = np.array([5, 9, 6, 6, 10, 7, 6, 4, 4, 5, 6, 4])
expenses = np.array([6, 6, 8, 3, 6, 9, 7, 8, 6, 6, 4, 8])ax6.plot(time, income, color="green")
ax6.plot(time, expenses, color="red")# 當(dāng)income > expenses填充綠色
ax6.fill_between(time, income, expenses, where=(income > expenses), interpolate=True, color="green", alpha=0.25, label="Positive"
)# 當(dāng)income <= expenses填充紅色
ax6.fill_between(time, income, expenses, where=(income <= expenses), interpolate=True, color="red", alpha=0.25,label="Negative"
)ax6.set_title('帶區(qū)域填充的多折線圖')
ax6.legend()plt.tight_layout() # 自動(dòng)調(diào)整間距
plt.show()

繪制時(shí)間序列圖

import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.dates as mdates# 導(dǎo)入數(shù)據(jù)
data = pd.read_csv("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/3_TwoNumOrdered.csv", delim_whitespace=True
)# 日期格式
data["date"] = pd.to_datetime(data["date"])date = data["date"]
value = data["value"]# 繪制時(shí)間序列圖
fig, ax = plt.subplots(figsize=(8, 6))# 設(shè)置6個(gè)月間隔為一刻度
half_year_locator = mdates.MonthLocator(interval=6) # 半年刻度
monthly_locator = mdates.MonthLocator() # 每月子刻度
year_month_formatter = mdates.DateFormatter("%Y-%m") # 格式化日期yyyy-MMax.xaxis.set_major_locator(half_year_locator)
ax.xaxis.set_minor_locator(monthly_locator)
ax.xaxis.set_major_formatter(year_month_formatter)ax.plot(date, value)fig.autofmt_xdate() # 自動(dòng)旋轉(zhuǎn)軸標(biāo)簽

通過(guò)plotly繪制多樣化的折線圖

import plotly.graph_objects as go
import numpy as np
import pandas as pd# 自定義數(shù)據(jù)dates = []
start = 1990
end = 2022
for date in range(start,end):dates.append(str(date))# 生成隨機(jī)序列，并計(jì)算累計(jì)和來(lái)生成隨機(jī)漫步1、2、3
random_steps = np.random.choice([-1, 1], size=end-start, p=[0.5, 0.5])
random_walk1 = np.cumsum(random_steps)random_steps = np.random.choice([-1, 1], size=end-start, p=[0.5, 0.5])
random_walk2 = np.cumsum(random_steps)random_steps = np.random.choice([-1, 1], size=end-start, p=[0.5, 0.5])
random_walk3 = np.cumsum(random_steps)# 具有三個(gè)隨機(jī)漫步的數(shù)據(jù)
df = pd.DataFrame({'date': dates,'value1': random_walk1,'value2': random_walk2,'value3': random_walk3,})fig = go.Figure()# 自定義變量1
fig.add_trace(go.Scatter(x=df['date'],y=df['value1'],mode='lines+markers', # 點(diǎn)線連接樣式name='Line1',marker=dict(symbol='square',    size=10,           color='red'),      line=dict(color='blue',        width=5)            
))# 自定義變量2
fig.add_trace(go.Scatter(x=df['date'], y=df['value2'], mode='lines+markers', name='Line2', marker=dict(symbol='circle',   size=7,           color='purple'),      line=dict(color='orange',       width=8)            
))# 自定義變量3
fig.add_trace(go.Scatter(x=df['date'], y=df['value3'], mode='lines+markers', name='Line3',marker=dict(symbol='diamond',    size=15,            color='yellow'),       line=dict(color='green',       width=4)             
))# 自定義布局
fig.update_layout(title='Customized Line Chart',xaxis_title='X Axis Label', yaxis_title='Y Axis Label',xaxis_tickangle=45,  showlegend=True,    plot_bgcolor='white',  paper_bgcolor='lightblue', 
)

通過(guò)pandas繪制多樣化的折線圖

修改參數(shù)

import pandas as pd
import matplotlib.pyplot as plt# 導(dǎo)入數(shù)據(jù)
url = 'https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/gapminderData.csv'
df = pd.read_csv(url)
df_france = df[df['country']=='France']# 繪制折線圖
ax = df_france.plot(x='year',y='lifeExp',grid=True,linestyle='--',alpha=0.5,color='purple',linewidth=2.0, marker='d',  markersize=8,  markerfacecolor='orange', label='France')# 標(biāo)題
ax.set_title('Evolution of \nthe life expectancy in France',weight='bold') # 軸標(biāo)簽
ax.set_ylabel('Life Expectancy')
ax.set_xlabel('Time (in year)')plt.show()

多變量折線圖

import pandas as pd
import random, numpy as np
import matplotlib.pyplot as plt# 自定義數(shù)據(jù)
num_time_points = 100
time_values = np.arange(num_time_points)temperature = np.random.uniform(200, 400, num_time_points) 
pressure = np.random.uniform(500, 700, num_time_points) 
humidity = np.random.uniform(800, 1000, num_time_points) data = {'Time': time_values,'Temperature': temperature,'Pressure': pressure,'Humidity': humidity
}
df = pd.DataFrame(data)# 繪制多變量折線圖
df.plot(x='Time',kind='line', grid=True, )
plt.legend(loc='upper right',bbox_to_anchor=(1.35, 1), )
plt.show()

分組折線圖

import pandas as pd
import random, numpy as np
import matplotlib.pyplot as plt# 自定義數(shù)據(jù)
num_data_points_per_country = 20# 設(shè)置多個(gè)國(guó)家的溫度
france_temperatures = np.random.uniform(10, 20, num_data_points_per_country)  
germany_temperatures = np.random.uniform(0, 10, num_data_points_per_country) 
italy_temperatures = np.random.uniform(25, 30, num_data_points_per_country) # 對(duì)應(yīng)的國(guó)家數(shù)據(jù)
countries = ['France', 'Germany', 'Italy']
country_labels = np.repeat(countries, num_data_points_per_country)# 時(shí)間數(shù)據(jù)
time_values = np.tile(np.arange(num_data_points_per_country), len(countries))data = {'Country': country_labels,'Temperature': np.concatenate([france_temperatures, germany_temperatures, italy_temperatures]),'Time': time_values
}df = pd.DataFrame(data)# 繪制折線圖
for name, group in df.groupby('Country'):plt.plot(group['Time'], group['Temperature'], label=name)# 設(shè)置圖表的標(biāo)題和坐標(biāo)軸標(biāo)簽，并顯示圖例
plt.title('Temperature Trend over Time by Country')
plt.xlabel('Time')
plt.ylabel('Temperature')
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')# 顯示圖表
plt.show()

總結(jié)

以上通過(guò)matplotlib、seaborn、plotly和pandas快速繪制折線圖。并通過(guò)修改參數(shù)或者輔以其他繪圖知識(shí)自定義各種各樣的折線圖來(lái)適應(yīng)相關(guān)使用場(chǎng)景。

共勉～

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