趋势（一）利用python绘制折线图

HsuHeinrich

发布于 2024-11-23 08:46:24

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代码可运行

趋势（一）利用python绘制折线图

折线图（ Line Chart）简介

折线图用于在连续间隔或时间跨度上显示定量数值，最常用来显示趋势和关系（与其他折线组合起来）。折线图既能直观地显示数量随时间的变化趋势，也能展示两个变量的关系。

快速绘制

基于matplotlib

import matplotlib.pyplot as plt
import numpy as np

# 自定义数据
values = np.cumsum(np.random.randn(1000,1))

# 绘制折线图
plt.plot(values)
plt.show()

基于seaborn

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# 自定义数据
values = np.cumsum(np.random.randn(1000,1))

# 绘制折线图
sns.lineplot(x=np.array(range(1, 1001)), y=values.ravel())  # 使用 ravel() 将 values 转化为一维
plt.show()

基于plotly

import plotly.graph_objects as go
import numpy as np

# 自定义数据
values = np.cumsum(np.random.randn(1000,1))

# 绘制折线图
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

# 自定义数据
values = np.cumsum(np.random.randn(1000,1))
df = pd.DataFrame(values, columns=['Values'])

# 绘制折线图
df.plot()
plt.show()

定制多样化的折线图

自定义折线图一般是结合使用场景对相关参数进行修改，并辅以其他的绘图知识。参数信息可以通过官网进行查看，其他的绘图知识则更多来源于实战经验，大家不妨将接下来的绘图作为一种学习经验，以便于日后总结。

通过matplotlib绘制多样化的折线图

matplotlib主要利用plot绘制折线图，可以通过matplotlib.pyplot.plot[1]了解更多用法

修改参数

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签

# 自定义数据
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))

# 自定义颜色
plt.subplot(1, 3, 1) 
plt.plot( 'x_values', 'y_values', data=df, color='skyblue')
plt.title('自定义颜色')

# 自定义透明度
plt.subplot(1, 3, 2) 
plt.plot( 'x_values', 'y_values', data=df, color='skyblue', alpha=0.3)
plt.title('自定义透明度')

# 自定义线条
plt.subplot(1, 3, 3) 
plt.plot( 'x_values', 'y_values', data=df, linestyle='dashed', linewidth=5)
plt.title('自定义线条')


plt.show()

带注释的折线图

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签

# 自定义数据
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-基本折线图
plt.subplot(2, 1, 1) 
plt.plot(df['date'], df['value'])

# 轴标签
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('基本折线图')

# 刻度
plt.xticks(rotation=45)


# 2-带注释的折线图
plt.subplot(2, 1, 2) 
plt.plot(df['date'], df['value'])

# 轴标签
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('带注释的折线图')

# 刻度
plt.xticks(rotation=45)

# 添加文本注释
plt.text(df['date'].iloc[38], # x位置
         df['value'].iloc[1], # y位置
         'What a nice chart!', # 文本注释
         fontsize=13,
         color='red')

# 找到最大值索引
highest_index = df['value'].idxmax()

# 最高值标记
plt.scatter(df['date'].iloc[highest_index],
            df['value'].iloc[highest_index],
            color='blue',
            marker='o', # 标记特殊的店
            s=100,
           )

# 计算均值
median_value = df['value'].median()

# 添加均值线
plt.axhline(y=median_value, color='green', linestyle='--', label='Reference Line (Median)')


fig.tight_layout() # 自动调整间距
plt.show()

对数变换的折线图

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

plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签

# 自定义数据
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-基本折线图
plt.subplot(1, 2, 1) 
plt.plot(df['x'], df['y'])

# 轴标签
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('基本折线图')

# 添加网格线
plt.grid(True, linestyle='--', alpha=0.6)

# 2-对数转化折线图
plt.subplot(1, 2, 2) 
plt.plot(df['x'], df['y'])

# y轴对数化
plt.yscale('log')

# 轴标签
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('对数化的折线图')

# 对数刻度的网格
y_major_locator = MultipleLocator(3000) 
plt.gca().yaxis.set_major_locator(y_major_locator)

# 添加网格线
plt.grid(True, linestyle='--', alpha=0.6)

plt.show()

双轴折线图

import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime, timedelta
from matplotlib import colors

plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签

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)

# 设置多子图
fig, axarr = plt.subplots(2, 2, figsize=(12, 8))

# 1-基础的双轴折线图
ax1 = axarr[0, 0]
ax2 = ax1.twinx()
ax1.plot(date, temperature)
ax2.plot(date, price)
ax1.set_title('基础的双轴折线图')

# 2-自定义颜色双轴
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('自定义颜色双轴')

# 3-折线与条形图组合
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("折线与条形图组合")

# 4-自定义组合图样式
color = list(colors.to_rgba(COLOR_TEMPERATURE))
color[3] = 0.4

ax1 = 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("自定义组合图样式")


fig.tight_layout() # 自动调整间距
plt.show()

多个折线图

import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime, timedelta
from matplotlib import colors
import matplotlib.gridspec as gridspec

plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签

# 创建 2x3 的大布局
fig = plt.figure(figsize=(18, 8))
gs = gridspec.GridSpec(2, 3, figure=fig)
# 获得每个子图的位置
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-基础的多折线图
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('基础的多折线图')



# 2-高亮显示一组
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)
# 增加注释
num=0
for i in df.values[9][1:]:
    num+=1
    name=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') # 高亮组的文本注释
ax2.set_title("高亮显示一组")



# 3-多折线模式（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+=1
    ax3.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("多折线模式（Spaghetti Plot）")



# 4-多折线小图
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创建3*3的子布局
ax4 = gridspec.GridSpecFromSubplotSpec(3, 3, subplot_spec=gs[1, 0])
# 在 3x3 的小布局中添加子图
axes = []
for i in range(3):
    for j in range(3):
        ax = fig.add_subplot(ax4[i, j])
        axes.append(ax)  # 将子图句柄添加到列表中
num = 0
for column in df.drop('x', axis=1):
    num += 1

    ax = 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)


    # 如果当前子图不在最左边，就不显示y轴的刻度标签
    if num not in [1,4,7] :
        ax.tick_params(labelleft=False)
        
    # 如果当前子图不在最下边，就不显示x轴的刻度标签
    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('多折线小图') # 通过设置3*3图的第二个子图的标题替代2*3图中的第4个图的子标题
    
    
# 5-多折线小图细节处理
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创建3*3的子布局
ax5 = gridspec.GridSpecFromSubplotSpec(3, 3, subplot_spec=gs[1, 1])
# 在 3x3 的小布局中添加子图
axes = []
for i in range(3):
    for j in range(3):
        ax = fig.add_subplot(ax5[i, j])
        axes.append(ax)  # 将子图句柄添加到列表中
num=0
for column in df.drop('x', axis=1):
    num+=1
 
    ax = 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)
 
     # 如果当前子图不在最左边，就不显示y轴的刻度标签
    if num not in [1,4,7] :
        ax.tick_params(labelleft=False)
        
    # 如果当前子图不在最下边，就不显示x轴的刻度标签
    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('多折线小图细节处理') # 通过设置3*3图的第二个子图的标题替代2*3图中的第5个图的子标题
    
    
# 6-带区域填充的多折线图
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")

# 当income > expenses填充绿色
ax6.fill_between(
    time, income, expenses, where=(income > expenses), 
    interpolate=True, color="green", alpha=0.25, 
    label="Positive"
)

# 当income <= expenses填充红色
ax6.fill_between(
    time, income, expenses, where=(income <= expenses), 
    interpolate=True, color="red", alpha=0.25,
    label="Negative"
)

ax6.set_title('带区域填充的多折线图')
ax6.legend()


plt.tight_layout() # 自动调整间距
plt.show()

绘制时间序列图

import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.dates as mdates

# 导入数据
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"]

# 绘制时间序列图
fig, ax = plt.subplots(figsize=(8, 6))

# 设置6个月间隔为一刻度
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-MM

ax.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() # 自动旋转轴标签

通过plotly绘制多样化的折线图

import plotly.graph_objects as go
import numpy as np
import pandas as pd

# 自定义数据

dates = []
start = 1990
end = 2022
for date in range(start,end):
    dates.append(str(date))
    
# 生成随机序列，并计算累计和来生成随机漫步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)

# 具有三个随机漫步的数据
df = pd.DataFrame({'date': dates,
                   'value1': random_walk1,
                   'value2': random_walk2,
                   'value3': random_walk3,})


fig = go.Figure()

# 自定义变量1
fig.add_trace(go.Scatter(
    x=df['date'],
    y=df['value1'],
    mode='lines+markers', # 点线连接样式
    name='Line1',
    
    marker=dict(
        symbol='square',    
        size=10,           
        color='red'),      
    
    line=dict(
        color='blue',        
        width=5)            
))

# 自定义变量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)            
))

# 自定义变量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)             
))

# 自定义布局
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', 
)

通过pandas绘制多样化的折线图

修改参数

import pandas as pd
import matplotlib.pyplot as plt


# 导入数据
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']


# 绘制折线图
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'
                   )

# 标题
ax.set_title('Evolution of \nthe life expectancy in France',
             weight='bold') 

# 轴标签
ax.set_ylabel('Life Expectancy')
ax.set_xlabel('Time (in year)')


plt.show()

多变量折线图

import pandas as pd
import random, numpy as np
import matplotlib.pyplot as plt

# 自定义数据
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)

# 绘制多变量折线图
df.plot(x='Time',
        kind='line', 
        grid=True, 
        )
plt.legend(loc='upper right',
           bbox_to_anchor=(1.35, 1), 
          )
plt.show()

分组折线图

import pandas as pd
import random, numpy as np
import matplotlib.pyplot as plt

# 自定义数据
num_data_points_per_country = 20

# 设置多个国家的温度
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) 

# 对应的国家数据
countries = ['France', 'Germany', 'Italy']
country_labels = np.repeat(countries, num_data_points_per_country)

# 时间数据
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)


# 绘制折线图
for name, group in df.groupby('Country'):
    plt.plot(group['Time'], group['Temperature'], label=name)

# 设置图表的标题和坐标轴标签，并显示图例
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')

# 显示图表
plt.show()