使用matplotlib包绘制折线图

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# 导入所需的python包
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# 设置绘图格式
plt.style.use('seaborn')
%matplotlib inline

# 创建示例数据
values=np.cumsum(np.random.randn(1000,1))

# 查看示例数据
values[1:10]
array([ 0.24297415,  0.8161543 ,  0.32656029, -0.17953043, -0.80225196,
       -1.27380905, -0.28938979, -3.42407114, -3.58398279])</pre>

绘制基础折线图

# use the plot function
plt.plot(values)

# show the graph
plt.show()

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# 绘制未排序数据的折线图

# import the iris dataset
# 加载示例数据
df = sns.load_dataset('iris')
df.head(10)

# plot
plt.plot( 'sepal_width', 'sepal_length', data=df)

# show the graph
plt.show()

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# 绘制排序后数据的折线图

# 构建示例数据
df=pd.DataFrame({'xvalues': range(1,101), 'yvalues': np.random.randn(100) })
df.head(10)

# plot
plt.plot( 'xvalues', 'yvalues', data=df)

# show the graph
plt.show()

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自定义线的颜色

# 构建示例数据
df=pd.DataFrame({'x_values': range(1,11), 'y_values': np.random.randn(10) })

# Draw plot
# 设置color参数自定义线的颜色
plt.plot( 'x_values', 'y_values', data=df, color='skyblue')
plt.show()

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# Draw line chart by modifiying transparency of the line
# 设置alpha参数更改线的透明度
plt.plot( 'x_values', 'y_values', data=df, color='red', alpha=0.3)

# Show plot
plt.show()

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自定义线的类型

# Draw line chart with dashed line
# 设置linestyle参数自定义闲的类型
plt.plot( 'x_values', 'y_values', data=df, linestyle='dashed')

# Show graph
plt.show()

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# 查看不同的线型
plt.plot( [1,1.1,1,1.1,1], linestyle='-' , linewidth=4)
plt.text(1.5, 1.3, "linestyle = '-' ", horizontalalignment='left', size='medium', color='C0', weight='semibold')

plt.plot( [2,2.1,2,2.1,2], linestyle='--' , linewidth=4 )
plt.text(1.5, 2.3, "linestyle = '--' ", horizontalalignment='left', size='medium', color='C1', weight='semibold')

plt.plot( [3,3.1,3,3.1,3], linestyle='-.' , linewidth=4 )
plt.text(1.5, 3.3, "linestyle = '-.' ", horizontalalignment='left', size='medium', color='C2', weight='semibold')

plt.plot( [4,4.1,4,4.1,4], linestyle=':' , linewidth=4 )
plt.text(1.5, 4.3, "linestyle = ':' ", horizontalalignment='left', size='medium', color='C3', weight='semibold')

plt.axis('off')
plt.show()

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自定义线的宽度

# Modify line width of the graph
# 设置linewidth参数自定义线的宽度
plt.plot( 'x_values', 'y_values', data=df, linewidth=22)

# Show graph
plt.show()

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绘制多线折线图

# Data
# 构建示例数据
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) })

df.head(10)

# multiple line plots
plt.plot( 'x_values', 'y1_values', data=df, marker='o', markerfacecolor='blue', markersize=12, color='skyblue', linewidth=4)
plt.plot( 'x_values', 'y2_values', data=df, marker='', color='olive', linewidth=2)
plt.plot( 'x_values', 'y3_values', data=df, marker='', color='red', linewidth=3, linestyle='dashed', label="toto")

# show legend
plt.legend()

# show graph
plt.show()

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高亮特定折线

# Change the style of plot
plt.style.use('seaborn-darkgrid')

# set figure size
my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)

# Make a data frame
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) })
df.head(10)

# 绘制多条折线图
# plot multiple lines
for column in df.drop('x', axis=1):
    plt.plot(df['x'], df[column], marker='', color='grey', linewidth=1, alpha=0.4)

# 高亮其中的一条折线
# Now re do the interesting curve, but biger with distinct color
plt.plot(df['x'], df['y5'], marker='', color='orange', linewidth=4, alpha=0.7)

# Change x axis limit
plt.xlim(0,12)

# 给每条折线添加注释信息
# Let's annotate the plot
num=0
for i in df.values[9][1:]:
    num+=1
    name=list(df)[num]
    if name != 'y5':
        plt.text(10.2, i, name, horizontalalignment='left', size='small', color='gray')

# And add a special annotation for the group we are interested in
plt.text(10.2, df.y5.tail(1), 'Mr Orange', horizontalalignment='left', size='small', color='orange')

# 添加标题和坐标轴
# Add titles
plt.title("Evolution of Mr Orange vs other students", loc='left', fontsize=12, fontweight=0, color='orange')
plt.xlabel("Time")
plt.ylabel("Score")

# Show the graph
plt.show()

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绘制分面折线图

# Initialize the figure style
plt.style.use('seaborn-darkgrid')

# create a color palette
palette = plt.get_cmap('Set1')

# 构建示例数据
# Make a data frame
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) })
df.head(10)

# multiple line plot
num=0
for column in df.drop('x', axis=1):
    num+=1

    # Find the right spot on the plot
    plt.subplot(3,3, num)

    # Plot the lineplot
    plt.plot(df['x'], df[column], marker='', color=palette(num), linewidth=1.9, alpha=0.9, label=column)

    # Same limits for every chart
    plt.xlim(0,10)
    plt.ylim(-2,22)

    # Not ticks everywhere
    if num in range(7) :
        plt.tick_params(labelbottom='off')
    if num not in [1,4,7] :
        plt.tick_params(labelleft='off')

    # Add title
    plt.title(column, loc='center', fontsize=12, fontweight=0, color=palette(num) )

# general title
plt.suptitle("How the 9 students improved\nthese past few days?", fontsize=13, fontweight=0, color='black', style='italic', y=1.02)

# Axis titles
plt.text(5, 0, 'Time', ha='center', va='center')
plt.text(0, 10, 'Note', ha='center', va='center', rotation='vertical')

# Show the graph
plt.show()

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# multiple line plot
num=0
for column in df.drop('x', axis=1):
    num+=1

    # Find the right spot on the plot
    plt.subplot(3,3, num)

    # plot every group, but discrete
    for v in df.drop('x', axis=1):
        plt.plot(df['x'], df[v], marker='', color='grey', linewidth=1, alpha=0.3)

    # Plot the lineplot
    plt.plot(df['x'], df[column], marker='', color=palette(num), linewidth=3, alpha=0.9, label=column)

    # Same limits for every chart
    plt.xlim(0,10)
    plt.ylim(-2,22)

    # Not ticks everywhere
    if num in range(7) :
        plt.tick_params(labelbottom='off')
    if num not in [1,4,7] :
        plt.tick_params(labelleft='off')

    # Add title
    plt.title(column, loc='center', fontsize=12, fontweight=0, color=palette(num) )

# general title
plt.suptitle("How the 9 students improved\nthese past few days?", fontsize=13, fontweight=0, color='black', style='italic', y=1.02)

# Axis titles
plt.text(5, 0, 'Time', ha='center', va='center')
plt.text(0, 10, 'Note', ha='center', va='center', rotation='vertical')

# Show the graph
plt.show()

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参考来源：https://www.python-graph-gallery.com/line-chart/