Hướng dẫn how do you draw a vertical line on a scatter plot in python? - làm cách nào để bạn vẽ một đường thẳng đứng trên biểu đồ phân tán trong python?

matplotlib.pyplot.vlines so với matplotlib.pyplot.axvline

• Các phương pháp này được áp dụng cho các lô được tạo ra với Seaborn và pandas.DataFrame.plot, cả hai đều sử dụng matplotlib.
• Sự khác biệt là vlines chấp nhận một hoặc nhiều vị trí cho

  import seaborn as sns
  
  # sample data
  fmri = sns.load_dataset("fmri")
  
  # x index for max y values for stim and cue
  c_max, s_max = fmri.pivot_table(index='timepoint', columns='event', values='signal', aggfunc='mean').idxmax()
  
  # plot
  g = sns.lineplot(data=fmri, x="timepoint", y="signal", hue="event")
  
  # y min and max
  ymin, ymax = g.get_ylim()
  
  # vertical lines
  g.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2)
  

  0, trong khi

  import seaborn as sns
  
  # sample data
  fmri = sns.load_dataset("fmri")
  
  # x index for max y values for stim and cue
  c_max, s_max = fmri.pivot_table(index='timepoint', columns='event', values='signal', aggfunc='mean').idxmax()
  
  # plot
  g = sns.lineplot(data=fmri, x="timepoint", y="signal", hue="event")
  
  # y min and max
  ymin, ymax = g.get_ylim()
  
  # vertical lines
  g.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2)
  

  1 cho phép một vị trí.
  • Vị trí đơn:

    import seaborn as sns
    
    # sample data
    fmri = sns.load_dataset("fmri")
    
    # x index for max y values for stim and cue
    c_max, s_max = fmri.pivot_table(index='timepoint', columns='event', values='signal', aggfunc='mean').idxmax()
    
    # plot
    g = sns.lineplot(data=fmri, x="timepoint", y="signal", hue="event")
    
    # y min and max
    ymin, ymax = g.get_ylim()
    
    # vertical lines
    g.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2)
    

    2.
  • Nhiều vị trí:

    import seaborn as sns
    
    # sample data
    fmri = sns.load_dataset("fmri")
    
    # x index for max y values for stim and cue
    c_max, s_max = fmri.pivot_table(index='timepoint', columns='event', values='signal', aggfunc='mean').idxmax()
    
    # plot
    g = sns.lineplot(data=fmri, x="timepoint", y="signal", hue="event")
    
    # y min and max
    ymin, ymax = g.get_ylim()
    
    # vertical lines
    g.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2)
    

    3.
• vlines lấy

  import seaborn as sns
  
  # sample data
  fmri = sns.load_dataset("fmri")
  
  # x index for max y values for stim and cue
  c_max, s_max = fmri.pivot_table(index='timepoint', columns='event', values='signal', aggfunc='mean').idxmax()
  
  # plot
  g = sns.lineplot(data=fmri, x="timepoint", y="signal", hue="event")
  
  # y min and max
  ymin, ymax = g.get_ylim()
  
  # vertical lines
  g.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2)
  

  5 và

  import seaborn as sns
  
  # sample data
  fmri = sns.load_dataset("fmri")
  
  # x index for max y values for stim and cue
  c_max, s_max = fmri.pivot_table(index='timepoint', columns='event', values='signal', aggfunc='mean').idxmax()
  
  # plot
  g = sns.lineplot(data=fmri, x="timepoint", y="signal", hue="event")
  
  # y min and max
  ymin, ymax = g.get_ylim()
  
  # vertical lines
  g.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2)
  

  6 làm vị trí trên trục y, trong khi

  import seaborn as sns
  
  # sample data
  fmri = sns.load_dataset("fmri")
  
  # x index for max y values for stim and cue
  c_max, s_max = fmri.pivot_table(index='timepoint', columns='event', values='signal', aggfunc='mean').idxmax()
  
  # plot
  g = sns.lineplot(data=fmri, x="timepoint", y="signal", hue="event")
  
  # y min and max
  ymin, ymax = g.get_ylim()
  
  # vertical lines
  g.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2)
  

  1 lấy

  import seaborn as sns
  
  # sample data
  fmri = sns.load_dataset("fmri")
  
  # x index for max y values for stim and cue
  c_max, s_max = fmri.pivot_table(index='timepoint', columns='event', values='signal', aggfunc='mean').idxmax()
  
  # plot
  g = sns.lineplot(data=fmri, x="timepoint", y="signal", hue="event")
  
  # y min and max
  ymin, ymax = g.get_ylim()
  
  # vertical lines
  g.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2)
  

  5 và

  import seaborn as sns
  
  # sample data
  fmri = sns.load_dataset("fmri")
  
  # x index for max y values for stim and cue
  c_max, s_max = fmri.pivot_table(index='timepoint', columns='event', values='signal', aggfunc='mean').idxmax()
  
  # plot
  g = sns.lineplot(data=fmri, x="timepoint", y="signal", hue="event")
  
  # y min and max
  ymin, ymax = g.get_ylim()
  
  # vertical lines
  g.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2)
  

  6 dưới dạng phần trăm của phạm vi trục y.
  • Khi chuyển nhiều dòng cho vlines, chuyển A

    import seaborn as sns
    
    # sample data
    fmri = sns.load_dataset("fmri")
    
    # used to get the index values (x) for max y for each event in each region
    fpt = fmri.pivot_table(index=['region', 'timepoint'], columns='event', values='signal', aggfunc='mean')
    
    # plot
    g = sns.relplot(data=fmri, x="timepoint", y="signal", col="region", hue="event", kind="line")
    
    # iterate through the axes
    for ax in g.axes.flat:
        # get y min and max
        ymin, ymax = ax.get_ylim()
        # extract the region from the title for use in selecting the index of fpt
        region = ax.get_title().split(' = ')[1]
        # get x values for max event
        c_max, s_max = fpt.loc[region].idxmax()
        # add vertical lines
        ax.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2, alpha=0.5)
    

    1 đến

    import seaborn as sns
    
    # sample data
    fmri = sns.load_dataset("fmri")
    
    # x index for max y values for stim and cue
    c_max, s_max = fmri.pivot_table(index='timepoint', columns='event', values='signal', aggfunc='mean').idxmax()
    
    # plot
    g = sns.lineplot(data=fmri, x="timepoint", y="signal", hue="event")
    
    # y min and max
    ymin, ymax = g.get_ylim()
    
    # vertical lines
    g.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2)
    

    5 và

    import seaborn as sns
    
    # sample data
    fmri = sns.load_dataset("fmri")
    
    # x index for max y values for stim and cue
    c_max, s_max = fmri.pivot_table(index='timepoint', columns='event', values='signal', aggfunc='mean').idxmax()
    
    # plot
    g = sns.lineplot(data=fmri, x="timepoint", y="signal", hue="event")
    
    # y min and max
    ymin, ymax = g.get_ylim()
    
    # vertical lines
    g.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2)
    

    6.
• Cũng

  import seaborn as sns
  
  # sample data
  fmri = sns.load_dataset("fmri")
  
  # used to get the index values (x) for max y for each event in each region
  fpt = fmri.pivot_table(index=['region', 'timepoint'], columns='event', values='signal', aggfunc='mean')
  
  # plot
  g = sns.relplot(data=fmri, x="timepoint", y="signal", col="region", hue="event", kind="line")
  
  # iterate through the axes
  for ax in g.axes.flat:
      # get y min and max
      ymin, ymax = ax.get_ylim()
      # extract the region from the title for use in selecting the index of fpt
      region = ax.get_title().split(' = ')[1]
      # get x values for max event
      c_max, s_max = fpt.loc[region].idxmax()
      # add vertical lines
      ax.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2, alpha=0.5)
  

  4 và

  import seaborn as sns
  
  # sample data
  fmri = sns.load_dataset("fmri")
  
  # used to get the index values (x) for max y for each event in each region
  fpt = fmri.pivot_table(index=['region', 'timepoint'], columns='event', values='signal', aggfunc='mean')
  
  # plot
  g = sns.relplot(data=fmri, x="timepoint", y="signal", col="region", hue="event", kind="line")
  
  # iterate through the axes
  for ax in g.axes.flat:
      # get y min and max
      ymin, ymax = ax.get_ylim()
      # extract the region from the title for use in selecting the index of fpt
      region = ax.get_title().split(' = ')[1]
      # get x values for max event
      c_max, s_max = fpt.loc[region].idxmax()
      # add vertical lines
      ax.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2, alpha=0.5)
  

  5 cho API hướng đối tượng.
  • Nếu bạn đang vẽ một con số với một cái gì đó như

    import seaborn as sns
    
    # sample data
    fmri = sns.load_dataset("fmri")
    
    # used to get the index values (x) for max y for each event in each region
    fpt = fmri.pivot_table(index=['region', 'timepoint'], columns='event', values='signal', aggfunc='mean')
    
    # plot
    g = sns.relplot(data=fmri, x="timepoint", y="signal", col="region", hue="event", kind="line")
    
    # iterate through the axes
    for ax in g.axes.flat:
        # get y min and max
        ymin, ymax = ax.get_ylim()
        # extract the region from the title for use in selecting the index of fpt
        region = ax.get_title().split(' = ')[1]
        # get x values for max event
        c_max, s_max = fpt.loc[region].idxmax()
        # add vertical lines
        ax.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2, alpha=0.5)
    

    6, thì hãy thay thế

    import seaborn as sns
    
    # sample data
    fmri = sns.load_dataset("fmri")
    
    # used to get the index values (x) for max y for each event in each region
    fpt = fmri.pivot_table(index=['region', 'timepoint'], columns='event', values='signal', aggfunc='mean')
    
    # plot
    g = sns.relplot(data=fmri, x="timepoint", y="signal", col="region", hue="event", kind="line")
    
    # iterate through the axes
    for ax in g.axes.flat:
        # get y min and max
        ymin, ymax = ax.get_ylim()
        # extract the region from the title for use in selecting the index of fpt
        region = ax.get_title().split(' = ')[1]
        # get x values for max event
        c_max, s_max = fpt.loc[region].idxmax()
        # add vertical lines
        ax.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2, alpha=0.5)
    

    7 hoặc

    import seaborn as sns
    
    # sample data
    fmri = sns.load_dataset("fmri")
    
    # used to get the index values (x) for max y for each event in each region
    fpt = fmri.pivot_table(index=['region', 'timepoint'], columns='event', values='signal', aggfunc='mean')
    
    # plot
    g = sns.relplot(data=fmri, x="timepoint", y="signal", col="region", hue="event", kind="line")
    
    # iterate through the axes
    for ax in g.axes.flat:
        # get y min and max
        ymin, ymax = ax.get_ylim()
        # extract the region from the title for use in selecting the index of fpt
        region = ax.get_title().split(' = ')[1]
        # get x values for max event
        c_max, s_max = fpt.loc[region].idxmax()
        # add vertical lines
        ax.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2, alpha=0.5)
    

    8 bằng

    import seaborn as sns
    
    # sample data
    fmri = sns.load_dataset("fmri")
    
    # used to get the index values (x) for max y for each event in each region
    fpt = fmri.pivot_table(index=['region', 'timepoint'], columns='event', values='signal', aggfunc='mean')
    
    # plot
    g = sns.relplot(data=fmri, x="timepoint", y="signal", col="region", hue="event", kind="line")
    
    # iterate through the axes
    for ax in g.axes.flat:
        # get y min and max
        ymin, ymax = ax.get_ylim()
        # extract the region from the title for use in selecting the index of fpt
        region = ax.get_title().split(' = ')[1]
        # get x values for max event
        c_max, s_max = fpt.loc[region].idxmax()
        # add vertical lines
        ax.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2, alpha=0.5)
    

    9 hoặc

    import pandas as pd
    import seaborn as sns
    
    # load data
    tips = sns.load_dataset('tips')
    
    # histogram
    ax = tips.plot(kind='hist', y='total_bill', bins=30, ec='k', title='Histogram with Vertical Line')
    _ = ax.vlines(x=16.5, ymin=0, ymax=30, colors='r')
    
    # barplot
    ax = tips.loc[5:25, ['total_bill', 'tip']].plot(kind='bar', figsize=(15, 4), title='Barplot with Vertical Lines', rot=0)
    _ = ax.vlines(x=[0, 17], ymin=0, ymax=45, colors='r')
    

    0, tương ứng.
• Xem câu trả lời này cho các đường ngang với

  import pandas as pd
  import seaborn as sns
  
  # load data
  tips = sns.load_dataset('tips')
  
  # histogram
  ax = tips.plot(kind='hist', y='total_bill', bins=30, ec='k', title='Histogram with Vertical Line')
  _ = ax.vlines(x=16.5, ymin=0, ymax=30, colors='r')
  
  # barplot
  ax = tips.loc[5:25, ['total_bill', 'tip']].plot(kind='bar', figsize=(15, 4), title='Barplot with Vertical Lines', rot=0)
  _ = ax.vlines(x=[0, 17], ymin=0, ymax=45, colors='r')
  

  1.

import numpy as np
import matplotlib.pyplot as plt

xs = np.linspace(1, 21, 200)

plt.figure(figsize=(10, 7))

# only one line may be specified; full height
plt.axvline(x=36, color='b', label='axvline - full height')

# only one line may be specified; ymin & ymax specified as a percentage of y-range
plt.axvline(x=36.25, ymin=0.05, ymax=0.95, color='b', label='axvline - % of full height')

# multiple lines all full height
plt.vlines(x=[37, 37.25, 37.5], ymin=0, ymax=len(xs), colors='purple', ls='--', lw=2, label='vline_multiple - full height')

# multiple lines with varying ymin and ymax
plt.vlines(x=[38, 38.25, 38.5], ymin=[0, 25, 75], ymax=[200, 175, 150], colors='teal', ls='--', lw=2, label='vline_multiple - partial height')

# single vline with full ymin and ymax
plt.vlines(x=39, ymin=0, ymax=len(xs), colors='green', ls=':', lw=2, label='vline_single - full height')

# single vline with specific ymin and ymax
plt.vlines(x=39.25, ymin=25, ymax=150, colors='green', ls=':', lw=2, label='vline_single - partial height')

# place the legend outside
plt.legend(bbox_to_anchor=(1.0, 1), loc='upper left')

plt.show()

Cốt truyện ở cấp độ biển

import seaborn as sns

# sample data
fmri = sns.load_dataset("fmri")

# x index for max y values for stim and cue
c_max, s_max = fmri.pivot_table(index='timepoint', columns='event', values='signal', aggfunc='mean').idxmax()

# plot
g = sns.lineplot(data=fmri, x="timepoint", y="signal", hue="event")

# y min and max
ymin, ymax = g.get_ylim()

# vertical lines
g.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2)

Cốt truyện ở mức độ biển

• Mỗi trục phải được lặp lại thông qua.

import seaborn as sns

# sample data
fmri = sns.load_dataset("fmri")

# used to get the index values (x) for max y for each event in each region
fpt = fmri.pivot_table(index=['region', 'timepoint'], columns='event', values='signal', aggfunc='mean')

# plot
g = sns.relplot(data=fmri, x="timepoint", y="signal", col="region", hue="event", kind="line")

# iterate through the axes
for ax in g.axes.flat:
    # get y min and max
    ymin, ymax = ax.get_ylim()
    # extract the region from the title for use in selecting the index of fpt
    region = ax.get_title().split(' = ')[1]
    # get x values for max event
    c_max, s_max = fpt.loc[region].idxmax()
    # add vertical lines
    ax.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2, alpha=0.5)

• Đối với

  import pandas as pd
  import seaborn as sns
  
  # load data
  tips = sns.load_dataset('tips')
  
  # histogram
  ax = tips.plot(kind='hist', y='total_bill', bins=30, ec='k', title='Histogram with Vertical Line')
  _ = ax.vlines(x=16.5, ymin=0, ymax=30, colors='r')
  
  # barplot
  ax = tips.loc[5:25, ['total_bill', 'tip']].plot(kind='bar', figsize=(15, 4), title='Barplot with Vertical Lines', rot=0)
  _ = ax.vlines(x=[0, 17], ymin=0, ymax=45, colors='r')
  

  2 giá trị tối đa của cả hai sự kiện xảy ra tại

  import pandas as pd
  import seaborn as sns
  
  # load data
  tips = sns.load_dataset('tips')
  
  # histogram
  ax = tips.plot(kind='hist', y='total_bill', bins=30, ec='k', title='Histogram with Vertical Line')
  _ = ax.vlines(x=16.5, ymin=0, ymax=30, colors='r')
  
  # barplot
  ax = tips.loc[5:25, ['total_bill', 'tip']].plot(kind='bar', figsize=(15, 4), title='Barplot with Vertical Lines', rot=0)
  _ = ax.vlines(x=[0, 17], ymin=0, ymax=45, colors='r')
  

  3.

Barplot và biểu đồ

• Lưu ý rằng các vị trí đánh dấu âm thanh của thanh có chỉ mục dựa trên không, bất kể nhãn đánh dấu trục, vì vậy hãy chọn

  import seaborn as sns
  
  # sample data
  fmri = sns.load_dataset("fmri")
  
  # x index for max y values for stim and cue
  c_max, s_max = fmri.pivot_table(index='timepoint', columns='event', values='signal', aggfunc='mean').idxmax()
  
  # plot
  g = sns.lineplot(data=fmri, x="timepoint", y="signal", hue="event")
  
  # y min and max
  ymin, ymax = g.get_ylim()
  
  # vertical lines
  g.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2)
  

  0 dựa trên chỉ mục thanh, không phải nhãn Tick.

  • import pandas as pd
    import seaborn as sns
    
    # load data
    tips = sns.load_dataset('tips')
    
    # histogram
    ax = tips.plot(kind='hist', y='total_bill', bins=30, ec='k', title='Histogram with Vertical Line')
    _ = ax.vlines(x=16.5, ymin=0, ymax=30, colors='r')
    
    # barplot
    ax = tips.loc[5:25, ['total_bill', 'tip']].plot(kind='bar', figsize=(15, 4), title='Barplot with Vertical Lines', rot=0)
    _ = ax.vlines(x=[0, 17], ymin=0, ymax=45, colors='r')
    

    5 sẽ hiển thị các vị trí và nhãn.

import pandas as pd
import seaborn as sns

# load data
tips = sns.load_dataset('tips')

# histogram
ax = tips.plot(kind='hist', y='total_bill', bins=30, ec='k', title='Histogram with Vertical Line')
_ = ax.vlines(x=16.5, ymin=0, ymax=30, colors='r')

# barplot
ax = tips.loc[5:25, ['total_bill', 'tip']].plot(kind='bar', figsize=(15, 4), title='Barplot with Vertical Lines', rot=0)
_ = ax.vlines(x=[0, 17], ymin=0, ymax=45, colors='r')

Trục thời gian

• Các ngày trong khung dữ liệu là trục x phải là

  import pandas as pd
  import seaborn as sns
  
  # load data
  tips = sns.load_dataset('tips')
  
  # histogram
  ax = tips.plot(kind='hist', y='total_bill', bins=30, ec='k', title='Histogram with Vertical Line')
  _ = ax.vlines(x=16.5, ymin=0, ymax=30, colors='r')
  
  # barplot
  ax = tips.loc[5:25, ['total_bill', 'tip']].plot(kind='bar', figsize=(15, 4), title='Barplot with Vertical Lines', rot=0)
  _ = ax.vlines(x=[0, 17], ymin=0, ymax=45, colors='r')
  

  6. Nếu cột hoặc chỉ mục không phải là loại chính xác, nó phải được chuyển đổi với

  import pandas as pd
  import seaborn as sns
  
  # load data
  tips = sns.load_dataset('tips')
  
  # histogram
  ax = tips.plot(kind='hist', y='total_bill', bins=30, ec='k', title='Histogram with Vertical Line')
  _ = ax.vlines(x=16.5, ymin=0, ymax=30, colors='r')
  
  # barplot
  ax = tips.loc[5:25, ['total_bill', 'tip']].plot(kind='bar', figsize=(15, 4), title='Barplot with Vertical Lines', rot=0)
  _ = ax.vlines(x=[0, 17], ymin=0, ymax=45, colors='r')
  

  7.
  • Nếu một mảng hoặc danh sách ngày đang được sử dụng, hãy tham khảo chuyển đổi mảng chuỗi numpy thành DateTime hoặc chuyển đổi danh sách DateTime thành Python ngày, tương ứng.

• import seaborn as sns
  
  # sample data
  fmri = sns.load_dataset("fmri")
  
  # x index for max y values for stim and cue
  c_max, s_max = fmri.pivot_table(index='timepoint', columns='event', values='signal', aggfunc='mean').idxmax()
  
  # plot
  g = sns.lineplot(data=fmri, x="timepoint", y="signal", hue="event")
  
  # y min and max
  ymin, ymax = g.get_ylim()
  
  # vertical lines
  g.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2)
  

  0 sẽ chấp nhận một ngày như

  import pandas as pd
  import seaborn as sns
  
  # load data
  tips = sns.load_dataset('tips')
  
  # histogram
  ax = tips.plot(kind='hist', y='total_bill', bins=30, ec='k', title='Histogram with Vertical Line')
  _ = ax.vlines(x=16.5, ymin=0, ymax=30, colors='r')
  
  # barplot
  ax = tips.loc[5:25, ['total_bill', 'tip']].plot(kind='bar', figsize=(15, 4), title='Barplot with Vertical Lines', rot=0)
  _ = ax.vlines(x=[0, 17], ymin=0, ymax=45, colors='r')
  

  9 hoặc

  import pandas_datareader as web  # conda or pip install this; not part of pandas
  import pandas as pd
  import matplotlib.pyplot as plt
  from datetime import datetime
  
  # get test data; this data is downloaded with the Date column in the index as a datetime dtype
  df = web.DataReader('^gspc', data_source='yahoo', start='2020-09-01', end='2020-09-28').iloc[:, :2]
  
  # display(df.head(2))
                     High          Low
  Date                                
  2020-09-01  3528.030029  3494.600098
  2020-09-02  3588.110107  3535.229980
  
  # plot dataframe; the index is a datetime index
  ax = df.plot(figsize=(9, 6), title='S&P 500', ylabel='Price')
  
  # add vertical lines
  ax.vlines(x=[datetime(2020, 9, 2), '2020-09-24'], ymin=3200, ymax=3600, color='r', label='test lines')
  
  ax.legend(bbox_to_anchor=(1, 1), loc='upper left')
  plt.show()
  

  0.

import pandas_datareader as web  # conda or pip install this; not part of pandas
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime

# get test data; this data is downloaded with the Date column in the index as a datetime dtype
df = web.DataReader('^gspc', data_source='yahoo', start='2020-09-01', end='2020-09-28').iloc[:, :2]

# display(df.head(2))
                   High          Low
Date                                
2020-09-01  3528.030029  3494.600098
2020-09-02  3588.110107  3535.229980

# plot dataframe; the index is a datetime index
ax = df.plot(figsize=(9, 6), title='S&P 500', ylabel='Price')

# add vertical lines
ax.vlines(x=[datetime(2020, 9, 2), '2020-09-24'], ymin=3200, ymax=3600, color='r', label='test lines')

ax.legend(bbox_to_anchor=(1, 1), loc='upper left')
plt.show()