如何修复 matplotlib 图中的重叠线
How to fix the overlapping lines in matplotlib plot
我的一些要点和线条重叠，因此不太清楚每年的结果是什么。
我已经阅读了之前关于同一问题的帖子（问题 55710343 和 40766909）。但是，他们对我没有帮助，因为他们假设像 alpha 和 linestyle 这样的参数可以在 plt.plots 中单独更改，而我的是 df.plot，里面有几个列值。
无论如何，这是您可以完全重现的我的代码。
我的实际数据有8行10年
#Downloading data
import numpy as np
import pandas as pd
from IPython.display import display, HTML
def install_module(module):
! conda install "$module" -y
js_cmd = ['IPython.notebook.kernel.restart();',
'IPython.notebook.select(1);',
'IPython.notebook.execute_cell();'
]
js = "<script>{0}</script>".format(' '.join(js_cmd))
display(HTML(js))
url = 'http://www.svpl.org.uk/index.php/2019-league-tables?year=2019-league-tables'
try:
df_list = pd.read_html(url)
except Exception as e:
print(e)
# #install necessary modules for read_html
module = str(e).split()[0]
install_module(module)
print('Number of Data Frames {}'.format(len(df_list)))
df_list[0].columns = df_list[0].iloc[0]
df19 = df_list[0].iloc[0:]
names=['Rank', 'Teams', 'Played','Points', 'Won','Lost','GF', 'GA', 'GD', 'PF', 'PA','PD','CurrentForm']
df19.columns = [names]
df19 = df19[['Rank', 'Teams','Points','GD','PD']]
url = 'http://www.svpl.org.uk/index.php/2018-league-tables?year=2018-league-tables'
try:
df_list = pd.read_html(url)
except Exception as e:
print(e)
# #install necessary modules for read_html
module = str(e).split()[0]
install_module(module)
print('Number of Data Frames {}'.format(len(df_list)))
df_list[0].columns = df_list[0].iloc[0]
df18 = df_list[0].iloc[0:]
names1=['Rank', 'Teams', 'Played','Points', 'Won','Lost','Drawn', 'GD', 'PF', 'PA','PD','CurrentForm']
df18.columns = [names1]
df18 = df18[['Rank', 'Teams','Points','GD','PD']]
url = 'http://www.svpl.org.uk/index.php/2017-league-tables?year=2017-league-tables'
try:
df_list = pd.read_html(url)
except Exception as e:
print(e)
# #install necessary modules for read_html
module = str(e).split()[0]
install_module(module)
print('Number of Data Frames {}'.format(len(df_list)))
df_list[0].columns = df_list[0].iloc[0]
df17 = df_list[0].iloc[0:]
names2=['Rank', 'Teams', 'Played','Points', 'Won','Lost', 'GD', 'PF', 'PA','PD','CurrentForm']
df17.columns = [names2]
df17 = df17[['Rank', 'Teams','Points','GD','PD']]
url = 'http://www.svpl.org.uk/index.php/2016-league-tables?year=2016-league-tables'
try:
df_list = pd.read_html(url)
except Exception as e:
print(e)
# #install necessary modules for read_html
module = str(e).split()[0]
install_module(module)
print('Number of Data Frames {}'.format(len(df_list)))
df_list[0].columns = df_list[0].iloc[0]
df16 = df_list[0].iloc[0:]
names3=['Rank', 'Teams','Played', 'Won','Lost', 'Points','GD', 'CurrentForm']
df16.columns = [names3]
df16 = df16[['Rank', 'Teams','Points','GD']]
url='http://www.svpl.org.uk/index.php/2015-league-tables?year=2015-league-tables'
try:
df_list = pd.read_html(url)
except Exception as e:
print(e)
# #install necessary modules for read_html
module = str(e).split()[0]
install_module(module)
print('Number of Data Frames {}'.format(len(df_list)))
df_list[0].columns = df_list[0].iloc[0]
df15 = df_list[0].iloc[0:]
names2=['Rank', 'Teams','Played', 'Points','Won','Lost', 'GD','PF','PA','PD','CurrentForm']
df15.columns = [names2]
df15 = df15[['Rank', 'Teams','Points','GD', 'PD']]
url='http://www.svpl.org.uk/index.php/2014-league-tables?year=2014-league-tables'
try:
df_list = pd.read_html(url)
except Exception as e:
print(e)
# #install necessary modules for read_html
module = str(e).split()[0]
install_module(module)
print('Number of Data Frames {}'.format(len(df_list)))
df_list[0].columns = df_list[0].iloc[0]
df14 = df_list[0].iloc[0:]
names2=['Rank', 'Teams', 'Played','Points', 'Won','Lost', 'GD', 'PF', 'PA','PD','CurrentForm']
df14.columns = [names2]
df14 = df14[['Rank', 'Teams','Points','GD','PD']]
# Filtering dataframes
AlveleyRoyals=df15[df15.values == 'Alveley Royals'].merge(df16[df16.values == 'Alveley Royals'], how='outer').merge(df17[df17.values == 'Alveley Royals'], how='outer').merge(df18[df18.values == 'Alveley Royals'], how='outer').merge(df19[df19.values == 'Alveley Royals'], how='outer')
AlveleyRoyals=AlveleyRoyals[['Teams','Points', 'GD']]
AlveleyRoyals.index.name='Year'
row=['Alveley Royals',0, np.NaN]
AlveleyRoyals.loc[-1] = row
AlveleyRoyals = AlveleyRoyals.sort_index().reset_index(drop=True)
AlveleyRoyals=AlveleyRoyals.rename(index={0: '2015', 1:'2016',2:'2017',3:'2018',4:'2019'},columns={'Points':'Aveley Royals'})
AlveleyRoyals=AlveleyRoyals[['Aveley Royals']]
TopPub=df15[df15.values == 'Top Pub'].merge(df16[df16.values == 'Top Pub'], how='outer').merge(df17[df17.values == 'Top Pub'], how='outer').merge(df18[df18.values == 'Top Pub'], how='outer').merge(df19[df19.values == 'Top Pub'], how='outer')
TopPub=TopPub[['Teams','Points', 'GD']]
TopPub.index.name='Year'
row=['Top Pub',0,np.NaN]
TopPub.loc[0.5] = row
TopPub = TopPub.sort_index().reset_index(drop=True)
TopPub=TopPub.rename(index={0: '2015', 1:'2016',2:'2017',3:'2018',4:'2019'},columns={'Points':'Top Pub'})
TopPub=TopPub[['Top Pub']]
AlveleyOaks=df14[df14.values == 'Alveley Oaks'].merge(df16[df16.values == 'Alveley Oaks'], how='outer').merge(df17[df17.values == 'Alveley Oaks'], how='outer').merge(df18[df18.values == 'Alveley Oaks'], how='outer').merge(df19[df19.values == 'Alveley Oaks'], how='outer')
AlveleyOaks=AlveleyOaks[['Teams','Points', 'GD']]
AlveleyOaks.index.name='Year'
row=['Alveley Oaks',0, np.NaN]
AlveleyOaks.loc[-0.2] = row #2015
AlveleyOaks.loc[-0.3] = row #2016
AlveleyOaks.loc[-0.4] = row #2017
AlveleyOaks = AlveleyOaks.sort_index().reset_index(drop=True)
AlveleyOaks = AlveleyOaks.rename(index={0: '2015', 1:'2016',2:'2017',3:'2018',4:'2019'}, columns={'Points':'Alveley Oaks'})
AlveleyOaks=AlveleyOaks[['Alveley Oaks']]
# Merging dataframes
merged=AlveleyRoyals.merge(TopPub, left_index=True, right_index=True).merge(AlveleyOaks,left_index=True, right_index=True)
merged
# Plotting lines
import matplotlib.pyplot as plt
from matplotlib.ticker import FixedLocator, FormatStrFormatter
#%matplotlib notebook
plt.style.use('seaborn-poster')
fig,ax = plt.subplots(figsize=(8,6))
my_colors=['steelblue','darkviolet','orangered']
ax=merged.plot( marker='o',figsize=(9,6),legend=None, color=my_colors, ax=ax)
for line, name in zip(ax.lines, merged.columns):
y = line.get_ydata()[-1]
ax.annotate(name, xy=(1,y), xytext=(6,0), color=line.get_color(),
xycoords = ax.get_yaxis_transform(), textcoords="offset points",
size=16, va="center")
x_axis = ax.axes.get_xaxis()
x_label = x_axis.get_label()
x_label.set_visible(False)
ax.set_ylabel('Points won', color='darkblue', fontdict={'fontsize': 15, 'fontweight': 'medium'})
years=[2015, 2016,2017,2018, 2019]
plt.xticks(range(len(years)),(years))
plt.margins(x=0.012,y=0.018)
ax.get_xaxis().tick_bottom()
#ax.get_yaxis().tick_left()
ax.tick_params(axis='y', which='major', labelsize=12,bottom=False, top=False, labelbottom=True,
left=False, right=False, labelright=False)
ax.tick_params(axis='x', which='major', labelsize=14,bottom=False, top=False, labelbottom=True,
left=False, right=False, labelright=False)
ax.yaxis.set_major_formatter(plt.FuncFormatter('{:.0f}'.format))
ax.grid(True, 'major', 'y', ls='--', lw=.7, c='darkgray', alpha=.5)
plt.show()
不知道为什么圆括号出现在标签上，如果有人能告诉我如何解决这个问题，我将不胜感激。
您可以在 pandas 绘图中使用与 plt.plot 相同的方式使用 alpha 关键字。
为了应用 y 偏移量转换，您需要逐列绘制数据框并以不同方式移动每个图。在这种情况下，您首先需要设置 y 轴限制，否则 pandas 无法正确计算。
圆括号来自多索引。它可以通过将其转换为常规索引来修复。
import matplotlib.transforms as mtrans
merged.columns = merged.columns.get_level_values(0)
ax.set_ylim(merged.min().min(), merged.max().max())
for i,col in enumerate(merged):
merged.plot(y=col, marker='o', figsize=(9,6), legend=None, color=my_colors[i], ax=ax, alpha=.5,
transform=mtrans.offset_copy(ax.transData, fig=fig, x=0.0, y=3*(i-1), units='points'))
我的一些要点和线条重叠，因此不太清楚每年的结果是什么。 我已经阅读了之前关于同一问题的帖子（问题 55710343 和 40766909）。但是，他们对我没有帮助，因为他们假设像 alpha 和 linestyle 这样的参数可以在 plt.plots 中单独更改，而我的是 df.plot，里面有几个列值。
无论如何，这是您可以完全重现的我的代码。 我的实际数据有8行10年
#Downloading data
import numpy as np
import pandas as pd
from IPython.display import display, HTML
def install_module(module):
! conda install "$module" -y
js_cmd = ['IPython.notebook.kernel.restart();',
'IPython.notebook.select(1);',
'IPython.notebook.execute_cell();'
]
js = "<script>{0}</script>".format(' '.join(js_cmd))
display(HTML(js))
url = 'http://www.svpl.org.uk/index.php/2019-league-tables?year=2019-league-tables'
try:
df_list = pd.read_html(url)
except Exception as e:
print(e)
# #install necessary modules for read_html
module = str(e).split()[0]
install_module(module)
print('Number of Data Frames {}'.format(len(df_list)))
df_list[0].columns = df_list[0].iloc[0]
df19 = df_list[0].iloc[0:]
names=['Rank', 'Teams', 'Played','Points', 'Won','Lost','GF', 'GA', 'GD', 'PF', 'PA','PD','CurrentForm']
df19.columns = [names]
df19 = df19[['Rank', 'Teams','Points','GD','PD']]
url = 'http://www.svpl.org.uk/index.php/2018-league-tables?year=2018-league-tables'
try:
df_list = pd.read_html(url)
except Exception as e:
print(e)
# #install necessary modules for read_html
module = str(e).split()[0]
install_module(module)
print('Number of Data Frames {}'.format(len(df_list)))
df_list[0].columns = df_list[0].iloc[0]
df18 = df_list[0].iloc[0:]
names1=['Rank', 'Teams', 'Played','Points', 'Won','Lost','Drawn', 'GD', 'PF', 'PA','PD','CurrentForm']
df18.columns = [names1]
df18 = df18[['Rank', 'Teams','Points','GD','PD']]
url = 'http://www.svpl.org.uk/index.php/2017-league-tables?year=2017-league-tables'
try:
df_list = pd.read_html(url)
except Exception as e:
print(e)
# #install necessary modules for read_html
module = str(e).split()[0]
install_module(module)
print('Number of Data Frames {}'.format(len(df_list)))
df_list[0].columns = df_list[0].iloc[0]
df17 = df_list[0].iloc[0:]
names2=['Rank', 'Teams', 'Played','Points', 'Won','Lost', 'GD', 'PF', 'PA','PD','CurrentForm']
df17.columns = [names2]
df17 = df17[['Rank', 'Teams','Points','GD','PD']]
url = 'http://www.svpl.org.uk/index.php/2016-league-tables?year=2016-league-tables'
try:
df_list = pd.read_html(url)
except Exception as e:
print(e)
# #install necessary modules for read_html
module = str(e).split()[0]
install_module(module)
print('Number of Data Frames {}'.format(len(df_list)))
df_list[0].columns = df_list[0].iloc[0]
df16 = df_list[0].iloc[0:]
names3=['Rank', 'Teams','Played', 'Won','Lost', 'Points','GD', 'CurrentForm']
df16.columns = [names3]
df16 = df16[['Rank', 'Teams','Points','GD']]
url='http://www.svpl.org.uk/index.php/2015-league-tables?year=2015-league-tables'
try:
df_list = pd.read_html(url)
except Exception as e:
print(e)
# #install necessary modules for read_html
module = str(e).split()[0]
install_module(module)
print('Number of Data Frames {}'.format(len(df_list)))
df_list[0].columns = df_list[0].iloc[0]
df15 = df_list[0].iloc[0:]
names2=['Rank', 'Teams','Played', 'Points','Won','Lost', 'GD','PF','PA','PD','CurrentForm']
df15.columns = [names2]
df15 = df15[['Rank', 'Teams','Points','GD', 'PD']]
url='http://www.svpl.org.uk/index.php/2014-league-tables?year=2014-league-tables'
try:
df_list = pd.read_html(url)
except Exception as e:
print(e)
# #install necessary modules for read_html
module = str(e).split()[0]
install_module(module)
print('Number of Data Frames {}'.format(len(df_list)))
df_list[0].columns = df_list[0].iloc[0]
df14 = df_list[0].iloc[0:]
names2=['Rank', 'Teams', 'Played','Points', 'Won','Lost', 'GD', 'PF', 'PA','PD','CurrentForm']
df14.columns = [names2]
df14 = df14[['Rank', 'Teams','Points','GD','PD']]
# Filtering dataframes
AlveleyRoyals=df15[df15.values == 'Alveley Royals'].merge(df16[df16.values == 'Alveley Royals'], how='outer').merge(df17[df17.values == 'Alveley Royals'], how='outer').merge(df18[df18.values == 'Alveley Royals'], how='outer').merge(df19[df19.values == 'Alveley Royals'], how='outer')
AlveleyRoyals=AlveleyRoyals[['Teams','Points', 'GD']]
AlveleyRoyals.index.name='Year'
row=['Alveley Royals',0, np.NaN]
AlveleyRoyals.loc[-1] = row
AlveleyRoyals = AlveleyRoyals.sort_index().reset_index(drop=True)
AlveleyRoyals=AlveleyRoyals.rename(index={0: '2015', 1:'2016',2:'2017',3:'2018',4:'2019'},columns={'Points':'Aveley Royals'})
AlveleyRoyals=AlveleyRoyals[['Aveley Royals']]
TopPub=df15[df15.values == 'Top Pub'].merge(df16[df16.values == 'Top Pub'], how='outer').merge(df17[df17.values == 'Top Pub'], how='outer').merge(df18[df18.values == 'Top Pub'], how='outer').merge(df19[df19.values == 'Top Pub'], how='outer')
TopPub=TopPub[['Teams','Points', 'GD']]
TopPub.index.name='Year'
row=['Top Pub',0,np.NaN]
TopPub.loc[0.5] = row
TopPub = TopPub.sort_index().reset_index(drop=True)
TopPub=TopPub.rename(index={0: '2015', 1:'2016',2:'2017',3:'2018',4:'2019'},columns={'Points':'Top Pub'})
TopPub=TopPub[['Top Pub']]
AlveleyOaks=df14[df14.values == 'Alveley Oaks'].merge(df16[df16.values == 'Alveley Oaks'], how='outer').merge(df17[df17.values == 'Alveley Oaks'], how='outer').merge(df18[df18.values == 'Alveley Oaks'], how='outer').merge(df19[df19.values == 'Alveley Oaks'], how='outer')
AlveleyOaks=AlveleyOaks[['Teams','Points', 'GD']]
AlveleyOaks.index.name='Year'
row=['Alveley Oaks',0, np.NaN]
AlveleyOaks.loc[-0.2] = row #2015
AlveleyOaks.loc[-0.3] = row #2016
AlveleyOaks.loc[-0.4] = row #2017
AlveleyOaks = AlveleyOaks.sort_index().reset_index(drop=True)
AlveleyOaks = AlveleyOaks.rename(index={0: '2015', 1:'2016',2:'2017',3:'2018',4:'2019'}, columns={'Points':'Alveley Oaks'})
AlveleyOaks=AlveleyOaks[['Alveley Oaks']]
# Merging dataframes
merged=AlveleyRoyals.merge(TopPub, left_index=True, right_index=True).merge(AlveleyOaks,left_index=True, right_index=True)
merged
# Plotting lines
import matplotlib.pyplot as plt
from matplotlib.ticker import FixedLocator, FormatStrFormatter
#%matplotlib notebook
plt.style.use('seaborn-poster')
fig,ax = plt.subplots(figsize=(8,6))
my_colors=['steelblue','darkviolet','orangered']
ax=merged.plot( marker='o',figsize=(9,6),legend=None, color=my_colors, ax=ax)
for line, name in zip(ax.lines, merged.columns):
y = line.get_ydata()[-1]
ax.annotate(name, xy=(1,y), xytext=(6,0), color=line.get_color(),
xycoords = ax.get_yaxis_transform(), textcoords="offset points",
size=16, va="center")
x_axis = ax.axes.get_xaxis()
x_label = x_axis.get_label()
x_label.set_visible(False)
ax.set_ylabel('Points won', color='darkblue', fontdict={'fontsize': 15, 'fontweight': 'medium'})
years=[2015, 2016,2017,2018, 2019]
plt.xticks(range(len(years)),(years))
plt.margins(x=0.012,y=0.018)
ax.get_xaxis().tick_bottom()
#ax.get_yaxis().tick_left()
ax.tick_params(axis='y', which='major', labelsize=12,bottom=False, top=False, labelbottom=True,
left=False, right=False, labelright=False)
ax.tick_params(axis='x', which='major', labelsize=14,bottom=False, top=False, labelbottom=True,
left=False, right=False, labelright=False)
ax.yaxis.set_major_formatter(plt.FuncFormatter('{:.0f}'.format))
ax.grid(True, 'major', 'y', ls='--', lw=.7, c='darkgray', alpha=.5)
plt.show()
不知道为什么圆括号出现在标签上，如果有人能告诉我如何解决这个问题，我将不胜感激。
您可以在 pandas 绘图中使用与 plt.plot 相同的方式使用 alpha 关键字。
为了应用 y 偏移量转换，您需要逐列绘制数据框并以不同方式移动每个图。在这种情况下，您首先需要设置 y 轴限制，否则 pandas 无法正确计算。
圆括号来自多索引。它可以通过将其转换为常规索引来修复。
import matplotlib.transforms as mtrans
merged.columns = merged.columns.get_level_values(0)
ax.set_ylim(merged.min().min(), merged.max().max())
for i,col in enumerate(merged):
merged.plot(y=col, marker='o', figsize=(9,6), legend=None, color=my_colors[i], ax=ax, alpha=.5,
transform=mtrans.offset_copy(ax.transData, fig=fig, x=0.0, y=3*(i-1), units='points'))