Python+matplotlib实现折线图的美化
1. 导入包
import pandas as pd import matplotlib.pyplot as plt import matplotlib.ticker as ticker import matplotlib.gridspec as gridspec
2. 获得数据
file_id = ' 1yM_F93NY4QkxjlKL3GzdcCQEnBiA2ltB '‘Python学习交流群：748989764 ’ url = f ' https://drive.google.com/uc?id={file_id} ' df = pd.read_csv(url, index_col= 0)
df
数据长得是这样的：
3. 对数据做一些预处理
按照需要，对数据再做一些预处理，代码及效果如下：
home_df = df.copy()
home_df = home_df.melt(id_vars = [ " date " , " home_team_name " , " away_team_name " ])
home_df[ " venue " ] = " H " home_df.rename(columns = { " home_team_name " : " team " , " away_team_name " : " opponent " }, inplace = True)
home_df.replace({ " variable " :{ " home_team_xG " : " xG_for " , " away_team_xG " : " xG_ag " }}, inplace = True)
away_df = df.copy()
away_df = away_df.melt(id_vars = [ " date " , " away_team_name " , " home_team_name " ])
away_df[ " venue " ] = " A " away_df.rename(columns = { " away_team_name " : " team " , " home_team_name " : " opponent " }, inplace = True)
away_df.replace({ " variable " :{ " away_team_xG " : " xG_for " , " home_team_xG " : " xG_ag " }}, inplace = True)
df = pd.concat([home_df, away_df]).reset_index(drop = True)
df 
4. 画图
# ---- Filter the data Y_for = df[(df[ " team " ] == " Lazio " ) & (df[ " variable " ] == " xG_for " )][ " value " ].reset_index(drop = True)
Y_ag = df[(df[ " team " ] == " Lazio " ) & (df[ " variable " ] == " xG_ag " )][ " value " ].reset_index(drop = True)
X_ = pd.Series(range(len(Y_for))) # ---- Compute rolling average Y_for = Y_for.rolling(window = 5, min_periods = 0).mean() # min_periods is for partial avg. Y_ag = Y_ag.rolling(window = 5, min_periods = 0).mean()
fig, ax = plt.subplots(figsize = (7,3), dpi = 200 )
ax.plot(X_, Y_for)
ax.plot(X_, Y_ag) 
使用matplotlib倒是可以快速把图画好了，但是太丑了。接下来进行优化。
4.1 优化：添加点
这里为每一个数据添加点
fig, ax = plt.subplots(figsize = (7,3), dpi = 200 ) # --- Remove spines and add gridlines ax.spines[ " left " ].set_visible(False)
ax.spines[ " top " ].set_visible(False)
ax.spines[ " right " ].set_visible(False)
ax.grid(ls = " -- " , lw = 0.5, color = " #4E616C " ) # --- The data ax.plot(X_, Y_for, marker = " o " )
ax.plot(X_, Y_ag, marker = " o " )
4.2 优化：设置刻度
fig, ax = plt.subplots(figsize = (7,3), dpi = 200 ) # --- Remove spines and add gridlines ax.spines[ " left " ].set_visible(False)
ax.spines[ " top " ].set_visible(False)
ax.spines[ " right " ].set_visible(False)
ax.grid(ls = " -- " , lw = 0.25, color = " #4E616C " ) # --- The data ax.plot(X_, Y_for, marker = " o " , mfc = " white " , ms = 5 )
ax.plot(X_, Y_ag, marker = " o " , mfc = " white " , ms = 5 ) # --- Adjust tickers and spine to match the style of our grid ax.xaxis.set_major_locator(ticker.MultipleLocator( 2)) # ticker every 2 matchdays xticks_ = ax.xaxis.set_ticklabels([x - 1 for x in range(0, len(X_) + 3, 2 )]) # This last line outputs # [-1, 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35] # and we mark the tickers every two positions. ax.xaxis.set_tick_params(length = 2, color = " #4E616C " , labelcolor = " #4E616C " , labelsize = 6 )
ax.yaxis.set_tick_params(length = 2, color = " #4E616C " , labelcolor = " #4E616C " , labelsize = 6 )
ax.spines[ " bottom " ].set_edgecolor( " #4E616C " )
4.3 优化：设置填充
fig, ax = plt.subplots(figsize = (7,3), dpi = 200 ) Python学习交流群：748989764
# --- Remove spines and add gridlines ax.spines[ " left " ].set_visible(False)
ax.spines[ " top " ].set_visible(False)
ax.spines[ " right " ].set_visible(False)
ax.grid(ls = " -- " , lw = 0.25, color = " #4E616C " ) # --- The data ax.plot(X_, Y_for, marker = " o " , mfc = " white " , ms = 5 )
ax.plot(X_, Y_ag, marker = " o " , mfc = " white " , ms = 5 ) # --- Fill between ax.fill_between(x = X_, y1 = Y_for, y2 = Y_ag, alpha = 0.5 ) # --- Adjust tickers and spine to match the style of our grid ax.xaxis.set_major_locator(ticker.MultipleLocator( 2)) # ticker every 2 matchdays xticks_ = ax.xaxis.set_ticklabels([x - 1 for x in range(0, len(X_) + 3, 2 )])
ax.xaxis.set_tick_params(length = 2, color = " #4E616C " , labelcolor = " #4E616C " , labelsize = 6 )
ax.yaxis.set_tick_params(length = 2, color = " #4E616C " , labelcolor = " #4E616C " , labelsize = 6 )
ax.spines[ " bottom " ].set_edgecolor( " #4E616C " )
4.4 优化：设置填充颜色
1.当橙色线更高时，希望填充为橙色。但是上面的还无法满足，这里再优化一下.
fig, ax = plt.subplots(figsize = (7,3), dpi = 200 ) # --- Remove spines and add gridlines ax.spines[ " left " ].set_visible(False)
ax.spines[ " top " ].set_visible(False)
ax.spines[ " right " ].set_visible(False)
ax.grid(ls = " -- " , lw = 0.25, color = " #4E616C " ) # --- The data ax.plot(X_, Y_for, marker = " o " , mfc = " white " , ms = 5 )
ax.plot(X_, Y_ag, marker = " o " , mfc = " white " , ms = 5 ) # --- Fill between # Identify points where Y_for > Y_ag pos_for = (Y_for > Y_ag)
ax.fill_between(x = X_[pos_for], y1 = Y_for[pos_for], y2 = Y_ag[pos_for], alpha = 0.5 )
pos_ag = (Y_for <= Y_ag)
ax.fill_between(x = X_[pos_ag], y1 = Y_for[pos_ag], y2 = Y_ag[pos_ag], alpha = 0.5 ) # --- Adjust tickers and spine to match the style of our grid ax.xaxis.set_major_locator(ticker.MultipleLocator( 2)) # ticker every 2 matchdays xticks_ = ax.xaxis.set_ticklabels([x - 1 for x in range(0, len(X_) + 3, 2 )])
ax.xaxis.set_tick_params(length = 2, color = " #4E616C " , labelcolor = " #4E616C " , labelsize = 6 )
ax.yaxis.set_tick_params(length = 2, color = " #4E616C " , labelcolor = " #4E616C " , labelsize = 6 )
ax.spines[ " bottom " ].set_edgecolor( " #4E616C " )
上面的图出现异常，再修改一下：
X_aux = X_.copy()
X_aux.index = X_aux.index * 10 # 9 aux points in between each match last_idx = X_aux.index[-1] + 1 X_aux = X_aux.reindex(range(last_idx))
X_aux = X_aux.interpolate() # --- Aux series for the xG created (Y_for) Y_for_aux = Y_for.copy()
Y_for_aux.index = Y_for_aux.index * 10 last_idx = Y_for_aux.index[-1] + 1 Y_for_aux = Y_for_aux.reindex(range(last_idx))
Y_for_aux = Y_for_aux.interpolate() # --- Aux series for the xG conceded (Y_ag) Y_ag_aux = Y_ag.copy()
Y_ag_aux.index = Y_ag_aux.index * 10 last_idx = Y_ag_aux.index[-1] + 1 Y_ag_aux = Y_ag_aux.reindex(range(last_idx))
Y_ag_aux = Y_ag_aux.interpolate()
fig, ax = plt.subplots(figsize = (7,3), dpi = 200 ) # --- Remove spines and add gridlines ax.spines[ " left " ].set_visible(False)
ax.spines[ " top " ].set_visible(False)
ax.spines[ " right " ].set_visible(False)
ax.grid(ls = " -- " , lw = 0.25, color = " #4E616C " ) # --- The data for_ = ax.plot(X_, Y_for, marker = " o " , mfc = " white " , ms = 5 )
ag_ = ax.plot(X_, Y_ag, marker = " o " , mfc = " white " , ms = 5 ) # --- Fill between for index in range(len(X_aux) - 1 ): # Choose color based on which line's on top if Y_for_aux.iloc[index + 1] > Y_ag_aux.iloc[index + 1 ]:
color = for_[0].get_color() else :
color = ag_[0].get_color() # Fill between the current point and the next point in pur extended series. ax.fill_between([X_aux[index], X_aux[index+1 ]],
[Y_for_aux.iloc[index], Y_for_aux.iloc[index +1 ]],
[Y_ag_aux.iloc[index], Y_ag_aux.iloc[index +1 ]],
color =color, zorder = 2, alpha = 0.2, ec = None) # --- Adjust tickers and spine to match the style of our grid ax.xaxis.set_major_locator(ticker.MultipleLocator( 2)) # ticker every 2 matchdays xticks_ = ax.xaxis.set_ticklabels([x - 1 for x in range(0, len(X_) + 3, 2 )])
ax.xaxis.set_tick_params(length = 2, color = " #4E616C " , labelcolor = " #4E616C " , labelsize = 6 )
ax.yaxis.set_tick_params(length = 2, color = " #4E616C " , labelcolor = " #4E616C " , labelsize = 6 )
ax.spines[ " bottom " ].set_edgecolor( " #4E616C " )
5. 把功能打包成函数
上面的样子都还不错啦，接下来把这些东西都打包成一个函数。方便后面直接出图。
def plot_xG_rolling(team, ax, window = 5, color_for = " blue " , color_ag = " orange " , data = df): ''' This function creates a rolling average xG plot for a given team and rolling
window.
team (str): The team's name
ax (obj): a Matplotlib axes.
window (int): The number of periods for our rolling average.
color_for (str): A hex color code for xG created.
color_af (str): A hex color code for xG conceded.
data (DataFrame): our df with the xG data. ''' # -- Prepping the data home_df = data.copy()
home_df = home_df.melt(id_vars = [ " date " , " home_team_name " , " away_team_name " ])
home_df[ " venue " ] = " H " home_df.rename(columns = { " home_team_name " : " team " , " away_team_name " : " opponent " }, inplace = True)
home_df.replace({ " variable " :{ " home_team_xG " : " xG_for " , " away_team_xG " : " xG_ag " }}, inplace = True)
away_df = data.copy()
away_df = away_df.melt(id_vars = [ " date " , " away_team_name " , " home_team_name " ])
away_df[ " venue " ] = " A " away_df.rename(columns = { " away_team_name " : " team " , " home_team_name " : " opponent " }, inplace = True)
away_df.replace({ " variable " :{ " away_team_xG " : " xG_for " , " home_team_xG " : " xG_ag " }}, inplace = True)
df = pd.concat([home_df, away_df]).reset_index(drop = True) # ---- Filter the data Y_for = df[(df[ " team " ] == team) & (df[ " variable " ] == " xG_for " )][ " value " ].reset_index(drop = True)
Y_ag = df[(df[ " team " ] == team) & (df[ " variable " ] == " xG_ag " )][ " value " ].reset_index(drop = True)
X_ = pd.Series(range(len(Y_for))) if Y_for.shape[0] == 0: raise ValueError(f " Team {team} is not present in the DataFrame " ) # ---- Compute rolling average Y_for = Y_for.rolling(window = 5, min_periods = 0).mean() # min_periods is for partial avg. Y_ag = Y_ag.rolling(window = 5, min_periods = 0).mean() # ---- Create auxiliary series for filling between curves X_aux = X_.copy()
X_aux.index = X_aux.index * 10 # 9 aux points in between each match last_idx = X_aux.index[-1] + 1 X_aux = X_aux.reindex(range(last_idx))
X_aux = X_aux.interpolate() # --- Aux series for the xG created (Y_for) Y_for_aux = Y_for.copy()
Y_for_aux.index = Y_for_aux.index * 10 last_idx = Y_for_aux.index[-1] + 1 Y_for_aux = Y_for_aux.reindex(range(last_idx))
Y_for_aux = Y_for_aux.interpolate() # --- Aux series for the xG conceded (Y_ag) Y_ag_aux = Y_ag.copy()
Y_ag_aux.index = Y_ag_aux.index * 10 last_idx = Y_ag_aux.index[-1] + 1 Y_ag_aux = Y_ag_aux.reindex(range(last_idx))
Y_ag_aux = Y_ag_aux.interpolate() # --- Plotting our data # --- Remove spines and add gridlines ax.spines[ " left " ].set_visible(False)
ax.spines[ " top " ].set_visible(False)
ax.spines[ " right " ].set_visible(False)
ax.grid(ls = " -- " , lw = 0.25, color = " #4E616C " ) # --- The data for_ = ax.plot(X_, Y_for, marker = " o " , mfc = " white " , ms = 4, color = color_for)
ag_ = ax.plot(X_, Y_ag, marker = " o " , mfc = " white " , ms = 4, color = color_ag) # --- Fill between for index in range(len(X_aux) - 1 ): # Choose color based on which line's on top if Y_for_aux.iloc[index + 1] > Y_ag_aux.iloc[index + 1 ]:
color = for_[0].get_color() else :
color = ag_[0].get_color() # Fill between the current point and the next point in pur extended series. ax.fill_between([X_aux[index], X_aux[index+1 ]],
[Y_for_aux.iloc[index], Y_for_aux.iloc[index +1 ]],
[Y_ag_aux.iloc[index], Y_ag_aux.iloc[index +1 ]],
color =color, zorder = 2, alpha = 0.2, ec = None) # --- Ensure minimum value of Y-axis is zero ax.set_ylim(0) # --- Adjust tickers and spine to match the style of our grid ax.xaxis.set_major_locator(ticker.MultipleLocator( 2)) # ticker every 2 matchdays xticks_ = ax.xaxis.set_ticklabels([x - 1 for x in range(0, len(X_) + 3, 2 )])
ax.xaxis.set_tick_params(length = 2, color = " #4E616C " , labelcolor = " #4E616C " , labelsize = 6 )
ax.yaxis.set_tick_params(length = 2, color = " #4E616C " , labelcolor = " #4E616C " , labelsize = 6 )
ax.spines[ " bottom " ].set_edgecolor( " #4E616C " ) # --- Legend and team name Y_for_last = Y_for.iloc[-1 ]
Y_ag_last = Y_ag.iloc[-1 ] # -- Add the team's name team_ = ax.text(
x = 0, y = ax.get_ylim()[1] + ax.get_ylim()[1]/20 ,
s = f ' {team} ' ,
color = " #4E616C " ,
va = ' center ' ,
ha = ' left ' ,
size = 7 ) # -- Add the xG created label for_label_ = ax.text(
x = X_.iloc[-1] + 0.75, y = Y_for_last,
s = f ' {Y_for_last:,.1f} xGF ' ,
color = color_for,
va = ' center ' ,
ha = ' left ' ,
size = 6.5 ) # -- Add the xG conceded label ag_label_ = ax.text(
x = X_.iloc[-1] + 0.75, y = Y_ag_last,
s = f ' {Y_ag_last:,.1f} xGA ' ,
color = color_ag,
va = ' center ' ,
ha = ' left ' ,
size = 6.5 )
6.测试函数
file_id = ' 1yM_F93NY4QkxjlKL3GzdcCQEnBiA2ltB ' url = f ' https://drive.google.com/uc?id={file_id} ' df = pd.read_csv(url, index_col=0)
再设置更加丰富的颜色：
fig = plt.figure(figsize=(5, 8), dpi = 200, facecolor = " #EFE9E6 " )
ax1 = plt.subplot(411, facecolor = " #EFE9E6 " )
ax2 = plt.subplot(412, facecolor = " #EFE9E6 " )
ax3 = plt.subplot(413, facecolor = " #EFE9E6 " )
ax4 = plt.subplot(414, facecolor = " #EFE9E6 " )
plot_xG_rolling( " Sassuolo " , ax1, color_for = " #00A752 " , color_ag = " black " , data = df)
plot_xG_rolling( " Lazio " , ax2, color_for = " #87D8F7 " , color_ag = " #15366F " , data = df)
plot_xG_rolling( " Hellas Verona " , ax3, color_for = " #153aab " , color_ag = " #fdcf41 " , data = df)
plot_xG_rolling( " Empoli " , ax4, color_for = " #00579C " , color_ag = " black " , data = df)
plt.tight_layout()
