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Python+matplotlib实现折线图的美化

来源：博客园


(相关资料图)

1. 导入包

import pandas as pdimport matplotlib.pyplot as pltimport matplotlib.ticker as tickerimport 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 dataY_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 averageY_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 gridlinesax.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 dataax.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 gridlinesax.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 dataax.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 gridax.xaxis.set_major_locator(ticker.MultipleLocator(2)) # ticker every 2 matchdaysxticks_ = 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 gridlinesax.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 dataax.plot(X_, Y_for, marker = "o", mfc = "white", ms = 5)ax.plot(X_, Y_ag, marker = "o", mfc = "white", ms = 5)# --- Fill betweenax.fill_between(x = X_, y1 = Y_for, y2 = Y_ag, alpha = 0.5)# --- Adjust tickers and spine to match the style of our gridax.xaxis.set_major_locator(ticker.MultipleLocator(2)) # ticker every 2 matchdaysxticks_ = 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 gridlinesax.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 dataax.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_agpos_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 gridax.xaxis.set_major_locator(ticker.MultipleLocator(2)) # ticker every 2 matchdaysxticks_ = 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 matchlast_idx = X_aux.index[-1] + 1X_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 * 10last_idx = Y_for_aux.index[-1] + 1Y_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 * 10last_idx = Y_ag_aux.index[-1] + 1Y_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 gridlinesax.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 datafor_ = ax.plot(X_, Y_for, marker = "o", mfc = "white", ms = 5)ag_ = ax.plot(X_, Y_ag, marker = "o", mfc = "white", ms = 5)# --- Fill betweenfor 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 gridax.xaxis.set_major_locator(ticker.MultipleLocator(2)) # ticker every 2 matchdaysxticks_ = 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()

关键词： 学习交流 是这样的 这些东西