python plt 折线图（Python+matplotlib实现折线图的美化）

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")

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 teams 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 lines 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 teams 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 )

file_id = 1yM_F93NY4QkxjlKL3GzdcCQEnBiA2ltB url = fhttps://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：