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 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
          )

file_id = '1yM_F93NY4QkxjlKL3GzdcCQEnBiA2ltB'
url = f'https://drive.google.com/uc?id={file_id}'
df = pd.read_csv(url, index_col=0)