Python+matplotlib实现折线图的美化
1. 导入包<br>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().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()
# 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 <br>
# --- 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()
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_, y1 = Y_for, y2 = Y_ag, alpha = 0.5)pos_ag = (Y_for
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