使用matplotlib生成折线图,matplotlib.pyplot画折线图

　　这篇文章主要和大家分享一个非常有趣的大蟒教程—如何美化一个绘制精美的图表折线图。文中的示例代码讲解详细，感兴趣的可以了解一下

目录

1. 导入包

　　进口熊猫作为螺纹中径

　　将matplotlib.pyplot作为血小板计数导入

　　将matplotlib.ticker作为心脏导入

　　将matplotlib.gridspec作为gridspec导入

2. 获得数据

　　file _ id= 1yM _ f93 ny 4 qkxjlkl 3 gzdccqenbia 2 ltb

　　url=fhttps://drive.google.com/uc？id={文件id}

　　df=pd.read_csv(网址，索引列=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。重命名(columns={ home _ team _ name : team ， away _ team _ name : opposite }，inplace=True)

　　home _ df。替换({ variable : { home _ team _ xG : xG _ for ， away_team_xG:xG_ag}}，inplace=True)

　　away_df=df.copy()

　　远离_ df=远离_ 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 : opposite }，inplace=True)

　　away _ df。替换({ 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

　　# -过滤数据

　　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)
　　# --- 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 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)
　　# --- 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 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
　　 )

6.测试函数

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

fig = plt.figure(figsize=(5, 2), dpi = 200)
　　ax = plt.subplot(111)
　　plot_xG_rolling("Sassuolo", ax, color_for = "#00A752", color_ag = "black", data = df)
　　plt.tight_layout()
　　

　　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()
　　

最后

　　其实本文主要是对两个折线图做了一系列的优化和改进而已，主要是强调细节部分。

　　涉及到的matplotlib的知识，也主要是在ticks、背景颜色、fill_between部分。

　　以上就是Python+matplotlib实现折线图的美化的详细内容，更多关于Python matplotlib折线图的资料请关注盛行IT软件开发工作室其它相关文章！

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