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ipynb 文件最好从上往下依次运行，不要回过头来重新搞，获取方式：
在 Google Colab 使用在线环境运行（推荐）下载 ipynb 文件 直接运行下载 运行所需文件准备 ¶导出聊天记录 ¶依照 WeChatMsg 的教程导出 csv 文件，只需要导出文本即可。
图 1 导出聊天记录安装所需包 ¶推荐在虚拟环境中安装。
1pip install numpy seaborn pandas wordcloud tqdm paddlepaddle paddlenlp引入包 ¶pandas: 基础数据框架matplotlib & seaborn: 绘图jieba: 中文分词wordcloud: 词云paddlenlp: 情感分析12345678910111213141516import reimport timeimport numpy as npimport pandas as pdimport jiebaimport jieba.posseg as psegfrom PIL import Imagefrom wordcloud import WordCloudimport seaborn as snsimport matplotlib.ticker as mtickerimport matplotlib.transforms as mtransformsfrom matplotlib.colors import ListedColormapfrom matplotlib import pyplot as pltfrom matplotlib import font_manager as fmfrom tqdm import tqdmfrom paddlenlp import Taskflow绘图设置 ¶font: 字体路径，至少支持中文，最好同时支持中文和 emoji1234sns.set_theme(style="ticks")font = "/usr/share/fonts/winfont/simsun.ttc"fp = fm.FontProperties(fname=font)plt.rcParams["axes.unicode_minus"] = False人名标签 ¶在这里修改双方的称呼：
1labels = ["LL", "FF"]数据读取 ¶filePath: 消息记录文件的路径dStart: 开始的时间dEnd: 结束的时间123456789101112131415161718192021222324filePath = "msg.csv"dStart = "2023-01-01 00:00:00"dEnd = "2023-12-31 23:59:59"df = pd.read_csv(filePath, encoding="utf-8")df = df.query( "CreateTime >= {:d} and CreateTime <= {:d}".format( int(time.mktime(time.strptime(dStart, "%Y-%m-%d %H:%M:%S"))), int(time.mktime(time.strptime(dEnd, "%Y-%m-%d %H:%M:%S"))), ))df.loc[:, "StrTime"] = pd.to_datetime(df["StrTime"])df.loc[:, "day"] = df["StrTime"].dt.dayofweekdf.loc[:, "hour"] = df["StrTime"].dt.hourdf.loc[:, "Count"] = 1# 如果出错就用df["StrTime"] = pd.to_datetime(df["StrTime"])df["day"] = df["StrTime"].dt.dayofweekdf["hour"] = df["StrTime"].dt.hourdf["Count"] = 1dfs = [df.query("IsSender == 0"), df.query("IsSender == 1")]消息过滤 ¶12345678910111213141516def textFilter(text: str): text = text.lower() # 过滤 emoji try: co = re.compile("[\U00010000-\U0010ffff]") except re.error: co = re.compile("[\uD800-\uDBFF][\uDC00-\uDFFF]") text = co.sub(" ", text) # 过滤微信表情 co = re.compile("\[[\u4e00-\u9fa5]+\]") return co.sub(" ", text)texts = [ [textFilter(i) for i in dfs[0].query("Type == 1")["StrContent"].to_list()], [textFilter(i) for i in dfs[1].query("Type == 1")["StrContent"].to_list()],]消息频率分析 ¶类型分析 ¶根据消息的类型进行分类，可以看出喜欢发送的消息类型，同时也可以看出谁发的多
1 = Text3 = Image34 = Voice43 = Video47 = Sticker48 = Location10000 = System12345678910111213141516171819202122232425262728data = {}for i in range(2): data[labels[i]] = [ len(dfs[i].query("Type == 1")), len(dfs[i].query("Type == 3")), len(dfs[i].query("Type == 34")), len(dfs[i].query("Type == 43")), len(dfs[i].query("Type == 47")), ]data = ( pd.DataFrame(data, index=["Text", "Image", "Voice", "Video", "Sticker"]) .reset_index() .melt("index") .rename(columns={"index": "Type", "variable": "Person", "value": "Count"}))g = sns.catplot(data, kind="bar", x="Type", y="Count", hue="Person", palette="dark", alpha=0.6, height=6)for ax in g.axes.ravel(): for i in range(2): ax.bar_label(ax.containers[i], fontsize=9)sns.move_legend(g, "upper right")plt.yscale("log")g.figure.set_size_inches(6, 5)g.figure.set_dpi(150)plt.show()plt.close()图 2 消息类型消息长度分析 ¶sN: 设置显示范围：μ+sN∗σ\mu + \mathrm{sN} * \sigmaμ+sN∗σ
multiple: 直方图堆叠格式12345678910111213141516171819202122232425262728293031323334sN = 3multiple = "dodge"mu, std = 0, 0data = {"Length": [], "Person": []}for i in range(2): length = [len(textFilter(i)) for i in texts[i]] data["Length"] += length data["Person"] += [labels[i]] * len(length) if np.mean(length) + sN * np.std(length) > mu + std: mu, std = np.mean(length), np.std(length)xlim = int(np.ceil(mu + sN * std))data = pd.DataFrame(data)bins = np.linspace(0, xlim, xlim + 1)ax = sns.histplot( data=data, x="Length", hue="Person", bins=bins, multiple=multiple, edgecolor=".3", linewidth=0.5, palette="dark", alpha=0.6,)ax.set_xlim(0, xlim)ax.set_xlabel("Length of Message")ax.figure.set_size_inches(8, 4)ax.figure.set_dpi(150)plt.show()plt.close()图 3 消息长度每日活跃分析 ¶划分每日 24 小时内每小时发送的消息数，可以得知每天的活跃的时间段
123456789101112131415161718192021222324252627282930data = {"Time": [], "Person": []}for i in range(2): hour = dfs[i]["hour"].to_list() data["Time"] += hour data["Person"] += [labels[i]] * len(hour)data = pd.DataFrame(data)bins = np.arange(0, 25, 1)ax = sns.histplot( data=data, x="Time", hue="Person", bins=bins, multiple=multiple, edgecolor=".3", linewidth=0.5, palette="dark", alpha=0.6,)ax.set_xticks(bins)ax.set_xticklabels(bins)ax.set_xlabel("Hour")ax.set_xlim(0, 24)sns.move_legend(ax, loc="upper center", bbox_to_anchor=(0.5, 1.2), ncol=2)ax.figure.set_size_inches(8, 4)ax.figure.set_dpi(150)plt.show()plt.close()图 4 每日活跃时间每周活跃分析 ¶查看一周内从周一到周日每天发送的消息数
12345678910111213grouper = pd.Grouper(key="day")data = df.groupby(grouper)["Count"].sum()data = data.sort_index()data.index = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]ax = sns.barplot(data=data, errorbar=None)ax.set_xlabel("Weekday")ax.bar_label(ax.containers[0], fontsize=10)ax.figure.set_size_inches(5, 5)ax.figure.set_dpi(150)plt.show()plt.close()图 5 每周活跃分析按周划分年度活跃分析 ¶划分每 7 天内发送的消息数，可以得知每周的活跃的时间段
wTicks: 每个刻度相差的数值请注意此处修改：
wStart: 当年或者聊天开始日期之后第一个周一的日期wEnd: 次年或者聊天结束日期之后第一个周一的日期1234567891011121314151617181920212223242526272829303132wTicks = 500wStart = "2023-01-02"wEnd = "2024-01-01"grouper = pd.Grouper(key="StrTime", freq="W-MON")data = df.groupby(grouper)["Count"].sum().to_frame()data.index = pd.date_range(start=wStart, end=wEnd, freq="W-MON").strftime("%m-%d")data.columns = ["Count"]vM = np.ceil(data["Count"].max() / wTicks) * wTicksnorm = plt.Normalize(0, vM)sm = plt.cm.ScalarMappable(cmap="Reds", norm=norm)ax = sns.barplot(x=data.index, y=data["Count"], hue=data["Count"], hue_norm=norm, palette="Reds")ax.set_xlabel("Date")plt.xticks(rotation=60)for bar in ax.containers: ax.bar_label(bar, fontsize=10, fmt="%.0f")ax.get_legend().remove()axpos = ax.get_position()caxpos = mtransforms.Bbox.from_extents(axpos.x1 + 0.02, axpos.y0, axpos.x1 + 0.03, axpos.y1)cax = ax.figure.add_axes(caxpos)locator = mticker.MultipleLocator(wTicks)formatter = mticker.StrMethodFormatter("{x:.0f}")cax.figure.colorbar(sm, cax=cax, ticks=locator, format=formatter)ax.figure.set_size_inches(20, 8)ax.figure.set_dpi(150)plt.show()plt.close()图 5 按周划分年度活跃分析按周划分聊天热情分析 ¶划分每 7 天内的聊天热情指数，聊天热情指数为发送的消息数减去收到的消息数与总消息数的比值：
E=QS−QRQS+QRE = \frac{Q_\mathrm{S} - Q_\mathrm{R}}{Q_\mathrm{S} + Q_\mathrm{R}}E=QS+QRQS−QR
12345678910111213141516171819202122232425262728293031grouper = pd.Grouper(key="StrTime", freq="W-MON")df_W1 = dfs[0].groupby(grouper)["Count"].sum()df_W2 = dfs[1].groupby(grouper)["Count"].sum()data = pd.DataFrame({"E": (df_W1 - df_W2) / (df_W1 + df_W2)})data.index = pd.date_range(start=wStart, end=wEnd, freq="W-MON").strftime("%m-%d")vM = data["E"].abs().max()norm = plt.Normalize(-vM, vM)sm = plt.cm.ScalarMappable(cmap="coolwarm", norm=norm)ax = sns.barplot(x=data.index, y=data["E"], hue=data["E"], hue_norm=norm, palette="coolwarm")ax.set_xlabel("Date")plt.xticks(rotation=60)ax.set_ylabel("Enthusiasm Index")for bar in ax.containers: ax.bar_label(bar, fontsize=10, fmt="%.2f")ax.get_legend().remove()axpos = ax.get_position()caxpos = mtransforms.Bbox.from_extents(axpos.x1 + 0.02, axpos.y0, axpos.x1 + 0.03, axpos.y1)cax = ax.figure.add_axes(caxpos)locator = mticker.MultipleLocator(0.1)formatter = mticker.StrMethodFormatter("{x:.1f}")cax.figure.colorbar(sm, cax=cax, ticks=locator, format=formatter)ax.figure.set_size_inches(20, 8)ax.figure.set_dpi(150)plt.show()plt.close()图 6 按周划分聊天热情分析按日划分年度活跃分析 ¶以热力图的方式展示按日划分的年度活跃情况
1234567891011121314151617181920212223242526272829grouper = pd.Grouper(key="StrTime", freq="D")data = df.groupby(grouper)["Count"].sum()data = data.to_frame()data["date"] = data.indexdata["week"] = data["date"].dt.isocalendar()["week"]data["day"] = data["date"].dt.dayofweekdata.index = range(len(data))for i in range(7): if data.loc[i, "week"] > 1: data.loc[i, "week"] = 0data = data.pivot(index="day", columns="week", values="Count")data.index = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]data.columns = pd.date_range(start=wStart, end=wEnd, freq="W-MON").strftime("%m-%d")ax = sns.heatmap( data, annot=False, linewidths=0.5, cbar_kws={"orientation": "vertical", "location": "left", "pad": 0.03}, cmap="Reds",)ax.set_xlabel("Week")ax.set_ylabel("Weekday")ax.figure.set_size_inches(24, 4)ax.figure.set_dpi(150)plt.show()plt.close()图 7 按日划分年度活跃分析词语分析 ¶分词词典、停止词与去除词性 ¶1234jieba.load_userdict("thuocl.txt")jieba.load_userdict("userdict.txt")stopwords = [line.strip() for line in open("stopwords.txt", "r").readlines()] + [" ", "\n", "\r\n"]wordclass = ["v", "u", "vd", "r", "p", "w"]分词函数 ¶1234567891011121314151617def wordSplit(texts, wordclass): words = [] pbar = tqdm(total=len(texts)) for i in range(len(texts)): res = pseg.lcut(texts[i]) for pair in res: if pair.word in stopwords: continue if pair.flag in wordclass: continue words.append(pair.word) if i % 1000 == 0: pbar.update(1000) pbar.close() return wordswords = [wordSplit(texts[i], wordclass) for i in range(2)]词云绘制 ¶mask: 词云的蒙版，影响词云的形状cmap: 色阶123456789101112131415161718192021222324252627282930313233mask = np.array(Image.open("mask.png"))masks = [np.array(Image.open("mask_L.jpg")), np.array(Image.open("mask_F.jpg"))]cmap = ListedColormap( [ "#fac1cf", "#a9d7ba", "#58b1db", "#f296ab", "#5dab81", "#3d9ec4", "#e16a8d", "#237b50", "#1e8299", "#8d3549", "#35563b", "#2d5d73", ])def wordCloud(text, font, mask, cmap): wc = WordCloud( background_color="white", scale=5, font_path=font, mask=mask, colormap=cmap, collocations=False, ).generate(text) plt.imshow(wc) plt.axis("off") plt.show()wordCloud(" ".join(words[0] + words[1]), font, mask, cmap)图 8 词云图高频词排行 ¶列出常用的 N 个词，并且展示双方的贡献
wN: 词的数目，默认为 501234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980818283848586878889wN = 50data = pd.DataFrame( { "words": words[0] + words[1], "L": [1] * len(words[0]) + [0] * len(words[1]), "F": [0] * len(words[0]) + [1] * len(words[1]), "S": [1] * len(words[0]) + [1] * len(words[1]), })grouper = pd.Grouper(key="words")data = data.groupby(grouper).sum()data = data.sort_values(by="S", ascending=False)data = data.iloc[:wN]# 将部分无法识别的 emoji 转化为文字tmp = data.index.to_list()for i in range(wN): if tmp[i] == "😘": tmp[i] = "[亲亲]" elif tmp[i] == "😂": tmp[i] = "[笑哭]" elif tmp[i] == "🤦": tmp[i] = "[捂脸]" elif tmp[i] == "😁": tmp[i] = "[呲牙]"data.index = tmpratio = data["L"] / data["S"]norm = plt.Normalize(0, 1)sm = plt.cm.ScalarMappable(cmap="coolwarm", norm=norm)fig = plt.figure(figsize=(10, 10), dpi=300)grid = plt.GridSpec(1, 4, wspace=0.5)ax0 = fig.add_subplot(grid[0, 0])sns.barplot(x=-data["L"], y=data.index, ax=ax0, hue=ratio, hue_norm=norm, palette="coolwarm")ax1 = fig.add_subplot(grid[0, 1:])sns.barplot(x=data["F"], y=data.index, ax=ax1, hue=(1 - ratio), hue_norm=norm, palette="coolwarm")ax0.set_xlabel("词频")ax0.set_ylabel("")ax0.set_xticks(range(-400, 1, 200))ax0.set_xticklabels([400, 200, 0])ax0.set_xlim(-400, 0)ax0.set_yticks([])ax0.spines["left"].set_visible(False)ax0.spines["top"].set_visible(False)ax0.spines["right"].set_visible(False)ax0.set_title("LL")ax0.get_legend().remove()ax1.set_xlabel("词频")ax1.set_ylabel("")ax1.set_xticks(range(0, 1201, 200))ax1.set_xticklabels([0, 200, 400, 600, 800, 1000, 1200])ax1.set_xlim(0, 1200)ax1.set_yticks([])ax1.spines["left"].set_visible(False)ax1.spines["top"].set_visible(False)ax1.spines["right"].set_visible(False)ax1.set_title("FF")ax1.get_legend().remove()axpos = ax1.get_position()caxpos = mtransforms.Bbox.from_extents(axpos.x0 + 0.06, axpos.y0 + 0.03, axpos.x1, axpos.y0 + 0.04)cax = ax1.figure.add_axes(caxpos)locator = mticker.MultipleLocator(0.1)formatter = mticker.StrMethodFormatter("{x:.1f}")cax.figure.colorbar(sm, cax=cax, orientation="horizontal", ticks=locator, format=formatter)cax.set_title("ratio")x0 = ax0.get_position().x1x1 = ax1.get_position().x0xm = (x0 + x1) / 2y0 = ax0.get_position().y0y1 = ax0.get_position().y1for i in range(wN): fig.text( xm, y0 + (y1 - y0) * (wN - i - 0.5) / wN, data.index[i], color="black", ha="center", va="center", fontproperties=fp )fig.set_dpi(150)plt.show()plt.close()图 9 高频词排行情感分析 ¶使用 paddlenlp 进行情感分析，得到的分数在 [-1, 1] 之间，越小越消极，越大越积极
123456789101112dfE = df.query("Type == 1")[["IsSender", "StrContent", "StrTime", "hour"]]dfE.index = range(len(dfE))senta = Taskflow("sentiment_analysis")scores = pd.DataFrame(senta([textFilter(i) for i in dfE["StrContent"].to_list()]))scores.loc[scores["label"] == "negative", "score"] = 1 - scores.loc[scores["label"] == "negative", "score"]dfE["score"] = scores["score"]dfE["score"] = 2 * dfE["score"] - 1dfE["Person"] = dfE.apply(lambda x: labels[x["IsSender"]], axis=1)dfEs = [dfE.query("IsSender == 0"), dfE.query("IsSender == 1")]年度总体情感分布 ¶12345678910ax = sns.histplot(data=dfE, x="score", hue="Person", palette="dark", alpha=0.6, bins=100)ax.set_xlabel("Sentiment Score")ax.set_ylabel("Count")ax.set_title("Sentiment Distribution")ax.set_xlim(-1, 1)ax.figure.set_size_inches(8, 3)ax.figure.set_dpi(150)plt.show()图 10 年度总体情感分布按周统计平均情感指数 ¶1234567891011121314151617181920212223242526272829303132333435def weekAvgSenScore(df): grouper = pd.Grouper(key="StrTime", freq="W-MON") data = df.groupby(grouper)["score"].mean().to_frame() data.index = pd.date_range(start=wStart, end=wEnd, freq="W-MON").strftime("%m-%d") data.columns = ["score"] vM = data["score"].abs().max() norm = plt.Normalize(-vM, vM) sm = plt.cm.ScalarMappable(cmap="coolwarm", norm=norm) ax = sns.barplot(x=data.index, y=data["score"], hue=data["score"], hue_norm=norm, palette="coolwarm") ax.set_xlabel("Date") plt.xticks(rotation=60) for bar in ax.containers: ax.bar_label(bar, fontsize=10, fmt="%.2f") ax.get_legend().remove() axpos = ax.get_position() caxpos = mtransforms.Bbox.from_extents(axpos.x1 + 0.02, axpos.y0, axpos.x1 + 0.03, axpos.y1) cax = ax.figure.add_axes(caxpos) locator = mticker.MultipleLocator(0.05) formatter = mticker.StrMethodFormatter("{x:.2f}") cax.figure.colorbar(sm, cax=cax, ticks=locator, format=formatter) ax.figure.set_size_inches(20, 8) ax.figure.set_dpi(150) plt.show() plt.close() return data["score"]avgSenScore0 = weekAvgSenScore(dfEs[0])avgSenScore1 = weekAvgSenScore(dfEs[1])_ = weekAvgSenScore(dfE)图 11 按周统计平均情感指数平均情感指数变化折线图 ¶12345678910111213ax = sns.lineplot(data=avgSenScore0, linewidth=3, marker="s", markersize=15, label=labels[0])ax = sns.lineplot(data=avgSenScore1, linewidth=3, marker="^", markersize=15, ax=ax, label=labels[1])ax.set_xlabel("Date")plt.xticks(rotation=60)ax.set_ylabel("Average Sentiment Score")ax.set_xlim(0, 52)ax.legend(prop={"size": 24})ax.figure.set_size_inches(20, 8)ax.figure.set_dpi(150)plt.show()plt.close()图 12 平均变化折线图按周统计累计情感指数 ¶1234567891011121314151617181920212223242526272829303132333435def weekTotSenScore(df): grouper = pd.Grouper(key="StrTime", freq="W-MON") data = df.groupby(grouper)["score"].sum().to_frame() data.index = pd.date_range(start=wStart, end=wEnd, freq="W-MON").strftime("%m-%d") data.columns = ["score"] vM = data["score"].abs().max() norm = plt.Normalize(-vM, vM) sm = plt.cm.ScalarMappable(cmap="coolwarm", norm=norm) ax = sns.barplot(x=data.index, y=data["score"], hue=data["score"], hue_norm=norm, palette="coolwarm") ax.set_xlabel("Date") plt.xticks(rotation=60) for bar in ax.containers: ax.bar_label(bar, fontsize=10, fmt="%.2f") ax.get_legend().remove() axpos = ax.get_position() caxpos = mtransforms.Bbox.from_extents(axpos.x1 + 0.02, axpos.y0, axpos.x1 + 0.03, axpos.y1) cax = ax.figure.add_axes(caxpos) locator = mticker.MultipleLocator(20) formatter = mticker.StrMethodFormatter("{x:.2f}") cax.figure.colorbar(sm, cax=cax, ticks=locator, format=formatter) ax.figure.set_size_inches(20, 8) ax.figure.set_dpi(150) plt.show() plt.close() return data["score"]totSenScore0 = weekTotSenScore(dfEs[0])totSenScore1 = weekTotSenScore(dfEs[1])_ = weekTotSenScore(dfE)图 13 按周统计累计情感指数累计情感指数变化折线图 ¶12345678910111213ax = sns.lineplot(data=totSenScore0, linewidth=3, marker="s", markersize=15, label=labels[0])ax = sns.lineplot(data=totSenScore1, linewidth=3, marker="^", markersize=15, ax=ax, label=labels[1])ax.set_xlabel("Date")plt.xticks(rotation=60)ax.set_ylabel("Total Sentiment Score")ax.set_xlim(0, 52)ax.legend(prop={"size": 24})ax.figure.set_size_inches(20, 8)ax.figure.set_dpi(150)plt.show()plt.close()图 14 累计变化折线图每日平均情感分析 ¶12345678910111213141516171819202122232425262728293031323334353637383940414243grouper = pd.Grouper(key="hour")data = []for k in range(2): tmp = dfEs[k].groupby(grouper)["score"].mean().sort_index() for i in range(24): if i in tmp.index: data.append(tmp[i]) else: data.append(0) data.append(0)data = pd.DataFrame( { "Score": data, "Person": [labels[0]] * 25 + [labels[1]] * 25, })xBins = [i for i in range(25)]ax = sns.histplot( data=data, x=xBins * 2, bins=xBins, weights="Score", hue="Person", multiple=multiple, edgecolor=".3", linewidth=0.5, palette="dark", alpha=0.6,)ax.set_xticks(range(25))ax.set_xticklabels(range(25))ax.set_xlabel("Hour")ax.set_xlim(0, 24)ax.set_ylim(np.min([0, np.floor(data["Score"].min() / 0.05) * 0.05]), np.ceil(data["Score"].max() / 0.05) * 0.05)sns.move_legend(ax, loc="upper center", bbox_to_anchor=(0.5, 1.2), ncol=2)ax.figure.set_size_inches(8, 4)ax.figure.set_dpi(150)plt.show()plt.close()图 15 每日平均情感分析每日累计情感分析 ¶12345678910111213141516171819202122232425262728293031323334353637383940414243grouper = pd.Grouper(key="hour")data = []for k in range(2): tmp = dfEs[k].groupby(grouper)["score"].sum().sort_index() for i in range(24): if i in tmp.index: data.append(tmp[i]) else: data.append(0) data.append(0)data = pd.DataFrame( { "Score": data, "Person": [labels[0]] * 25 + [labels[1]] * 25, })xBins = [i for i in range(25)]ax = sns.histplot( data=data, x=xBins * 2, bins=xBins, weights="Score", hue="Person", multiple=multiple, edgecolor=".3", linewidth=0.5, palette="dark", alpha=0.6,)ax.set_xticks(range(25))ax.set_xticklabels(range(25))ax.set_xlabel("Hour")ax.set_xlim(0, 24)ax.set_ylim(np.min([0, np.floor(data["Score"].min() / 0.05) * 0.05]), np.ceil(data["Score"].max() / 0.05) * 0.05)sns.move_legend(ax, loc="upper center", bbox_to_anchor=(0.5, 1.2), ncol=2)ax.figure.set_size_inches(8, 4)ax.figure.set_dpi(150)plt.show()plt.close()图 16 每日累计情感分析工具 ¶WeChatMsgPaddleNLPTHUOCL：清华大学开放中文词库中文常用停用词表jieba 中文分词参考资料 ¶wechat_analysisPython 制作日历图｜可视化聊天记录1 年的聊天记录写成论文作为老婆生日礼物当我把和男朋友两年半的聊天记录可视化90 万人看过的聊天记录可视化第二弹来啦Make seaborn show a colorbar instead of a legend when using hue in a bar plot?Matplotlib 系列：colorbar 的设置matplotlib 绘图之坐标变换