微信聊天记录分析：去年和你的猪聊了什么？

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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。

1	pip 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

import re
import time
import numpy as np
import pandas as pd
import jieba
import jieba.posseg as pseg
from PIL import Image
from wordcloud import WordCloud
import seaborn as sns
import matplotlib.ticker as mticker
import matplotlib.transforms as mtransforms
from matplotlib.colors import ListedColormap
from matplotlib import pyplot as plt
from matplotlib import font_manager as fm
from tqdm import tqdm
from 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emoji

sns.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：

1	labels = ["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

filePath = "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.dayofweek
df.loc[:, "hour"] = df["StrTime"].dt.hour
df.loc[:, "Count"] = 1

# 如果出错就用
df["StrTime"] = pd.to_datetime(df["StrTime"])
df["day"] = df["StrTime"].dt.dayofweek
df["hour"] = df["StrTime"].dt.hour
df["Count"] = 1

dfs = [df.query("IsSender == 0"), df.query("IsSender == 1")]

消息过滤¶

def 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 = Text
3 = Image
34 = Voice
43 = Video
47 = Sticker
48 = Location
10000 = System

data = {}
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：

\mu + \mathrm{sN} * \sigma

multiple: 直方图堆叠格式

sN = 3
multiple = "dodge"

mu, std = 0, 0
data = {"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

data = {"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

grouper = 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

wTicks = 500
wStart = "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) * wTicks
norm = 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 = \frac{Q_\mathrm{S} - Q_\mathrm{R}}{Q_\mathrm{S} + Q_\mathrm{R}}

grouper = 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

grouper = pd.Grouper(key="StrTime", freq="D")
data = df.groupby(grouper)["Count"].sum()
data = data.to_frame()

data["date"] = data.index
data["week"] = data["date"].dt.isocalendar()["week"]
data["day"] = data["date"].dt.dayofweek
data.index = range(len(data))
for i in range(7):
    if data.loc[i, "week"] > 1:
        data.loc[i, "week"] = 0

data = 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¶

jieba.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¶

def 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 words

words = [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

mask = 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50

wN = 50

data = 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 = tmp

ratio = 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().x1
x1 = ax1.get_position().x0
xm = (x0 + x1) / 2
y0 = ax0.get_position().y0
y1 = ax0.get_position().y1

for 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

dfE = 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"] - 1
dfE["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¶

ax = 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¶

def 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¶

ax = 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¶

def 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¶

ax = 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¶

grouper = 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¶

grouper = 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