pandas数据分析，matplotlib绘图并解决中文乱码问题
批量将数据写入多个sheet的excel表，并将图片和数据写入同一个sheet，查了不少api，希望对大家有帮助
代码就不一一解释了，自己看吧
最终生成效果图

# -*- coding: utf-8 -*-

# @File    : data_statistics_and_draw_imgs.py
# @Date    : 2023-03-27
# @Author  : jiang

import sys

sys.path.append("..")
print(sys.path)
import traceback
import pandas as pd
import logging
import math
from datetime import datetime
from datetime import timedelta
import matplotlib.pyplot as plt
import matplotlib as mpl

plt.switch_backend('agg')

# 设置背景为白色
plt.rcParams['axes.facecolor'] = 'w'
# mac电脑字体
plt.rcParams['font.sans-serif'] = ['Arial Unicode MS']
mpl.rcParams['font.serif'] = ['Arial Unicode MS']
# linux字体
# plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']  # 用来正常显示中文标签
# mpl.rcParams['font.serif'] = ['Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号

logging.basicConfig(stream=sys.stdout, level=logging.INFO,
                    datefmt='%Y-%m-%d %H:%M:%S',
                    format='%(asctime)s-%(levelname)s: -> %(message)s')


class ProfileUtils(object):
    def __init__(self):
        self.dir = "/Users/jiang/Downloads/"        
        self.data_path = "{}/data.txt".format(self.dir)
        self.save_path = "{}/用户画像.xls".format(self.dir)
        self.columns = ['账号',
                        '访问日期',
                        '画像丰满度',
                        'GPS',
                        '最近点击过的v1',
                        '最近点击过的v2',
                        '最近点击过的guid',
                        '最近点击过的topic',
                        '最近点击过的实体',
                        '最近点击过的泛化实体',
                        '关注',
                        '操作系统',
                        '机型',
                        '更新时间',
                        '版本',
                        'white_group']

    def read_data(self, path, columns):
        return pd.read_csv(path, sep='\t', header=None, names=columns)

    def save_df(self, path, df):
        return df.to_csv(path, sep='\t', index=None, header=None)

    def save_data_to_excel(self, save_name, all_cols_list, img_path_dic):
        writer = pd.ExcelWriter(save_name, engine='xlsxwriter')
        for sheet_name, df_item in all_cols_list:
            if sheet_name == '画像明细':
                df_item.to_excel(writer, sheet_name=sheet_name, index=False)
            else:
                df_item.to_excel(writer, sheet_name=sheet_name, index=False)
                sheet = writer.book.sheetnames[sheet_name]
                sheet.insert_image('H2', img_path_dic[sheet_name])

        writer.save()
        writer.close()

    def days(self, day1, day2):
        d1 = datetime(int(day1[:4]), int(day1[4:6]), int(day1[-2:]))
        d2 = datetime(int(day2[:4]), int(day2[4:6]), int(day2[-2:]))
        interval = d2 - d1  # 两日期差距
        return interval.days  # 具体的天数

    def parse_gps(self, row, city_dic):
        try:
            GPS = row['GPS']
            if pd.isna(GPS):
                city = "未知"
            elif GPS == 'None':
                city = "未知"
            else:
                lis = GPS.split("|")
                if len(lis) > 1:
                    if '中国' in GPS:
                        city = GPS.split("|")[1]
                    else:
                        city = GPS.split("|")[0]
                elif len(lis) == 1:
                    city = GPS.split("|")[0]
                else:
                    city = "未知"
                if "�" in city or len(city) == 1:
                    city = "未知"

                if city not in city_dic:
                    city_dic[city] = 0
            city_dic[city] += 1
        except Exception as e:
            logging.error(f"parse_gps error {traceback.format_exc()}")

    def click_tag(self, row, data_dic, col):
        try:
            tag = row[col]
            if not (pd.isna(tag) or tag == 'None'):
                lis = tag.split(",")
                # 财经|1|4,其他|1|5
                for v1_s in lis:
                    ll = v1_s.split("|")
                    if len(ll) == 3:
                        v1_cv = ll[0]
                        c = int(ll[1])
                        e = int(ll[2])
                        if v1_cv not in data_dic:
                            data_dic[v1_cv] = {
                                "曝光": 0,
                                "点击": 0,
                                "用户数": 0
                            }
                        data_dic[v1_cv]['曝光'] += e
                        data_dic[v1_cv]['点击'] += c
                        data_dic[v1_cv]['用户数'] += 1
        except Exception as e:
            logging.error(f"click_tag error {traceback.format_exc()}")

    def subscribe(self, row, ts_dic, col):
        # 关注
        try:
            subs = row[col]
            if not (pd.isna(subs) or subs == 'None'):
                lis = subs.split(",")
                # 账号维度：账号关注的人数，关注的日期，关注的小时数
                # 时间维度：某一天，某小时，关注了哪些账号
                # "4000000089717384490|20230227 15:56:00,80572276|20230101 11:29:01'
                for sub_s in lis:
                    ll = sub_s.split("|")
                    if len(ll) == 2:
                        # guid = ll[0]
                        ts = ll[1]
                        ts_ll = ts.split(" ")
                        # day = ts_ll[0]
                        hour = ts_ll[1][:2]
                        if hour not in ts_dic:
                            ts_dic[hour] = 0
                        ts_dic[hour] += 1
        except Exception as e:
            logging.error(f"subscribe error {traceback.format_exc()}")

    def calc_data(self, df):
        v1_dic = {}
        df.apply(self.click_tag, axis=1, args=(v1_dic, "最近点击过的v1",))

        v2_dic = {}
        df.apply(self.click_tag, axis=1, args=(v2_dic, "最近点击过的v2",))

        entity_dic = {}
        df.apply(self.click_tag, axis=1, args=(entity_dic, "最近点击过的实体",))

        generate_en_dic = {}
        df.apply(self.click_tag, axis=1, args=(generate_en_dic, "最近点击过的泛化实体",))

        topic_dic = {}
        df.apply(self.click_tag, axis=1, args=(topic_dic, "最近点击过的topic",))

        guid_dic = {}
        df.apply(self.click_tag, axis=1, args=(guid_dic, "最近点击过的guid",))
        return v1_dic, v2_dic, entity_dic, generate_en_dic, topic_dic, guid_dic

    def dic2df(self, data_dic, keys, sort=False, sort_value=False):
        try:
            if sort and sort_value:
                data_dic = dict(sorted(data_dic.items(), key=lambda item: item[1], reverse=True))
            elif sort:
                data_dic = dict(sorted(data_dic.items(), key=lambda item: item[0]))
            sub_list = []
            for tag, cnt in data_dic.items():
                sub_list.append([tag, cnt])
            return pd.DataFrame(sub_list, columns=keys)
        except Exception as e:
            logging.error(f"dic2df error {traceback.format_exc()}")
            return None

    def calc_mean_freq(self, freq_list):
        try:
            if len(freq_list) == 1:
                now = (datetime.now() - timedelta(days=0)).strftime('%Y%m%d')
                return round(self.days(freq_list[0], now))
            elif freq_list and len(freq_list) > 1:
                freq_list.sort()
                days_list = []
                for i in range(len(freq_list)):
                    if i + 1 < len(freq_list):
                        dd = self.days(freq_list[i], freq_list[i + 1])
                        days_list.append(dd)
                if len(days_list) > 0:
                    return round(math.ceil(sum(days_list) / len(days_list), ))
                else:
                    return -1
            else:
                return -1
        except Exception as e:
            logging.error(f"calc_mean_freq error {freq_list} {traceback.format_exc()}")
        return -1

    def visit_day(self, row, visit_dic, col):
        try:
            visits = row[col]
            if not (pd.isna(visits) or visits == 'None'):
                # 不同日期访问的数量
                # 访问日期频率
                # 20230101|20221214
                lis = visits.split("|")
                mean_freq = self.calc_mean_freq(lis)
                if mean_freq not in visit_dic['freq']:
                    visit_dic['freq'][mean_freq] = 0
                visit_dic['freq'][mean_freq] += 1

                times = len(lis)
                if times not in visit_dic['times']:
                    visit_dic['times'][times] = 0
                visit_dic['times'][times] += 1
        except Exception as e:
            logging.error(f"visit_day error {traceback.format_exc()}")

    def cur_level(self, dic):
        new_dic = {}
        try:
            for k, v in dic.items():
                if k <= 1:
                    key = '1'
                elif k <= 2:
                    key = '2'
                elif k <= 5:
                    key = "5"
                elif k <= 8:
                    key = '8'
                elif k <= 10:
                    key = "10"
                elif k <= 14:
                    key = "14"
                elif k <= 18:
                    key = "18"
                elif k <= 20:
                    key = "20"
                elif k <= 30:
                    key = "30"
                else:
                    key = "50"
                if key not in new_dic:
                    new_dic[key] = 0
                new_dic[key] += v
            new_dic = dict(sorted(new_dic.items(), key=lambda item: int(item[0])))
        except Exception as e:
            logging.error(f"cur_level error {traceback.format_exc()}")
        return new_dic

    def plot_line(self, save_path, x_data, y_data_list, colors, title, rotation=90):
        try:
            # 图表大小
            fig = plt.figure(figsize=(20, 16))
            # 设置点大小
            for i, y_data in enumerate(y_data_list):
                # 画图
                plt.plot(x_data, y_data, color=colors[i], marker='o', label='曝光', alpha=0.25)
                # # 标记数值
                for x, y in zip(x_data, y_data):
                    plt.text(x, y, y, ha='center', va='bottom', fontsize=14)

            # 设置网格线
            plt.grid(color='black', axis="y", linestyle='-.', linewidth=1, alpha=0.15)

            # x轴斜向展示
            plt.xticks(rotation=rotation)
            # 坐标轴字体
            plt.xticks(fontsize=16)
            plt.yticks(fontsize=16)
            # 设置坐标轴颜色
            ax = plt.gca()
            ax.spines['left'].set_color('black')
            ax.spines['bottom'].set_color('black')

            plt.title(title, fontsize=20)
            # plt.show()

            fig.savefig(save_path, dpi=400, facecolor="white")
        except Exception as e:
            logging.error(f"plot_line error {save_path} {traceback.format_exc()}")

    def merge_click_df(self, data_dic, columns):
        try:
            tag_list = []
            for tag, dd in data_dic.items():
                sub_ = [tag, dd['曝光'], dd['点击'], dd['用户数']]
                tag_list.append(sub_)
            return pd.DataFrame(tag_list, columns=columns)
        except Exception as e:
            logging.error(f"merge_click_df error {data_dic} {traceback.format_exc()}")
            return None

    def get_path(self, name):
        return f"{self.dir}/img/{name}"

    def plot_df(self):        
        df = self.read_data(self.data_path, self.columns)

        logging.info(f"df: {df.shape} {df.columns} \n {df.head()}")

        city_dic = {}
        df.apply(self.parse_gps, axis=1, args=(city_dic,))
        logging.info(f"city_dic: {len(city_dic)} {city_dic}")

        v1_dic, v2_dic, entity_dic, generate_en_dic, topic_dic, guid_dic = self.calc_data(df)
        logging.info(f"v1_dic: {len(v1_dic)} {v1_dic}")
        logging.info(f"v2_dic: {len(v2_dic)}")
        logging.info(f"entity_dic: {len(entity_dic)}")
        logging.info(f"generate_en_dic: {len(generate_en_dic)}")
        logging.info(f"topic_dic: {len(topic_dic)}")
        logging.info(f"guid_dic: {len(guid_dic)}")

        ts_dic = {}
        df.apply(self.subscribe, axis=1, args=(ts_dic, "关注",))
        logging.info(f"ts_dic: {len(ts_dic)} {ts_dic}")

        hour_df = self.dic2df(ts_dic, ['小时(关注)', '人数'], sort=True)

        visit_dic = {
            "freq": {},
            "times": {}
        }
        df.apply(self.visit_day, axis=1, args=(visit_dic, "访问日期",))

        logging.info(f"freq: {len(visit_dic['freq'])} {visit_dic['freq']}")
        logging.info(f"times: {len(visit_dic['times'])} {visit_dic['times']}")

        freq_dic = self.cur_level(visit_dic['freq'])
        freq_df = self.dic2df(freq_dic, ['访问频率', '人数'], sort=False)
        times_df = self.dic2df(visit_dic['times'], ['小时(访问)', '人数'], sort=True)
        city_df = self.dic2df(city_dic, ['城市', '人数'], sort=True, sort_value=True)
        logging.info(f"city_df: {city_df.shape} {city_df.head()}")

        v1_df = self.merge_click_df(v1_dic, ["一级分类", "曝光", "点击", "用户数"])
        v2_df = self.merge_click_df(v2_dic, ["二级分类", "曝光", "点击", "用户数"])
        entity_df = self.merge_click_df(entity_dic, ["实体", "曝光", "点击", "用户数"])
        generate_en_df = self.merge_click_df(generate_en_dic, ["泛化实体", "曝光", "点击", "用户数"])
        topic_df = self.merge_click_df(topic_dic, ["话题", "曝光", "点击", "用户数"])
        guid_df = self.merge_click_df(guid_dic, ["账号", "曝光", "点击", "用户数"])

        img_path_dic = {
            "一级分类": self.get_path("v1.png"),
            "二级分类": self.get_path("v2.png"),
            "实体": self.get_path("entity.png"),
            "泛化实体": self.get_path("generate.png"),
            "话题": self.get_path("topic.png"),
            "账号": self.get_path("guid.png"),
            "小时关注人数": self.get_path("hour.png"),
            "访问频率": self.get_path("freq.png"),
            "访问次数": self.get_path("times.png"),
            "城市分布": self.get_path("city.png")
        }
        self.plot_line(img_path_dic['一级分类'], v1_df['一级分类'], [v1_df['曝光'], v1_df['点击'], v1_df['用户数']],
                       ['red', 'blue', 'green'],
                       "一级分类: 曝光(红)，点击(蓝)，点击人数(绿)")
        self.plot_line(img_path_dic['二级分类'], v2_df['二级分类'].iloc[:50],
                       [v2_df['曝光'].iloc[:50], v2_df['点击'].iloc[:50], v2_df['用户数'].iloc[:50]],
                       ['red', 'blue', 'green'],
                       "二级分类: 曝光(红)，点击(蓝)，点击人数(绿)")
        self.plot_line(img_path_dic['实体'], entity_df['实体'].iloc[:50],
                       [entity_df['曝光'].iloc[:50], entity_df['点击'].iloc[:50], entity_df['用户数'].iloc[:50]],
                       ['red', 'blue', 'green'], "实体: 曝光(红)，点击(蓝)，点击人数(绿)")
        self.plot_line(img_path_dic['泛化实体'], generate_en_df['泛化实体'].iloc[:50],
                       [generate_en_df['曝光'].iloc[:50], generate_en_df['点击'].iloc[:50], entity_df['用户数'].iloc[:50]],
                       ['red', 'blue', 'green'], "泛化实体: 曝光(红)，点击(蓝)，点击人数(绿)")
        self.plot_line(img_path_dic['话题'], topic_df['话题'].iloc[:50],
                       [topic_df['曝光'].iloc[:50], topic_df['点击'].iloc[:50], topic_df['用户数'].iloc[:50]],
                       ['red', 'blue', 'green'], "话题: 曝光(红)，点击(蓝)，点击人数(绿)")
        self.plot_line(img_path_dic['账号'], guid_df['账号'].iloc[:50],
                       [guid_df['曝光'].iloc[:50], guid_df['点击'].iloc[:50], guid_df['用户数'].iloc[:50]],
                       ['red', 'blue', 'green'], "guid: 曝光(红)，点击(蓝)，点击人数(绿)")

        self.plot_line(img_path_dic['小时关注人数'], hour_df['小时(关注)'], [hour_df['人数']], ['red'], "关注人数", rotation=0)
        self.plot_line(img_path_dic['访问频率'], freq_df['访问频率'], [freq_df['人数']], ['red'], "访问频率", rotation=0)
        self.plot_line(img_path_dic['访问次数'], times_df['小时(访问)'], [times_df['人数']], ['red'], "访问次数", rotation=0)
        self.plot_line(img_path_dic['城市分布'], city_df['城市'], [city_df['人数']], ['red'], "城市分布", rotation=90)

        all_cols_list = [
            ["画像明细", df],
            ['一级分类', v1_df],
            ['二级分类', v2_df],
            ['实体', entity_df],
            ['泛化实体', generate_en_df],
            ['话题', topic_df],
            ['账号', guid_df],
            ['小时关注人数', hour_df],
            ['访问频率', freq_df],
            ['访问次数', times_df],
            ['城市分布', city_df]
        ]

        self.save_data_to_excel(self.save_path, all_cols_list, img_path_dic)


obj = ProfileUtils()
obj.plot_df()

数据格式

81890649	20221112	0.6179316052346939	中国|上海市|上海市|普陀区|大渡河路565号(长风公园地铁站2号口步行200米)|友缘茶人(大渡河路店)	生活|1|1	生活分享|1|1	4000000089719214312|1|1	8KkYtoHh2Jc|1|1	None	None	None	iphone_16.1	iphone13_4,iphone	2022-11-18 23:14:39	7.58.1	None
76919736	20221227	0.6179316052346939	中国|北京市|北京市	文化|1|1	东方智慧|1|1	4000000089718963493|1|1	8M4LIgYEjmy|1|1	None	None	None	android_10	m2007j22c,redmi	2023-01-02 22:43:38	7.60.0	None
80404085	20221125	0.6179316052346939	中国|上海市|上海市|嘉定区|张掖路|	文化|1|2	文化理论|1|2	4000000089719025918|1|2	8LEB2OXOCxs|1|3	None	None	None	android_12	m2102k1c,xiaomi	2022-12-01 22:24:33	7.58.2	None
70407321	None	None	None	None	None	None	None	None	None	94088003|20230303 10:38:56,50478422|20230303 10:38:55,4000000087352550627|20230303 10:38:54	None	None	None	None	None
4000000089719227369	20230101|20221214	0.6262705367812866	中国|广东省|深圳市|龙岗区|兴龙路|	旅游|2|8,体育|1|12	旅游政策性资讯|2|2,体育娱乐八卦|1|3	4000000089719227369|2|4,40275996|1|1	8Ljco89fQmm|2|3,8L1FvIG3rRw|1|11	达州市|2|2,四川省|2|2	冬天应季游|2|2,冰雪游|2|2,温泉度假游|2|2	None	android_12	lge-an00,honor	2023-01-03 11:13:39	7.61.0	None
9565171	20221121	0.6179316052346939	中国|广东省|中山市	情感|1|1	心灵鸡汤|1|1	87813829|1|1	8L6Xb96FvEm|1|2	None	None	None	android_10	lio-an00,huawei	2022-11-27 23:24:59	7.58.2	None
4000000089716853269	None	None	None	None	None	None	None	None	None	4000000089716827825|20221228 09:17:04	None	None	None	None	None
4000000015087800646	None	None	None	None	None	None	None	None	None	4000000089717384490|20230227 15:56:00,80572276|20230101 11:29:01	None	None	None	None	None
4000000089718249026	20221227	0.6179316052346939	中国|内蒙古自治区|赤峰市|元宝山区|G16(丹锡高速)|	军事|1|1	军事知识|1|1	4000000089718249026|1|3	8LTtiqUpgGm|1|1	None	None	88407282|20230315 21:23:17,4000000089718514440|20230308 04:23:18,4000000089719449903|20230306 09:53:16,73812971|20230224 00:18:12,97499832|20230222 22:11:21,74526440|20230213 15:04:46,94297008|20230203 20:40:05,93545605|20230201 16:40:30,4000000089719077377|20230123 18:06:33,78575652|20230123 18:04:09,4000000089718631016|20230121 18:15:06,4000000089718926745|20230113 11:45:35,4000000089717533288|20230108 19:04:20,4000000089716849322|20230108 18:54:49,4000000009675440627|20230103 01:03:18,4000000087958970623|20230101 16:39:17,4000000089366840623|20221222 21:24:08,67962554|20221218 18:30:04,40275996|20221215 20:53:24,4000000089716876908|20221214 19:44:27,4000000046052300623|20221213 17:21:16,4000000089718868282|20221206 17:55:54,4000000089717609772|20221206 09:40:10,4000000089717505210|20221205 22:44:57,4000000089719271110|20221201 19:02:30,93425221|20221130 13:13:17,4000000089719273042|20221130 02:03:53,75924071|20221129 20:16:40,4000000089717557380|20221124 21:40:26,61980778|20221123 18:17:15,86531178|20221122 09:51:56,4000000075239060628|20221121 19:02:44,4000000089719243384|20221119 23:18:21,94192268|20221119 08:52:43,4000000089717578344|20221118 12:30:55,4000000089718915089|20221118 12:26:42,4000000089718831949|20221115 06:32:14,21534852|20221113 17:54:07,4000000089719215032|20221113 11:46:22,4000000087352550627|20221110 08:52:20,85354052|20221109 20:58:27,4000000047203130623|20221109 19:34:24,4000000089719094567|20221109 19:15:06,4000000089719150656|20221107 00:11:51,4000000089719174178|20221031 09:03:02,4000000089718532186|20221024 16:23:45,4000000089716816483|20221022 10:39:45,4000000089718754511|20221019 15:07:59,4000000006494000623|20221014 19:41:37,92240778|20221011 16:02:16,4000000089719113390|20221009 23:22:40,4000000051390650623|20221005 21:05:24,4000000089717544773|20220930 18:08:46,4000000025878980624|20220930 18:08:42,4000000089717610763|20220930 18:08:01,4000000089717261533|20220929 13:26:11,4000000064983110623|20220929 09:03:13,4000000051766160624|20220928 20:31:00,4000000089718528657|20220928 20:30:11,97518683|20220928 12:38:52,94367337|20220927 20:05:04,4000000049817060623|20220926 17:54:11,4000000086140400625|20220923 17:38:59,4000000089719064228|20220923 12:13:07,97518705|20220923 12:12:03,4000000030670280633|20220922 17:57:49,4000000086773280625|20220921 19:22:34,4000000089718987397|20220919 22:49:12,4000000089717599001|20220915 22:55:41,4000000089719020521|20220910 21:38:08,4000000089718199219|20220909 12:41:23,48954335|20220906 14:14:06,91459300|20220904 12:53:39,4000000024873620623|20220902 18:08:22,4000000089718584953|20220609 11:15:17,4000000089717639139|20220609 11:15:11,4000000089716613916|20220309 18:31:50,4000000089717630531|20220302 11:46:23	android_12	ana-an00,huawei	2023-01-02 22:53:30	7.61.0	None
4000000089719137203	None	None	None	None	None	None	None	None	None	76279380|20230105 16:58:54	None	None	None	None	None
4000000089719281486	20221222	0.6218582440451138	中国|上海市|上海市	时政|1|1,社会|1|3	灾情播报及预报|1|1,东亚和环太平洋时政|1|1	87813829|1|1,4000000089719060681|1|3	8Lx0VBH5wv2|1|1,8Lx7sCa3Wim|1|1	None	None	None	android_10	hma-al00,huawei	2023-01-03 19:03:46	7.60.0	None
4000000029094140626	None	None	None	None	None	None	None	None	None	4000000030829220624|20221204 05:46:40,4000000056781080624|20200609 05:26:38,95409948|20190718 11:56:37	None	None	None	None	None
4000000061880780623	20221127	0.6179316052346939	中国|北京市|北京市|东城区|东长安街1号|东方新天地	文化|1|1	文化理论|1|1	4000000089719025918|1|1	8LEB2OXOCxs|1|2	None	None	None	iphone_15.5	iphone14_2,iphone	2022-12-03 22:14:30	7.58.2	None
4000000089719408157	20230101	0.6179316052346939		文化|1|1	解读小说|1|1	4000000089719237515|1|1	8Kr9qtFToVE|1|1,8KoB3WyCx60|1|1	None	None	None	iphone_13.6	iphone9_2,iphone	2023-01-04 22:43:29	7.61.0	None
4000000089719072754	20230103|20230101|20221230|20221228|20221227|20221224	0.6448956506309339	中国|河南省|洛阳市	社会|5|22,三农|1|1,其他|1|5	城市与乡村建设|3|6,三农政策|1|1,政府在行动|1|2,正能量|1|2	4000000089719072754|7|24	8LsIB6nCVBg|1|3,8Kpo25R4FDk|1|2	肖亚庆|1|3	影视剧点评|1|6	None	android_13	sm-s9080,samsung	2023-01-04 08:53:45	7.61.0	None
4000000006327020631	20221225	0.6179316052346939	中国|四川省|成都市	生活|1|1	节日祝福（祝福）|1|1	4000000089718963493|1|1	8LyVfEkHMsy|1|1	None	None	None	iphone_14.7.1	iphone13_1,iphone	2022-12-31 22:43:46	7.60.0	None