manyi/script/sort_dealer.py

# coding=utf-8
import datetime

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
from pylab import mpl

from apps.web.core.bridge.wechat.v3api import WechatApiProxy
from apps.web.core.models import WechatServiceProvider
from apps.web.core.networking import MessageSender
from apps.web.device.models import DeviceDict
from apps.web.merchant.models import MerchantSourceInfo


def make_autopct(total):
    def my_autopct(pct):
        val = int(round(pct*total/100.0))
        # 同时显示数值和占比的饼图
        return '{p:.2f}%  ({v:d})'.format(p=pct,v=val)
    return my_autopct


def parse_data1():
    """
    解析数据
    """
    # 读取数据
    df = pd.read_csv("dealerMerchantStatus1.csv", header=0)

    # 标准化画布
    plt.axes(aspect='equal')

    allCount = df.id.count()

    #
    df.merStatus.value_counts().sort_values().plot.pie(autopct=make_autopct(allCount), fontsize=10, counterclock=False, explode=(0.2,0.3,0.4,0.2, 0))

    # df.set_index('merStatus', inplace=True)
    plt.title(u'商户开通整体情况', fontsize=16, fontweight='bold')
    plt.ylabel(u"发出邀请总人数 {}".format(df["merStatus"].count()), fontsize=16, fontweight='bold')

    plt.show()


def parse_data2():
    """
    解析数据
    """
    # 读取数据
    df = pd.read_csv("dealerMerchantStatus1.csv", header=0)

    # 标准化画布
    plt.axes(aspect='equal')

    # 条件筛选出未开通商户的
    online4 =  df[df["merStatus"] == 4].onlineCount.sum()
    online3 =  df[df["merStatus"] == 3].onlineCount.sum()
    online2=  df[df["merStatus"] == 2].onlineCount.sum()
    online1 =  df[df["merStatus"] == 1].onlineCount.sum()
    online0 =  df[df["merStatus"] == 0].onlineCount.sum()

    allOnline = df.onlineCount.sum()

    s = pd.Series([
        online0,
        online1,
        online2,
        online3,
        online4,
    ],[
        u"未提交资料",
        u"京东入驻中",
        u"申请失败",
        u"申请成功（微信审核）",
        u"已经切换"
    ]
    )

    s.sort_values().plot.pie(autopct=make_autopct(allOnline), fontsize=10, counterclock=False, explode=(0.3,0,0,0,0))

    plt.title(u"设备分布图", fontsize=16, fontweight='bold')
    plt.ylabel(u"设备总数{}".format(allOnline), fontsize=16, fontweight='bold')
    #
    plt.show()


def parse_data3():
    """
    最后登录
    """
    # 读取数据
    df = pd.read_csv("dealerMerchantStatus1.csv", header=0)

    # 标准化画布
    plt.axes(aspect='equal')

    # 找出所有没有提交资料的经销商
    df = df[df["merStatus"] == 0]

    # 比较登录日期
    now = datetime.datetime.now()
    login14 = df[df.lastLogin.astype('datetime64') >= now - datetime.timedelta(days=14)].onlineCount.sum()

    login15_30 = df[(df.lastLogin.astype('datetime64') < now - datetime.timedelta(days=14)) & (df.lastLogin.astype('datetime64') >= now - datetime.timedelta(days=30))].onlineCount.sum()
    login30_90 = df[(df.lastLogin.astype('datetime64') < now - datetime.timedelta(days=30)) & (df.lastLogin.astype('datetime64') >= now - datetime.timedelta(days=90))].onlineCount.sum()
    login90 = df[(df.lastLogin.astype('datetime64') < now - datetime.timedelta(days=90))].onlineCount.sum()

    allCount = df.onlineCount.sum()


    s = pd.Series([
        login14,
        login15_30,
        login30_90,
        login90,
    ], [
        u"14天以内",
        u"14天到30天",
        u"30天到90天",
        u"3个月以上"
    ])

    s.sort_values().plot.pie(autopct=make_autopct(allCount), fontsize=10, counterclock=False, explode=(0.3,0.3,0.3,0))

    plt.title(u"最后登录分布图(设备)", fontsize=16, fontweight='bold')
    plt.ylabel(u"总数{}".format(allCount), fontsize=16, fontweight='bold')
    #
    plt.show()


def parse_data4():

    # 读取数据
    df = pd.read_csv("dealerMerchantStatus1.csv", header=0)

    # 读取所有的里歌经销商ID
    with open("lgdid") as f:
        lg = f.read().split()
    with open('hpdid') as f:
        hp = f.read().split()

    ids = lg + hp
    # 标准化画布
    # plt.axes(aspect='equal')

    # 分种类经销商
    df0 = df[df.merStatus == 0]
    df1 = df[df.merStatus == 1]
    df2 = df[df.merStatus == 2]
    df3 = df[df.merStatus == 3]
    df4 = df[df.merStatus == 4]


    num_list = [
        df0.onlineCount.sum(),
        df1.onlineCount.sum(),
        df2.onlineCount.sum(),
        df3.onlineCount.sum(),
        df4.onlineCount.sum(),
    ]

    num_list1 = [
        df0[df0.id.isin(ids)].onlineCount.sum(),
        df1[df1.id.isin(ids)].onlineCount.sum(),
        df2[df2.id.isin(ids)].onlineCount.sum(),
        df3[df3.id.isin(ids)].onlineCount.sum(),
        df4[df4.id.isin(ids)].onlineCount.sum(),
    ]

    # 比较登录日期
    name_list = [u"未提交", u"入驻中", u"设备失败", u"入驻成功（微信申请）", u"已切换"]
    x = list(range(len(name_list)))

    total_width, n = 0.6, 2
    width = total_width / n
    plt.bar(x, num_list, width=width, label=u"设备总数量", fc="b")

    for a, b in zip(x, num_list):  # 柱子上的数字显示
        plt.text(a, b, '%.3f' % b, ha='center', va='bottom', fontsize=10)

    for i in range(len(x)):
        x[i] = x[i] + width
    plt.bar(x, num_list1, width=width, label=u"霍珀里歌", tick_label=name_list, fc="r")
    plt.xlabel(u"类型", fontsize=10)
    plt.ylabel(u"设备数量", fontsize=10)
    plt.title(u"霍珀里歌", fontsize=15)
    for a, b in zip(x, num_list1):  # 柱子上的数字显示
        plt.text(a, b, '%.3f' % b, ha='center', va='bottom', fontsize=10)
    plt.legend(fontsize=8)
    plt.show()


def parse_data5():
    """
    最后登录
    """
    # 读取数据
    df = pd.read_csv("dealerMerchantStatus1.csv", header=0)

    # 读取所有的里歌经销商ID
    with open("lgdid") as f:
        lg = f.read().split()
    with open('hpdid') as f:
        hp = f.read().split()

    ids = lg + hp
    # 标准化画布
    # plt.axes(aspect='equal')

    # 分种类经销商
    df0 = df[df.merStatus == 0]
    df1 = df[df.merStatus == 1]
    df2 = df[df.merStatus == 2]
    df3 = df[df.merStatus == 3]
    df4 = df[df.merStatus == 4]


    num_list = [
        df0.onlineCount.count(),
        df1.onlineCount.count(),
        df2.onlineCount.count(),
        df3.onlineCount.count(),
        df4.onlineCount.count(),
    ]

    num_list1 = [
        df0[df0.partnerId == "[]"].onlineCount.count(),
        df1[df1.partnerId == "[]"].onlineCount.count(),
        df2[df2.partnerId == "[]"].onlineCount.count(),
        df3[df3.partnerId == "[]"].onlineCount.count(),
        df4[df4.partnerId == "[]"].onlineCount.count(),
    ]

    # 比较登录日期
    name_list = [u"未提交", u"入驻中", u"设备失败", u"入驻成功（微信申请）", u"已切换"]
    x = list(range(len(name_list)))

    total_width, n = 0.6, 2
    width = total_width / n
    plt.bar(x, num_list, width=width, label=u"申请数量", fc="b")

    for a, b in zip(x, num_list):  # 柱子上的数字显示
        plt.text(a, b, '%d' % b, ha='center', va='bottom', fontsize=10)

    for i in range(len(x)):
        x[i] = x[i] + width
    plt.bar(x, num_list1, width=width, label=u"没有合伙人", tick_label=name_list, fc="r")
    plt.xlabel(u"类型", fontsize=10)
    plt.ylabel(u"设备数量", fontsize=10)
    plt.title(u"没有合伙人情况", fontsize=15)
    for a, b in zip(x, num_list1):  # 柱子上的数字显示
        plt.text(a, b, '%d' % b, ha='center', va='bottom', fontsize=10)
    plt.legend(fontsize=8)
    plt.show()


def aa():
    """
    """

    from apps.web.dealer.models import TodoMessage, Dealer
    from apps.web.device.models import Device

    num = 0
    for t in TodoMessage.objects.filter(title=u"商户开通提醒"):
        if num >= 1:
            break

        if Device.objects.filter(ownerId=t.ownerId).count() >= 10:
            continue

        dealer = Dealer.objects.get(id=t.ownerId)   # type: Dealer

        if "forceMerchant" in dealer.features:
            continue

        dealer.features.append("forceMerchant")
        dealer.save()
        print("dealerId = {}".format(dealer.id))
        num += 1










if __name__ == '__main__':
    parse_data3()