• 多進程學習
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import pandas as pd
from geopy.distance import geodesic
from multiprocessing import Manager, Pool
import time
import warnings
warnings.filterwarnings("ignore")


def count_points(index, match_table, to_match_table1, to_match_table2, cabinet_affiliation, res):
    for i in match_table.index:
        # 統(tǒng)計字段初始化
        within_10m = dict()
        num_10m = 0
        name_10m = []

        within_30m = dict()
        num_30m = 0
        name_30m = []

        within_50m = dict()
        num_50m = 0
        name_50m = []

        # 第一個點經(jīng)緯度坐標
        lgt1 = match_table['delivery_lat'][i]
        lat1 = match_table['delivery_lgt'][i]
        # zno_code = match_table['zno_code'][i]

        # 遍歷to_match_table1
        for j in to_match_table1.index:
            # if to_match_table1['所在網(wǎng)點代碼'][j] == zno_code:
            # 第二個點經(jīng)緯度坐標
            lgt2 = to_match_table1['y'][j]
            lat2 = to_match_table1['x'][j]

            # 統(tǒng)計字段初始化
            name_10m_dict_zc = dict()
            name_30m_dict_zc = dict()
            name_50m_dict_zc = dict()

            # 距離計算及邏輯判斷
            distance1 = geodesic((lgt1, lat1), (lgt2, lat2)).m
            if distance1 <= 10:
                num_10m  = 1
                name_10m_dict_zc['name'] = to_match_table1['name'][j]
                name_10m_dict_zc['comp_name'] = to_match_table1['comp_name'][j]
                name_10m.append(name_10m_dict_zc)

                num_30m  = 1
                name_30m_dict_zc['name'] = to_match_table1['name'][j]
                name_30m_dict_zc['comp_name'] = to_match_table1['comp_name'][j]
                name_30m.append(name_30m_dict_zc)

                num_50m  = 1
                name_50m_dict_zc['name'] = to_match_table1['name'][j]
                name_50m_dict_zc['comp_name'] = to_match_table1['comp_name'][j]
                name_50m.append(name_50m_dict_zc)
            elif distance1 <= 30:
                num_30m  = 1
                name_30m_dict_zc['name'] = to_match_table1['name'][j]
                name_30m_dict_zc['comp_name'] = to_match_table1['comp_name'][j]
                name_30m.append(name_30m_dict_zc)

                num_50m  = 1
                name_50m_dict_zc['name'] = to_match_table1['name'][j]
                name_50m_dict_zc['comp_name'] = to_match_table1['comp_name'][j]
                name_50m.append(name_50m_dict_zc)
            elif distance1 <= 50:
                num_50m  = 1
                name_50m_dict_zc['name'] = to_match_table1['name'][j]
                name_50m_dict_zc['comp_name'] = to_match_table1['comp_name'][j]
                name_50m.append(name_50m_dict_zc)
            else:
                pass

        # 遍歷to_match_table2
        for k in to_match_table2.index:
            # 第二個點經(jīng)緯度坐標
            lgt2 = to_match_table2['latitude'][k]
            lat2 = to_match_table2['longitude'][k]

            # 統(tǒng)計字段初始化
            name_10m_dict_fc = dict()
            name_30m_dict_fc = dict()
            name_50m_dict_fc = dict()

            # 距離計算及邏輯判斷
            distance2 = geodesic((lgt1, lat1), (lgt2, lat2)).m
            if distance2 <= 10:
                num_10m  = 1
                name_10m_dict_fc['name'] = 'fc'
                name_10m_dict_fc['belongorg'] = cabinet_affiliation[to_match_table2['belongorg'][k]]
                name_10m.append(name_10m_dict_fc)

                num_30m  = 1
                name_30m_dict_fc['name'] = 'fc'
                name_30m_dict_fc['belongorg'] = cabinet_affiliation[to_match_table2['belongorg'][k]]
                name_30m.append(name_30m_dict_fc)

                num_50m  = 1
                name_50m_dict_fc['name'] = 'fc'
                name_50m_dict_fc['belongorg'] = cabinet_affiliation[to_match_table2['belongorg'][k]]
                name_50m.append(name_50m_dict_fc)
            elif distance2 <= 30:
                num_30m  = 1
                name_30m_dict_fc['name'] = 'fc'
                name_30m_dict_fc['belongorg'] = cabinet_affiliation[to_match_table2['belongorg'][k]]
                name_30m.append(name_30m_dict_fc)

                num_50m  = 1
                name_50m_dict_fc['name'] = 'fc'
                name_50m_dict_fc['belongorg'] = cabinet_affiliation[to_match_table2['belongorg'][k]]
                name_50m.append(name_50m_dict_fc)
            elif distance2 <= 50:
                num_50m  = 1
                name_50m_dict_fc['name'] = 'fc'
                name_50m_dict_fc['belongorg'] = cabinet_affiliation[to_match_table2['belongorg'][k]]
                name_50m.append(name_50m_dict_fc)
            else:
                pass

        # 組裝字段-字典
        within_10m['points_num'] = num_10m
        within_10m['name-or-code'] = name_10m
        within_30m['points_num'] = num_30m
        within_30m['name-or-code'] = name_30m
        within_50m['points_num'] = num_50m
        within_50m['name-or-code'] = name_50m

        # DataFrame對應字段賦值
        match_table['80_10m'][i] = within_10m
        match_table['80_30m'][i] = within_30m
        match_table['80_50m'][i] = within_50m
    res[index] = match_table


if __name__ == '__main__':
    cabinet_affiliation = {
        "1": "a",
        "2": "b",
        "3": "c",
        "4": "d",
        "5": "e",
        "6": "f",
        "7": "g",
        "8": "h",
        "9": "i",
        "10": "j"
    }
    # 讀入數(shù)據(jù)
    coord_80 = pd.read_csv(r'', dtype=str)
    to_match_1_zc = pd.read_csv(r'', sep='\t', dtype=str)
    to_match_2_fc = pd.read_csv(r'', dtype=str)

    # 添加字段及字段值類型轉(zhuǎn)換
    coord_80['80_10m'] = ''
    coord_80['80_30m'] = ''
    coord_80['80_50m'] = ''
    coord_80['delivery_lgt'] = coord_80['delivery_lgt'].apply(pd.to_numeric)
    coord_80['delivery_lat'] = coord_80['delivery_lat'].apply(pd.to_numeric)
    to_match_1_zc['x'] = to_match_1_zc['x'].apply(pd.to_numeric)
    to_match_1_zc['y'] = to_match_1_zc['y'].apply(pd.to_numeric)
    to_match_2_fc['longitude'] = to_match_2_fc['longitude'].apply(pd.to_numeric)
    to_match_2_fc['latitude'] = to_match_2_fc['latitude'].apply(pd.to_numeric)

    # 多進程
    time_start = time.time()
    manager = Manager()
    res = manager.dict()
    pool_nums = 8
    pool_band = 10
    p = Pool(pool_nums - 1)

    for i in range(pool_nums):
        index = i * pool_band
        coord_80_temp = coord_80.iloc[index:index   pool_band, :]
        p.apply_async(count_points,
                      args=(index, coord_80_temp, to_match_1_zc, to_match_2_fc, cabinet_affiliation, res,))

    p.close()
    p.join()

    df_temp = pd.DataFrame()
    for values in res.values():
        df_temp = pd.concat((df_temp, values))
    df_temp.to_csv(r'', index=False)
    time_end = time.time()
    print('用時{}s'.format(time_end - time_start))