Pandas：基于两个不同的列创建唯一值的索引

Shipping Lane Rate Cost 20_21_RDWY_Purple_AL_AR_KY_LA_MS_SC_TN_PE 50.80% 120 20_21_RDWY_Purple_AZ 50.80% 155 20_21_RDWY_Purple_CA 62.40% 145 20_21_RDWY_Purple_CO_ND_WY_MB_NF_PQ 62.40% 155 20_21_RDWY_Purple_CT_DE_MN_NE 50.00% 145 20_21_RDWY_Purple_DC_IA_KS_MD_MI_OH_OK_WI 49.00% 125 20_21_RDWY_Purple_FL 48.30% 125

def remove_dups(input, output): input.sort() n_list = list(input for input, _ in itertools.groupby(input)) output.append(n_list) def get_matches_discount(state): state_groups = [] state_rates = [] state_cost = [] final_format = [] match = [] c_match = [] for i, x in enumerate(df_d[state]): #checks within the column for identical values then maps where the identical values are match1 = [j for j, y in enumerate(df_d[state].isin([x])) if y is True] match.append(match1) remove_dups(match, c_match) for list in c_match: for elements in list: r = elements[0] state_g = df_d.index[elements] state_groups.append(state_g) state_r = df_d[state][r] state_rates.append(state_r) print(state_rates) match_cost = df_m[state][r] state_cost.append(match_cost) for i in state_groups: delimiter = "_" join_str = delimiter.join(i) j_str = "20_21_RDWY_Purple_" + join_str final_format.append(j_str) master_frame = pd.DataFrame( {'Shipping Lane': final_format, 'Rate': state_rates, 'Cost': state_cost, } ) print(master_frame) return master_frame m_col_names = ['AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL', 'GA', 'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MA', 'MD', 'ME', 'MI', 'MN', 'MO', 'MS', 'MT', 'NC', 'ND', 'NE', 'NH', 'NJ', 'NM', 'NV', 'NY', 'OH', 'OK', 'OR', 'PA', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VA', 'VT', 'WA', 'WI', 'WV', 'WY', 'AB', 'BC', 'MB', 'NB', 'NF', 'NS', 'ON', 'PE', 'PQ', 'SK'] # calls the function in a loop to process one column at a time # creates the master data frame outside of the function calling for loop master_dataframe0 = pd.DataFrame() for state in m_col_names: temp_df = get_matches_discount(state) # Stores the function call as a variable master_dataframe0 = master_dataframe0.append(temp_df) # Creates an appended dataframe outside of the function print(master_dataframe0) master_dataframe0.to_excel("shipping_lanes_revised00.xlsx")

State AL AR AZ CA CO CT DC AL 120.00 120.00 155.00 145.00 155.00 145.00 125.00 AR 120.00 120.00 155.00 155.00 145.00 155.00 145.00 AZ 155.00 155.00 120.00 120.00 125.00 185.00 185.00 CA 145.00 164.30 120.00 120.00 170.00 185.00 185.00 CO 155.00 145.00 125.00 145.00 120.00 155.00 155.00 CT 145.00 155.00 185.00 185.00 155.00 120.00 120.00 DC 125.00 155.00 185.00 185.00 155.00 120.00 185.00 DE 145.00 155.00 185.00 185.00 155.00 120.00 120.00 FL 125.00 145.00 145.00 185.00 145.00 155.00 145.00 GA 120.00 120.00 155.00 145.00 155.00 145.00 120.00 IA 125.00 125.00 155.00 145.00 125.00 155.00 145.00 ID 145.00 155.00 145.00 145.00 125.00 185.00 185.00 IL 120.00 120.00 155.00 145.00 145.00 125.00 125.00 IN 120.00 120.00 155.00 145.00 145.00 125.00 120.00 KS 125.00 120.00 155.00 155.00 120.00 155.00 145.00 KY 120.00 120.00 155.00 145.00 145.00 125.00 125.00 LA 120.00 120.00 155.00 145.00 155.00 155.00 155.00 MA 155.00 155.00 185.00 185.00 145.00 120.00 120.00 MD 125.00 145.00 185.00 185.00 155.00 120.00 120.00 ME 155.00 155.00 185.00 185.00 145.00 120.00 125.00 MI 125.00 125.00 145.00 145.00 155.00 125.00 120.00 MN 145.00 125.00 155.00 145.00 145.00 155.00 145.00 MO 120.00 120.00 155.00 155.00 125.00 145.00 145.00 MS 120.00 120.00 155.00 155.00 145.00 155.00 145.00 MT 145.00 155.00 155.00 155.00 125.00 185.00 185.00 NC 120.00 125.00 145.00 185.00 155.00 125.00 120.00 ND 155.00 155.00 145.00 145.00 155.00 155.00 155.00 NE 145.00 125.00 155.00 155.00 120.00 155.00 155.00 NH 155.00 155.00 185.00 185.00 145.00 120.00 120.00 NJ 145.00 155.00 185.00 185.00 155.00 120.00 120.00 NM 155.00 125.00 120.00 145.00 120.00 145.00 145.00 NV 145.00 155.00 120.00 120.00 145.00 185.00 185.00 NY 145.00 145.00 185.00 185.00 155.00 120.00 120.00 OH 125.00 125.00 145.00 145.00 155.00 120.00 120.00 OK 125.00 120.00 145.00 155.00 120.00 155.00 155.00 OR 185.00 145.00 155.00 125.00 155.00 185.00 185.00 PA 145.00 145.00 185.00 185.00 155.00 120.00 120.00 RI 155.00 155.00 185.00 185.00 145.00 120.00 120.00 SC 120.00 120.00 145.00 185.00 155.00 125.00 120.00 SD 155.00 145.00 155.00 155.00 120.00 155.00 145.00 TN 120.00 120.00 155.00 145.00 155.00 145.00 125.00 TX 125.00 120.00 145.00 155.00 125.00 145.00 155.00 UT 170.00 164.30 132.50 132.50 127.20 145.00 145.00 VA 120.00 145.00 145.00 185.00 155.00 120.00 120.00

State AL AR AZ CA CO CT DC AL 50.80% 44.10% 54.30% 73.10% 53.90% 50.00% 49.00% AR 50.80% 50.80% 53.90% 65.70% 50.00% 53.90% 50.00% AZ 56.70% 55.80% 50.80% 54.10% 49.60% 59.50% 64.40% CA 62.40% 61.00% 54.30% 61.40% 43.00% 52.30% 54.30% CO 54.30% 67.10% 49.00% 65.70% 50.80% 54.30% 54.30% CT 50.00% 53.90% 64.40% 72.50% 54.30% 50.80% 50.80% DC 49.00% 53.90% 64.40% 64.40% 54.30% 50.80% 64.40% DE 50.00% 53.90% 64.40% 64.40% 54.30% 50.80% 50.80% FL 48.30% 35.00% 55.50% 55.50% 55.10% 66.40% 62.30% GA 67.90% 44.10% 71.00% 64.60% 56.00% 50.00% 44.10% IA 49.00% 49.00% 54.30% 61.80% 49.00% 53.90% 50.00% ID 61.80% 54.30% 50.00% 75.90% 49.00% 64.40% 64.40% IL 44.10% 44.10% 54.30% 64.00% 50.00% 49.00% 49.00% IN 44.10% 1.60% 11.70% 26.10% -0.70% 49.00% 44.10% KS 49.00% 63.40% 61.00% 67.70% 72.50% 72.20% 50.00% KY 50.80% 44.10% 54.30% 61.50% 50.00% 49.00% 49.00% LA 50.80% 44.10% 54.30% 61.80% 53.90% 54.30% 53.90% MA 63.50% 53.90% 67.70% 63.90% 53.00% 63.50% 44.10% MD 49.00% 50.00% 64.40% 73.80% 54.30% 50.80% 50.80% ME 53.90% 54.30% 64.40% 64.40% 61.80% 50.80% 49.00% MI 49.00% 49.00% 61.80% 55.10% 53.90% 49.00% 44.10% MN 50.00% 49.00% 54.30% 61.80% 50.00% 53.90% 50.00% MO 44.10% 50.80% 53.90% 56.10% 49.00% 50.00% 50.00% MS 50.80% 50.80% 54.30% 63.90% 50.00% 53.90% 50.00% MT 61.80% 54.30% 53.90% 75.80% 49.00% 64.40% 64.40% NC 44.10% 59.20% 53.50% 58.60% 57.90% 42.90% 69.60% ND 54.30% 53.90% 61.80% 61.80% 54.30% 53.90% 53.90% NE 50.00% 49.00% 54.30% 54.30% 44.10% 53.90% 53.90% NH 53.90% 54.30% 64.40% 64.40% 61.80% 50.80% 44.10% NJ 50.50% 51.50% 70.50% 66.20% 59.70% 67.10% 50.80% NM 53.90% 49.00% 44.10% 68.20% 44.10% 61.80% 61.80% NV 61.80% 54.30% 52.70% 73.50% 50.00% 64.40% 64.40% NY 61.10% 69.00% 65.50% 68.90% 63.00% 68.40% 50.80% OH 49.00% 49.00% 68.50% 71.50% 72.30% 60.70% 44.10% OK 49.00% 50.80% 50.00% 54.30% 44.10% 54.30% 54.30% OR 64.40% 61.80% 53.90% 64.00% 53.90% 64.40% 64.40% PA 47.20% 57.00% 33.70% 51.90% 45.50% 50.80% 50.80% RI 53.90% 54.30% 64.40% 64.40% 61.80% 50.80% 44.10% SC 50.80% 44.10% 61.80% 58.70% 54.30% 49.00% 44.10% SD 53.90% 50.00% 54.30% 54.30% 44.10% 54.30% 61.80% TN 50.80% 50.80% 52.50% 62.60% 61.30% 53.30% 49.00% TX 56.60% 46.00% 51.40% 58.30% 53.20% 63.10% 65.10% UT 45.00% 60.60% 73.50% 73.50% 70.30% 44.40% 61.90% VA 57.90% 50.00% 61.80% 72.10% 54.30% 44.10% 50.80%

Shipping Lane Rate Cost 0 20_21_RDWY_Purple_AL_AR_KY_LA_MS_SC_TN_PE 50.80% 120.0 1 20_21_RDWY_Purple_AZ 56.70% 155.0 2 20_21_RDWY_Purple_CA 62.40% 145.0 3 20_21_RDWY_Purple_CO_ND_WY_MB_NF_PQ 54.30% 155.0 4 20_21_RDWY_Purple_CT_DE_MN_NE 50.00% 145.0 5 20_21_RDWY_Purple_DC_IA_KS_MD_MI_OH_OK_WI 49.00% 125.0 6 20_21_RDWY_Purple_FL 48.30% 125.0 7 20_21_RDWY_Purple_GA 67.90% 120.0 8 20_21_RDWY_Purple_ID_MT_NV_AB_SK 61.80% 145.0 9 20_21_RDWY_Purple_IL_IN_MO_NC_WV 44.10% 120.0 10 20_21_RDWY_Purple_MA 63.50% 155.0 11 20_21_RDWY_Purple_ME_NH_NM_RI_SD_VT_NB_NS 53.90% 155.0 12 20_21_RDWY_Purple_NJ 50.50% 145.0 13 20_21_RDWY_Purple_NY 61.10% 145.0 14 20_21_RDWY_Purple_OR_WA_BC 64.40% 185.0 15 20_21_RDWY_Purple_PA 47.20% 145.0 16 20_21_RDWY_Purple_TX 56.60% 125.0 17 20_21_RDWY_Purple_UT 45.00% 170.0 18 20_21_RDWY_Purple_VA 57.90% 120.0 19 20_21_RDWY_Purple_ON 37.30% 145.0 0 20_21_RDWY_Purple_AL_GA_IL_KY_LA_SC 44.10% 120.0 1 20_21_RDWY_Purple_AR_MO_MS_OK_TN_NB_NF_NS_PE 50.80% 120.0 2 20_21_RDWY_Purple_AZ 55.80% 155.0 3 20_21_RDWY_Purple_CA 61.00% 164.3 4 20_21_RDWY_Purple_CO 67.10% 145.0 5 20_21_RDWY_Purple_CT_DC_DE_MA_ND_MB 53.90% 155.0 6 20_21_RDWY_Purple_FL 35.00% 145.0 7 20_21_RDWY_Purple_IA_MI_MN_NE_NM_OH_WI_WV 49.00% 125.0 8 20_21_RDWY_Purple_ID_ME_MT_NH_NV_RI_VT_PQ_SK 54.30% 155.0 9 20_21_RDWY_Purple_IN 1.60% 120.0 10 20_21_RDWY_Purple_KS 63.40% 120.0 11 20_21_RDWY_Purple_MD_SD_VA_WY 50.00% 145.0 12 20_21_RDWY_Purple_NC 59.20% 125.0 13 20_21_RDWY_Purple_NJ 51.50% 155.0 14 20_21_RDWY_Purple_NY 69.00% 145.0 15 20_21_RDWY_Purple_OR_WA_AB 61.80% 145.0 16 20_21_RDWY_Purple_PA 57.00% 145.0 17 20_21_RDWY_Purple_TX 46.00% 120.0 18 20_21_RDWY_Purple_UT 60.60% 164.3 19 20_21_RDWY_Purple_BC 64.40% 185.0 20 20_21_RDWY_Purple_ON 32.10% 145.0 0 20_21_RDWY_Purple_AL_CA_IA_IL_KY_LA_MN_MS_NE_SD_WA_AB_BC 54.30% 155.0 1 20_21_RDWY_Purple_AR_MO_MT_OR 53.90% 155.0 2 20_21_RDWY_Purple_AZ_NB_NF_NS_PE 50.80% 120.0 3 20_21_RDWY_Purple_CO 49.00% 125.0 4 20_21_RDWY_Purple_CT_DC_DE_MD_ME_NH_RI_VT_ON_PQ_SK 64.40% 185.0 5 20_21_RDWY_Purple_FL 55.50% 145.0 6 20_21_RDWY_Purple_GA 71.00% 155.0 7 20_21_RDWY_Purple_ID_OK_WY 50.00% 145.0 8 20_21_RDWY_Purple_IN 11.70% 155.0 9 20_21_RDWY_Purple_KS 61.00% 155.0 10 20_21_RDWY_Purple_MA 67.70% 185.0 11 20_21_RDWY_Purple_MI_ND_SC_VA_WV_MB 61.80% 145.0 12 20_21_RDWY_Purple_NC 53.50% 145.0 13 20_21_RDWY_Purple_NJ 70.50% 185.0 14 20_21_RDWY_Purple_NM 44.10% 120.0 15 20_21_RDWY_Purple_NV 52.70% 120.0 16 20_21_RDWY_Purple_NY 65.50% 185.0 17 20_21_RDWY_Purple_OH 68.50% 145.0 18 20_21_RDWY_Purple_PA 33.70% 185.0 19 20_21_RDWY_Purple_TN 52.50% 155.0 20 20_21_RDWY_Purple_TX 51.40% 145.0

2条回答

网友

1楼 · 编辑于 2024-10-01 13:35:51

利用Erickson对.groupby和lambda函数的帮助，我们得出了正确的解决方案：

pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)

df_d = pd.read_excel(path,
                        sheet_name=0,
                        header=0,
                        index_col=False,
                        keep_default_na=True)
df_m = pd.read_excel(path2,
                       sheet_name=0,
                       header=0,
                       index_col=False,
                       keep_default_na=True)

m_col_names = ['AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL', 'GA', 'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA',
               'MA', 'MD', 'ME', 'MI', 'MN', 'MO', 'MS', 'MT', 'NC', 'ND', 'NE', 'NH', 'NJ', 'NM', 'NV', 'NY', 'OH',
               'OK', 'OR', 'PA', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VA', 'VT', 'WA', 'WI', 'WV', 'WY', 'AB', 'BC',
               'MB', 'NB', 'NF', 'NS', 'ON', 'PE', 'PQ', 'SK']

final_frame = pd.DataFrame()
for state in m_col_names:

    master_dataframe0 = (pd.merge(df_d[['State', state]], df_m[['State', state]], how='inner', on='State')
                         .rename({state + '_x': 'Rate', state + '_y': 'Cost'}, axis=1)
                         .groupby(['Rate', 'Cost'])['State'].apply(lambda x: '_'.join(x)).reset_index()
                         .sort_values('State'))
    master_dataframe0['Origin'] = state
    master_dataframe0 = master_dataframe0[['State', 'Rate', 'Cost', 'Origin']].assign(
        State='20_21_RDWY_Purple_' + master_dataframe0['State'])

    final_frame = final_frame.append(master_dataframe0)

    print(final_frame)
    final_frame.to_excel("w3llshipmeright.xlsx")

正确输出：

网友

2楼 · 编辑于 2024-10-01 13:35:51

状态有多行，但它们也在列上。看起来您只是在显示AL列的示例输出？您可以合并State上的两个数据帧，然后合并.groupby{}和Cost。然后，返回具有相同速率和成本的状态的连接字符串（带有.apply(lambda x: '_'.join(x))）（因为按它们分组，它们将具有相同的速率和成本）：

master_dataframe0 = (pd.merge(df_d[['State', 'AL']], df_m[['State', 'AL']], how='inner', on='State')
                    .rename({'AL_x' : 'Rate', 'AL_y' : 'Cost'}, axis=1)
                    .groupby(['Rate', 'Cost'])['State'].apply(lambda x: '_'.join(x)).reset_index()
                    .sort_values('State'))
master_dataframe0 = master_dataframe0[['State', 'Rate', 'Cost']].assign(State='20_21_RDWY_Purple_' + master_dataframe0['State'])
master_dataframe0
Out[1]: 
                                     State    Rate   Cost
7   20_21_RDWY_Purple_AL_AR_KY_LA_MS_SC_TN  50.80%  120.0
11                    20_21_RDWY_Purple_AZ  56.70%  155.0
15                    20_21_RDWY_Purple_CA  62.40%  145.0
9                  20_21_RDWY_Purple_CO_ND  54.30%  155.0
5            20_21_RDWY_Purple_CT_DE_MN_NE  50.00%  145.0
4   20_21_RDWY_Purple_DC_IA_KS_MD_MI_OH_OK  49.00%  125.0
3                     20_21_RDWY_Purple_FL  48.30%  125.0
18                    20_21_RDWY_Purple_GA  67.90%  120.0
14              20_21_RDWY_Purple_ID_MT_NV  61.80%  145.0
0            20_21_RDWY_Purple_IL_IN_MO_NC  44.10%  120.0
16                    20_21_RDWY_Purple_MA  63.50%  155.0
8         20_21_RDWY_Purple_ME_NH_NM_RI_SD  53.90%  155.0
6                     20_21_RDWY_Purple_NJ  50.50%  145.0
13                    20_21_RDWY_Purple_NY  61.10%  145.0
17                    20_21_RDWY_Purple_OR  64.40%  185.0
2                     20_21_RDWY_Purple_PA  47.20%  145.0
10                    20_21_RDWY_Purple_TX  56.60%  125.0
1                     20_21_RDWY_Purple_UT  45.00%  170.0
12                    20_21_RDWY_Purple_VA  57.90%  120.0

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