一种在多个数据集中映射数据的更好方法，具有多个数据映射规则

Question

我有三个数据集（final_NN，ppt_code，herd_id），我想MapValue在final_NN数据框中添加一个名为的新列，并且可以从其他两个数据框中检索要添加的值，规则在代码后的底部。

import pandas as pd

final_NN = pd.DataFrame({
    "number": [123, 456, "Unknown", "Unknown", "Unknown", "Unknown", "Unknown", "Unknown", "Unknown", "Unknown"],
    "ID": ["", "", "", "", "", "", "", "", 799, 813],
    "code": ["", "", "AA", "AA", "BB", "BB", "BB", "CC", "", ""]
})

ppt_code = pd.DataFrame({
    "code": ["AA", "AA", "BB", "BB", "CC"],
    "number": [11, 11, 22, 22, 33]
})

herd_id = pd.DataFrame({
    "ID": [799, 813],
    "number": [678, 789]
})

new_column = pd.Series([])
for i in range(len(final_NN)):
    if final_NN["number"][i] != "" and final_NN["number"][i] != "Unknown":
        new_column[i] = final_NN['number'][i]

    elif final_NN["code"][i] != "":
        for p in range(len(ppt_code)):
            if ppt_code["code"][p] == final_NN["code"][i]:
                new_column[i] = ppt_code["number"][p]

    elif final_NN["ID"][i] != "":
        for h in range(len(herd_id)):
            if herd_id["ID"][h] == final_NN["ID"][i]:
                new_column[i] = herd_id["number"][h]

    else:
        new_column[i] = ""

final_NN.insert(3, "MapValue", new_column)
print(final_NN)

final_NN：

    number   ID code
0      123          
1      456          
2  Unknown        AA
3  Unknown        AA
4  Unknown        BB
5  Unknown        BB
6  Unknown        BB
7  Unknown        CC
8  Unknown  799     
9  Unknown  813 

ppt_code:

  code  number
0   AA      11
1   AA      11
2   BB      22
3   BB      22
4   CC      33

herd_id:

    ID  number
0  799     678
1  813     789

预期输出：

    number   ID code   MapValue
0      123                  123
1      456                  456
2  Unknown        AA         11
3  Unknown        AA         11
4  Unknown        BB         22
5  Unknown        BB         22
6  Unknown        BB         22
7  Unknown        CC         33
8  Unknown  799             678
9  Unknown  813             789

规则是：

1. 如果number在 final_NN 中不是Unknown，MapValue=number在final_NN；
2. 如果number在final_NN中是Unknown但codeinfinal_NN不是Null，则搜索ppt_code数据帧，并使用code和它对应的“编号”来映射并填入“MapValue”中final_NN；
3. 如果两者number和codeinfinal_NN分别为Unknown和null，但IDinfinal_NN不为Null，则搜索herd_iddataframe，并使用ID和它对应number的填充在MapValue第一个dataframe中。我在数据帧中应用了一个循环，这是实现此目的的缓慢方法，如上所述。但我知道可能有更快的方法来做到这一点。只是想知道有人会帮助我有一种快速简便的方法来实现相同的结果吗？

Answer 1

ppt_code首先从和数据帧创建映射系列herd_id，然后使用替换列中的值Series.replace来创建新列，然后使用两个连续的和根据规则填充列中缺失的值：MapNumberUnknownnumbernp.NaNSeries.fillnaSeries.mapMapNumber

ppt_map = ppt_code.drop_duplicates(subset=['code']).set_index('code')['number']
hrd_map = herd_id.drop_duplicates(subset=['ID']).set_index('ID')['number']

final_NN['MapNumber'] = final_NN['number'].replace({'Unknown': np.nan})
final_NN['MapNumber'] = (
    final_NN['MapNumber']
    .fillna(final_NN['code'].map(ppt_map))
    .fillna(final_NN['ID'].map(hrd_map))
)

结果：

# print(final_NN)

    number   ID code  MapNumber
0      123                123.0
1      456                456.0
2  Unknown        AA       11.0
3  Unknown        AA       11.0
4  Unknown        BB       22.0
5  Unknown        BB       22.0
6  Unknown        BB       22.0
7  Unknown        CC       33.0
8  Unknown  799           678.0
9  Unknown  813           789.0