5

我有三个数据集(final_NN, ppt_code, herd_id),我想MapValuefinal_NNdataframe中添加一个新的列,并且可以从其他两个dataframe中检索要添加的值,规则在代码之后的底部。

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numberin final_NN 不是Unknown, MapValue= numberin final_NN;
  2. 如果numberin final_NN is Unknownbut codein final_NNis not Null,则搜索ppt_code数据框,并使用code和其对应的“数字”映射并填写“MapValue”中final_NN
  3. 如果两者numbercodeinfinal_NN分别是Unknown和null,但IDinfinal_NN不是Null,则搜索herd_iddataframe,并使用ID和其对应number的填充MapValue第一个dataframe。如上所述,我通过数据框应用了一个循环,这是实现此目的的缓慢方法。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个快速和简单的方法来达到同样的结果吗?
4

2 回答 2

4

ppt_code首先从和数据框创建一个映射系列herd_id,然后通过用 替换列中的值Series.replace来创建一个新列,然后使用两个连续的 with来根据规则填充列中的缺失值: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
于 2020-06-23T05:23:19.700 回答
0

我们简单地组合了三个数据框。

  1. 原始 DF 删除了“未知”行。
  2. 'ppt_code' 更改列名。
  3. 'pandas.concat()' 将它们连接在一起。
final_NN['number'].replace('Unknown', np.NaN, inplace=True)
final_NN.dropna(inplace=True, how='any')
ppt_code.rename(columns={'code':'ID'}, inplace=True)
new_df = pd.concat([final_NN, ppt_code, herd_id], axis=0, ignore_index=True)

new_df
    number  ID  code
0   123.0       
1   456.0       
2   11.0    AA  NaN
3   11.0    AA  NaN
4   22.0    BB  NaN
5   22.0    BB  NaN
6   33.0    CC  NaN
7   678.0   799 NaN
8   789.0   813 NaN
于 2020-06-23T05:45:56.673 回答