Question

我有以下数据框，

ip_df:
ble           gw_mac       Uuid                             rawData
ac233f264920  ac233fc01403 e2c56db5dffb48d2b060d0f5a71096e0 0
ac233f264920  ac233fc015f6 e2c56db5dffb48d2b060d0f5a71096e0 0
ac233f264920  ac233fc02eaa e2c56db5dffb48d2b060d0f5a71096e0 0201060303aafe1116aafe20000bdf160006c39e0b1484bee4
ac233f26492b  ac233fc01403 e2c56db5dffb48d2b060d0f5a71096e0 0201060303aafe1116aafe20000bf115000618d2ef1484c2fe
ac233f26492b  ac233fc015f6 e2c56db5dffb48d2b060d0f5a71096e0 0
ac233f26492b  ac233fc02eaa 0                                0201060303aafe1116aafe20000bdf160006c39e0b1484bee4

我需要根据以下条件对新列进行分组和框架

如果Uuid不等于0＆rawData = 0，则有效负载应为Uuid的值
如果Uuid不等于0并且rawData不等于0，则有效负载应为Uuid的值
如果Uuid = 0并且rawData不等于0，则有效负载应为rawData的值

我的结果应该是

ip_df:
ble           gw_mac       type    payload                                                         
ac233f264920  ac233fc01403 iBeacon e2c56db5dffb48d2b060d0f5a71096e0     
ac233f264920  ac233fc015f6 iBeacon e2c56db5dffb48d2b060d0f5a71096e0   
ac233f264920  ac233fc02eaa iBeacon e2c56db5dffb48d2b060d0f5a71096e0    
ac233f26492b  ac233fc01403 iBeacon e2c56db5dffb48d2b060d0f5a71096e0     
ac233f26492b  ac233fc015f6 iBeacon e2c56db5dffb48d2b060d0f5a71096e0     
ac233f26492b  ac233fc02eaa other 0201060303aafe1116aafe20000bdf160006c39e0b1484bee4

现在我正在迭代数据帧以检查上述条件，

for index, row in ip_df.iterrows():
    if row['Uuid'] != 0 and row['rawData'] == 0:
        ip_df.loc[index, 'payload'] = row['Uuid']
        ip_df.loc[index, 'type'] = str("iBeacon")
    elif row['Uuid'] != 0 and row['rawData'] != 0:
        ip_df.loc[index, 'payload'] = row['Uuid']
        ip_df.loc[index, 'type'] = str("iBeacon")
    elif row['Uuid'] == 0 and row['rawData'] != 0:
        ip_df.loc[index, 'payload'] = row['rawData']
        ip_df.loc[index, 'type'] = str("other")

如何用大熊猫数据框的groupby实现相同的逻辑？

Python Pandas groupby：针对两个不同的列返回具有多个过滤条件的新列

0 个答案: