使用 Pandas df.apply 创建新列

Question

我有一个巨大的 NetFlow 数据库，（它包含时间戳、源 IP、目标 IP、协议、源和目标端口号、交换的数据包、字节等）。我想根据当前行和以前的行创建自定义属性。

我想根据当前行的源 ip 和时间戳计算新列。这就是我想要在逻辑上做的事情：

• 获取当前行的源IP。
• 获取当前行的时间戳。
• 基于源 IP 和时间戳，我想获取与源 IP 匹配的整个数据帧的所有先前行，并且通信发生在最后半小时内。这非常重要。
• 对于符合条件（源 ip 并且发生在过去半小时内）的行（在我的示例中为流），我想计算所有数据包和所有字节的总和和平均值。

One row from the dataset

相关代码片段：

df = pd.read_csv(path, header = None, names=['ts','td','sa','da','sp','dp','pr','flg','fwd','stos','pkt','byt','lbl'])

df['ts'] = pd.to_datetime(df['ts'])

def prev_30_ip_sum(ts,sa,size):
global joined
for (x,y) in zip(df['sa'], df['ts']):
    ...
return sum

df['prev30ipsumpkt'] = df.apply(lambda x: prev_30_ip_sum(x['ts'],x['sa'],x['pkt']), axis = 1)

我知道可能有更好、更有效的方法来做到这一点，但遗憾的是我不是最好的程序员。

谢谢。

Answer 1

内嵌文档

from datetime import timedelta

def fun(df, i):
  # Current timestamp
  current = df.loc[i, 'ts']
  # timestamp of last 30 minutes
  last = current - timedelta(minutes=30)
  # Current IP
  ip = df.loc[i, 'sa']
  
  # df matching the criterian
  adf = df[(last <= df['ts']) & (current > df['ts']) & (df['sa'] == ip)]

  # Return sum and mean
  return adf['pkt'].sum(), adf['pkt'].mean()

# Apply the fun over each row
result = [fun(df, i) for i in df.index]

# Create new columns
df['sum'] = [i[0] for i in result]
df['mean'] = [i[1] for i in result]

Answer 2

df = pd.read_csv(path, header = None, names=['ts','td','sa','da','sp','dp','pr','flg','fwd','stos','pkt','byt','lbl'])
        
df['ts'] = pd.to_datetime(df['ts'])
   
def prev_30_ip_sum(df, i):
  #current time from current row
  current = df.loc[i, 'ts']
  # timestamp of last 30 minutes 
  last = current - timedelta(minutes=30)

  # Current source address
  sa = df.loc[i, 'sa']

  # new dataframe for timestamp less than 30 min and same ip as current one
  new_df = df[(last <= df['ts']) & (current > df['ts']) & (df['sa'] == sa)]

  # Return sum and mean
  return new_df['pkt'].sum(), new_df['pkt'].mean()


# Take sa and timestamp of each row and create new dataframe
result = [prev_30_ip_sum(df, i) for i in df.index]

# Create new columns in current database.
df['sum'] = [i[0] for i in result]
df['mean'] = [i[1] for i in result]

refer this to understand timedelta