从数据框中提取行

Question

我有这个数据框：

我想提取客户端在 Block 操作和 Alow 操作中同时所在的行，所以我想要行：0、2、4 和 6。

使用行索引的解决方案，我无法使用它，因为我有数百万行。

Answer 1

如果 action 列仅包含 block 和 allow 值，您可以按 [=12] 对数据框进行分组=] 然后，计算唯一操作的数量。

例如：

df.groupby("client")["action"].nunique()

如果提取的值大于 1，则特定客户端同时拥有块和允许值。

Answer 2

使用groupby、filter和nunique：

indexes = df.groupby('client')['action'].filter(lambda x: x.nunique() >= 2).index
filtered = df.loc[indexes]

输出：

>>> indexes.tolist()
[0, 2, 4, 6]

>>> filtered
  action   client
0  block  client1
2  allow  client1
4  block  client8
6  allow  client8

Answer 3

这是对您问题的回答，它主要依赖于 Python 逻辑而不是 Pandas 逻辑。

它还包括 timeit 与主要基于 Pandas 的方法的性能比较，这似乎表明 Python 逻辑对于所选示例快 50 倍以上超过 100,000 行。

import pandas as pd

# Sample data
n = 100000
recordData = [['allow' if i < n // 2 else 'block', 'ip="128.03.03.29"', 'source="29E9t 99 94"', 'destination="12300rtgR30"', 'client'+f'{i}'] for i in range(n)]
nDual = 20000
recordData += [['block'] + recordData[i % n][1:] for i in range(1, 7 * nDual + 1, 7)]
df = pd.DataFrame(data=recordData, columns=['action', 'adresse_ip', 'source_ip', 'destin_ip', 'client'])
print(f"Sample dataframe of length {len(df)}:")
print(df)

import timeit

def foo(df):
    # Selection
    blocks = {*list(df['client'][df['action'] == 'block'])}
    allows = {*list(df['client'][df['action'] == 'allow'])}
    duals = blocks & allows
    rowsWithDuals = df[df['client'].apply(lambda x: x in duals)]

    # Diagnostics
    print(f"Number of rows for clients with dual actions: {len(rowsWithDuals)}")

    return rowsWithDuals

print("\nPrimarily Python approach:")
t = timeit.timeit(lambda: foo(df), number = 1)
print(f"timeit: {t}")

def bar(df):
    indexes = df.groupby('client')['action'].filter(lambda x: x.nunique() >= 2).index
    filtered = df.loc[indexes]

    print(f"Number of rows for clients with dual actions: {len(filtered)}")

    return filtered

print("\nPrimarily Pandas approach:")
t = timeit.timeit(lambda: bar(df), number = 1)
print(f"timeit: {t}")

输出是：

Sample dataframe of length 120000:
       action         adresse_ip             source_ip                  destin_ip       client
0       allow  ip="128.03.03.29"  source="29E9t 99 94"  destination="12300rtgR30"      client0
1       allow  ip="128.03.03.29"  source="29E9t 99 94"  destination="12300rtgR30"      client1
2       allow  ip="128.03.03.29"  source="29E9t 99 94"  destination="12300rtgR30"      client2
3       allow  ip="128.03.03.29"  source="29E9t 99 94"  destination="12300rtgR30"      client3
4       allow  ip="128.03.03.29"  source="29E9t 99 94"  destination="12300rtgR30"      client4
...       ...                ...                   ...                        ...          ...
119995  block  ip="128.03.03.29"  source="29E9t 99 94"  destination="12300rtgR30"  client39966
119996  block  ip="128.03.03.29"  source="29E9t 99 94"  destination="12300rtgR30"  client39973
119997  block  ip="128.03.03.29"  source="29E9t 99 94"  destination="12300rtgR30"  client39980
119998  block  ip="128.03.03.29"  source="29E9t 99 94"  destination="12300rtgR30"  client39987
119999  block  ip="128.03.03.29"  source="29E9t 99 94"  destination="12300rtgR30"  client39994

[120000 rows x 5 columns]

Primarily Python approach:
Number of rows for clients with dual actions: 25714
timeit: 0.04522189999988768

Primarily Pandas approach:
Number of rows for clients with dual actions: 25714
timeit: 3.1578059000021312

这似乎表明使用主要 Python（而非 Pandas）方法更适合大型数据集。

从数据框中提取行

Extracting rows from a dataframe

python

extract

dataframe

pandas