您可以使用replicated()标记所有重复的行，并过滤掉标记的行。如果你以后需要将列赋值给new_df，确保调用.copy()，这样你以后就不会得到SettingWithCopyWarning。

new_df = df[~df.duplicated(subset=['A', 'C'], keep=False)].copy()

该方法的一个很好的特性是，您可以有条件地使用它删除重复项。例如，仅当列A等于'foo'时删除所有重复的行，您可以使用以下代码。

new_df = df[~( df.duplicated(subset=['A', 'B', 'C'], keep=False) & df['A'].eq('foo') )].copy()

此外，如果您不希望按名称写出列，您可以传递df的切片。列到子集=。对于drop_duplicate()也是如此。

# to consider all columns for identifying duplicates
df[~df.duplicated(subset=df.columns, keep=False)].copy()

# the same is true for drop_duplicates
df.drop_duplicates(subset=df.columns, keep=False)

# to consider columns in positions 0 and 2 (i.e. 'A' and 'C') for identifying duplicates
df.drop_duplicates(subset=df.columns[[0, 2]], keep=False)

如果你想将结果存储在另一个数据集中:

df.drop_duplicates(keep=False)

or

df.drop_duplicates(keep=False, inplace=False)

如果需要更新相同的数据集:

df.drop_duplicates(keep=False, inplace=True)

上面的例子将删除所有重复项并保留一个，类似于SQL中的DISTINCT *

试试这些不同的方法

df = pd.DataFrame({"A":["foo", "foo", "foo", "bar","foo"], "B":[0,1,1,1,1], "C":["A","A","B","A","A"]})

>>>df.drop_duplicates( "A" , keep='first')

or

>>>df.drop_duplicates( keep='first')

or

>>>df.drop_duplicates( keep='last')

只是想添加到Ben关于drop_duplicate的答案:

keep: {' first '， ' last '， False}，默认' first '

first:删除除第一次出现之外的重复项。 last:删除除最后一次出现之外的重复项。 False:删除所有副本。

所以将keep设置为False会给你想要的答案。

DataFrame.drop_duplicates(*args, **kwargs) Return DataFrame with duplicate rows removed, optionally only considering certain columns Parameters: subset : column label or sequence of labels, optional Only consider certain columns for identifying duplicates, by default use all of the columns keep : {‘first’, ‘last’, False}, default ‘first’ first : Drop duplicates except for the first occurrence. last : Drop duplicates except for the last occurrence. False : Drop all duplicates. take_last : deprecated inplace : boolean, default False Whether to drop duplicates in place or to return a copy cols : kwargs only argument of subset [deprecated] Returns: deduplicated : DataFrame

您可以使用replicated()标记所有重复的行，并过滤掉标记的行。如果你以后需要将列赋值给new_df，确保调用.copy()，这样你以后就不会得到SettingWithCopyWarning。

new_df = df[~df.duplicated(subset=['A', 'C'], keep=False)].copy()

该方法的一个很好的特性是，您可以有条件地使用它删除重复项。例如，仅当列A等于'foo'时删除所有重复的行，您可以使用以下代码。

new_df = df[~( df.duplicated(subset=['A', 'B', 'C'], keep=False) & df['A'].eq('foo') )].copy()

此外，如果您不希望按名称写出列，您可以传递df的切片。列到子集=。对于drop_duplicate()也是如此。

# to consider all columns for identifying duplicates
df[~df.duplicated(subset=df.columns, keep=False)].copy()

# the same is true for drop_duplicates
df.drop_duplicates(subset=df.columns, keep=False)

# to consider columns in positions 0 and 2 (i.e. 'A' and 'C') for identifying duplicates
df.drop_duplicates(subset=df.columns[[0, 2]], keep=False)

使用drop_duplicate和keep参数，这在pandas中要容易得多。

import pandas as pd
df = pd.DataFrame({"A":["foo", "foo", "foo", "bar"], "B":[0,1,1,1], "C":["A","A","B","A"]})
df.drop_duplicates(subset=['A', 'C'], keep=False)