下面是一个包含数据和2组的完整示例:

进口:

from StringIO import StringIO
import pandas as pd
#pandas config
pd.set_option('display.max_rows', 20)

有2个组的数据示例:G1:Group 1。G2:第二组:

TESTDATA = StringIO("""G1;G2;Value
1;A;1.6
1;A;5.1
1;A;7.1
1;A;8.1

1;B;21.1
1;B;22.1
1;B;24.1
1;B;30.6

2;A;40.6
2;A;51.1
2;A;52.1
2;A;60.6

2;B;80.1
2;B;70.6
2;B;90.6
2;B;85.1
""")

读取文本数据到pandas数据框架:

df = pd.read_csv(TESTDATA, sep=";")

使用标准偏差定义离群值

stds = 1.0
outliers = df[['G1', 'G2', 'Value']].groupby(['G1','G2']).transform(
           lambda group: (group - group.mean()).abs().div(group.std())) > stds

定义过滤后的数据值和异常值:

dfv = df[outliers.Value == False]
dfo = df[outliers.Value == True]

打印结果:

print '\n'*5, 'All values with decimal 1 are non-outliers. In the other hand, all values with 6 in the decimal are.'
print '\nDef DATA:\n%s\n\nFiltred Values with %s stds:\n%s\n\nOutliers:\n%s' %(df, stds, dfv, dfo)

由于我还没有看到处理数值和非数值属性的答案，这里有一个补充答案。

您可能只希望删除数值属性上的异常值(类别变量几乎不可能是异常值)。

函数定义

我扩展了@tanemaki的建议，当非数值属性也存在时处理数据:

from scipy import stats

def drop_numerical_outliers(df, z_thresh=3):
    # Constrains will contain `True` or `False` depending on if it is a value below the threshold.
    constrains = df.select_dtypes(include=[np.number]) \
        .apply(lambda x: np.abs(stats.zscore(x)) < z_thresh, reduce=False) \
        .all(axis=1)
    # Drop (inplace) values set to be rejected
    df.drop(df.index[~constrains], inplace=True)

使用

drop_numerical_outliers(df)

例子

想象一个数据集df，其中包含一些关于房屋的值:小巷、土地轮廓、销售价格……例:数据文档

首先，你想要在散点图上可视化数据(z-score Thresh=3):

# Plot data before dropping those greater than z-score 3. 
# The scatterAreaVsPrice function's definition has been removed for readability's sake.
scatterAreaVsPrice(df)

# Drop the outliers on every attributes
drop_numerical_outliers(train_df)

# Plot the result. All outliers were dropped. Note that the red points are not
# the same outliers from the first plot, but the new computed outliers based on the new data-frame.
scatterAreaVsPrice(train_df)

下面是一个包含数据和2组的完整示例:

进口:

from StringIO import StringIO
import pandas as pd
#pandas config
pd.set_option('display.max_rows', 20)

有2个组的数据示例:G1:Group 1。G2:第二组:

TESTDATA = StringIO("""G1;G2;Value
1;A;1.6
1;A;5.1
1;A;7.1
1;A;8.1

1;B;21.1
1;B;22.1
1;B;24.1
1;B;30.6

2;A;40.6
2;A;51.1
2;A;52.1
2;A;60.6

2;B;80.1
2;B;70.6
2;B;90.6
2;B;85.1
""")

读取文本数据到pandas数据框架:

df = pd.read_csv(TESTDATA, sep=";")

使用标准偏差定义离群值

stds = 1.0
outliers = df[['G1', 'G2', 'Value']].groupby(['G1','G2']).transform(
           lambda group: (group - group.mean()).abs().div(group.std())) > stds

定义过滤后的数据值和异常值:

dfv = df[outliers.Value == False]
dfo = df[outliers.Value == True]

打印结果:

print '\n'*5, 'All values with decimal 1 are non-outliers. In the other hand, all values with 6 in the decimal are.'
print '\nDef DATA:\n%s\n\nFiltred Values with %s stds:\n%s\n\nOutliers:\n%s' %(df, stds, dfv, dfo)

这个答案类似于@tanemaki提供的答案，但使用了lambda表达式而不是scipy stats。

df = pd.DataFrame(np.random.randn(100, 3), columns=list('ABC'))

standard_deviations = 3
df[df.apply(lambda x: np.abs(x - x.mean()) / x.std() < standard_deviations)
   .all(axis=1)]

要过滤只有一个列的数据帧(例如:B)在三个标准差之内:

df[((df['B'] - df['B'].mean()) / df['B'].std()).abs() < standard_deviations]

关于如何在滚动的基础上应用这个z-score:滚动z-score应用于pandas数据框架

#------------------------------------------------------------------------------
# accept a dataframe, remove outliers, return cleaned data in a new dataframe
# see http://www.itl.nist.gov/div898/handbook/prc/section1/prc16.htm
#------------------------------------------------------------------------------
def remove_outlier(df_in, col_name):
    q1 = df_in[col_name].quantile(0.25)
    q3 = df_in[col_name].quantile(0.75)
    iqr = q3-q1 #Interquartile range
    fence_low  = q1-1.5*iqr
    fence_high = q3+1.5*iqr
    df_out = df_in.loc[(df_in[col_name] > fence_low) & (df_in[col_name] < fence_high)]
    return df_out

scipy。Stats有方法trim1()和trimboth()，根据排名和被删除值的引入百分比，在单行中删除异常值。