熊猫试图告诉我的警告是什么?

时间:2016-01-14 05:38:00

标签: python pandas dataframe sklearn-pandas

我有以下代码,它只有一个函数,它接收输入数据帧并输出一个版本,按label对它们进行分组并对它们求和。

import pandas as pd
import random
import numpy as np

random.seed = 10

input_data = np.array(
[
[random.randint(0,9) for x in range(4)]+['g'],
[random.randint(0,9) for x in range(4)]+['g'],
[random.randint(0,9) for x in range(4)]+['a'],
[random.randint(0,9) for x in range(4)]+['b'],
[random.randint(0,9) for x in range(4)]+['b']
]
)

input_df = pd.DataFrame(data=input_data, columns=['A','B', 'C', 'D', 'label'])

def group_and_sum(input_df):
    final_df = pd.DataFrame()
    for gr,subdf in input_df.groupby('label'):
        new_df = pd.DataFrame()

        new_df['label'] = [gr]
        columns = [x for x in input_df.columns if x!='label']
        subdf[columns] = subdf[columns].values.astype(float)
        for col in columns:
            new_df[col] = [sum(subdf[col].values)]

        new_df['sum'] = sum([new_df[x].values for x in columns])
        final_df = pd.concat([final_df, new_df])
    final_df.index = np.array(range(len(final_df)))
    return final_df

final_df = group_and_sum(input_df)

会抛出以下警告:

Warning (from warnings module):
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.17.0-py2.7-macosx-10.6-intel.egg/pandas/core/frame.py", line 2269
    self.ix._setitem_with_indexer((slice(None), indexer), value)
SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

两件事:

1)当我查看警告here时,它似乎与我无关。我没有做chained-indexing之类的事情,因为警告中提供的链接表示。

2)当我尝试在函数之外重现错误时,我不能出于某种原因:

input_df[['A']]=input_df[['A']].astype(float)
input_df[['A','B']]=input_df[['A','B']].astype(float)

......那些都完美无缺。

有没有其他方法可以重现此警告,是否适用于此处?感谢。

1 个答案:

答案 0 :(得分:1)

我认为警告是因为你的行subdf[columns] = subdf[columns].values.astype(float)。您从groupby获得subdf,因此subdf是对原始DataFrame的某些行的引用。在此切片上设置值会导致警告。换句话说,就好像你做了链式索引:

input_df[rows_that_are_part_of_this_group][columns] = ...