如何处理Pandas中的SettingWithCopyWarning [英] How to deal with SettingWithCopyWarning in Pandas

问题描述

我刚刚将熊猫从0.11升级到0.13.0rc1。现在，该应用程序会弹出许多新警告。像这样的一个：

I just upgraded my Pandas from 0.11 to 0.13.0rc1. Now, the application is popping out many new warnings. One of them like this:

E:\FinReporter\FM_EXT.py:449: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_index,col_indexer] = value instead
  quote_df['TVol']   = quote_df['TVol']/TVOL_SCALE

我想知道这到底是什么意思？我需要更改某些内容吗？

I want to know what exactly it means? Do I need to change something?

如果我坚持使用 quote_df ['TVol'] = quote_df ['TVol'] / TVOL_SCALE，应该如何暂停警告？ ？

def _decode_stock_quote(list_of_150_stk_str):
    """decode the webpage and return dataframe"""

    from cStringIO import StringIO

    str_of_all = "".join(list_of_150_stk_str)

    quote_df = pd.read_csv(StringIO(str_of_all), sep=',', names=list('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefg')) #dtype={'A': object, 'B': object, 'C': np.float64}
    quote_df.rename(columns={'A':'STK', 'B':'TOpen', 'C':'TPCLOSE', 'D':'TPrice', 'E':'THigh', 'F':'TLow', 'I':'TVol', 'J':'TAmt', 'e':'TDate', 'f':'TTime'}, inplace=True)
    quote_df = quote_df.ix[:,[0,3,2,1,4,5,8,9,30,31]]
    quote_df['TClose'] = quote_df['TPrice']
    quote_df['RT']     = 100 * (quote_df['TPrice']/quote_df['TPCLOSE'] - 1)
    quote_df['TVol']   = quote_df['TVol']/TVOL_SCALE
    quote_df['TAmt']   = quote_df['TAmt']/TAMT_SCALE
    quote_df['STK_ID'] = quote_df['STK'].str.slice(13,19)
    quote_df['STK_Name'] = quote_df['STK'].str.slice(21,30)#.decode('gb2312')
    quote_df['TDate']  = quote_df.TDate.map(lambda x: x[0:4]+x[5:7]+x[8:10])
    
    return quote_df

更多错误消息

E:\FinReporter\FM_EXT.py:449: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_index,col_indexer] = value instead
  quote_df['TVol']   = quote_df['TVol']/TVOL_SCALE
E:\FinReporter\FM_EXT.py:450: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_index,col_indexer] = value instead
  quote_df['TAmt']   = quote_df['TAmt']/TAMT_SCALE
E:\FinReporter\FM_EXT.py:453: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_index,col_indexer] = value instead
  quote_df['TDate']  = quote_df.TDate.map(lambda x: x[0:4]+x[5:7]+x[8:10])

推荐答案

<$ c创建$ c> SettingWithCopyWarning 来标记可能造成混淆的链接邮件。分配，例如下面的分配，并不总是能按预期工作，特别是当第一个选择返回 copy 时。 [请参见 GH5390 和 GH5597 供背景讨论。]

The SettingWithCopyWarning was created to flag potentially confusing "chained" assignments, such as the following, which does not always work as expected, particularly when the first selection returns a copy. [see GH5390 and GH5597 for background discussion.]

df[df['A'] > 2]['B'] = new_val  # new_val not set in df

中未设置的new_val警告提供了如下重写建议：

The warning offers a suggestion to rewrite as follows:

df.loc[df['A'] > 2, 'B'] = new_val

但是，这不适合您的用法，等效于：

However, this doesn't fit your usage, which is equivalent to:

df = df[df['A'] > 2]
df['B'] = new_val

很显然，您并不关心写入使其回到原始框架（因为您正在覆盖对它的引用），不幸的是，该模式无法与第一个链接的分配示例区分开。因此，（误报）警告。 有关索引的文档，如果您想进一步阅读的话。您可以通过以下分配安全地禁用此新警告。

While it's clear that you don't care about writes making it back to the original frame (since you are overwriting the reference to it), unfortunately this pattern cannot be differentiated from the first chained assignment example. Hence the (false positive) warning. The potential for false positives is addressed in the docs on indexing, if you'd like to read further. You can safely disable this new warning with the following assignment.

import pandas as pd
pd.options.mode.chained_assignment = None  # default='warn'

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其他资源

• pandas用户指南：索引和选择数据


• Python数据科学手册：数据索引和选择


• 真实的Python：熊猫中的SettingWithCopyWarning：视图与副本


• 数据查询： SettingwithCopyWarning：如何在熊猫中解决此警告


• 迈向数据科学：在熊猫中解释SettingWithCopyWarning



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Other Resources

• pandas User Guide: Indexing and selecting data
• Python Data Science Handbook: Data Indexing and Selection
• Real Python: SettingWithCopyWarning in Pandas: Views vs Copies
• Dataquest: SettingwithCopyWarning: How to Fix This Warning in Pandas
• Towards Data Science: Explaining the SettingWithCopyWarning in pandas

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