使用 loc 更新数据框 python pandas
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Use of loc to update a dataframe python pandas
提问by Data Enthusiast
I have a pandas dataframe (df) with the column structure :
我有一个列结构的Pandas数据框（df）：
month a b c d
this dataframe has data for say Jan, Feb, Mar, Apr. A,B,C,D are numeric columns. For the month of Feb , I want to recalculate column A and update it in the dataframe i.e. for month = Feb, A = B + C + D
此数据框包含 Jan、Feb、Mar、Apr 的数据。A、B、C、D 是数字列。对于 Feb 月份，我想重新计算 A 列并在数据框中更新它，即月份 = Feb, A = B + C + D
Code I used :
我使用的代码：
df[df['month']=='Feb']['A']=df[df['month']=='Feb']['B'] + df[df['month']=='Feb']['C'] + df[df['month']=='Feb']['D']
This ran without errors but did not change the values in column A for the month Feb. In the console, it gave a message that :
这运行没有错误，但没有更改 2 月份 A 列中的值。在控制台中，它给出了一条消息：
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
试图在来自 DataFrame 的切片副本上设置值。
尝试使用 .loc[row_indexer,col_indexer] = value 代替
I tried to use .loc but right now the dataframe I am working on, I had used .reset_index()on it and I am not sure how to set index and use .loc. I followed documentation but not clear. Could you please help me out here?
This is an example dataframe :
我尝试使用 .loc 但现在我正在处理的数据帧，我已经使用.reset_index()过它，但我不确定如何设置索引和使用 .loc。我遵循了文档，但不清楚。你能帮我一下吗？这是一个示例数据框：
import pandas as pd import numpy as np
dates = pd.date_range('1/1/2000', periods=8)
df = pd.DataFrame(np.random.randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
I want to update say one date : 2000-01-03. I am unable to give the snippet of my data as it is real time data.
我想更新一个日期：2000-01-03。我无法提供我的数据片段，因为它是实时数据。
回答by Anton Protopopov
As you could see from the warning you should use loc[row_index, col_index]. When you subsetting your data you get index values. You just need to pass for row_indexand then with comma col_name:
正如您从警告中看到的，您应该使用loc[row_index, col_index]. 当您对数据进行子集化时，您将获得索引值。你只需要传递 forrow_index然后用逗号col_name：
df.loc[df['month'] == 'Feb', 'A'] = df.loc[df['month'] == 'Feb', 'B'] + df.loc[df['month'] == 'Feb', 'C'] + df.loc[df['month'] == 'Feb', 'D']
回答by DeepSpace
While not being the most beautiful, the way I would achieve your goal (without explicitly iterating over the rows) is:
虽然不是最漂亮的，但我实现目标的方式（不显式迭代行）是：
df.ix[df['month'] == 'Feb', 'a'] = df[df['month'] == 'Feb']['b'] + df[df['month'] == 'Feb']['c']
Note: ixhas been deprecatedsince Pandas v0.20.0 in favour of iloc/ loc.
注意：自 Pandas v0.20.0ix起已弃用iloc/ loc。
