数据框列的平均值 [英] Average of Dataframe columns

问题描述

我想获取多年来每个国家的平均GDP，列2006、2007 ... 2015包含GDP数据...我的代码返回一个错误，该错误意味着mean(axis = 1)至少需要1个变量，并且已经为其分配了1 ...这很奇怪..我也发现我们使用均值而不是avg很奇怪，但是却找不到groupby的avg函数

I want to get the average GDP of each country across the years, the columns 2006, 2007...2015 contain the GDP numbers... My code returns an error that mean(axis=1) needs at least 1 variable, and 1 has been assign to it... which is weird..I also find it weird that we are using mean instead of avg, but coulnd't find an avg function for groupby

这是我的代码

    Top15 = ANSWER
    Top15 = Top15[['Country', '2006', '2007', '2008', '2009', '2010', 
    '2011', '2012', '2013', '2014', '2015']]
    return Top15.groupby('Country').agg({"avg": np.mean(axis=1)})

推荐答案

GroupBy在这里不是必需的，因为您正在执行计算而不是聚合.您可以只使用 pd.DataFrame.mean .这是一个最小的示例:

GroupBy is not necessary here as you are performing a calculation rather than an aggregation. You can just use pd.DataFrame.mean. Here's a minimal example:

df = pd.DataFrame({'Country': ['UK', 'US'],
                   '2006': [1, 2],
                   '2007': [3, 4],
                   '2008': [5, 6]})

df['mean'] = df[['2006', '2007', '2008']].mean(1)

print(df)

   2006  2007  2008 Country  mean
0     1     3     5      UK   3.0
1     2     4     6      US   4.0

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