Question

我想使用groupby计算多列的平均值。下面是一个玩具示例

df = pd.DataFrame({'company': ['dell', 'microsoft', 'toshiba', 'apple'], 
'measure': ['sales', 'speed', 'wait time', 'service'], 'category': ['laptop', 
'tablet', 'smartphone', 'desktop'], '10/6/2015': [234, 333, 456, 290], 
'10/13/2015': [134, 154, 123, 177], '10/20/2015': [57, 57, 63, 71]})

我想计算df中日期列中每一行的平均值。我认为使用groupby的最佳方法是更改列名，以使每个月的列名都不唯一，就像这样：

def maybe_rename(col_name):
if re.match('\\d+/\\d+/\\d+', col_name):
    return re.split('/', col_name)[0] + re.split('/', col_name)[2]
else:
    return col_name

df = df.rename(columns = maybe_rename)

df

     company    measure    category  102015  102015  102015
0       dell      sales      laptop     234     134      57
1  microsoft      speed      tablet     333     154      57
2    toshiba  wait time  smartphone     456     123      63
3      apple    service     desktop     290     177      71

然后我尝试像这样计算mean：

df = df.groupby(df.columns, axis = 1).mean()

哪个返回以下错误：DataError: No numeric types to aggregate

我该如何解决？我想要的结果如下：

df

     company    measure    category  102015
0       dell      sales      laptop  141.66
1  microsoft      speed      tablet  181.33
2    toshiba  wait time  smartphone   214.0
3      apple    service     desktop   79.33

Answer 1

尝试一下：

import pandas as pd
df = pd.DataFrame({'company': ['dell', 'microsoft', 'toshiba', 'apple'],
'measure': ['sales', 'speed', 'wait time', 'service'], 'category': ['laptop',
'tablet', 'smartphone', 'desktop'], '10/6/2015': [234, 333, 456, 290],
'10/13/2015': [134, 154, 123, 177], '10/20/2015': [57, 57, 63, 71]})

columns_to_average = ['10/6/2015','10/20/2015','10/13/2015']
df['means'] = df[columns_to_average].mean(axis=1)

如果您有很多日期列，我建议将其转换为时间序列数据...

tdf = df[['category','10/6/2015','10/20/2015','10/13/2015']].transpose()
tdf = tdf.rename(columns=tdf.iloc[0]).drop(tdf.index[0])
print(tdf['laptop'].mean())

熊猫-DataError没有要聚合的数字类型

1 个答案: