<blockquote>
<p>I get the following error: TypeError: unorderable types: int() < str()</p>
</blockquote>
<p>是不是你的数据类型不正确?例如str.而不是int?
您可以尝试使用以下内容创建数据帧:</p>
<pre class="lang-py prettyprint-override"><code>In [18]: import numpy as np; import pandas as pd
In [19]: col1 = ['adidas','nike','yourturn','zara','nike','nike','bla','bla','zalando','amazon']
In [20]: data = {'Major Brand':col1, '2016 Units':range(len(col1)), '2015 Units':range(len(col1),len(col1)*2)}
In [21]: x = pd.DataFrame(data, dtype=np.int64 )
In [22]:
In [22]: x.groupby(by="Major Brand").sum()
Out[22]:
2015 Units 2016 Units
Major Brand
adidas 10 0
amazon 19 9
bla 33 13
nike 40 10
yourturn 12 2
zalando 18 8
zara 13 3
In [23]: x.groupby(by="Major Brand")["2016 Units","2015 Units"].sum()
Out[23]:
2016 Units 2015 Units
Major Brand
adidas 0 10
amazon 9 19
bla 13 33
nike 10 40
yourturn 2 12
zalando 8 18
zara 3 13
In [24]: x.dtypes
Out[24]:
2015 Units int64
2016 Units int64
Major Brand object
dtype: object
In [25]: x.groupby(by="Major Brand").agg(['count','sum','mean','median'])
Out[25]:
2015 Units 2016 Units
count sum mean median count sum mean median
Major Brand
adidas 1 10 10.000000 10.0 1 0 0.000000 0.0
amazon 1 19 19.000000 19.0 1 9 9.000000 9.0
bla 2 33 16.500000 16.5 2 13 6.500000 6.5
nike 3 40 13.333333 14.0 3 10 3.333333 4.0
yourturn 1 12 12.000000 12.0 1 2 2.000000 2.0
zalando 1 18 18.000000 18.0 1 8 8.000000 8.0
zara 1 13 13.000000 13.0 1 3 3.000000 3.0
</code></pre>