Question

我有数据框：

df = pd.DataFrame({'Continent':['North America','North America','North America','Europe','Europe','Europe','Europe'],
                'Country': ['US','Canada','Mexico','France','Germany','Spain','Italy'],
                'Status': ['Member','Non-Member','Non-Member','Member','Non-Member','Member','Non-Member'],
                'Units': [27,5,4,10,15,8,8]})

print df

       Continent  Country      Status  Units
0  North America       US      Member     27
1  North America   Canada  Non-Member      5
2  North America   Mexico  Non-Member      4
3         Europe   France      Member     10
4         Europe  Germany  Non-Member     15
5         Europe    Spain      Member      8
6         Europe    Italy  Non-Member      8

我需要添加2列，这些列是关于大陆的摘要统计信息。我需要一个列为成员国和非成员国的单位总和。

以便最终输出如下：

       Continent  Member Units  Non-Member Units  Country      Status  Units
0  North America            27                 9       US      Member     27
1  North America            27                 9   Canada  Non-Member      5
2  North America            27                 9   Mexico  Non-Member      4
3         Europe            18                23   France      Member     10
4         Europe            18                23  Germany  Non-Member     15
5         Europe            18                23    Spain      Member      8
6         Europe            18                23    Italy  Non-Member      8

似乎我需要使用groupby，但我无法弄清楚如何获取groupby值并将它们作为新列重新插入数据帧。

summary_stats = df.groupby(['Continent','Status'])['Units'].sum()
print summary_stats

Continent      Status    
Europe         Member        18
               Non-Member    23
North America  Member        27
               Non-Member     9
Name: Units, dtype: int64

我也尝试过不使用groupby：

df['Member Units'] = df['Units'][df['Status'] == 'Member'].sum()
df['Non-Member Units'] = df['Units'][df['Status'] == 'Non-Member'].sum()

但是这并没有被大陆区分，所以它只是将所有会员和非会员加起来

任何帮助都非常有用！

Answer 1

我认为您需要先groupby和transform sum来创建新的Series all_sum。然后我认为最好使用numpy.where，如果是成员，则从Series获取值，如果不是，则获取0。与非成员相似：

all_sum = df.groupby(['Continent','Status'])['Units'].transform(sum)
print all_sum
0    27
1     9
2     9
3    18
4    23
5    18
6    23
dtype: int64

df['Member Units'] = np.where(df['Status'] == 'Member', all_sum, 0)
df['Non-Member Units'] = np.where(df['Status'] != 'Member', all_sum, 0)
print df
       Continent  Country      Status  Units  Member Units  Non-Member Units
0  North America       US      Member     27            27                 0
1  North America   Canada  Non-Member      5             0                 9
2  North America   Mexico  Non-Member      4             0                 9
3         Europe   France      Member     10            18                 0
4         Europe  Germany  Non-Member     15             0                23
5         Europe    Spain      Member      8            18                 0
6         Europe    Italy  Non-Member      8             0                23

Answer 2

一旦你summary_stats，我认为你可以这样做：

df['Member Units'] = summary_stats[zip(df['Continent'].values, df['Status'].values)]

您需要zip系列值的原因是df['Continent']会返回带索引的系列，但您不希望这种情况发生。

Answer 3

由于您有summary_stats，因此您可以在重塑后使用merge()：

summary = summary_stats.reset_index().pivot(index='Continent', columns='Status', values='Units')

summary['Continent'] = summary.index

df = df.merge(summary, on='Continent')

然后根据需要重命名列

根据列的总和添加新列，并在Pandas中按2个其他列分组

3 个答案: