Question

我有一个pandas数据集，其中的列是以逗号分隔的字符串，例如1,2,3,10：

data = [
  { 'id': 1, 'score': 9, 'topics': '11,22,30' },
  { 'id': 2, 'score': 7, 'topics': '11,18,30' },
  { 'id': 3, 'score': 6, 'topics': '1,12,30' },
  { 'id': 4, 'score': 4, 'topics': '1,18,30' }
]
df = pd.DataFrame(data)

我想获得topics中每个值的计数和平均分数。所以：

topic_id,count,mean
1,2,5
11,2,8
12,1,6

等等。我怎么能这样做？

我到目前为止：

df['topic_ids'] = df.topics.str.split()

但是现在我想我要爆炸topic_ids，所以整个值集中的每个唯一值都有一列......？

Answer 1

不需要groupby和agg

df.topics=df.topics.str.split(',')
New_df=pd.DataFrame({'topics':np.concatenate(df.topics.values),'id':df.id.repeat(df.topics.apply(len)),'score':df.score.repeat(df.topics.apply(len))})

New_df.groupby('topics').score.agg(['count','mean'])

Out[1256]: 
        count  mean
topics             
1           2   5.0
11          2   8.0
12          1   6.0
18          2   5.5
22          1   9.0
30          4   6.5

Answer 2

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Answer 3

这是一种方式。 Reindex＆amp;堆栈，然后groupby＆amp; AGG

import pandas as pd

data = [
  { 'id': 1, 'score': 9, 'topics': '11,22,30' },
  { 'id': 2, 'score': 7, 'topics': '11,18,30' },
  { 'id': 3, 'score': 6, 'topics': '1,12,30' },
  { 'id': 4, 'score': 4, 'topics': '1,18,30' }
]
df = pd.DataFrame(data)
df.topics = df.topics.str.split(',')
df2 = pd.DataFrame(df.topics.tolist(), index=[df.id, df.score])\
                   .stack()\
                   .reset_index(name='topics')\
                   .drop('level_2', 1)

df2.groupby('topics').score.agg(['count', 'mean']).reset_index()

pandas：拆分字符串和计数值？

3 个答案: