python - 使用条件计算 pandas 数据框中出现的总数

Question

我有这个数据框:

cat_df.head()

   category depth
0   food    0.0
1   food    1.0
2   sport   1.0
3   food    3.0
4   school  0.0
5   school  0.0
6   school  1.0
...

其中depth = 0代表根发布，depth > 0是评论。

对于每个类别，我想计算根出版物的数量 (depth=0) 和评论的数量 (depth>0)。

我使用 value_counts() 来计算唯一值:

cat_df['category'].value_counts().head(15)

category     total number 
food         44062
sport        38004
school       11080
life         8810
...

我以为我可以将 ['depth'] == 0 作为数据框内的条件，但它不起作用:

cat_df[cat_df['depth'] == 0].value_counts().head(5)

如何获取 depth=0 和 depth>0 的总出现次数？

我想把它放在这样的表格中:

category | total number | depth=0 | depth>0 
...

Answer 1

您只能使用一个groupby 来提高性能:

df = (cat_df['depth'].ne(0)
                     .groupby(cat_df['category'])
                     .value_counts()
                     .unstack(fill_value=0)
                     .rename(columns={0:'depth=0', 1:'depth>0'})
                     .assign(total=lambda x: x.sum(axis=1))
                     .reindex(columns=['total','depth=0','depth>0']))

print (df)
depth     total  depth=0  depth>0
category                         
food          3        1        2
school        3        2        1
sport         1        0        1

解释:

1. 首先比较 depth 列不等于 Series.ne (!=)
2. groupby按 category 列 SeriesGroupBy.value_counts
3. reshape unstack
4. 按字典重命名列
5. 按 assign 创建新的 total 列
6. 要自定义列的顺序，请添加 reindex

编辑:

cat_df = pd.DataFrame({'category': ['food', 'food', 'sport', 'food', 'school', 'school', 'school'], 'depth': [0.0, 1.0, 1.0, 3.0, 0.0, 0.0, 1.0], 'num_of_likes': [10, 10, 10, 20, 20, 20, 20]})

---

print (cat_df)
  category  depth  num_of_likes
0     food    0.0            10
1     food    1.0            10
2    sport    1.0            10
3     food    3.0            20
4   school    0.0            20
5   school    0.0            20
6   school    1.0            20

df = (cat_df['depth'].ne(0)
                     .groupby([cat_df['num_of_likes'], cat_df['category']])
                     .value_counts()
                     .unstack(fill_value=0)
                     .rename(columns={0:'depth=0', 1:'depth>0'})
                     .assign(total=lambda x: x.sum(axis=1))
                     .reindex(columns=['total','depth=0','depth>0'])
                     .reset_index()
                     .rename_axis(None, axis=1)
)

print (df)
   num_of_likes category  total  depth=0  depth>0
0            10     food      2        1        1
1            10    sport      1        0        1
2            20     food      1        0        1
3            20   school      3        2        1

编辑1:

s = cat_df.groupby('category')['num_of_likes'].sum()
print (s)
category
food      40
school    60
sport     10
Name: num_of_likes, dtype: int64

df = (cat_df['depth'].ne(0)
                     .groupby(cat_df['category'])
                     .value_counts()
                     .unstack(fill_value=0)
                     .rename(columns={0:'depth=0', 1:'depth>0'})
                     .assign(total=lambda x: x.sum(axis=1))
                     .reindex(columns=['total','depth=0','depth>0'])
                     .reset_index()
                     .rename_axis(None, axis=1)
                     .assign(num_of_likes=lambda x: x['category'].map(s))
)
print (df)
  category  total  depth=0  depth>0  num_of_likes
0     food      3        1        2            40
1   school      3        2        1            60
2    sport      1        0        1            10