使用条件计算pandas数据帧中出现的总次数

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
...

depth = 0代表根发布，depth > 0是注释。

对于每个类别，我想计算根发布的数量（depth=0）和评论数量（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作为条件放在数据框内但不起作用：

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

如何获得深度= 0和深度> 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来提高效果：

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

<强>解释：

1. 首先比较depth列不等于Series.ne（!=）
{li> groupby列category与SeriesGroupBy.value_counts
2. 重塑unstack
3. Rename列词典
4. 按assign
创建新的total列
5. 对于列的自定义顺序，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

EDIT1：

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

Answer 2

这是使用pandas.concat的一种方式：

total = df.groupby('category').size()
zero = df[df.depth == 0].groupby('category').size()
nonzero = df[df.depth > 0].groupby('category').size()

res = pd.concat([total, zero, nonzero], axis=1)\
        .rename(columns={0: 'total', 1: 'zero', 2: 'nonzero'})\
        .fillna(0).astype(int)

print(res)

#         total  zero   nonzero
# food        3     1         2
# school      3     2         1
# sport       1     0         1

Answer 3

我会做什么crosstab

pd.crosstab(df.category,df.depth.ne(0),margins=True).iloc[:-1,:]
Out[618]: 
depth     False  True  All
category                  
food          1     2    3
school        2     1    3
sport         0     1    1

如果需要添加重命名的名称

pd.crosstab(df.category,df.depth.ne(0),margins=True).iloc[:-1,:].rename(columns={True:'depth>0',False:'depth=0'})