Groupby 多列使用 python pandas 查找一列的唯一计数

Question

I have dataframe like:

column1    column2    column3
 ram        tall        good
 rohan      short       fine
 ajay       tall        best
 alia       tall        good
 aman       medium      fine
 john       short       good
 jack       short       fine

now i need output like:

unique count of good in tall, short, medium on basis of column1->

tall=2 , short=1 , medium=0

unique count of fine in tall, short, medium on basis of column1->

tall=0 , short=2 , medium=1

unique count of best in tall, short, medium on basis of column1->

tall=1 , short=0 , medium=0

I am beginner in pandas. Thanks in advance

Answer 1

Let's try pd.crosstab :

pd.crosstab(df['column3'], df['column2'])

---

column2  medium  short  tall
column3                     
best          0      0     1
fine          1      2     0
good          0      1     2

Answer 2

Use value_counts + unstack

res = df[['column3', 'column2']].value_counts().unstack('column2', fill_value=0)
print(res)

Output

column2  medium  short  tall
column3                     
best          0      0     1
fine          1      2     0
good          0      1     2

As an alternative groupby + unstack :

res = df.groupby(['column3', 'column2']).count().unstack('column2', fill_value=0)
print(res)

Output (groupby)

        column1           
column2  medium short tall
column3                   
best          0     0    1
fine          1     2    0
good          0     1    2

The idea behind both approaches is to create an index and then unstack it. If you want to match the same order as specify in your question, convert to Categorical first:

df['column2'] = pd.Categorical(df['column2'], categories=['tall', 'short', 'medium'], ordered=True)
res = df[['column3', 'column2']].value_counts().unstack('column2', fill_value=0)
print(res) 

Output

column2  tall  short  medium
column3                     
best        1      0       0
fine        0      2       1
good        2      1       0