根据其他列值的组合检查唯一列值

Question

I want to be able to check if one column always has a unique set in the other columns. For example, in this dataframe, I would like to test if Orange always has a value of yes, yes in stale and stock and apple always has a value of yes, no. Please give me instructions on how to do this for a larger dataframe in order to check if one volumn is mapped form the unique combination of another two columns.

type    stale    stock      
orange    yes     yes    
apple     yes     no     
orange    yes     yes

Answer 1

you can use drop_duplicates to remove all duplicate rows (based on relevant columns) from the df, then use groupby on type and check that size() is equal to 1 .

try this:

print(df.drop_duplicates(['type', 'stale', 'stock']).groupby('type').size().eq(1))

Output:

type
apple     True
orange    True
dtype: bool

and here's a bigger example with some values that also don't have a unique set:

import pandas as pd
from io import StringIO

s = """
type    stale    stock      
orange    yes     yes    
apple     yes     no     
orange    yes     yes
orange    yes     yes
banana    yes     yes
banana    yes     no
peach     no      no
peach     yes     no
"""

df = pd.read_csv(StringIO(s), sep="\s+")

print(df.drop_duplicates(['type', 'stale', 'stock']).groupby('type').size().eq(1))

Output:

type
apple      True
banana    False
orange     True
peach     False
dtype: bool

Answer 2

我不确定您是想找出数据框中存在的所有值，还是仅针对单个给定值，例如，如果您想为“橙色”执行此操作，则可以使用以下命令：

df.loc[df["type"] == "orange"].nunique().stale == 1 & df.loc[df["typee"] == "orange"].nunique().stock == 1

Answer 3

Can groupby size twice. The first collapses to unique combinations, the second checks if there is only one per type.

df.groupby([*df]).size().groupby('type').size().eq(1)
#type
#apple     True
#orange    True
#dtype: bool