我有这样的DataFrame:
product_id dt stock_qty
226870 2948259 2017-11-11 17.000
233645 2948259 2017-11-12 17.000
240572 2948260 2017-11-13 5.000
247452 2948260 2017-11-14 5.000
233644 2948260 2017-11-12 5.000
226869 2948260 2017-11-11 5.000
247451 2948262 2017-11-14 -2.000
226868 2948262 2017-11-11 -1.000 <- not duplicated
240571 2948262 2017-11-13 -2.000
240570 2948264 2017-11-13 5.488
233643 2948264 2017-11-12 5.488
244543 2948269 2017-11-11 2.500
247450 2948276 2017-11-14 3.250
226867 2948276 2017-11-11 3.250
我必须删除stock_qty
不同但product_id
值相同的行。所以我应该得到这样的DataFrame:
product_id dt stock_qty
226870 2948259 2017-11-11 17.000
233645 2948259 2017-11-12 17.000
240572 2948260 2017-11-13 5.000
247452 2948260 2017-11-14 5.000
233644 2948260 2017-11-12 5.000
226869 2948260 2017-11-11 5.000
240570 2948264 2017-11-13 5.488
233643 2948264 2017-11-12 5.488
244543 2948269 2017-11-11 2.500
247450 2948276 2017-11-14 3.250
226867 2948276 2017-11-11 3.250
感谢您的帮助!
答案 0 :(得分:2)
您需要drop_duplicates
才能获取所有product_id
值,然后isin
将其排除在xor
(^)
链接的其他条件之外:
m1 = df['product_id'].isin(df.drop_duplicates('stock_qty', keep=False)['product_id'])
m2 = df.duplicated('product_id', keep=False)
df = df[m1 ^ m2]
print (df)
product_id dt stock_qty
226870 2948259 2017-11-11 17.000
233645 2948259 2017-11-12 17.000
240572 2948260 2017-11-13 5.000
247452 2948260 2017-11-14 5.000
233644 2948260 2017-11-12 5.000
226869 2948260 2017-11-11 5.000
240570 2948264 2017-11-13 5.488
233643 2948264 2017-11-12 5.488
244543 2948269 2017-11-11 2.500
247450 2948276 2017-11-14 3.250
226867 2948276 2017-11-11 3.250
详情:
print (m1)
226870 False
233645 False
240572 False
247452 False
233644 False
226869 False
247451 True
226868 True
240571 True
240570 False
233643 False
244543 True
247450 False
226867 False
Name: product_id, dtype: bool
print (m2)
226870 True
233645 True
240572 True
247452 True
233644 True
226869 True
247451 True
226868 True
240571 True
240570 True
233643 True
244543 False
247450 True
226867 True
dtype: bool
答案 1 :(得分:1)
@jezrael解决方案是最佳的,但另一种方法是使用groupby
和filter
:
df.groupby(['product_id','stock_qty']).filter(lambda x: len(x)>1)
输出:
product_id dt stock_qty
226870 2948259 2017-11-11 17.000
233645 2948259 2017-11-12 17.000
240572 2948260 2017-11-13 5.000
247452 2948260 2017-11-14 5.000
233644 2948260 2017-11-12 5.000
226869 2948260 2017-11-11 5.000
247451 2948262 2017-11-14 -2.000
240571 2948262 2017-11-13 -2.000
240570 2948264 2017-11-13 5.488
233643 2948264 2017-11-12 5.488
247450 2948276 2017-11-14 3.250
226867 2948276 2017-11-11 3.250
答案 2 :(得分:1)
使用drop_duplicates
df.drop(df.drop_duplicates(['stock_qty', 'product_id'], keep=False).index)
Out[797]:
product_id dt stock_qty
226870 2948259 2017-11-11 17.000
233645 2948259 2017-11-12 17.000
240572 2948260 2017-11-13 5.000
247452 2948260 2017-11-14 5.000
233644 2948260 2017-11-12 5.000
226869 2948260 2017-11-11 5.000
247451 2948262 2017-11-14 -2.000
240571 2948262 2017-11-13 -2.000
240570 2948264 2017-11-13 5.488
233643 2948264 2017-11-12 5.488
247450 2948276 2017-11-14 3.250
226867 2948276 2017-11-11 3.250
答案 3 :(得分:0)
使用 loc[]
,您可以仅过滤重复的行并分配给您的原始数据框。
df = df.loc[df.duplicated(subset=['product_id','stock_qty'], keep=False)]
还有 keep=False
参数将所有重复的行标记为 True,如果您只想要第一个或最后一个使用 keep='first'
或 keep='last'