用python计算库存

考虑一个产品库存的数据框架（转换为字典以便在此处发布）

[
  {
    "production_batch_start_date": "2019-07-15",
    "rm/pm_code": "abc",
    "consumed_qty": 0,
    "purchased_quantity": 0,
    "opening_stock": 58819.8
  },
  {
    "production_batch_start_date": "2019-07-15",
    "rm/pm_code": "efg",
    "consumed_qty": 0,
    "purchased_quantity": 0,
    "opening_stock": 0
  },
  {
    "production_batch_start_date": "2019-07-15",
    "rm/pm_code": "hij",
    "consumed_qty": 150,
    "purchased_quantity": 10,
    "opening_stock": 39715.4
  },
  {
    "production_batch_start_date": "2019-07-16",
    "rm/pm_code": "abc",
    "consumed_qty": 6807,
    "purchased_quantity": 0,
    "opening_stock": "nan"
  },
  {
    "production_batch_start_date": "2019-07-16",
    "rm/pm_code": "efg",
    "consumed_qty": 11432,
    "purchased_quantity": 0,
    "opening_stock": "nan"
  },
  {
    "production_batch_start_date": "2019-07-16",
    "rm/pm_code": "hij",
    "consumed_qty": 20,
    "purchased_quantity": 5,
    "opening_stock": "nan"
  }
]

我想添加另一个名为

库存

的专栏，它将为我提供每天结束时剩余的数量值。它可以通过将当天的

购买数量

与前一天的库存相加，然后减去该特定日期的

消费数量

来计算

期初库存

为我提供了盘点第一天的初始价值库存。其他所有日子都是

NaN

预期产出：

[
  {
    "production_batch_start_date": "2019-07-15",
    "rm/pm_code": "hij",
    "purchased_quantity": 10,
    "opening_stock": 39715.4,
    "consumed_qty": 150,
    "stock_in_hand": 39575.4
  },
  {
    "production_batch_start_date": "2019-07-16",
    "rm/pm_code": "hij",
    "purchased_quantity": 5,
    "opening_stock": "NaN",
    "consumed_qty": 20,
    "stock_in_hand": 39560
  }
]

---

请让我知道如何在Python中实现这一点

我认为你需要用以下公式减去值：

---

你能添加预期的输出数据帧吗？@jezrael我已经添加了！！我要试试这个！谢谢

#if necessary sort data
df = df.sort_values(['production_batch_start_date', 'rm/pm_code'])

#if necessary aggregate data
#df=df.groupby(['production_batch_start_date', 'rm/pm_code'], as_index=False).sum(min_count=1)
df["stock_in_hand"] = df['opening_stock'].sub(df['consumed_qty'], fill_value=0)
df['stock_in_hand'] = df.groupby('rm/pm_code')['stock_in_hand'].cumsum()
print (df)

  production_batch_start_date rm/pm_code  consumed_qty  purchased_quantity  \
0                  2019-07-15        abc             0                   0   
1                  2019-07-15        efg             0                   0   
2                  2019-07-15        hij           150                  10   
3                  2019-07-16        abc          6807                   0   
4                  2019-07-16        efg         11432                   0   
5                  2019-07-16        hij            20                   5   

   opening_stock  stock_in_hand  
0        58819.8        58819.8  
1            0.0            0.0  
2        39715.4        39565.4  
3            NaN        52012.8  
4            NaN       -11432.0  
5            NaN        39545.4