Pandas Convert float column, содержащий значения nan, в int для операции слияния

Question

Попытка # 1

s["order_id"].apply(lambda x: int(x) if pd.notnull(x) else np.nan)

Попытка # 2

def to_int(x):
   if(pd.notnull(x)):
       return int(x)

Попытка # 3

 s["order_id"] = s.loc[pd.notnull(s["order_id"]),"order_id].astype(int)

Все они возвращают серию, в которой значения по-прежнему отформатированы как числа с плавающей запятой.

Мне интересно, могу ли я использовать функцию обновления или воспользоваться переиндексацией.

Попытка использовать решение для индексирования:

null = np.nan
data = {"time":{"0":1528971021539,"1":1529289904697,"2":1529572773525,"3":1529892602301,"4":1530082881098,"5":1530069453264,"6":1528985491630,"7":1529236762719,"8":1529475504491,"9":1529814085541,"10":1529906568681,"11":1530160346468,"12":1529833559160,"13":1530051985183,"14":1530240956273,"15":1529794554495,"16":1529892989425,"17":1529386510176,"18":1529118607780,"19":1529404958912,"20":1529812956409,"21":1530012703548,"22":1527815420250,"23":1527826735070,"24":1527832343938,"25":1527853694229,"26":1527889066223,"27":1527986243670,"28":1528070794031,"29":1528149294729,"30":1528158483701,"31":1528172242288,"32":1528173686892,"33":1528174729282,"34":1528175624472,"35":1528184014365,"36":1528184994544,"37":1528199211274,"38":1528204822424,"39":1528236692102,"40":1528246124079,"41":1528251449061,"42":1528254158311,"43":1528324045380,"44":1528409837346,"45":1528429172972,"46":1528453372400,"47":1528525996756,"48":1528530493509,"49":1528539093472},"user_id":{"0":1754627236948496,"1":4702200191313171,"2":4778254911976758,"3":8293985621789157,"4":5156436454415407,"5":4445821205748907,"6":6872300957263521,"7":579402494860,"8":2010389994610194,"9":3378398685582335,"10":2923987501904097,"11":7254681572754712,"12":2280706641994510,"13":5853777483445659,"14":1790488830140089,"15":4649841298300342,"16":8296801793054868,"17":6074985077237804,"18":7512067556495704,"19":7449962479289671,"20":931159100938705,"21":4303206141550631,"22":4931136210605885,"23":910152652690726,"24":213367265258802,"25":59665205254502,"26":7375134691043656,"27":5112755499047871,"28":1511225869347102,"29":6553192205018264,"30":5758319280291333,"31":5654341500640968,"32":8149628703137465,"33":6808112291514009,"34":3363098540596606,"35":4205809380744263,"36":3662128280212665,"37":986809097179824,"38":3834989038766064,"39":3561701388137551,"40":3363098540596606,"41":7998995390673240,"42":188780187662080,"43":290955994841187,"44":7996996554339358,"45":2624074855751159,"46":8317830532715985,"47":4819555707307085,"48":6662202062763635,"49":1363740504674809},"order_id":{"0":1161.0,"1":1175.0,"2":1186.0,"3":1200.0,"4":1217.0,"5":1213.0,"6":1162.0,"7":1171.0,"8":1183.0,"9":1192.0,"10":1205.0,"11":1219.0,"12":1195.0,"13":1212.0,"14":1221.0,"15":1190.0,"16":1201.0,"17":1166.0,"18":1167.0,"19":1181.0,"20":1191.0,"21":1211.0,"22":null,"23":null,"24":null,"25":null,"26":null,"27":null,"28":null,"29":null,"30":null,"31":null,"32":null,"33":null,"34":null,"35":null,"36":null,"37":null,"38":null,"39":null,"40":null,"41":null,"42":null,"43":null,"44":null,"45":null,"46":null,"47":null,"48":null,"49":null}}
s = pd.DataFrame(data=data)

orders = {"order_id":{"0":1161,"1":1175,"2":1205,"3":1219,"4":1195,"5":1212,"6":1221,"7":1190,"8":1201,"9":1166,"10":1167,"11":1181,"12":1186,"13":1191,"14":1211,"15":1200,"16":1217,"17":1213,"18":1162,"19":1171,"20":1183,"21":1192},"order_total":{"0":"206.50","1":"369.00","2":"313.65","3":"158.74","4":"164.50","5":"156.83","6":"184.50","7":"137.50","8":"120.00","9":"85.00","10":"369.00","11":"156.83","12":"184.50","13":"191.25","14":"297.50","15":"180.00","16":"394.40","17":"75.00","18":"191.25","19":"386.33","20":"95.00","21":"200.00"}}
o = pd.DataFrame(data=orders)

orders = s.loc[pd.notnull(s["order_id"])]
orders["order_id"] = orders["order_id"].astype(int)
s["order_total"] = np.nan
s.update(orders.merge(o, on='order_id', how='left').set_index(o.index)["order_total"])

Answer 1

Это возможно путем взлома, но не рекомендуется, потому что некоторые функции должны выйти из строя и ухудшить производительность:

s = pd.DataFrame({'order_id':[np.nan,8,9,4,2,3]})

s["order_id"] = s["order_id"].astype(object)
print (s)
  order_id
0      NaN
1        8
2        9
3        4
4        2
5        3

Docs

В отсутствие поддержки высокой производительности NA, встроенной в NumPy с нуля, основной жертвой является возможность представлять NA в целочисленных массивах.

In [20]: s = pd.Series([1, 2, 3, 4, 5], index=list('abcde'))

In [21]: s
Out[21]: 
a    1
b    2
c    3
d    4
e    5
dtype: int64

In [22]: s.dtype
Out[22]: dtype('int64')

In [23]: s2 = s.reindex(['a', 'b', 'c', 'f', 'u'])

In [24]: s2
Out[24]: 
a    1.0
b    2.0
c    3.0
f    NaN
u    NaN
dtype: float64

In [25]: s2.dtype
Out[25]: dtype('float64')

Этот компромисс сделан в основном из-за памяти и производительности, а также из-за того, что полученный ряд продолжает оставаться «числовым». Одной из возможностей является использование dtype = object массивов.

EDIT:

Возможно удалить NaN s на dropna и затем преобразовать в float, если merge не удалось соединить между float s и integer s:

orders = s = s.dropna(subset=['order_id'])
orders['order_id'] = orders['order_id'].astype(int)
#if want select only one column there was typos - ] and ) after s["order_id"]  
orders = s.loc[pd.notnull(s["order_id"]),"order_id"].astype(int)

orders.merge(df, on="order_id", how="left")

EDIT1:

orders = o.set_index('order_id')["order_total"]

s["order_total"] = s["order_id"].map(orders)
print (s.head(20))

             time           user_id  order_id order_total
0   1528971021539  1754627236948496    1161.0      206.50
1   1529289904697  4702200191313171    1175.0      369.00
10  1529906568681  2923987501904097    1205.0      313.65
11  1530160346468  7254681572754712    1219.0      158.74
12  1529833559160  2280706641994510    1195.0      164.50
13  1530051985183  5853777483445659    1212.0      156.83
14  1530240956273  1790488830140089    1221.0      184.50
15  1529794554495  4649841298300342    1190.0      137.50
16  1529892989425  8296801793054868    1201.0      120.00
17  1529386510176  6074985077237804    1166.0       85.00
18  1529118607780  7512067556495704    1167.0      369.00
19  1529404958912  7449962479289671    1181.0      156.83
2   1529572773525  4778254911976758    1186.0      184.50
20  1529812956409   931159100938705    1191.0      191.25
21  1530012703548  4303206141550631    1211.0      297.50
22  1527815420250  4931136210605885       NaN         NaN
23  1527826735070   910152652690726       NaN         NaN
24  1527832343938   213367265258802       NaN         NaN
25  1527853694229    59665205254502       NaN         NaN
26  1527889066223  7375134691043656       NaN         NaN