從另一個資料幀的選定列填充NaN值

我有這樣的 df1

       id        name  level personality      type  weakness    atk    def     hp  stage
0    53.0     Persian   40.0        mild    normal  fighting  104.0  116.0    NaN    2.0
1   126.0      Magmar   44.0      docile       NaN     water   96.0   83.0  153.0    1.0
2    57.0    Primeape    9.0      lonely  fighting    flying    NaN   66.0   43.0    2.0
3     3.0    Venusaur   44.0       sassy     grass      fire  136.0  195.0   92.0    3.0
4    11.0     Metapod    4.0       naive     grass      fire    NaN  114.0    NaN    2.0
5   126.0      Magmar   96.0      modest      fire     water   62.0  114.0    NaN    1.0
6   137.0     Porygon   96.0     relaxed       NaN  fighting   68.0   50.0  127.0    1.0
7    69.0  Bellsprout   84.0      lonely     grass      fire    NaN    NaN    NaN    1.0
8    10.0    Caterpie    3.0     serious       NaN    flying    NaN    NaN   15.0    1.0
9    12.0  Butterfree   12.0       hasty       NaN    flying   20.0    NaN    NaN    3.0
10   35.0    Clefairy   18.0      impish     fairy    poison   33.0    NaN    NaN    1.0
11   59.0    Arcanine   35.0      gentle      fire     water   45.0   60.0   80.0    2.0
12  111.0     Rhyhorn   31.0     naughty      rock     water   40.0    NaN  175.0    1.0
13  136.0     Flareon   75.0        bold       NaN     water    NaN  143.0    NaN    2.0
14   51.0     Dugtrio   82.0      gentle    ground     water  152.0  161.0  168.0    2.0
15   38.0   Ninetales    5.0       brave      fire     water    NaN  179.0  173.0    2.0
16  102.0   Exeggcute   88.0        rash       NaN      fire    NaN  124.0    NaN    1.0 
........

和 df2 作為

    weakness      type  count
3       fire     grass     11
10     water      fire      9
0   fighting    normal      6
4     flying  fighting      3
8     poison     fairy      3
6      grass     water      1
9       rock      fire      1
7     ground  electric      1

我想使用 df2 更新型別列中的 NaN 值，并在兩個 dfs 中匹配弱列。例如，在 df1 的第 8 行和第 9 行中，'type' 值為 NaN。我想用 df2 更新它們匹配 df1 中的弱點列。所以那些 8,9 型別值應該是“戰斗”等。這就像 df2 和 df1 之間的一對多關系。

我試過

df1.update(df2)

和

df1.fillna(df2)

但他們沒有給出想要的輸出。任何幫助將不勝感激。

uj5u.com熱心網友回復：

1. 創建一個 Series from df2，將weakness值映射到type值：

   mapping = df2.set_index("weakness")["type"]

2. 地圖df1["weakness"]使用這個映射創建默認值：

   defaults = df1["weakness"].map(mapping)

3. 使用默認值作為fillna方法的引數：

   df1["type"] = df1["type"].fillna(defaults)

uj5u.com熱心網友回復：

您可以從 df2 創建一個字典，以弱列作為鍵，型別列作為它們各自的值，然后使用該字典到fillnadf1 中的型別列map：

m = dict(zip(df2.weakness,df2.type))
df1.type = df1.type.fillna(df1.weakness.map(m))

印刷：

>>> df1[['weakness','type']]

    weakness      type
0   fighting    normal
1      water      fire
2     flying  fighting
3       fire     grass
4       fire     grass
5      water      fire
6   fighting    normal
7       fire     grass
8     flying  fighting
9     flying  fighting
10    poison     fairy
11     water      fire
12     water      rock
13     water      fire
14     water    ground
15     water      fire
16      fire     grass

uj5u.com熱心網友回復：

行內記錄的代碼

# Merge both dataframes using "weakness" as key
df = pd.merge(df1, df2[['weakness', 'type']], 
               on="weakness",  suffixes=("", "_y"), how="left")
# Replace nans
df['type'].fillna(df['type_y'], inplace=True)
# Drop additional columns resulted from Merge
df.drop(columns=['type_y'])

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