从另一个数据框的选定列中填充 NaN 值

Question

我有这样的 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 为

    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 更新类型列中的 NaN 值，并在两个 df 中匹配弱点列。例如，在 df1 的第 8 行和第 9 行中，'type' 值为 NaN。我想用 df2 更新它们匹配 df1 中的弱点列。所以那些 8,9 类型值应该是 'fighting' 等。这有点像 df2 和 df1 之间的一对多关系。

我试过了

df1.update(df2)

和

df1.fillna(df2)

但是他们没有给出想要的输出。任何帮助将不胜感激。

Answer 1

从 df2 创建一个系列，它将 weakness 值映射到 type 值：

mapping = df2.set_index("weakness")["type"]
map df1["weakness"] 使用此映射创建默认值：

defaults = df1["weakness"].map(mapping)
使用默认值作为 fillna 方法的参数：

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

Answer 2

您可以从 df2 创建一个字典，以 weakness 列作为键，type 列作为它们各自的值，然后使用该字典 fillna df1 中的 type 列，使用 map：

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

打印：

>>> 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

Answer 3

内联记录的代码

# 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'])

从另一个数据框的选定列中填充 NaN 值

fill NaN values from selected columns of another dataframe

python

dataframe

pandas

fillna