合并从应用功能返回到数据框的数据范围？

Question

我的问题与这个问题有关：

这是我的代码版本：

col = ['State','Annual Salary']
dat = [['New York', 132826], ['New Hampshire',128704], ['California',127388], ['Vermont',121599], ['Idaho',120011]]
df = pd.DataFrame(dat, columns=col)

def get_taxes_from_api(state, annual_salary):
    return pd.DataFrame({'State': [state, state], 
                         'annual.fica.amount': [int(annual_salary * 0.067),
                                                int(annual_salary * 1.067)], 
                         'annual.federal.amount': [int(annual_salary * 0.3),
                                                   int(annual_salary * 1.3)], 
                         'annual.state.amount': [int(annual_salary * 0.048),
                                                 int(annual_salary * 1.048)]})

如何将get_taxes_from_api应用于每一行DF并将返回的数据范围合并到数据框架上？

唯一的区别是我的函数返回多行数据框，而不是1行数据框。因此，上述问题的解决方案对我的情况不起作用。 （而且我没有足够的声誉来在那里发表评论。）

Answer 1

您可以使用 concat 作为嵌套数据框

nested_df = df.apply(lambda x: get_taxes_from_api(x["State"],x["Annual Salary"]),axis=1)

result = pd.DataFrame()

for element in nested_df:
    result = pd.concat([result,element])

结果：

print(result)

	State	annual.fica.amount	annual.federal.amount	annual.state.amount
0	New York	8899	39847	6375
1	New York	141725	172673	139201
0	New Hampshire	8623	38611	6177
1	New Hampshire	137327	167315	134881
0	California	8534	38216	6114
1	California	135922	165604	133502
0	Vermont	8147	36479	5836
1	Vermont	129746	158078	127435
0	IDAHO	8040	36003	5760
1	IDAHO	128051	156014	1257714

Answer 2

您可以在两个DFS中创建一个新的JOIN密钥，然后做PD.Merge。请参阅此处：

df["df_merge_key"] = "#"
df_after_apply["df_merge_key"] = "#"
details_df = pd.merge(df, df_after_apply, how="left", on="df_merge_key").drop(labels=["df_merge_key"], axis=1)

我认为这更简单，更整洁。

Answer 3

这不是直接回答您的问题，但是这是不使用应用程序

col = ['State','Annual Salary']
dat = [['New York', 132826], ['New Hampshire',128704], ['California',127388], ['Vermont',121599], ['Idaho',120011]]
df = pd.DataFrame(dat, columns=col)

#Create the "first" row of each state from your function by adding columns
df['annual.fica.amount'] = df['Annual Salary'].multiply(0.067)
df['annual.federal.amount'] = df['Annual Salary'].multiply(0.3)
df['annual.state.amount'] = df['Annual Salary'].multiply(0.048)

#Create the "second" row of each state as a new df
cumulative_df = df.copy()
cumulative_df['annual.fica.amount'] += cumulative_df['Annual Salary']
cumulative_df['annual.federal.amount'] += cumulative_df['Annual Salary']
cumulative_df['annual.state.amount'] += cumulative_df['Annual Salary']

#Concatenate the two tables and sort so the states are right next to each other
final_df = pd.concat((df,cumulative_df)).sort_values('State').reset_index(drop=True)

输出