[英]Compare two columns in two dataframes with a condition on another column
我有一個多級數據框,我想將 column secret
的值與 column group
上的條件進行比較。 如果 group = A,我們允許另一個數據幀中的值為空或 na。 否則,為不匹配的輸出 INVALID。
多級數據框:
name secret group
df1 df2 df1 df2 df1 df2
id
1 Tim Tim random na A A
2 Tom Tom tree A A
3 Alex Alex apple apple B B
4 May May file cheese C C
秘密的預期輸出
id name secret group
1 Tim na A
2 Tom A
3 Alex apple B
4 May INVALID C
到目前為止我有:
result_df['result'] = multilevel_df.groupby(level=0, axis=0).apply(lambda x: secret_check(x))
#take care of the rest by compare column by column
result_df = multilevel_df.groupby(level=0, axis=1).apply(lambda x: validate(x))
def validate(x):
if x[0] == x[1]:
return x[1]
else:
return 'INVALID'
def secret_check(x):
if (x['group'] == 'A' and pd.isnull(['secret']): #this line is off
return x[1]
elif x[0] == x[1]:
return x[1]
else:
return 'INVALID'
如果我理解正確,如果df1
和df2
的機密不同並且組不是 A,您想將df2
“機密”標記為無效。
condition = (df[('secret', 'df1')] != df[('secret', 'df2')]) &\
df[('group', 'df1')] != 'A')
df.loc[condition, ('secret', 'df2')] = 'INVALID'
假設我們有以下數據框:
df = pd.DataFrame({0: {0: 1, 1: 2, 2: 3, 3: 4},
1: {0: 'Tim', 1: 'Tom', 2: 'Alex', 3: 'May'},
2: {0: 'Tim', 1: 'Tom', 2: 'Alex', 3: 'May'},
3: {0: 'random', 1: 'tree', 2: 'apple', 3: 'file'},
4: {0: 'na', 1: '', 2: 'apple', 3: 'cheese'},
5: {0: 'A', 1: 'A', 2: 'B', 3: 'C'},
6: {0: 'A', 1: 'A', 2: 'B', 3: 'C'}})
df
df.columns = pd.MultiIndex.from_tuples([('id',''), ('name', 'df1'), ('name', 'df2'),
('secret', 'df1'), ('secret', 'df2'), ('group', 'df1'), ('group', 'df2')])
df
In[1]:
id name secret group
df1 df2 df1 df2 df1 df2
0 1 Tim Tim random na A A
1 2 Tom Tom tree A A
2 3 Alex Alex apple apple B B
3 4 May May file cheese C C
np.select()
根據條件返回結果。.droplevel()
退出多.droplevel()
數據幀df.loc[:,~df.columns.duplicated()]
刪除重復的列。 由於我們將答案設置為df1
列,因此不需要df2
列。df[('secret', 'df1')] = np.select([(df[('group', 'df2')] != 'A') &
(df[('secret', 'df1')] != df[('secret', 'df2')])], #condition 1
[df[('secret', 'df1')] + ' > ' + df[('secret', 'df2')]], #result 1
df[('secret', 'df2')]) #alterantive if conditions not met
df.columns = df.columns.droplevel(1)
df = df.loc[:,~df.columns.duplicated()]
df
Out[1]:
id name secret group
0 1 Tim na A
1 2 Tom A
2 3 Alex apple B
3 4 May file > cheese C
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