Schlüssel des ersten Datenrahmens, der in zweiten und Flagge angezeigt, die Tatsache

Question

Ich habe zwei Datenrahmen:

data = { 
    'year': ['11:23:19', '11:23:19', '11:24:19', '11:25:19', '11:25:19', '11:23:19', '11:23:19', '11:23:19', '11:23:19', '11:23:19'], 
    'store_number': ['1944', '1945', '1946', '1948', '1948', '1949', '1947', '1948', '1949', '1947'], 
    'retailer_name': ['Walmart', 'Walmart', 'CRV', 'CRV', 'CRV', 'Walmart', 'Walmart', 'CRV', 'CRV', 'CRV'], 
    'amount': [5, 5, 8, 6, 1, 5, 10, 6, 12, 11], 
    'id': [10, 10, 11, 11, 11, 10, 10, 11, 11, 10] 
} 

df1 = pd.DataFrame(data, columns = ['retailer_name', 'store_number', 'year', 'amount', 'id']) 
df1.set_index(['retailer_name', 'store_number', 'year'], inplace = True) 

---

retailer_name store_number year  amount id 
Walmart  1944   11:23:19  5 10 
       1945   11:23:19  5 10 
CRV   1946   11:24:19  8 11 
       1948   11:25:19  6 11 
          11:25:19  1 11 
Walmart  1949   11:23:19  5 10 
       1947   11:23:19  10 10 
CRV   1948   11:23:19  6 11 
       1949   11:23:19  12 11 
       1947   11:23:19  11 10 

Und die zweite:

data2 = { 
    'year': ['11:23:19', '11:23:19', '13:23:19'], 
    'store_number': [1944, 1947, 1978], 
    'retailer_name': ['Walmart', 'CRV', 'CRV12'], 
    'amount': [5, 11, 11] 
} 

df2 = pd.DataFrame(data2, columns = ['retailer_name', 'store_number', 'year', 'amount']) 
df2.set_index(['retailer_name', 'store_number', 'year'], inplace = True) 

---

retailer_name store_number year  amount 
Walmart  1944   11:23:19  5 
CRV   1947   11:23:19  11 
CRV12   1978   13:23:19  11 

Wie kann ich die Schlüssel von DF2 überprüfen, die in df1 erscheinen und Flagge 1 auf diejenigen, die erscheinen und 0 wenn nicht:

retailer_name store_number year  amount flag 
Walmart  1944   11:23:19  5 1 
CRV   1947   11:23:19  11 1 
CRV12   1978   13:23:19  11 0 

Answer 1

Sie MultiIndex.intersection() Methode verwenden können, wenn Sie sicherstellen, dass beide multiindexes die gleichen dtypes haben :

In [74]: df2['flag'] = 0 

In [75]: df2.ix[df2.index.intersection(df.index), 'flag'] = 1 
c:\envs\py35\lib\site-packages\IPython\terminal\ipapp.py:344: PerformanceWarning: indexing past lexsort depth may impact performance. 
    self.shell.mainloop() 

In [76]: df2 
Out[76]: 
            amount flag 
retailer_name store_number year 
Walmart  1944   11:23:19  5  1 
CRV   1947   11:23:19  11  1 
CRV12   1978   13:23:19  11  0 

HINWEIS: es wird nicht mit Ihrer Probe Verschlechterungsfaktoren arbeiten, weil Spalte store_number verschiedene dtypes hat: string in df und int in df2