for loop using iterrows in pandas

Question

I have 2 dataframes as follows:

data1 looks like this:

id          address       
1          11123451
2          78947591

data2 loo

Answer 1

You can use first cross join with merge and then filter values by boolean indexing. Last remove unecessary columns by drop:

data1['tmp'] = 1
data2['tmp'] = 1
df = pd.merge(data1, data2, on='tmp', how='outer')
df = df[(df.lowerbound_address <= df.address) & (df.upperbound_address >= df.address)]
df = df.drop(['lowerbound_address','upperbound_address', 'tmp'], axis=1)
print (df)
   id   address place
1   1  11123451     Y
2   2  78947591     X

Another solution with itertuples, last create DataFrame.from_records:

places = []
for row1 in data1.itertuples():
    for row2 in data2.itertuples():
        #print (row1.address)
        if (row2.lowerbound_address <= row1.address <= row2.upperbound_address):
            places.append((row1.id, row1.address, row2.place))    
print (places)
[(1, 11123451, 'Y'), (2, 78947591, 'X')]

df = pd.DataFrame.from_records(places)
df.columns=['id','address','place']
print (df)
   id   address place
0   1  11123451     Y
1   2  78947591     X

Another solution with apply:

def f(x):
    for row2 in data2.itertuples():
        if (row2.lowerbound_address <= x <= row2.upperbound_address):
            return pd.Series([x, row2.place], index=['address','place'])

df = data1.set_index('id')['address'].apply(f).reset_index()
print (df)
   id   address place
0   1  11123451     Y
1   2  78947591     X

EDIT:

Timings:

N = 1000:

If saome values are not in range, in solution b and c are omited. Check last row of df1.

In [73]: %timeit (data1.set_index('id')['address'].apply(f).reset_index())
1 loop, best of 3: 2.06 s per loop

In [74]: %timeit (a(df1a, df2a))
1 loop, best of 3: 82.2 ms per loop

In [75]: %timeit (b(df1b, df2b))
1 loop, best of 3: 3.17 s per loop

In [76]: %timeit (c(df1c, df2c))
100 loops, best of 3: 2.71 ms per loop

Code for timings:

np.random.seed(123)
N = 1000
data1 = pd.DataFrame({'id':np.arange(1,N+1), 
                   'address': np.random.randint(N*10, size=N)}, columns=['id','address'])

#add last row with value out of range
data1.loc[data1.index[-1]+1, ['id','address']] = [data1.index[-1]+1, -1]
data1 = data1.astype(int)
print (data1.tail())

data2 = pd.DataFrame({'lowerbound_address':np.arange(1, N*10,10), 
                      'upperbound_address':np.arange(10,N*10+10, 10),
                      'place': np.random.randint(40, size=N)})

print (data2.tail())
df1a, df1b, df1c = data1.copy(),data1.copy(),data1.copy()
df2a, df2b ,df2c = data2.copy(),data2.copy(),data2.copy()

---

def a(data1, data2):
    data1['tmp'] = 1
    data2['tmp'] = 1
    df = pd.merge(data1, data2, on='tmp', how='outer')
    df = df[(df.lowerbound_address <= df.address) & (df.upperbound_address >= df.address)]
    df = df.drop(['lowerbound_address','upperbound_address', 'tmp'], axis=1)
    return (df)

---

def b(data1, data2):
    places = []
    for row1 in data1.itertuples():
        for row2 in data2.itertuples():
            #print (row1.address)
            if (row2.lowerbound_address <= row1.address <= row2.upperbound_address):
                places.append((row1.id, row1.address, row2.place))    

        df = pd.DataFrame.from_records(places)
        df.columns=['id','address','place']

    return (df)

---

def f(x):
    #use for ... else for add NaN to values out of range
    #http://stackoverflow.com/q/9979970/2901002
    for row2 in data2.itertuples():
        if (row2.lowerbound_address <= x <= row2.upperbound_address):
             return pd.Series([x, row2.place], index=['address','place'])
    else:
        return pd.Series([x, np.nan], index=['address','place'])

---

def c(data1,data2):
    data1 = data1.sort_values('address')
    data2 = data2.sort_values('lowerbound_address')
    df = pd.merge_asof(data1, data2, left_on='address', right_on='lowerbound_address')
    df = df.drop(['lowerbound_address','upperbound_address'], axis=1)
    return df.sort_values('id')


print (data1.set_index('id')['address'].apply(f).reset_index())
print (a(df1a, df2a))
print (b(df1b, df2b))
print (c(df1c, df2c))

Only solution c with merge_asof works very nice with large DataFrame:

N=1M:

In [84]: %timeit (c(df1c, df2c))
1 loop, best of 3: 525 ms per loop

More about merge asof in docs.