One to many relationships

Joining Data with pandas

Aaren Stubberfield

Instructor

One-to-one

Image of two DataFrames put together horizontally

One-To-One = Every row in the left table is related to only one row in the right table

One-to-one example

  ward  alderman         address          zip
0 1     Proco "Joe" ...  2058 NORTH W...  60647
1 2     Brian Hopkins    1400 NORTH  ...  60622
2 3     Pat Dowell       5046 SOUTH S...  60609
3 4     William D. B...  435 EAST 35T...  60616
4 5     Leslie A. Ha...  2325 EAST 71...  60649

  ward  pop_2000  pop_2010  change  address          zip
0 1     52951     56149     6%      2765 WEST SA...  60647
1 2     54361     55805     3%      WM WASTE MAN...  60622
2 3     40385     53039     31%     17 EAST 38TH...  60653
3 4     51953     54589     5%      31ST ST HARB...  60653
4 5     55302     51455     -7%     JACKSON PARK...  60637

One-to-many

Image of two DataFrames put together horizontally with a one-to-many relationship

One-To-Many = Every row in left table is related to one or more rows in the right table

One-to-many example

Map of Chicago Wards

Business owner #1 pointing to ward map

Business owner #2 pointing to ward map

One-to-many example

licenses = pd.read_csv('Business_Licenses.csv')
print(licenses.head())
print(licenses.shape)

  account  ward  aid  business         address          zip
0 307071   3     743  REGGIE'S BAR...  2105 S STATE ST  60616
1 10       10    829  HONEYBEERS       13200 S HOUS...  60633
2 10002    14    775  CELINA DELI      5089 S ARCHE...  60632
3 10005    12    nan  KRAFT FOODS ...  2005 W 43RD ST   60609
4 10044    44    638  NEYBOUR'S TA...  3651 N SOUTH...  60613
(10000, 6)

One-to-many example

  ward  alderman         address          zip
0 1     Proco "Joe" ...  2058 NORTH W...  60647
1 2     Brian Hopkins    1400 NORTH  ...  60622
2 3     Pat Dowell       5046 SOUTH S...  60609
3 4     William D. B...  435 EAST 35T...  60616
4 5     Leslie A. Ha...  2325 EAST 71...  60649

  account  ward  aid  business         address          zip
0 307071   3     743  REGGIE'S BAR...  2105 S STATE ST  60616
1 10       10    829  HONEYBEERS       13200 S HOUS...  60633
2 10002    14    775  CELINA DELI      5089 S ARCHE...  60632
3 10005    12    nan  KRAFT FOODS ...  2005 W 43RD ST   60609
4 10044    44    638  NEYBOUR'S TA...  3651 N SOUTH...  60613

One-to-many example

ward_licenses = wards.merge(licenses, on='ward', suffixes=('_ward','_lic'))
print(ward_licenses.head())

  ward  alderman         address_ward     zip_ward  account  aid  business         address_lic      zip_lic
0 1     Proco "Joe" ...  2058 NORTH W...  60647     12024    nan  DIGILOG ELEC...  1038 N ASHLA...  60622  
1 1     Proco "Joe" ...  2058 NORTH W...  60647     14446    743  EMPTY BOTTLE...  1035 N WESTE...  60622  
2 1     Proco "Joe" ...  2058 NORTH W...  60647     14624    775  LITTLE MEL'S...  2205 N CALIF...  60647  
3 1     Proco "Joe" ...  2058 NORTH W...  60647     14987    nan  MR. BROWN'S ...  2301 W CHICA...  60622  
4 1     Proco "Joe" ...  2058 NORTH W...  60647     15642    814  Beat Kitchen     2000-2100 W ...  60622

One-to-many example

print(wards.shape)

(50, 4)

print(ward_licenses.shape)

(10000, 9)

Let's practice!

Joining Data with pandas