pandas: merged (inner join) data frame has more rows than the original ones

Question

I am using python 3.4 on Jupyter Notebook, trying to merge two data frame like below:

df_A.shape
(204479, 2)

df_B.shape
(178, 3)

new_df = pd.merge(df_A, df_B,  how='inner', on='my_icon_number')
new_df.shape
(266788, 4)

I thought the new_df merged above should have few rows than df_A since merge is like an inner join. But why new_df here actually has more rows than df_A?

Here is what I actually want:

my df_A is like:

 id           my_icon_number
-----------------------------
 A1             123             
 B1             234
 C1             123
 D1             235
 E1             235
 F1             400

and my df_B is like:

my_icon_number    color      size
-------------------------------------
  123              blue      small
  234              red       large 
  235              yellow    medium

Then I want new_df to be:

 id           my_icon_number     color       size
--------------------------------------------------
 A1             123              blue        small
 B1             234              red         large
 C1             123              blue        small
 D1             235              yellow      medium
 E1             235              yellow      medium

I don't really want to remove duplicates of my_icon_number in df_A. Any idea what I missed here?

Answer 1

Because you have duplicates of the merge column in both data sets, you'll get k * m rows with that merge column value, where k is the number of rows with that value in data set 1 and m is the number of rows with that value in data set 2.

try drop_duplicates

dfa = df_A.drop_duplicates(subset=['my_icon_number'])
dfb = df_B.drop_duplicates(subset=['my_icon_number'])

new_df = pd.merge(dfa, dfb, how='inner', on='my_icon_number')

---

Example

In this example, the only value in common is 4 but I have it 3 times in each data set. That means I should get 9 total rows in the resulting merge, one for every combination.

df_A = pd.DataFrame(dict(my_icon_number=[1, 2, 3, 4, 4, 4], other_column1=range(6)))
df_B = pd.DataFrame(dict(my_icon_number=[4, 4, 4, 5, 6, 7], other_column2=range(6)))

pd.merge(df_A, df_B,  how='inner', on='my_icon_number')

   my_icon_number  other_column1  other_column2
0               4              3              0
1               4              3              1
2               4              3              2
3               4              4              0
4               4              4              1
5               4              4              2
6               4              5              0
7               4              5              1
8               4              5              2