Prevent line duplication during merge

Question

long lurker, but the first poster on Stackoverflow.

I hit a wall with a data analysis project I'm working on.

Essentially, if I have a CSV 'A' example:

id   | item_num
A123 |     1
A123 |     2
B456 |     1

And I have an example CSV 'B':

id   | description
A123 | Mary had a...
A123 | ...little lamb.
B456 | ...Its fleece...

If I execute mergewith Pandas, it ends as follows:

id   | item_num | description
A123 |     1    | Mary had a...
A123 |     2    | Mary had a...
A123 |     1    | ...little lamb.
A123 |     2    | ...little lamb.
B456 |     1    | Its fleece...

How could I do this instead:

id   | item_num | description
A123 |     1    | Mary had a...
A123 |     2    | ...little lamb...
B456 |     1    | Its fleece...

This is my code:

import pandas as pd

# Import CSVs
first = pd.read_csv("../PATH_TO_CSV/A.csv")
print("Imported first CSV: " + str(first.shape))
second = pd.read_csv("../PATH_TO_CSV/B.csv")
print("Imported second CSV: " + str(second.shape))


# Create a resultant, but empty, DF, and then append the merge.
result = pd.DataFrame()
result = result.append(pd.merge(first, second), ignore_index = True)
print("Merged CSVs... resulting DataFrame is: " + str(result.shape))

# Lets do a "dedupe" to deal with an issue on how Pandas handles datetime merges
# I read about an issue where if datetime is involved, duplicate entires will be created.
result = result.drop_duplicates()
print("Deduping... resulting DataFrame is: " + str(result.shape))

# Save to another CSV
result.to_csv("EXPORT.csv", index=False)
print("Saved to file.")

I would really appreciate any help - I am very stuck! And I am dealing with 20,000 lines.

Thank.

Edit: my post has been flagged as a potential duplicate. This is not the case, since I'm not necessarily trying to add a column - I'm just trying to prevent multiplication descriptionby a number item_numthat is assigned to a specific one id.

---

UPDATE, 6/21:

, DF ?

id   | item_num | other_col
A123 |     1    | lorem ipsum
A123 |     2    | dolor sit
A123 |     3    | amet, consectetur
B456 |     1    | lorem ipsum

CSV 'B':

id   | item_num | description
A123 |     1    | Mary had a...
A123 |     2    | ...little lamb.
B456 |     1    | ...Its fleece...

, :

id   | item_num |  other_col  | description
A123 |     1    | lorem ipsum | Mary Had a...
A123 |     2    | dolor sit   | ...little lamb.
B456 |     1    | lorem ipsum | ...Its fleece...

, , 3, "amet, consectetur" "other_col", .

Answer 1

:

In [135]: result = A.merge(B.assign(item_num=B.groupby('id').cumcount()+1))

In [136]: result
Out[136]:
     id  item_num       description
0  A123         1     Mary had a...
1  A123         2   ...little lamb.
2  B456         1  ...Its fleece...

: "virtual" item_num B DF :

In [137]: B.assign(item_num=B.groupby('id').cumcount()+1)
Out[137]:
     id       description  item_num
0  A123     Mary had a...         1
1  A123   ...little lamb.         2
2  B456  ...Its fleece...         1

Answer 2

df, :

df = df.set_index(['id', 'item_num']).drop_duplicates()

Answer 3

, concat

result = pd.concat([df1.set_index('id'), df2.set_index('id')],axis = 1).reset_index()

    id      item_no     description
0   A123    1           Mary had a...
1   A123    2           ...little lamb
2   B456    1           ...Its fleece...