Home/ Questions/Q 10392
Next
In Process

askthedev.com Latest Questions

Asked: 2024-09-26T03:30:38+05:30 In:

How can I merge two pandas DataFrames in Python while keeping all the data from both, even if some entries don’t match?

I’ve been having a bit of a struggle with merging two pandas DataFrames in Python and could really use some help from you all. So here’s the deal: I’ve got these two DataFrames, let’s call them `df1` and `df2`, and they contain some similar columns, but not all of them match perfectly.

`df1` has info on various products like this:

“`
ProductID Name Category
0 1 T-shirt Clothes
1 2 Sneakers Shoes
2 3 Backpack Accessories
“`

And then `df2` has sales data, which looks something like this:

“`
ProductID Sales Date
0 1 200 2023-01-01
1 2 150 2023-01-02
2 4 300 2023-01-03
“`

As you can see, the `ProductID` is the common key, but `df2` has a `ProductID` (4) that doesn’t exist in `df1`. Ideally, what I want is to merge these two DataFrames in such a way that I keep all the entries from `df1` and `df2`, regardless of whether they have matching `ProductID`s.

I’ve tried using the `merge()` function with different ‘how’ parameters like ‘left’ and ‘inner’, but they just aren’t cutting it for my needs because they end up dropping some data that I actually want to keep. I don’t want to lose any information, so ‘outer’ seems like the way to go, but I’m not sure if I’m setting it up correctly or if there’s something I’m missing.

Can someone help me with the right way to do this? Maybe some example code or tips on how to use `merge()` effectively? I really want to combine these DataFrames but maintain all the data from both, even if some `ProductID`s don’t match. Would love to hear your suggestions! Thanks!

Share

    Leave an answer

    You must login to add an answer.

    Need An Account,

    2 Answers



    1. Merging DataFrames in Pandas

      Merging Two Pandas DataFrames

      It sounds like you’re on the right track wanting to use the merge() function! If you want to keep all the entries from both DataFrames, you definitely need to go for the ‘outer’ merge. This will include all rows from both DataFrames, and where there are no matches, you’ll get NaN for the missing values.

      Here’s a little code snippet to help you out: 

      
import pandas as pd

# Sample DataFrames
df1 = pd.DataFrame({
    'ProductID': [1, 2, 3],
    'Name': ['T-shirt', 'Sneakers', 'Backpack'],
    'Category': ['Clothes', 'Shoes', 'Accessories']
})

df2 = pd.DataFrame({
    'ProductID': [1, 2, 4],
    'Sales': [200, 150, 300],
    'Date': ['2023-01-01', '2023-01-02', '2023-01-03']
})

# Merging DataFrames
merged_df = pd.merge(df1, df2, on='ProductID', how='outer')

print(merged_df)

      In this code:

      • on='ProductID' tells pandas which column to use as the key to merge the DataFrames.
      • how='outer' ensures you keep all rows from both DataFrames.

      After running the above code, your merged_df DataFrame should look something like this: 

      
   ProductID      Name   Category  Sales        Date
0          1   T-shirt     Clothes  200.0  2023-01-01
1          2  Sneakers      Shoes  150.0  2023-01-02
2          3   Backpack  Accessories    NaN         NaN
3          4       NaN       NaN  300.0  2023-01-03

      See how it keeps all entries, putting NaN where there’s no matching data? This way, you won’t lose any information. Give it a try and see if it works for you!


    2. “`html

      To merge your two DataFrames, `df1` and `df2`, while preserving all entries from both, you should indeed use the `merge()` function with the ‘outer’ join option. This will ensure that all rows from both DataFrames are included in the final result, with NaN values filling in for those missing from either DataFrame. Here’s how you can do it:

      import pandas as pd

# Sample data
df1 = pd.DataFrame({
    'ProductID': [1, 2, 3],
    'Name': ['T-shirt', 'Sneakers', 'Backpack'],
    'Category': ['Clothes', 'Shoes', 'Accessories']
})

df2 = pd.DataFrame({
    'ProductID': [1, 2, 4],
    'Sales': [200, 150, 300],
    'Date': ['2023-01-01', '2023-01-02', '2023-01-03']
})

# Merging the two DataFrames
merged_df = pd.merge(df1, df2, on='ProductID', how='outer')

print(merged_df)

      Running the above code will yield a DataFrame that combines the data from `df1` and `df2`, like so:

         ProductID     Name   Category  Sales        Date
0          1   T-shirt     Clothes  200.0  2023-01-01
1          2  Sneakers       Shoes  150.0  2023-01-02
2          3   Backpack  Accessories    NaN         NaN
3          4        NaN        NaN  300.0  2023-01-03

      This output shows that `ProductID`s 1 and 2 from both DataFrames have been matched, while `ProductID` 3 is from `df1` and has no corresponding entry in `df2`, resulting in NaNs, and similarly for `ProductID` 4 from `df2`. This way, you ensure no data is lost in the merge process.

      “`

    Related Questions