Скачать или смотреть Combining Two DataFrames in Pandas on Multiple Columns with Mismatched Values in One Column

Learn how to effectively merge two Pandas DataFrames with non-matching values in a specific column. This guide breaks down the process and provides clear examples for easy understanding.
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Merging Two DataFrames in Pandas: A Challenge with Mismatched Values

When working with data, it's common to encounter the need to combine two DataFrames. However, this task can become complicated when one of the columns you want to merge on contains non-matching values. In this guide, we will explore how to effectively merge two DataFrames in Pandas, even when faced with such challenges.

The DataFrames

Let’s consider two DataFrames for our example:

DataFrame 1 (df1)

timelatlonlevval111010110DataFrame 2 (df2)

timelatlonlevval211010220As you can see, while both DataFrames have the same time, lat, and lon values, the lev column contains different entries. This discrepancy can pose a challenge when attempting to join the two DataFrames.

The Desired Output

Our goal is to merge these two DataFrames into a single DataFrame (df_total) that looks like this:

timelatlonlevval1val211010110NaN110102NaN20Notice how the values from lev do not overlap, and we have unspecified entries represented as NaN (Not a Number). So, how can we accomplish this in Pandas?

The Solution

To achieve this, we can utilize the pd.concat() method in Pandas. Here’s how it's done step-by-step:

Step 1: Import Pandas

Make sure you have Pandas installed in your environment. Import it as follows:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Create the DataFrames

Define your DataFrames as specified:

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Concatenate the DataFrames

Now, to merge the two DataFrames, use the following code:

[[See Video to Reveal this Text or Code Snippet]]

This line of code combines both DataFrames while ignoring the index, effectively stacking them on top of each other.

Step 4: Checking the Output

You can now check the contents of df_total to see the result:

[[See Video to Reveal this Text or Code Snippet]]

The output will be:

timelatlonlevval1val211010110NaN110102NaN20Conclusion

By utilizing the pd.concat() method, we successfully merged two DataFrames in Pandas, even with non-matching values in one of the target columns. This method allows us to maintain the integrity of our data while achieving the desired output format.

Whether you're a beginner or an experienced data analyst, mastering DataFrame merging techniques in Pandas will elevate your data manipulation skills. Happy coding!