Third-party libraries are one of the best things about Python. The programming language brings a lot of powerful options even in its default state. But third-party options like Pandas and NumPy open up a wide variety of advanced mathematical functions. Even better, those options are fully integrated into Python’s easy-to-use syntax. However, there are instances where quirks within these libraries can lead to some confusing error messages. And the “valueerror: cannot reindex from a duplicate axis” error is one of the more complex examples. However, solving this problem is made considerably easier by understanding exactly what it means.
The Value Error’s Basics
The exact cause of a “valueerror: cannot reindex from a duplicate axis” can vary considerably on a case-by-case basis. However, the core of the problem comes down to an attempt to work with a Pandas dataframe that contains duplicate values as an index. This type of structure isn’t inherently problematic. However, it limits your ability to perform some operations, such as rendering or resampling.
A Deeper Look Into the Value Error
This valueerror is fairly descriptive. But as is often the case, it can be a little misleading underneath that veneer of simplicity. One of the larger questions surrounding this valueerror is the entire concept of data types which can be both valid and invalid at the same time. It’s hard to image, for example, an integer that didn’t support addition operations. Why can a valid dataframe support some operations but not others?
The answer comes down to what the Pandas developers wanted for the language. Pandas is intended to be a library that can work with any real-world data. This means information that isn’t specifically created for digital storage. Pandas should be able to clean up any of the random collections of information that exist in the world. And this ultimately means being able to import messy collections of data. Such as, in this case, dataframes that might not be ready for full integration into a larger codebase.
The “valueerror: cannot reindex from a duplicate axis” error message is essentially a requirement for Pandas if it’s going to be able to handle messy data sets. This is also why it’s generally a good idea to automate some data validation in your codebase. You can’t always assume that an interpreter or modules will force your code to do things in a specific way. However, the positive flip side to Pandas design choices is that the library also provides a wide variety of tools to validate and fix data.
How To Fix the Value Error
One of the more difficult aspects of this particular error is that it typically springs from automatically generated data. You’ll typically see it when importing large data sets from multiple databases, excel, etc. If you’re not looking at an immense data set then you can often implement a fairly quick fix by simply looking for duplicate index values in your dataframe. Basically, you just need to manually fix any instance of index duplication.
However, if you can’t manually look through the data then you’re still in luck. As previously noted, Pandas works off an assumption that you may need to perform automated validation and cleanup on your datasets. You might be envisioning elaborate loops to manually work through everything. But it can be as simple as a single line of Python code. Consider the following example.
import pandas as pd
df = pd.DataFrame({
‘state’: [‘Alabama’, ‘Alabama’, ‘Alaska’, ‘Arizona’, ‘Arkansas’],
‘capital’: [‘Montgomery’, ‘Montgomery’, ‘Juneau’, ‘Phoenix’, ‘Little Rock’],
‘year’: [1819, 1819, 1959, 1912, 1836]
}, index =[‘alpha’,’alpha’,’beta’,’gamma’,’delta’])
df.reindex([‘State 1′,’State 1′,’State 2′,’State 3′,’State 4’])
Running this Python code will generate the “valueerror: cannot reindex from a duplicate axis” Python error. You’ll note that there’s no issue with actually creating a dataframe with a non-unique index. What raises the error is our attempt to reindex it. But this also produces something of a contradiction. How can we work with a dataframe if we can’t predict how it’ll interact with the larger environment? Thankfully, this is where the various tools in Pandas come in. Take a look at the following code.
import pandas as pd
df = pd.DataFrame({
‘state’: [‘Alabama’, ‘Alabama’, ‘Alaska’, ‘Arizona’, ‘Arkansas’],
‘capital’: [‘Montgomery’, ‘Montgomery’, ‘Juneau’, ‘Phoenix’, ‘Little Rock’],
‘year’: [1819, 1819, 1959, 1912, 1836]
}, index =[‘alpha’,’alpha’,’beta’,’gamma’,’delta’])
print(df)
print(df.index.is_unique)
df = df.drop_duplicates()
print(df)
print(df.index.is_unique)
df.reindex([‘State 1′,’State 1′,’State 2′,’State 3′,’State 4’])
print(df)
print(df.index.is_unique)
This isn’t the tidiest way to fix a dataframe. But we’re working through the process in a slower manner to illustrate some of the options available with Pandas.
We start out in a similar way to the previous code example. After creating the dataframe as df we print it to the screen. This is where we first see that there are duplicates. Of course, if this was a huge mass of data we wouldn’t be able to see this just by glancing at a data dump. This is where the next line comes in. With a larger data set, we’d want to have Pandas verify that the index is unique. The result of index.is_unique is printed on the screen. And in this case, as expected, it comes out as false because the index isn’t unique.
The code then uses the Pandas drop_duplicates and assigns the newly cleaned dataframe back to df. We proceed to once again print df to the screen. Things look a lot cleaner now and the redundancy in our dataframe is gone. Python’s interpreter proceeds to test df for a unique index again. This time around, it returns true. In theory, this should fix the “valueerror: cannot reindex from a duplicate axis” error.
We proceed to actually test that theory and run the reindex. It now proceeds cleanly and without the error message. The code proceeds to print out df to the screen again. You’ll note that the index has been replaced thanks to the reindex. Finally, we verify that the index is unique for one last time and print the results to the screen.
This process is obviously filled with redundancies. However, this code demonstrates every step of the process to check, fix, and verify your dataframe. This can be easily wrapped into a custom function for your own projects as needed. For example, you might run dataframes through a conditional that checks and fixes them after an initial import from a csv file or the like.