Python is an unquestionably powerful language. And one of its strongest points comes from the fact that it’s so easy to expand its capabilities with 3rd party libraries like NumPy and Pandas. Both of these libraries, alone or in tandom, add advanced mathematical capabilities to Python. But this can come with some unanticipated difficulties.
Powerful additions to Python can significantly change the coding experience. And it can bring about some equally significant and obtuse errors. For example, “valueerror: length of values does not match length of index” might seem perplexing at first glance. But it’s easier to fix than you might think once you consider how different Python libraries work together.
Taking a Closer Look at the Underlying Issues
The error message can seem intimidating at first. In large part due to the fact that it’s often a little ambiguous about the specific cause of the problem. However, the main point to focus on is the fact that it’s a ValueError. This error relates to the nature of the values in multiple containers. It usually comes down to a conflict between the format of a NumPy array and a Pandas DataFrame.
Arrays and DataFrames
The Python error is somewhat similar to a hypothetical situation where you have two containers which held a specific number of oranges. But one container is longer than the other. Even if you had five oranges in each container you wouldn’t be able to properly stack them due to the fundamental differences in the container’s dimensions. Properly stacking the containers for shipment would require either moving the oranges to a new container or modifying one container to properly fit the other. And this is the easiest way to fix a conflict between two Python containers as well.
How To Fix the ValueError
One of the major problems when using multiple 3rd party libraries together is that they don’t always work together seamlessly. A library working in isolation might provide automated error correction which isn’t present when combining it with another library. And this is the case with the ValueError arising from use of a NumPy array and Pandas DataFrame. The easiest solution is to simply stick with a single container format. For example, consider the following Python code.
import pandas as pn
import numpy as np
dafr = pn.DataFrame({‘a’: [1, 2, 3, 4],’b’: [5, 6, 7, 8]})
df[‘c’] = np.array([9, 10, 11])
print(dafr)
You’ll see the familiar ValueError appear as Python attempts to work with a different sized array and DataFrame. You can get past that problem by simply using a Pandas series to create a new column. Pandas will use automatic error correction to correct for the size difference. This is something it has less leeway with when working with different containers. For example, consider this implementation of a similar idea using a Pandas series to replicate the data that was initially in the NumPy array.
import pandas as pn
import numpy as np
dafr = pn.DataFrame({‘a’: [1, 2, 3, 4],’b’: [5, 6, 7, 8]})
dafr[‘c’] = pn.Series([9, 10, 11])
print(dafr)
This time around Python returns all of the expected values without an error message. However, there is one element that might come as a surprise. You’ll see a NaN value at the end of the printed data. NaN (Not a Number) is the missing value that was causing a problem when formatted as an array. In this example Pandas was able to automatically correct for the discrepancy in a way that it wouldn’t when using a NumPy array.
You’ll generally want an actual number in the position currently held by the NaN. This is easily taken care of with Pandas fillna() method. This can change any NaN to any value. The following example will use fillna() as the finishing touch when compensating for the size differences.
import pandas as pn
import numpy as np
dafr = pn.DataFrame({‘a’: [1, 2, 3, 4],’b’: [5, 6, 7, 8]})
dafr[‘c’] = pn.Series([9, 10, 11])
dafr = dafr.fillna(0)
print(dafr)
At this point the returned values are neat, organized, and ready to work with any other code. Of course, this isn’t the only possible solution. The previous example can be thought of as a foundation to build upon. You might want to modify the fillna() to pass values dependent on different situations. Or you might want to automate the conversion of an array to a series. But this method of dealing with the ValueError gives you room to build upon in any number of ways that matches your needs.