[Solved] Key of Type tuple not found and not a multiindex

You might have got the “key of type tuple not found and not a multiindex” error while working on a Pandas data frame in Python. Go through this blog to know how you can resolve this error.

About the error

The “key of type tuple not found and not a multiindex” is an error that occurs while you are working on a data frame and trying to access a column through a tuple. Moreover, in this case, multi-indexing doesn’t exist. Thus, a column that has been single-indexed can’t be accessed through a tuple.

What is multi-indexing?

Multi-indexing helps you create a hierarchy. It helps in efficient data analysis and provides specialized indices. It enables grouping and segregation of data at the same time.

product_names = ['Product A', 'Product B', 'Product C']
regions = ['North America', 'Europe', 'Asia']
years = [2021, 2022, 2023]

multi_index = pd.MultiIndex.from_arrays([product_names, regions, years])

Example of error

Let us see when this error occurs using the example given below. Here, no multi-level indexing is present. We are trying to access the element using a tuple; therefore, we will get the “key of type tuple not found and not a multiindex” error.

import pandas as pd

# Create a dataframe with a regular index
data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
df = pd.DataFrame(data)

# Attempt to access a column using a tuple as the key
result = df[('A', 'B')]

Methods of resolving the error

If you are trying to obtain a value in the data frame using a tuple as a key, the key should be a tuple. It should not be single-indexed.

  • [] The accessor may not always fetch the correct value. It is better to use a loc accessor.
  • Check if multi-indexing exists in the dataframe.
  • Use quotation marks with column names.
  • Check if proper indexing exists in the dataframe.

Use loc to access tuple as a key

Some sort of hierarchy exists in multi-indexing. You can use the loc accessor to access multiple indices at one point in time. An example given below demonstrates how you can use loc to access the multi-indexed elements.

Here, the tuple comprises X and A. Multi-indexing happens here. At the first level, index X is accessed, followed by the value at A. Thus, two levels exist- the index level and then the column level.

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, index=['X', 'Y', 'Z'])
df.loc[('X', 'A')]  

Therefore, the output will be 1.

Change to Regular Index

The reset_index() method can help you change the multi-index to a regular index if you want to access the columns through a string and not a tuple. This is fine in cases where you have a dataset that supports multi-indexing, but you can remove it easily.

Let’s understand this with an example of multi-indexed data.

import pandas as pd

# Create a DataFrame with a MultiIndex
data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
index = pd.MultiIndex.from_tuples([('X', '1'), ('X', '2'), ('Y', '1')], names=('level1', 'level2'))
df = pd.DataFrame(data, index=index)

# Convert the MultiIndex to a regular index, dropping the 'level1' level
df = df.reset_index(level='level1')

# Access columns using strings as keys
print(df['A'])  
print(df['B']) 

In the first place, you can create a data frame with the given index values. This example follows multi-indexing. It specifies two levels: level 1 and level 2 for the given data. So, the given values will be segregated in this manner:

level1level2AB
X114
X225
Y136

After the grouping into levels is done, you can use the reset_index() method to drop the levels. You can specify the level, too. You can now access the values with the help of strings. Therefore, the output of this code will be: [1 2 3][4 5 6]

Other tips while working with multi-indexing

Multi-indexing in pandas is a fairly easy concept. Check out some tips to do it effortlessly.

  • In order to manipulate the data well, you should develop the hierarchy properly.
  • Some accessors like loc help in easy indexing.
  • If you change your mind regarding the multi-indexed dataset, use reset_index() to change it to a regular index.
  • fillna() or dropna() can help you to handle missing values. Sometimes, multi-indexed data can cause problems.

FAQs

What type of column names are suggested in multi-indexed columns?

The Python community suggests using descriptive column names that are easy to remember.

Conclusion

This article elaborates the reason for the ‘key of type tuple not found and not a multiindex’ error. It enlists ways through which you can resolve this error in Python. Besides, you can follow some tips when you are working on multi-indexed columns.

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