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This tutorial explores the concept of KeyError in Pandas.

What is Pandas KeyError?

While working with Pandas, analysts may encounter multiple errors thrown by the code interpreter. These errors are wide ranging and can help us better investigate the issue.

In this tutorial, we aim to gain a better understanding of the KeyError raised by Pandas, why it is raised, and potential ways to resolve it.

First, let's understand what this error means. A KeyError means that the key or element you tried to find in a DataFrame, or even a column in a DataFrame, does not exist.

This means that you are trying to query or find something which is not there in the way you expect it to be. In such cases, we have to face Pandas KeyError.

Analysts often face this error; it is ubiquitous in poorly formatted or poorly labeled DataFrames. Now, let’s understand why this error occurs.

However, before we do that, let's create a dummy DataFrame to work with. We'll call this DataFrame dat1.

Let us create this DataFrame using the following code.

import pandas as pd

dat1 = pd.DataFrame({"dat1": [9, 5]})

The above code creates a DataFrame with some entries namely 9and 5. To see the entries in the data, we use the following code.

print(dat1)

The above code gives the following output.

   dat1
0     9
1     5

As shown in the figure, we have 2 columns and 2 rows, one column represents the index and the second column represents the values ​​in the DataFrame.

Why do we encounter KeyError in Pandas?

Now let's replicate the error. We can do this using the following code.

print(dat1["Date"])

The code tries dat1to get Datea column named from the DataFrame which theoretically does not exist. Hence, we get the following output.

KeyError: 'Date'
Traceback (most recent call last)

So, we can see that if we access the DataFrame by a column name that does not exist, we may have to face a KeyError.

This error may occur because you may not have the entire DataFrame you are trying to reference. In such cases, a KeyError is thrown, making it difficult for the analyst to understand the exact reason behind it.

It is best to avoid this by checking all variable names before referencing or querying through theoretically non-existent data.

Hence, this tutorial taught us about the meaning, causes, and possible solutions of KeyError thrown in Pandas.