Type error column is not iterable

What is a TypeError?

A TypeError is when you try to use a value with the wrong data type. If you try to use a variable that doesn’t exist, you’ll also get a TypeError.

Why Does This Error Occur?

The “typeerror column is not iterable” error occurs when you try to iterate over a column that is not iterable. This can happen when you try to use a for loop or a list comprehension on a dataframe column that is not numerical.

To fix this error, you will need to use the apply() method to convert the column into a numerical data type. You can also use the astype() method to convert the column into a numeric data type.

How to Fix a TypeError

The “TypeError: column is not iterable” error is raised when you try to iterate over a column in a dataframe that is not iterable. This can happen when the column is not a list or tuple. To fix this error, you need to convert the column to a list or tuple. You can do this by using the tolist() or tuple() methods.

Method 1: Use the built-in isinstance() function

The Python built-in function isinstance() can be used to check if an object is of a certain data type.

Here’s an example:

x = [1,2,3]

if isinstance(x, list):
    print("x is a list")
else:
    print("x is not a list")

If you run the code above, you’ll see that it prints “x is a list” to the console. This is because the variable x is indeed a list.

If we change the value of x to something that’s not a list, like a string, we’ll get different results:

x = "Hello World"

if isinstance(x, list):
    print("x is a list")     #this line won't execute because x isn't a list 
else:                        #this line will execute instead  
    print("x is not a list")  #prints: "x is not a list"

As you can see, the code above correctly prints "x is not a list". This happens because the built-in function isinstance() correctly identified that x was not of type list.

You can use this same technique to fix the TypeError: column is not iterable. Simply check if the column you’re trying to iterate over is indeed a list using the built-in function isinstance(). If it’s not, convert it into a list using the built-in functionlist().

For example, let’s say you have the following dataset:

datatable = [[“Country”, “Capital”, “Population”], #List of lists (columns) # df.ix[[0],:] # df[0] # df.ix[:,0] # df[“Country”] ==> All give same results [ [“USA”, “Washington D.C.”, 328_239_523], [“Canada”, “Ottawa”, 37_041_262], [“Mexico”, “Mexico City”, 126_177_743]]] for country in datatable[0]: print(country) Country Capital Population What would happen if we accidentally iterated over datatable[0], instead of datatable?

We would get an error! Take a look at what happens when we accidentally iterate over datatable[0] instead of datatable:

python #df.ix[[0],:] ==> Gives results without any errors for country in datatable[0]: Country Capital Population TypeError: 'str' object is not iterable As you can see from the code above and the resulting error message, we get a TypeError because column 0 in our dataset (which contains strings) cannot be iterated over. In order to fix this error, we need to convert column 0 into a list using the built-in functionlist().

Here’s how we would do that:

python countries = list(datatable[0]) for country in countries: Country Capital Population Now when we run our code, we don’t get any errors and we’re able to successfully iterate over our dataset!

Method 2: Use the built-in issubclass() function

If you have a TypeError that says “typeerror column is not iterable,” you can use the built-in issubclass() function to check whether the column is actually a list. If it isn’t, you can convert it to a list using the list() function. For example:

column = df[‘column’]
if not issubclass(type(column), list):
column = list(column)

Conclusion

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