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[,:] # df # 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: print(country) Country Capital Population What would happen if we accidentally iterated over datatable, instead of datatable?
We would get an error! Take a look at what happens when we accidentally iterate over datatable instead of datatable:
python #df.ix[,:] ==> Gives results without any errors for country in datatable: 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) 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)
There are a few things to keep in mind when choosing the right roast for your coffee. The type of roast you choose will impact the flavor of your coffee, so be sure to select a roast that you think will taste good. Additionally, the roast you choose can also affect the caffeine level in your coffee. If you are looking for a high caffeine content, you may want to choose a light roast, as they tend to have higher levels of caffeine. Finally, keep in mind that the perfect roast is a personal choice, so don’t be afraid to experiment until you find the perfect one for you.