This is in the Life Expectancy and GDP project, but it’s really a question about Jupyter Notebook or Python itself.
I was trying to rename a column of a Dataframe, but for some reason, Jupyter was implicitly type-casting the Dataframe to a Series object, whose rename method does not have a “columns” keyword. So I keep getting this error about the rename() method not taking a column keyword, which was making me go insane because all the documentation said that it does. But I eventually found a random comment on stackoverflow that clued me in to checking the type. And lo and behold, the Dataframe was a series.
The problem is that I have no idea why it was happening. I fixed it by running the code a few more times and clicking different cells before hitting run.
The type() function was saying Series, but then suddenly switched to Dataframe and it worked. I’m so confused… 
Can you post the code that you’re referring to?
The differences between a Series and a DF in Pandas:
https://www.geeksforgeeks.org/creating-a-dataframe-from-pandas-series/
Think of a series as a column (of any data type) in a google sheet (or Excel).
You can create a df from a couple series:
https://www.geeksforgeeks.org/combine-two-pandas-series-into-a-dataframe/
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Thanks for your reply. My problem is actually much more specific.
This is my code after the import statements. The only significant code are the first and last lines.
df = pd.read_csv("all_data.csv")
print(df.head())
print(type(df))
print(df["Country"].unique())
print(df["Year"].unique())
print(df.head())
**print(type(df))**
df.rename(columns = {'Life expectancy at birth (years)': 'LEABY'}, inplace=True)
The last line was throwing an error because it said a Series object has no rename() method with a columns keyword parameter. The bolded print statement actually returned a Series object for a few minutes. I tried running the cell, running all the cells, and it somehow switched to being a Dataframe again.
Does what I’m trying to say make any more sense?
Did it work or is it still throwing an error? Maybe it’s just a hiccup w/Jupyter or something…
To get the data types of the cols in a dataframe:
print(df.dtypes)
to get the dtype of a single col:
print(df1['Score'].dtypes)
It’s actually working now. That’s the part that has me so confused. I wasted a bunch of time looking for what I was doing wrong, but in the end, I just tried running it a few ways and suddenly it began working. And I saw the print statement change from <class ‘pandas.core.series.Series’> to <class ‘pandas.core.frame.DataFrame’>
Yeah, I figured it might be an issue with Jupyter, but I didn’t know if maybe I was causing the issue because I just don’t know how to use Jupyter correctly. It’s all good now so maybe I should just move on and forget about it, but I wanted to see if anyone else ever experienced something like this and had a better answer so I can avoid it in the future.
Thanks for your replies. I appreciate you taking the time
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That is odd. It’s probably gremlins or something. 
Is this project in the python 3 course or elsewhere?
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It is one of the capstone projects in Visualize Data with Python
The project isn’t actually very difficult at all. Just gremlins. We’ll blame the gremlins. 
https://www.codecademy.com/learn/paths/visualize-data-with-python
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For what it’s worth, my educated guess is that a cell got run out of order somewhere, the result of which was the DataFrame being reduced to a single column, which Pandas then automatically converts into a Series type. Whenever something weird like that happens in a Jupyter notebook, just restart the kernel, clear all cell output and then run through them in order from the top. That typically either removes or exposes the gremlins 
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