Pandas - weird renaming columns problem (python implicitly typecasts DataFrame to Series)

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#1

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… :confused:

#2

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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#3

To rename cols in a DF:

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#4

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?

#5

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)

#6

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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#7

That is odd. It’s probably gremlins or something. :slight_smile:

Is this project in the python 3 course or elsewhere?

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#8

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. :space_invader: :space_invader: :space_invader:

https://www.codecademy.com/learn/paths/visualize-data-with-python

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#9

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 :space_invader: :slightly_smiling_face:

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