Pandas syntax question

So when using Pandas I always get confused about one part of the syntax.

Whenever I need to get info from a dataframe I usually assign a variable by doing:

variable = df[value]

although when a bool is needed I often do this:

variable = df[value] == “value”]
While it needs to be:
variable = df[df[value] == “value”]

can someone explain to me why it needs to be the ladder? I can kinda guess that it’s because the first df specifies that it’s in that dataframe, but why is it repeated? wouldn’t
variable = df[value] == “value”]
be the same thing?

Regardless I just need a little better of an explanation of how it all works together so I don’t keep having to fix that error in my code lol.

Hi @foxtrot.a.t , welcome to the community!

When you do variable = df[value] you assign an entire column from the DataFrame to a variable.

When you do df[value] == “value” you are not filtering the DataFrame, but creating a boolean mask, which assigns boolean values for each row based on the condition you established.

If you display the output of the boolean maks, you’ll see something like this:

0       False
1       False
2       True
3       True
4       False

Now, if want to actually filter your DataFrame and come up with a new DataFrame with only the rows that match your criteria, then you have to apply this mask to the DataFrame by doing df[df[value] == “value”]. Note that the first df is the DataFrame that is being filtered itself while the second one is just part of the boolean mask.

A better way to visualize this is to separate the process like this:

mask = df[value] == “value”
filtered_df = df[mask]

I hope this helps you.

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This helps great! Thank you!

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