How to access a row in a pandas dataframe with custom index labels?-CodePudding

I'm trying to create an Inverted File index to hold TF-IDF for my vocabulary, and I've created a pandas dataframe to hold the data like so:

TF_IDF = pd.DataFrame(index = vocab)
TF_IDF['0'] = 0

so when I print it, it looks like this:

           0
life       0
math       0
student    0
experi     0
control    0
...       ..
slave      0
linga      0
31-32      0
democrat   0
unsustain  0

how would I access these custom rows? am I able to do something like TF_IDF["life","1_1"] to reference row "life" in column "1_1"

(I have tried a few versions of this but none seem to work)

CodePudding user response：

TF_IDF['0']['life'] will get you there

Instead of removing the question, I'll leave it up, just in case someone else also cant find the answer anywhere else online

CodePudding user response：

You should use .loc[] rather than chained indexing (as in your answer) as it's more efficient, and chained indexing can sometimes raise a SettingWithCopy warning (read more here).

To use .loc, you would call it like below:

df.loc[row_index(es), col_names]

Which would be the below in your example:

TF_IDF.loc['life', '0']

returns: