Given a dataframe with three columns of text blobs to search through, which can be found in this Gist.

And three keywords that I want to identify in this text:

branches_of_sci = ['bio', 'chem', 'physics']

I've written the following code to identify the presence of these keywords:

dfq_col = ['Text A', 'Text B', 'Text C']

for branch in branches_of_sci:
    for col in dfq_col:
        temp_list = []
        for row in df[col]:
            if type(row) is not str:
            elif type(row) is str:
        df[branch] |= temp_list

This is the result of the data I linked to:

table result

I think the main problem here is that I'm using a for-loop when I should be using some sort of dataframe-specific function, but I'm not sure how to restructure the code to accomplish this.

import pandas as pd

df = pd.read_clipboard(sep=',') # copied data from the gist

branches_of_sci = ['bio', 'chem', 'physics']

for branch in branches_of_sci:
    df[branch] = df.astype(str).sum(axis=1).str.contains(branch)

In my limited experience, for loops are almost always wrong when using Pandas. The primary benefit of Pandas is vectorization, so using the built-in methods is typically best.

Here is a breakdown of the main function:

  1. df[branch] creates a new dataframe column
  2. df.astype(str) converts all of the dtypes in the dataframe to strings
  3. .sum(axis=1) concatenates all dataframe columns horizontally (i.e. axis=1)
  4. .str.contains() use built-in string search (see docs)

Hopefully that helps.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.