Select Pandas DF Rows including any string in a list of strings

Try this. I'm unable to load your DF.

df4[df4["story"].isin(selected_words)]

Here you can see a solution https://stackoverflow.com/a/26577689/12322720

Basically str.contains supports Regular Expressions so you can concatenate with an or pipe

df4[df4.story.str.contains('|'.join(selected_words))]

I'm currently learning more Pandas myself so I wanted to contribute an answer I just learned from a book.

It's possible to create a "mask" using a Pandas Series and use that to filter the Dataframe.

import pandas as pd

# This URL doesn't return CSV.
CSV_URL = "https://drive.google.com/open?id=1rwg8c2GmtqLeGGv1xm9w6kS98iqgd6vW"
# Data file saved from within a browser to help with question.

# I stored the BitcoinData.csv data on my Minio server.
df = pd.read_csv("https://minio.apps.selfip.com/mymedia/csv/BitcoinData.csv")


selected_words = [
    "accept",
    "believe",
    "trust",
    "accepted",
    "accepts",
    "trusts",
    "believes",
    "acceptance",
    "trusted",
    "trusting",
    "accepting",
    "believes",
    "believing",
    "believed",
    "normal",
    "normalize",
    " normalized",
    "routine",
    "belief",
    "faith",
    "confidence",
    "adoption",
    "adopt",
    "adopted",
    "embrace",
    "approve",
    "approval",
    "approved",
    "approves",
]

# %%timeit run in Jupyter notebook

mask = pd.Series(any(word in item for word in selected_words) for item in df["story"])

# results 18.2 ms ± 94.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

# %%timeit run in Jupyter notebook

df[mask]

# results: 955 µs ± 6.74 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)


# %%timeit run in Jupyter notebook

df[df.story.str.contains('|'.join(selected_words))]

# results 129 ms ± 738 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

# True for all
df[mask] == df[df.story.str.contains('|'.join(selected_words))]

# It is possible to calculate the mask inside of the index operation though of course a time penalty is taken rather than using the calculated and stored mask.

# %%timeit run in Jupyter notebook

df[[any(word in item for word in selected_words) for item in df["story"]]]

# results 18.2 ms ± 94.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

# This is still faster than using the alternative `df.story.str.contains`

#

The mask way of searching is significantly faster.