尝试一下。我无法加载您的DF。

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

在这里您可以看到解决方案https://stackoverflow.com/a/26577689/12322720

基本上，str.contains支持正则表达式，因此您可以使用or或管道连接

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

我目前正在自己​​学习更多熊猫，所以我想贡献我刚从book学到的答案。

可以使用Pandas系列创建“蒙版”，并使用它来过滤数据框。

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","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`

#

掩码搜索方式明显更快。