Question

我试图选择“故事”列包含列表“ selected_words”中任何字符串的行。

我尝试了多个选项，包括isin和str.contains，但是我通常只会得到错误，否则会得到一个空的数据框。

df4=pd.read_csv("https://drive.google.com/file/d/1rwg8c2GmtqLeGGv1xm9w6kS98iqgd6vW/view?usp=sharing")
df4["story"] = df4["story"].astype(str) 
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']
#At this point I am lost as to what to do next

根据我尝试执行的操作，我得到的是空数据框或错误消息。

Answer 1

尝试一下。我无法加载您的DF。

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

Answer 2

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))]

Answer 3

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",
    "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`

#

掩码搜索方式明显更快。

选择Pandas DF行，包括字符串列表中的任何字符串

3 个答案: