从 Dataframe Pandas 中的句子中计算最常见的 100 个单词
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Count most frequent 100 words from sentences in Dataframe Pandas
提问by swati saoji
I have text reviews in one column in Pandas dataframe and I want to count the N-most frequent words with their frequency counts (in whole column - NOT in single cell). One approach is Counting the words using a counter, by iterating through each row. Is there a better alternative?
我在 Pandas 数据框中的一列中有文本评论，我想用频率计数来计算 N 个最频繁的单词（在整列中 - 而不是在单个单元格中）。一种方法是使用计数器通过迭代每一行来计算单词。有更好的选择吗？
Representative data.
代表性数据。
0 a heartening tale of small victories and endu
1 no sophomore slump for director sam mendes w
2 if you are an actor who can relate to the sea
3 it's this memory-as-identity obviation that g
4 boyd's screenplay ( co-written with guardian
回答by Joran Beasley
from collections import Counter
Counter(" ".join(df["text"]).split()).most_common(100)
im pretty sure would give you what you want (you might have to remove some non-words from the counter result before calling most_common)
我很确定会给你你想要的（你可能需要在调用 most_common 之前从计数器结果中删除一些非单词）
回答by Zero
Along with @Joran's solution you could also you use series.value_countsfor large amounts of text/rows
与@Joran 的解决方案一起，您还可以series.value_counts用于大量文本/行
pd.Series(' '.join(df['text']).lower().split()).value_counts()[:100]
You would find from the benchmarks series.value_countsseems twice (2X) faster than Countermethod
您会从基准测试中发现series.value_counts似乎比Counter方法快两倍 (2X)
For Movie Reviews dataset of 3000 rows, totaling 400K characters and 70k words.
对于 3000 行的电影评论数据集，总共 40 万个字符和 7 万个单词。
In [448]: %timeit Counter(" ".join(df.text).lower().split()).most_common(100)
10 loops, best of 3: 44.2 ms per loop
In [449]: %timeit pd.Series(' '.join(df.text).lower().split()).value_counts()[:100]
10 loops, best of 3: 27.1 ms per loop
