Python 如何根据值计数过滤 Pandas DataFrame？
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How do I filter a pandas DataFrame based on value counts?
提问by uchuujin
I'm working in Python with a pandas DataFrame of video games, each with a genre. I'm trying to remove any video game with a genre that appears less than some number of times in the DataFrame, but I have no clue how to go about this. I did find a StackOverflow questionthat seems to be related, but I can't decipher the solution at all (possibly because I've never heard of R and my memory of functional programming is rusty at best).
我正在 Python 中使用 Pandas DataFrame 的视频游戏，每个游戏都有一个流派。我正在尝试删除任何类型在 DataFrame 中出现次数少于一定次数的视频游戏，但我不知道如何解决这个问题。我确实找到了一个似乎相关的 StackOverflow 问题，但我根本无法破译解决方案（可能是因为我从未听说过 R 并且我对函数式编程的记忆充其量是生疏的）。
Help?
帮助？
采纳答案by Andy Hayden
Use groupby filter:
使用groupby 过滤器：
In [11]: df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B'])
In [12]: df
Out[12]:
A B
0 1 2
1 1 4
2 5 6
In [13]: df.groupby("A").filter(lambda x: len(x) > 1)
Out[13]:
A B
0 1 2
1 1 4
I recommend reading the split-combine-section of the docs.
我建议阅读文档的split-combine-section。
回答by jezrael
Solutions with better performance should be GroupBy.transformwith sizefor count per groups to Series with same size like original df, so possible filter by boolean indexing:
性能更好的解决方案应该是GroupBy.transform与size每团体计数系列与像原来一样大小df的，所以可能的过滤器boolean indexing：
df1 = df[df.groupby("A")['A'].transform('size') > 1]
Or use Series.mapwith Series.value_counts:
或者使用Series.map具有Series.value_counts：
df1 = df[df['A'].map(df['A'].value_counts()) > 1]
