Pandas 查找列值在数据集中出现的次数
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Pandas find how many times a column value appears in dataset
提问by if __name__ is None
I am trying to sort data by the Namecolumn, by popularity.
我正在尝试Name按受欢迎程度按列对数据进行排序。
Right now, I'm doing this:
现在，我正在这样做：
df['Count'] = df.apply(lambda x: len(df[df['Name'] == x['Name']]), axis=1)
df[df['Count'] > 50][['Name', 'Description', 'Count']].drop_duplicates('Name').sort_values('Count', ascending=False).head(100)
However this query is very slow, it takes hours to run.
但是这个查询很慢，需要几个小时才能运行。
What would be a more efficient way to do this?
什么是更有效的方法来做到这一点？
回答by if __name__ is None
The solution I have been looking for is:
我一直在寻找的解决方案是：
df['Count'] = df.groupby('Name')['Name'].transform('count')
Big thanks to @Lynob for providing a link with an answer.
非常感谢@Lynob 提供带有答案的链接。
回答by Alex
You can use Series.value_counts.
您可以使用Series.value_counts.
df = pd.DataFrame([[0, 1], [1, 0], [1, 1]], columns=['a', 'b'])
print(df['b'].value_counts())
outputs
输出
1 2
0 1
Name: b, dtype: int64
回答by Merlin
Try this:
尝试这个：
a = ["jim"]*5 + ["jane"]*10 + ["john"]*15
n = pd.Series(a)
sorted((n.value_counts()[n.value_counts() > 5]).index)
['jane', 'john']
