[英]Pandas most efficient way to filter dataframe based on groupby mask
I would like to filter a dataframe based on the values in that df's groupby
results on a column.我想根据列上 df 的
groupby
结果中的值过滤数据框。 For example, if I have a dataframe with columns := ticker, year, price
, I'd like to filter out of the df tickers whose first year is >= 1990.例如,如果我有一个包含
columns := ticker, year, price
的数据框,我想过滤掉第一年 >= 1990 的 df 代码。
or more technically where the ticker evaluates to True in df.groupby('ticker').['year'].min() < 1990
或者从技术上讲,代码在
df.groupby('ticker').['year'].min() < 1990
中评估为 True
I am currently doing it this way:我目前正在这样做:
ticker_min_date_bool = df.groupby('ticker')['year'].min() < 1990 # get booleans
tickers_filt = [i for i in ticker_min_date_bool.index if ticker_min_date_bool[i]] # make list of tickers with criteria
df_new = df[df.ticker.isin(tickers_filt)] # filter df based on above list
However this feels a little clumsy to do in 3 lines and doesn't seem to scale well for larger datasets.然而,这在 3 行中做起来感觉有点笨拙,而且似乎不能很好地扩展到更大的数据集。
Are there any dataframe methods that accomplish this more efficiently?是否有任何数据框方法可以更有效地完成此任务?
Just do transform
只做
transform
ticker_min_date_bool = df.groupby('ticker')['year'].transform('min') < 1990
df_new = df[ticker_min_date_bool]
Or without groupby
或者没有
groupby
s = df.loc[df['year']<1990,'ticker']
df_new = df[df['ticker'].isin(s)]
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