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Efficient way to apply multiple filters to pandas DataFrame or Series
...numpy) allow for boolean indexing, which will be much more efficient:
In [11]: df.loc[df['col1'] >= 1, 'col1']
Out[11]:
1 1
2 2
Name: col1
In [12]: df[df['col1'] >= 1]
Out[12]:
col1 col2
1 1 11
2 2 12
In [13]: df[(df['col1'] >= 1) & (df['col1'] <=1 )]
Out...
mongodb group values by multiple fields
...addr": "address5", "book": "book1" }
{ "addr": "address77", "book": "book11" }
{ "addr": "address1", "book": "book1" }
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