Is there a way to more efficiently filter a result on a data frame without having to explicitly save it in a variable and then filter? For instance, in the code below I would like to add something to line 3 to be able to achieve either df1 or df2.
import pandas as pd
df=pd.DataFrame({'A' : [1,2,3,4,5], 'B' : [0,0,0,0,0]})
df=df.var()
df1 = df[df!=0]
df2 = df[df > 3]
If you want to filter Pandas Series "on the fly", you can use .loc[] indexer in conjunction with the callable method (for example using lambda function).
Demo:
In [8]: df.var()
Out[8]:
A 2.5
B 0.0
dtype: float64
In [9]: df.var().loc[lambda ser: ser!=0]
Out[9]:
A 2.5
dtype: float64
In [10]: df.var().loc[lambda ser: ser>3]
Out[10]: Series([], dtype: float64)
If you want to filter a DataFrame, then you can use DF.query(...) method:
In [11]: df
Out[11]:
A B
0 1 0
1 2 0
2 3 0
3 4 0
4 5 0
In [12]: df.query("A >= 3")
Out[12]:
A B
2 3 0
3 4 0
4 5 0
I believe you are looking for this:
import pandas as pd
df=pd.DataFrame({'A' : [1,2,3,4,5], 'B' : [0,0,0,0,0]})
df1 = df[df['B'] != 0]
df2 = df[df['A'] > 3]
Depending on what you are trying to accomplish, you can modify this to meet your needs.
