I have the below dataframe
In [62]: df Out[62]: coverage name reports year Cochice 45 Jason 4 2012 Pima 214 Molly 24 2012 Santa Cruz 212 Tina 31 2013 Maricopa 72 Jake 2 2014 Yuma 85 Amy 3 2014 Basically i can filter the rows as below
df[df["coverage"] > 30 and i can drop/delete a single row as below
df.drop(['Cochice', 'Pima']) But i want to delete a certain number of rows based on a condition, how can i do so?
The best is boolean indexing but need invert condition - get all values equal and higher as 72:
print (df[df["coverage"] >= 72]) coverage name reports year Pima 214 Molly 24 2012 Santa Cruz 212 Tina 31 2013 Maricopa 72 Jake 2 2014 Yuma 85 Amy 3 2014 It is same as ge function:
print (df[df["coverage"].ge(72)]) coverage name reports year Pima 214 Molly 24 2012 Santa Cruz 212 Tina 31 2013 Maricopa 72 Jake 2 2014 Yuma 85 Amy 3 2014 Another possible solution is invert mask by ~:
print (df["coverage"] < 72) Cochice True Pima False Santa Cruz False Maricopa False Yuma False Name: coverage, dtype: bool print (~(df["coverage"] < 72)) Cochice False Pima True Santa Cruz True Maricopa True Yuma True Name: coverage, dtype: bool print (df[~(df["coverage"] < 72)]) coverage name reports year Pima 214 Molly 24 2012 Santa Cruz 212 Tina 31 2013 Maricopa 72 Jake 2 2014 Yuma 85 Amy 3 2014 we can use pandas.query() functionality as well
import pandas as pd dict_ = {'coverage':[45,214,212,72,85], 'name': ['jason','Molly','Tina','Jake','Amy']} df = pd.DataFrame(dict_) print(df.query('coverage > 72')) 
df[df["coverage"] >= 72]