filter rows pandas
newdf = df[(df.origin == "JFK") & (df.carrier == "B6")]filter rows pandas
newdf = df[(df.origin == "JFK") & (df.carrier == "B6")]panda - subset based on column value
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': 'foo bar foo bar foo bar foo foo'.split(),
'B': 'one one two three two two one three'.split(),
'C': np.arange(8), 'D': np.arange(8) * 2})
print(df)
sub_df = df.loc[df['A'] == 'foo'] # Subset based on specific A valuepandas filter rows by value in list
df.loc[df['col name'].isin(ls_conditions)]pandas filter rows by value
# does year equals to 2002?
# is_2002 is a boolean variable with True or False in it
>is_2002 = gapminder['year']==2002
>print(is_2002.head())
0 False
1 False
2 False
3 False
4 False
# filter rows for year 2002 using the boolean variable
>gapminder_2002 = gapminder[is_2002]
>print(gapminder_2002.shape)
(142, 6)how to apply filters in pandas
# filter rows for year 2002 using the boolean expression
>gapminder_2002 = gapminder[gapminder.year.eq(2002)]
>print(gapminder_2002.shape)
(142, 6)pandas filter
df[(df['year'] > 2012) & (df['reports'] < 30)]Copyright © 2021 Codeinu
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