new dataframe based on certain row conditions
filterinfDataframe = dfObj[(dfObj['Sale'] > 30) & (dfObj['Sale'] < 33) ]new dataframe based on certain row conditions
filterinfDataframe = dfObj[(dfObj['Sale'] > 30) & (dfObj['Sale'] < 33) ]slice dataframe pandas based on condition
# To slice pandas dataframe by condition
frioMurteira = data.loc[(data["POM"] == "Murteira") & (data["TMP"] > 7.2), ["DTM","TMP"]]new dataframe based on certain row conditions
# Create variable with TRUE if nationality is USA
american = df['nationality'] == "USA"
# Create variable with TRUE if age is greater than 50
elderly = df['age'] > 50
# Select all cases where nationality is USA and age is greater than 50
df[american & elderly]conditional value if else pandas np.select
# create a list of our conditions
conditions = [
(df['likes_count'] <= 2),
(df['likes_count'] > 2) & (df['likes_count'] <= 9),
(df['likes_count'] > 9) & (df['likes_count'] <= 15),
(df['likes_count'] > 15)
]
# create a list of the values we want to assign for each condition
values = ['tier_4', 'tier_3', 'tier_2', 'tier_1']
# create a new column and use np.select to assign values to it using our lists as arguments
df['tier'] = np.select(conditions, values)
# display updated DataFrame
df.head()python pandas dataframe conditional subset
In [722]: df[(df['C']==1) | df['B']]
Out[722]:
A B C
0 1 True 1
2 2 False 1
3 2 True 2
4 3 True 1
In [723]: df.query('C==1 or B==True')
Out[723]:
A B C
0 1 True 1
2 2 False 1
3 2 True 2
4 3 True 1
In [724]: df[df.eval('C==1 or B==True')]
Out[724]:
A B C
0 1 True 1
2 2 False 1
3 2 True 2
4 3 True 1Copyright © 2021 Codeinu
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