pandas select row by index
#for single row
df.loc[ index , : ]
# for multiple rows
indices = [1, 20, 33, 47, 52 ]
new_df= df.iloc[indices, :]pandas select row by index
#for single row
df.loc[ index , : ]
# for multiple rows
indices = [1, 20, 33, 47, 52 ]
new_df= df.iloc[indices, :]retrieve row by index pandas
rowData = dfObj.loc[ 'b' , : ]pandas select rows by index level
### w3sources ###
d = {'num_legs': [4, 4, 4, 2, 2],
'num_wings': [0, 0, 0, 2, 2],
'class': ['mammal', 'mammal', 'mammal', 'bird', 'bird'],
'animal': ['tiger', 'lion', 'fox', 'eagle', 'penguin'],
'locomotion': ['walks', 'walks', 'walks', 'flies', 'walks']}
df = pd.DataFrame(data=d)
df = df.set_index(['class', 'animal', 'locomotion'])
# num_legs num_wings
# class animal locomotion
# mammal| tiger walks 4 0
# | lion walks 4 0
# | fox walks 4 0
# __________________________________________
# bird | eagle flies 2 2
# | penguin walks 2 2
df.xs('mammal')
df.xs(('mammal', 'fox'))
df.xs('lion', level=1)
df.xs(('bird', 'walks'),level=[0, 'locomotion'])Copyright © 2021 Codeinu
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