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, :]isolate row based on index pandas
dfObj.iloc[: , [0, 2]]pandas df by row index
indices = [133, 22, 19, 203, 14, 1]
df_by_indices = df.iloc[indices, :]retrieve row by index pandas
rowData = dfObj.loc[ 'b' , : ]dataframe select row by index value
In [1]: df = pd.DataFrame(np.random.rand(5,2),index=range(0,10,2),columns=list('AB'))
In [2]: df
Out[2]:
A B
0 1.068932 -0.794307
2 -0.470056 1.192211
4 -0.284561 0.756029
6 1.037563 -0.267820
8 -0.538478 -0.800654
In [5]: df.iloc[[2]]
Out[5]:
A B
4 -0.284561 0.756029
In [6]: df.loc[[2]]
Out[6]:
A B
2 -0.470056 1.192211pandas 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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