pandas select column by index
# A B C
# 0 1 3 5
# 1 2 4 6
column_B = a_dataframe.iloc[:, 1]
print(column_B)
# OUTPUT
# 0 3
# 1 4pandas select column by index
# A B C
# 0 1 3 5
# 1 2 4 6
column_B = a_dataframe.iloc[:, 1]
print(column_B)
# OUTPUT
# 0 3
# 1 4isolate row based on index pandas
dfObj.iloc[: , [0, 2]]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'])add an index column in range dataframe
df = df.loc[df.index.repeat(df['a'])]
df['c'] = df.groupby(level=0).cumcount() + 1
df = df.reset_index(drop=True)
print (df)
a b c
0 1 x 1
1 2 y 1
2 2 y 2
3 3 z 1
4 3 z 2
5 3 z 3Copyright © 2021 Codeinu
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