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, :]select rows from dataframe pandas
from pandas import DataFrame
boxes = {'Color': ['Green','Green','Green','Blue','Blue','Red','Red','Red'],
'Shape': ['Rectangle','Rectangle','Square','Rectangle','Square','Square','Square','Rectangle'],
'Price': [10,15,5,5,10,15,15,5]
}
df = DataFrame(boxes, columns= ['Color','Shape','Price'])
select_color = df.loc[df['Color'] == 'Green']
print (select_color)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 1.192211add 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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