how to get a row from a dataframe in python
df.iloc[[index]]how to get a row from a dataframe in python
df.iloc[[index]]isolate row based on index pandas
dfObj.iloc[: , [0, 2]]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 1.192211pandas return row
1. Selecting data by row numbers (.iloc)
# myrow = data.iloc[<row selection>]
myrow = data.iloc[7]
myrow = data.iloc[0:9]
2. Selecting data by label or by a conditional statement (.loc)
# myrow = data.loc[<row selection.]
myrow = data.loc['University ABC']
3. Selecting in a hybrid approach (.ix) (now Deprecated in Pandas 0.20.1)
# Works like a .loc but also accepts integers - may lead to unexpected resultsCopyright © 2021 Codeinu
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