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, :]how to get a row from a dataframe in python
df.iloc[[index]]select specific rows from dataframe in python
select_color = df.loc[df['Color'] == 'Green']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)pandas df by row index
indices = [133, 22, 19, 203, 14, 1]
df_by_indices = df.iloc[indices, :]pandas 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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