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 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)retrieve row by index pandas
rowData = dfObj.loc[ 'b' , : ]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 resultsadd 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
Forgot your account's password or having trouble logging into your Account? Don't worry, we'll help you to get back your account. Enter your email address and we'll send you a recovery link to reset your password. If you are experiencing problems resetting your password contact us
