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 loc iloc
# Selecting Datafrmae Information:
# iloc
# selecting a single row:
df.iloc[3]
# selecting a range of rows:
df.iloc[0:3]
# selecting all rows, with columns within an index range:
# all rows, 1st- 3rd columns, sliced at second index:
df.iloc[:, 0:3]
# selecting a range of rows and a range of columns:
# 1st to 3rd rows, 5th & 6th columns:
df.iloc[0:3, 4:6]
# by multiple noconsecutive rows and columns:
# selecting rows 1, 4, 6 with columns 2, 3, 5:
df.iloc[[0, 3, 5], [1, 2, 4]]
# a) .loc label-based indexing- selecting columns based on index:
# all rows:
df.loc[:, 'column_name']
# or:
df['column_name']
# selected rows:
df.loc[0:5, 'column_name']
# b) boolean indexing using .loc:
df.loc[df['column_name'] < 5]
#boolean indexing fro one column:
df.loc[df['column_condition'] < 12, ['column_desired']]how to use loc and iloc in pandas
>>> df.iloc[0, 1]
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