Access Rows by integers and Columns by labels Pandas
My data is like this:
[First row is headers]
Name,Email,Age
Sachith,[email protected],23
Sim,[email protected],234
Yoshi,[email protected],2345
sarla,[email protected],234
I would like to access elements such that rows are specified as integers and columns by labels. i.e for Sim I would like to access it as [1,'Name'] and so-on
My question is should I use loc or ix?
Looking at the documentation, I am confused as to what is a pandas index? Is it used to access rows or columns or both? When I try to print the indices of this data, I get a (4,) dtype=int64 array of [0,1,2,3]. So , are columns not part of the index?
Answer
Use loc or iloc, because ix is deprecated.
print (df)
Name Email Age
0 Sachith [email protected] 23
1 Sim [email protected] 234
2 Yoshi [email protected] 2345
3 sarla [email protected] 234
#select by label 1 and label Name
a = df.loc[1, 'Name']
print (a)
Sim
But if need select index by position (need iloc) and columns by labels (need loc) together:
df = df.set_index('Email')
print (df)
Name Age
Email
[email protected] Sachith 23
[email protected] Sim 234
[email protected] Yoshi 2345
[email protected] sarla 234
get label of second index by df.index[1]:
a = df.loc[df.index[1], 'Name']
print (a)
Sim
Or get position of label by get_loc:
a = df.iloc[1, df.columns.get_loc('Name')]
print (a)
Sim