Example 1: isolate row based on index pandas
dfObj.iloc[: , [0, 2]]
Example 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)
Example 3: pandas df by row index
indices = [133, 22, 19, 203, 14, 1]
df_by_indices = df.iloc[indices, :]
Example 4: retrieve row by index pandas
rowData = dfObj.loc[ 'b' , : ]
Example 5: get column index pandas
df = pd.read_csv('thanksgiving_w_age_income.csv')
gravy = df.columns.get_loc("Do you typically have gravy?")
meet_friends = df.columns.get_loc('Have you ever tried to meet up with hometown friends on Thanksgiving night?')
friendsgiving = df.columns.get_loc('Have you ever attended a "Friendsgiving?"')
Example 6: loc and iloc in pandas
iloc slicing gives all the data upto the position that is passed as argument
loc gives all the data upto the label that is passed as argument
n = pd.Series([1,2,3,4],index = [0,1,2,3])
print("With iloc we got")
print(n.iloc[:2])
print("With loc we got")
print(n.loc[:2])
<Output>
With iloc we got
0 1
1 2
dtype: int64
With loc we got
0 1
1 2
2 3
dtype: int64