Title: Iterating over a DataFrame Slug: Iterating_over_a_dataframe Summary: Iterating over a Pandas DataFrame with a generator Date: 2017-10-14 20:33 Category: Python Tags: Data Wrangling Authors: Guillaume Redoulès ### Create a sample dataframe ```python # Import modules import pandas as pd ``` ```python # Example dataframe raw_data = {'fruit': ['Banana', 'Orange', 'Apple', 'lemon', "lime", "plum"], 'color': ['yellow', 'orange', 'red', 'yellow', "green", "purple"], 'kcal': [89, 47, 52, 15, 30, 28] } df = pd.DataFrame(raw_data, columns = ['fruit', 'color', 'kcal']) df ```
fruit color kcal
0 Banana yellow 89
1 Orange orange 47
2 Apple red 52
3 lemon yellow 15
4 lime green 30
5 plum purple 28
### Using the iterrows method Pandas DataFrames can return a generator with the iterrrows method. It can then be used to loop over the rows of the DataFrame ```python for index, row in df.iterrows(): print("At line {0} there is a {1} which is {2} and contains {3} kcal".format(index, row["fruit"], row["color"], row["kcal"])) ``` At line 0 there is a Banana which is yellow and contains 89 kcal At line 1 there is a Orange which is orange and contains 47 kcal At line 2 there is a Apple which is red and contains 52 kcal At line 3 there is a lemon which is yellow and contains 15 kcal At line 4 there is a lime which is green and contains 30 kcal At line 5 there is a plum which is purple and contains 28 kcal