Introduction

This is the workbook component of the "Indexing, selecting, assigning" section. For the reference component, click here.

Selecting specific values of a pandas DataFrame or Series to work on is an implicit step in almost any data operation you'll run, so one of the first things you need to learn in working with data in Python is how to go about selecting the data points relevant to you quickly and effectively.

In this set of exercises we will work on exploring the Wine Reviews dataset.

Relevant Resources

• Quickstart to indexing and selecting data
• Indexing and Selecting Data section of pandas documentation
• Pandas Cheat Sheet

Set Up

Fork this notebook using the button towards the top of the screen.

Run the following cell to load your data and some utility functions

import pandas as pd
import seaborn as sns

import sys
sys.path.append('../input/advanced-pandas-exercises/')
from indexing_selecting_assigning import *

reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv", index_col=0)
pd.set_option("display.max_rows", 5)

Look at an overview of your data by running the following line

reviews.head()

Checking Answers

You can check your answers in each of the exercises that follow using the check_qN function provided in the code cell above (replacing N with the number of the exercise). For example here's how you would check an incorrect answer to exercise 1:

check_q1(pd.DataFrame())

For the first set of questions, if you use check_qN on your answer, and your answer is right, a simple True value will be returned.

For the second set of questions, using this function to check a correct answer will present you will an informative graph!

If you get stuck, use answer_qN function to print the answer outright.

Exercises

Exercise 1: Select the description column from reviews.

# Your code here

Exercise 2: Select the first value from the description column of reviews.

# Your code here

Exercise 3: Select the first row of data (the first record) from reviews. Hint: from this exercise onwards I strongly recommend using loc or iloc.

# Your code here

Exercise 4: Select the first 10 values from the description column in reviews. Hint: format your output as a pandas Series.

# Your code here

Exercise 5: Select the records with the 1, 2, 3, 5, and 8 row index positions. In other words, generate the followingDataFrame:

# Your code here

Exercise 6: Select the country, province, region_1, and region_2 columns of the records with the 0, 1, 10, and 100 index positions. In other words, generate the following DataFrame:

# Your code here

Exercise 7: Select the country and variety columns of the first 100 records.

Hint: you may use loc or iloc. When working on the answer this question and the several of the ones that follow, keep the following "gotcha" described in the reference for this tutorial section:

iloc uses the Python stdlib indexing scheme, where the first element of the range is included and the last one excluded. So 0:10 will select entries 0,...,9. loc, meanwhile, indexes inclusively. So 0:10 will select entries 0,...,10.

[...]

...[consider] when the DataFrame index is a simple numerical list, e.g. 0,...,1000. In this case df.iloc[0:1000] will return 1000 entries, while df.loc[0:1000] return 1001 of them! To get 1000 elements using iloc, you will need to go one higher and ask for df.iloc[0:1001].

# Your code here

Exercise 8: Select wines made in Italy. Hint: reviews.country equals what?

# Your code here

Exercise 9: Select wines whose region_2 is not NaN.

# Your code here

The remaining exercises are visual.

Exercise 10: Select the points column.

# Your code here

Exercise 11: Select the points column for the first 1000 wines.

# Your code here

Exercise 12: Select the points column for the last 1000 wines.

# Your code here

Exercise 13: Select the points column, but only for wines made in Italy.

# Your code here

Exercise 14: Who produces more above-averagely good wines, France or Italy? Select the country column, but only when said country is one of those two options, and the points column is greater than or equal to 90.

Your output should look roughly like this:

119       France
120        Italy
           ...  
129969    France
129970    France
Name: country, Length: 15840, dtype: object

# Your code here

Keep going

Move on to the Summary functions and maps workbook.