grouping_and_sorting.py

# **This notebook is an exercise in the [Pandas](https://www.kaggle.com/learn/pandas) course.  
#  You can reference the tutorial at [this link](https://www.kaggle.com/residentmario/grouping-and-sorting).**
# 
# ---
# 

# # Introduction
# 
# In these exercises we'll apply groupwise analysis to our dataset.
# 
# Run the code cell below to load the data before running the exercises.

import pandas as pd

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

from learntools.core import binder; binder.bind(globals())
from learntools.pandas.grouping_and_sorting import *
print("Setup complete.")

# # Exercises

# ## 1.
# Who are the most common wine reviewers in the dataset? 
# Create a `Series` whose index is the `taster_twitter_handle` category from the dataset,
# and whose values count how many reviews each person wrote.

# Your code here
reviews_written = reviews.groupby('taster_twitter_handle')['taster_name'].count()

# Check your answer
q1.check()

# ## 2.
# What is the best wine I can buy for a given amount of money?
# Create a `Series` whose index is wine prices and whose values is the maximum number of points
# a wine costing that much was given in a review. 
# Sort the values by price, ascending (so that `4.0` dollars is at the top and `3300.0` dollars is at the bottom).

best_rating_per_price = reviews.groupby('price').points.max()

# Check your answer
q2.check()

# ## 3.
# What are the minimum and maximum prices for each `variety` of wine? 
# Create a `DataFrame` whose index is the `variety` category from the dataset
# and whose values are the `min` and `max` values thereof.

price_extremes = reviews.groupby('variety')['price'].agg(['min', 'max'])

# Check your answer
q3.check()

# ## 4.
# What are the most expensive wine varieties?
# Create a variable `sorted_varieties` containing a copy of the dataframe from the previous question
# where varieties are sorted in descending order based on minimum price, then on maximum price (to break ties).

sorted_varieties = price_extremes.sort_values(['min','max'], ascending=(False, False))

# Check your answer
q4.check()

# ## 5.
# Create a `Series` whose index is reviewers and whose values is the average review score
# given out by that reviewer. Hint: you will need the `taster_name` and `points` columns.

reviewer_mean_ratings = reviews.groupby('taster_name')['points'].mean()

# Check your answer
q5.check()

# Are there significant differences in the average scores assigned by the various reviewers? 
# Run the cell below to use the `describe()` method to see a summary of the range of values.

reviewer_mean_ratings.describe()

# ## 6.
# 
# What combination of countries and varieties are most common?
# Create a `Series` whose index is a `MultiIndex`of `{country, variety}` pairs.
# For example, a pinot noir produced in the US should map to `{"US", "Pinot Noir"}`. 
# Sort the values in the `Series` in descending order based on wine count.

country_variety_counts = reviews.groupby(['country', 'variety'])['variety'].count().sort_values(ascending=False)

# Check your answer
q6.check()