trjhtr

I am diving into Data Analysis with pandas, and I have just written this Python script to calculate the average of hotel review scores of each country. The dataset contains an individual average score for each customer review, like: 8.86 or 7.95. My goal was to average all these individual scores for a particular country.

For example, if the hotels in United Kingdom got the following hotel review scores: 8.65, 7.89, 4.35, and 6.98, I would average these four scores and create a dataframe where the first column is "Country" and the second column is the "Overall Average Score" for that country.

I tried to write a concise code as much as I could. Would you mind giving your opinions and recommendations about it? I'll be adding this to my portfolio. What should be kept and/or avoided in a professional and real-world setting?

Script:

# Average all scores that belong to a particular country.

import pandas as pd

# Reading original hotel reviews dataset.
df = pd.read_csv(DATASET_PATH)

# Getting a dataframe with two columns: 'Hotel_Address' and 'Average_Score'.
df = df.loc[:, ["Hotel_Address", "Average_Score"]]

# List of tuples.
countries_w_avg_list = 
for _, row in df.iterrows():
 address = row[0].split()
 country_name = address[len(address) - 1]
 countries_w_avg_list.append( (country_name, row[1]) )

# Getting the sum of all 'Average_Score' values for each country.
d = # Empty dictionary. It will be a dictionary with list values, like: "Netherlands": [sum, counter]
counter = 0
for country, individual_average in countries_w_avg_list:
 if country not in d:
 d[country] = [0, 0]

 d[country][0] += individual_average
 d[country][1] += 1

# Getting the average of all 'Average_Score' values for each country.
for key, value in d.items():
 d[key] = round((d[key][0] / d[key][1]), 2)
# print(d)

# Now, I believe there are two ways to transform this dictionary in the df I want.
# 1 - Transform d in a df, and then transpose it. Then rename the columns.
# 2 - Create a dataframe with the column names "Country" and "Overall Average Score"
# and their values as d's keys as the value for the first column and d's values as the
# values for the second column.
df = pd.DataFrame("Country": list(d.keys()), "Overall Average Score": list(d.values()))
print(df)

You should probably use pandas.DataFrame.groupby.

The string manipulations to extract the countries can also be simplified using pandas.DataFrame.str methods.

import pandas as pd

# Reading original hotel reviews dataset.
# Reduce dataframe to two columns: 'Hotel_Address' and 'Average_Score'.
df = pd.read_csv(DATASET_PATH).loc[:, ["Hotel_Address", "Average_Score"]]

# Extract country from address
df["Country"] = df.Hotel_Address.str.split().str[-1]
df.drop(columns=["Hotel_Address"], inplace=True)

# Get average average score per country, rounded to two decimal places
print(df.groupby("Country").mean().round(2))