Python for Research — Case Study 2 Solution

Introduction to Problem from HarvardX

In the six exercises of this case study, we will find and plot the distribution of word frequencies for different translations of Hamlet. Perhaps the distribution of word frequencies of Hamlet depends on the translation — let’s find out!
For this case study, the functions count_words_fast and word_stats are defined as in the Case 2 Videos (Videos 3.2.1 through 3.2.6). The code for these functions, which you will need for the following exercises, is given here:

import os 
import pandas as pd 
import numpy as np 
from collections import Counter


def count_words_fast(text): 
    text = text.lower() 
    skips = [".", ",", ";", ":", "'", '"', "\n", "!", "?", "(", ")"] 
    for ch in skips: 
        text = text.replace(ch, "") 
    word_counts = Counter(text.split(" ")) 
    return word_counts

def word_stats(word_counts): 
    num_unique = len(word_counts) 
    counts = word_counts.values() 
    return (num_unique, counts)

Observe that we have used functions defined in the previous case study which has recorded videos, not the one we have the solutions and also called Case Study 2: Language Processing. I highly encourage watching the videos and answering the exercises.

Exercises

Exercise 1

Note that book_titles is a nested dictionary, containing book titles within authors within languages, all of which are strings. These books are all stored online, and are accessed throughout this case study. In Exercise 1, we will first read in and store each translation of Hamlet.
Instructions
Read in the data as a pandas dataframe using pd.read_csv. Use the index_col argument to set the first column in the csv file as the index for the dataframe. The data can be found at this link within the courseware, and at this link when coming from outside the courseware.
Complete the following line of code to read in the data:

hamlets = pd.read_csv("./hamlets.csv", index_col="Unnamed: 0") ## Complete this line of code! ##
hamlets.head()

It’s good practice to check whether or not our dataframe is correctly initialized. In this code block, we have used read_csv to read our .csv file and used index_col to select our index column as languages. Our expected output is shown below. Since this case study is much harder than the previous one, I will try to share my results as well as the code.

Exercise 2

In Exercise 2, we will summarize the text for a single translation of Hamlet in a pandas dataframe.
Instructions
Find the dictionary of word frequency in text by calling count_words_fast(). Store this as counted_text.
Create a pandas dataframe named data.
Using counted_text, define two columns in data:
word, consisting of each unique word in text.
count, consisting of the number of times each word in word is included in the text.
Here’s the code to get you started:
How many times does the word Hamlet appear in the text?

language, text = hamlets.iloc[0]

# Enter your code here.
counted_text = count_words_fast(text)

data = pd.DataFrame(list(counted_text.items()), columns=['word', 'count'])
counted_text["hamlet"]

And the expected output is 97. Here list(counted_text.items()) gives us a list of tuples of key and value pairs, which we can use to have the rows of our dataframe.

Exercise 3

In Exercise 3, we will continue to define summary statistics for a single translation of Hamlet.
Instructions Add a column to data named length, defined as the length of each word.
Add another column named frequency, which is defined as follows for each word in data:
-If count > 10, frequency is “frequent”.
-If 1 < count <= 10, frequency is “infrequent”.
-If count == 1, frequency is “unique”.
How many unique words appear in the text?

Now we are coming to harder parts. From now on,I will try to elaborate more with the methods and variables I have used.

# write your code here!
def freq(row):
    if row["count"] > 10:
        return "frequent"
    elif 1< row["count"] <=10:
        return "infrequent"
    else:
        return "unique"

data["length"] = data["word"].apply(len)
data["frequency"] = data.apply(freq, axis=1)

len(data[data["frequency"] == "unique"])

Expected output is 3348.

What does apply do?

Now we need to understand what apply() method does. In the official documentation, it says “Apply a function along an axis of the DataFrame.”. However, there is one more point waiting to be explained. What is an “axis”? By default, the axis value is equal to 0. This means Pandas will apply the corresponding function (our first input) to each column. If it was equal to 1, it would apply to each row.

Exercise 4

In Exercise 4, we will summarize the statistics in data into a smaller pandas dataframe.
Instructions
Create a pandas dataframe named sub_data including the following columns:
language, which is the language of the text (defined in Exercise 2).
frequency, which is a list containing the strings “frequent”, “infrequent”, and “unique”.
mean_word_length, which is the mean word length of each value in frequency.
num_words, which is the total number of words in each frequency category.
What is the average word length of the infrequent words?

Here comes the hardest part of this case study for me.

# write your code here!

languages = ["English", "German", "Portuguese"]
frequencies = ["frequent", "infrequent", "unique"]

data_list = []

for lang in languages:
    for freq in frequencies:
        data_prime = data[data["frequency"] == freq]
        mean_word_length = data_prime["length"].mean()
        num_words = data_prime["count"].sum()
        data_list.append([lang, freq, mean_word_length, num_words])

sub_data = pd.DataFrame(data_list, columns=['language', 'frequency', 'mean_word_length', 'num_words'])

sub_data.loc[sub_data['frequency'] == 'infrequent', 'mean_word_length'].values[0]

Expected result is 5.825242718446602.

Alright, I mostly do not remember how I wrote this part of the code but I will try to elaborate with it. Moreover, in later exercises, HarvardX recommends another approach. I will stick with mine for now.

Since we need to group by frequency — if you check what we need you can easily see that — first we create a new dataframe called data_prime to store our new dataframe with selected frequency. After that, rest is history. In the last line, we needed to find average word length of the infrequent words and we have found that with using loc[] attribute.

Exercise 5

In the last two exercises, we wrap up what we have covered until now.

In Exercise 5, we will join all the data summaries for text Hamlet translation.
Instructions
The previous code for summarizing a particular translation of Hamlet is consolidated into a single function called summarize_text. Create a pandas dataframe grouped_data consisting of the results of summarize_text for each translation of Hamlet in hamlets.
Use a for loop across the row indices of hamlets to assign each translation to a new row.
Obtain the ith row of hamlets to variables using the .iloc method, and assign the output to variables language and text.
Call summarize_text using language and text, and assign the output to sub_data.
Use the pandas .append() function to append pandas dataframes row-wise to grouped_data.
The code below defines summarize_text:
What is the average word length of the frequent words in the German translation?
How many frequent words are there in the Portugese translation?

def summarize_text(language, text):
    counted_text = count_words_fast(text)

    data = pd.DataFrame({
        "word": list(counted_text.keys()),
        "count": list(counted_text.values())
    })
    
    data.loc[data["count"] > 10,  "frequency"] = "frequent"
    data.loc[data["count"] <= 10, "frequency"] = "infrequent"
    data.loc[data["count"] == 1,  "frequency"] = "unique"
    
    data["length"] = data["word"].apply(len)
    
    sub_data = pd.DataFrame({
        "language": language,
        "frequency": ["frequent","infrequent","unique"],
        "mean_word_length": data.groupby(by = "frequency")["length"].mean(),
        "num_words": data.groupby(by = "frequency").size()
    })
    
    return(sub_data)
    
# write your code here!
grouped_data = pd.DataFrame()
for idx in range(3):
    language, text = hamlets.iloc[idx]["language"], hamlets.iloc[idx]["text"]
    sub_data = summarize_text(language, text)
    grouped_data = pd.concat([grouped_data, sub_data])

grouped_data

Expected output is a dataframe:

Here you can easily find expected results. Now, let’s check how our solution works. First, we have created a dataframe called grouped_data. Then, from 0 to 2, we have a loop and we have one-by-one assign language and text and use these as input while using summarize_text().

Since the last exercise is abnormally easy, I won’t be talking about it.