fclesio

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

• "Build tools for others that you want to be built for you." - Kenneth Reitz
• "Simplicity is alway better than functionality." - Pieter Hintjens

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import matplotlib.pyplot as plt
import nltk
import numpy as np
import os 
import pandas as pd
import pyLDAvis
import pyLDAvis.sklearn
import random
import re
import seaborn as sns

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def get_language(text):
    text = str(text)
    b = TextBlob(text)
    return b.detect_language()

# Include language in the DF
df_raw_lyrics['lang'] = df_raw_lyrics['lyric'].apply(get_language)

# Show stats about the language per artist
df_raw_lyrics.groupby(['artist', 'lang']).size().reset_index()

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# Filtering out non-EN songs
df_raw_lyrics = df_raw_lyrics[df_raw_lyrics['lang'] == 'en']

# Lyrics per album
df_raw_lyrics.groupby(['artist', 'album']).size().reset_index()

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# Average songs per album
df_albuns = df_raw_lyrics.groupby(['artist', 'album']).size().reset_index()
df_albuns.columns = ['artist', 'album', 'qty_tracks']
df_albuns.groupby(['artist']).agg({'qty_tracks': [np.size, np.mean]}).reset_index()

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# Convert the lyrics to string to not break the posterior converts
df_raw_lyrics['lyric'] = df_raw_lyrics['lyric'].astype(str)

# Remove all stopwords
df_raw_lyrics['lyric'] = df_raw_lyrics['lyric']\
    .apply(lambda x: ' '.join([item for item in x.lower()\
    .split() if item not in stoplist]))

# Quick check
df_raw_lyrics.head(5)

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# Data exploration in some specific class to see the most frequent words
def get_word_frequency(artist):

    # Word Frequency per Category
    def cleanup_text(docs, logging=False):
        texts = []
        counter = 1
        for doc in docs:
            if counter % 1000 == 0 and logging:
                print("Processed %d out of %d documents." % (counter, len(docs)))

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# Most Common words: Angra
get_word_frequency('angra')

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# Most Common words: Sepultura
get_word_frequency('sepultura')

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# Word cloud with most common words
def show_wordcloud(text, artist):
    # Create and generate a word cloud image:
    wordcloud = WordCloud(stopwords=stoplist, background_color="white").generate(text)

    # Display the generated image:
    fig = plt.figure(figsize=(25,10))
    plt.imshow(wordcloud, interpolation='bilinear')
    plt.title(f'Word Cloud for {artist}', fontsize=20)
    plt.axis("off")