Plotly & Seaborn to Explore Text Dataset

Github Repository

• Plotly & Seaborn to Explore Text Dataset
  • Dataset
    • Data Cleaning
    • Remove Contractions
  • Feature Engineering
    • Numerical Plots
      • Sentiment Polarity
      • Ratings vs. Age
    • Word Count vs Average Word Length
    • Categorical Plots
      • Department, Division and Class
    • Unigram, Bigram and Trigram
      • Get Top Words Function
      • Unigrams Feature Extraction
      • Bigrams Feature Extraction
      • Trigrams Feature Extraction
      • Removing Stop Words
    • Parts-of-Speech (PoS-Tags)
    • Bivariant Analysis
      • Recommendation Sentiment Polarity Distribution
      • Recommendation Rating Distribution
      • Joint Plot

Dataset

Women's E-Commerce Clothing Reviews

Content

This dataset includes 23486 rows and 10 feature variables. Each row corresponds to a customer review, and includes the variables:

• Clothing ID: Integer Categorical variable that refers to the specific piece being reviewed.
• Age: Positive Integer variable of the reviewers age.
• Title: String variable for the title of the review.
• Review Text: String variable for the review body.
• Rating: Positive Ordinal Integer variable for the product score granted by the customer from 1 Worst, to 5 Best.
• Recommended IND: Binary variable stating where the customer recommends the product where 1 is recommended, 0 is not recommended.
• Positive Feedback Count: Positive Integer documenting the number of other customers who found this review positive.
• Division Name: Categorical name of the product high level division.
• Department Name: Categorical name of the product department name.
• Class Name: Categorical name of the product class name.

import json
import cufflinks as cf
import matplotlib.pyplot as plt
import nltk
import numpy as np
import pandas as pd
import plotly as py
import seaborn as sns
from sklearn.feature_extraction.text import CountVectorizer
from textblob import TextBlob

from contractions import contractions

from plotly.offline import iplot
py.offline.init_notebook_mode(connected=True)
cf.go_offline()

! wget 'https://github.com/ya-stack/Women-s-Ecommerce-Clothing-Reviews/raw/master/Womens%20Clothing%20E-Commerce%20Reviews.csv' -P dataset

reviews_df = pd.read_csv('dataset/Womens_Clothing_E-Commerce_Reviews.csv')
reviews_df.head(5)

	Unnamed: 0	Clothing ID	Age	Title	Review Text	Rating	Recommended IND	Positive Feedback Count	Division Name	Department Name	Class Name
0	0	767	33	NaN	Absolutely wonderful - silky and sexy and comf...	4	1	0	Initmates	Intimate	Intimates
1	1	1080	34	NaN	Love this dress! it's sooo pretty. i happene...	5	1	4	General	Dresses	Dresses
2	2	1077	60	Some major design flaws	I had such high hopes for this dress and reall...	3	0	0	General	Dresses	Dresses
3	3	1049	50	My favorite buy!	I love, love, love this jumpsuit. it's fun, fl...	5	1	0	General Petite	Bottoms	Pants
4	4	847	47	Flattering shirt	This shirt is very flattering to all due to th...	5	1	6	General	Tops	Blouses

Data Cleaning

reviews_df_drop= reviews_df.drop(labels=['Unnamed: 0', 'Clothing ID', 'Title'], axis=1)
reviews_df_drop.head(5)

	Age	Review Text	Rating	Recommended IND	Positive Feedback Count	Division Name	Department Name	Class Name
0	33	Absolutely wonderful - silky and sexy and comf...	4	1	0	Initmates	Intimate	Intimates
1	34	Love this dress! it's sooo pretty. i happene...	5	1	4	General	Dresses	Dresses
2	60	I had such high hopes for this dress and reall...	3	0	0	General	Dresses	Dresses
3	50	I love, love, love this jumpsuit. it's fun, fl...	5	1	0	General Petite	Bottoms	Pants
4	47	This shirt is very flattering to all due to th...	5	1	6	General	Tops	Blouses

reviews_df_drop.sum(numeric_only=True)

Age	1014561
Rating	98548
Recommended IND	19314
Positive Feedback Count	59559
dtype: int64

reviews_df_drop.dropna(subset=['Review Text', 'Division Name'], inplace=True)

reviews_df_drop.sum(numeric_only=True)

Remove Contractions

' '.join(reviews_df_drop['Review Text'].tolist())

def expand_contractions(x):
    if type(x) is str:
        # remove backslashes
        x = x.replace('\\', '')
        # expand contractions
        for key in contractions:
            value = contractions[key]
            x = x.replace(key, value)
        return x
    else:
        return x

# test function on text
x = "y'all it\'s sooo pretty, i am 5\' 5\'\', about 135"
print(expand_contractions(x))
# you all it is sooo pretty, i am 5' 5'', about 135

# test on a dataframe
string_series = pd.Series([
    "i\'m glad i did bc  it\'s never...",
    "I\'m 5\"5\' and ain't 125 lbs."
], dtype="string")

string_df = pd.DataFrame({'strings':string_series.values})
string_df['strings'].apply(lambda x: expand_contractions(x)).head(2)

# 0    I am glad i did because  it is never...
# 1              I am 5"5' and is not 125 lbs.
# Name: strings, dtype: object

reviews_df_expanded = reviews_df_drop.copy()
reviews_df_expanded['Review Text'] = reviews_df_drop['Review Text'].apply(
    lambda x: expand_contractions(x)
)

' '.join(reviews_df_expanded['Review Text'].tolist())

Feature Engineering

reviews_df_expanded['Polarity'] = reviews_df_expanded['Review Text'].apply(
    lambda x: TextBlob(x).sentiment.polarity
)

reviews_df_expanded['Review Length'] = reviews_df_expanded['Review Text'].apply(
    lambda x: len(x)
)

reviews_df_expanded['Word Count'] = reviews_df_expanded['Review Text'].apply(
    lambda x: len(x.split())
)

def get_avg_word_len(x):
    words = x.split()
    word_len = 0
    for word in words:
        word_len = word_len + len(word)
        
    return word_len/len(words)

reviews_df_expanded['Average Word Length'] = reviews_df_expanded['Review Text'].apply(
    lambda x: get_avg_word_len(x)
)

reviews_df_expanded.head(5)

	Age	Review Text	Rating	Recommended IND	Positive Feedback Count	Division Name	Department Name	Class Name	Polarity	Review Length	Word Count	Average Word Length
0	33	Absolutely wonderful - silky and sexy and comf...	4	1	0	Initmates	Intimate	Intimates	0.633333	53	8	5.750000
1	34	Love this dress! it is sooo pretty. i happen...	5	1	4	General	Dresses	Dresses	0.339583	316	65	3.800000
2	60	I had such high hopes for this dress and reall...	3	0	0	General	Dresses	Dresses	0.073675	500	98	4.112245
3	50	I love, love, love this jumpsuit. it is fun, f...	5	1	0	General Petite	Bottoms	Pants	0.550000	125	23	4.478261
4	47	This shirt is very flattering to all due to th...	5	1	6	General	Tops	Blouses	0.512891	192	36	4.361111

Numerical Plots

Sentiment Polarity

reviews_df_expanded['Polarity'].iplot(
    title='Sentiment Polarity',
    theme='polar',
    yTitle='Sentiment [-1,1]',
    xTitle='Reviews'    
)

reviews_df_expanded['Polarity'].iplot(
    kind='hist',
    title='Sentiment Polarity',
    theme='polar',
    xTitle='Sentiment [-1,1]',
    yTitle='Frequency'
)

Ratings vs. Age

reviews_df_expanded['Rating'].iplot(
    kind='hist',
    theme='polar',
    title='Rating Histogram',
    xTitle='Rating',
    yTitle='Frequency'
)

reviews_df_expanded['Age'].iplot(
    kind='hist',
    theme='polar',
    title='Age Histogram',
    xTitle='Age',
    yTitle='Frequency'
)

sns.jointplot(
    data=reviews_df_expanded,
    x='Age',
    y='Polarity',
    kind='scatter',
    hue='Rating',
    palette='winter',
    alpha=0.4
)

plt.savefig('../assets/E-Commerce_Clothing_Reviews_05.webp', bbox_inches='tight')

Word Count vs Average Word Length

reviews_df_expanded['Word Count'].iplot(
    kind='hist',
    theme='polar',
    title='Word Count',
    xTitle='Word Count',
    yTitle='Frequency'
)

reviews_df_expanded['Average Word Length'].iplot(
    kind='hist',
    theme='polar',
    title='Average Word Length',
    xTitle='Average Word Length',
    yTitle='Frequency'
)

sns.jointplot(
    data=reviews_df_expanded,
    x='Word Count',
    y='Average Word Length',
    kind='scatter',
    hue='Rating',
    palette='winter',
    alpha=0.4
)

plt.savefig('../assets/E-Commerce_Clothing_Reviews_08.webp', bbox_inches='tight')

Categorical Plots

Department, Division and Class

departments = reviews_df_expanded['Department Name'].value_counts()
divisions = reviews_df_expanded['Division Name'].value_counts()
classes = reviews_df_expanded['Class Name'].value_counts()

Class Names	Count	Division Name	Count	Department Name	Count
Dresses	6145	Tops	10048	General	13365
Knits	4626	Dresses	6145	General Petite	7837
Blouses	2983	Bottoms	3662	Initmates	1426
Sweaters	1380	Intimate	1653
Pants	1350	Jackets	1002
Jeans	1104	Trend	118
Fine gauge	1059
Skirts	903
Jackets	683
Lounge	669
Swim	332
Outerwear	319
Shorts	304
Sleep	214
Legwear	158
Intimates	147
Layering	132
Trend	118
Casual bottoms	1
Chemises	1
Name: Class Name, dtype: int64

reviews_df_expanded['Department Name'].value_counts().iplot(
    kind='bar',
    theme='polar',
    title='Reviews by Department',
    xTitle='Departments',
    yTitle='Review Count'
)

reviews_df_expanded['Division Name'].value_counts().iplot(
    kind='bar',
    theme='polar',
    title='Reviews by Division',
    xTitle='Division',
    yTitle='Review Count'
)

reviews_df_expanded['Class Name'].value_counts().iplot(
    kind='bar',
    theme='polar',
    title='Reviews by Class',
    xTitle='Class',
    yTitle='Review Count'
)

Unigram, Bigram and Trigram

Get Top Words Function

# pre-processing example - get word frequency from string
x = ['i would have, you would have ordered it online because it is petite.  i bought a petite and am 5\'8\".']
vec = CountVectorizer().fit(x)
vec.vocabulary_.items()

# vocabulary items and position
# dict_items([('would', 10), ('have', 4), ('you', 11), ('ordered', 8), ('it', 6), ('online', 7), ('because', 2), ('is', 5), ('petite', 9), ('bought', 3), ('and', 1), ('am', 0)])

words = vec.transform(x)
words_sum = words.sum(axis=0)
words_sum

# frequency of words (correlates to position index above)
# matrix([[1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1]])

word_frequency = []

for word, idx in vec.vocabulary_.items():
    word_frequency.append((word, words_sum[0, idx]))
    
word_frequency = sorted(word_frequency, key=lambda x: x[1], reverse=True)

word_frequency
# [('would', 2),
#  ('have', 2),
#  ('it', 2),
#  ('petite', 2),
#  ('you', 1),
#  ('ordered', 1),
#  ('online', 1),
#  ('because', 1),
#  ('is', 1),
#  ('bought', 1),
#  ('and', 1),
#  ('am', 1)]

def get_ngrams_by_frequency(nrange, data, n):
    # ngram_range : tuple (min_n, max_n), default=(1, 1)
    vec = CountVectorizer(ngram_range=nrange).fit(data)
    vec.vocabulary_.items()
    
    words = vec.transform(data)
    words_sum = words.sum(axis=0)
    
    word_frequency = []

    for word, idx in vec.vocabulary_.items():
        word_frequency.append((word, words_sum[0, idx]))

    top_words = sorted(word_frequency, key=lambda data: data[1], reverse=True)
    
    return top_words[:n]

# return 5 top words
get_ngrams_by_frequency((1, 1), x, 5)
# [('would', 2), ('have', 2), ('it', 2), ('petite', 2), ('you', 1)]

Unigrams Feature Extraction

top_20_unigrams = get_ngrams_by_frequency(
    nrange=(1, 1),
    data=reviews_df_expanded['Review Text'],
    n=20
)

top_20_unigrams_df = pd.DataFrame(top_20_unigrams, columns=['Unigram', 'Frequency'])
top_20_unigrams_df = top_20_unigrams_df.set_index('Unigram')
top_20_unigrams_df

Unigram	Frequency
the	76159
it	49162
and	48993
is	37696
this	25758
to	24592
in	20722
not	18781
but	16549
on	15325
for	13994
of	13472
was	12923
with	12797
so	12017
am	11625
my	11027
dress	10561
that	9560
love	8945

top_20_unigrams_df.iplot(
    kind='bar',
    theme='polar',
    title='Top 20 Unigrams',
    yTitle='Frequency',
    xTitle='Unigram'
)

Bigrams Feature Extraction

top_20_bigrams = get_ngrams_by_frequency(
    nrange=(2, 2),
    data=reviews_df_expanded['Review Text'],
    n=20
)

top_20_bigrams_df = pd.DataFrame(top_20_bigrams, columns=['Bigram', 'Frequency'])
top_20_bigrams_df = top_20_bigrams_df.set_index('Bigram')
top_20_bigrams_df

Bigram	Frequency
it is	12528
in the	7169
and the	5611
this dress	4755
on the	4337
of the	3933
and it	3723
it was	3287
does not	3228
this top	2937
this is	2772
is very	2725
the fabric	2653
love the	2641
did not	2486
love this	2331
to wear	2266
but it	2196
the dress	2178
do not	2118

top_20_bigrams_df.iplot(
    kind='bar',
    theme='polar',
    title='Top 20 Bigrams',
    yTitle='Frequency',
    xTitle='Bigram'
)

Trigrams Feature Extraction

top_20_trigrams = get_ngrams_by_frequency(
    nrange=(3, 3),
    data=reviews_df_expanded['Review Text'],
    n=20
)

top_20_trigrams_df = pd.DataFrame(top_20_trigrams, columns=['Trigram', 'Frequency'])
top_20_trigrams_df = top_20_trigrams_df.set_index('Trigram')
top_20_trigrams_df

Trigram	Frequency
do not does	1759
not does not	1758
true to size	1316
the fabric is	1301
and it is	1124
this dress is	1123
it is very	976
but it is	921
it is not	910
the material is	896
if you are	733
in the store	728
on the model	722
the fit is	696
this top is	672
the color is	630
love this dress	603
it is so	585
tried it on	574
and it was	550

top_20_trigrams_df.iplot(
    kind='bar',
    theme='polar',
    title='Top 20 Trigrams',
    yTitle='Frequency',
    xTitle='Trigram'
)

Removing Stop Words

def get_ngrams_no_stop_by_frequency(nrange, data, n):
    # ngram_range : tuple (min_n, max_n), default=(1, 1)
    vec = CountVectorizer(ngram_range=nrange, stop_words='english').fit(data)
    vec.vocabulary_.items()
    
    words = vec.transform(data)
    words_sum = words.sum(axis=0)
    
    word_frequency = []

    for word, idx in vec.vocabulary_.items():
        word_frequency.append((word, words_sum[0, idx]))

    top_words = sorted(word_frequency, key=lambda data: data[1], reverse=True)
    
    return top_words[:n]

top_20_unigrams_no_stop = get_ngrams_no_stop_by_frequency(
    nrange=(1, 1),
    data=reviews_df_expanded['Review Text'],
    n=20
)

top_20_unigrams_no_stop_df = pd.DataFrame(top_20_unigrams_no_stop, columns=['Unigram', 'Frequency'])
top_20_unigrams_no_stop_df = top_20_unigrams_no_stop_df.set_index('Unigram')

top_20_unigrams_no_stop_df.iplot(
    kind='bar',
    theme='polar',
    title='Top 20 Unigrams',
    yTitle='Frequency',
    xTitle='Unigram'
)

top_20_bigrams_no_stop = get_ngrams_no_stop_by_frequency(
    nrange=(2, 2),
    data=reviews_df_expanded['Review Text'],
    n=20
)

top_20_bigrams_no_stop_df = pd.DataFrame(top_20_bigrams_no_stop, columns=['Bigram', 'Frequency'])
top_20_bigrams_no_stop_df = top_20_bigrams_no_stop_df.set_index('Bigram')

top_20_bigrams_no_stop_df.iplot(
    kind='bar',
    theme='polar',
    title='Top 20 Bigrams',
    yTitle='Frequency',
    xTitle='Bigram'
)

top_20_trigrams_no_stop = get_ngrams_no_stop_by_frequency(
    nrange=(3, 3),
    data=reviews_df_expanded['Review Text'],
    n=20
)

top_20_trigrams_no_stop_df = pd.DataFrame(top_20_trigrams_no_stop, columns=['Bigram', 'Frequency'])
top_20_trigrams_no_stop_df = top_20_trigrams_no_stop_df.set_index('Bigram')

top_20_trigrams_no_stop_df.iplot(
    kind='bar',
    theme='polar',
    title='Top 20 Trigrams',
    yTitle='Frequency',
    xTitle='Trigrams'
)

Parts-of-Speech (PoS-Tags)

nltk.download('averaged_perceptron_tagger')
nltk.download('punkt')
nltk.download('tagsets')

blob = TextBlob(str(reviews_df_expanded['Review Text']))
pos_df = pd.DataFrame(blob.tags, columns=['Words', 'PoS'])
pos_df

	Words	PoS
0	0	CD
1	Absolutely	RB
2	wonderful	JJ
3	silky	NN
4	and	CC
...
106	Text	NNP
107	Length	NNP
108	22628	CD
109	dtype	NN
110	object	NN
111 rows × 2 columns

nltk.help.upenn_tagset()

$: dollar
    $ -$ --$ A$ C$ HK$ M$ NZ$ S$ U.S.$ US$
'': closing quotation mark
    ' ''
(: opening parenthesis
    ( [ {
): closing parenthesis
    ) ] }
,: comma
    ,
--: dash
    --
.: sentence terminator
    . ! ?
:: colon or ellipsis
    : ; ...
CC: conjunction, coordinating
    & 'n and both but either et for less minus neither nor or plus so
    therefore times v. versus vs. whether yet
CD: numeral, cardinal
    mid-1890 nine-thirty forty-two one-tenth ten million 0.5 one forty-
    seven 1987 twenty '79 zero two 78-degrees eighty-four IX '60s .025
    fifteen 271,124 dozen quintillion DM2,000 ...
DT: determiner
    all an another any both del each either every half la many much nary
    neither no some such that the them these this those
EX: existential there
    there
FW: foreign word
    gemeinschaft hund ich jeux habeas Haementeria Herr K'ang-si vous
    lutihaw alai je jour objets salutaris fille quibusdam pas trop Monte
    terram fiche oui corporis ...
IN: preposition or conjunction, subordinating
    astride among uppon whether out inside pro despite on by throughout
    below within for towards near behind atop around if like until below
    next into if beside ...
JJ: adjective or numeral, ordinal
    third ill-mannered pre-war regrettable oiled calamitous first separable
    ectoplasmic battery-powered participatory fourth still-to-be-named
    multilingual multi-disciplinary ...
JJR: adjective, comparative
    bleaker braver breezier briefer brighter brisker broader bumper busier
    calmer cheaper choosier cleaner clearer closer colder commoner costlier
    cozier creamier crunchier cuter ...
JJS: adjective, superlative
    calmest cheapest choicest classiest cleanest clearest closest commonest
    corniest costliest crassest creepiest crudest cutest darkest deadliest
    dearest deepest densest dinkiest ...
LS: list item marker
    A A. B B. C C. D E F First G H I J K One SP-44001 SP-44002 SP-44005
    SP-44007 Second Third Three Two * a b c d first five four one six three
    two
MD: modal auxiliary
    can cannot could couldn't dare may might must need ought shall should
    shouldn't will would
NN: noun, common, singular or mass
    common-carrier cabbage knuckle-duster Casino afghan shed thermostat
    investment slide humour falloff slick wind hyena override subhumanity
    machinist ...
NNP: noun, proper, singular
    Motown Venneboerger Czestochwa Ranzer Conchita Trumplane Christos
    Oceanside Escobar Kreisler Sawyer Cougar Yvette Ervin ODI Darryl CTCA
    Shannon A.K.C. Meltex Liverpool ...
NNPS: noun, proper, plural
    Americans Americas Amharas Amityvilles Amusements Anarcho-Syndicalists
    Andalusians Andes Andruses Angels Animals Anthony Antilles Antiques
    Apache Apaches Apocrypha ...
NNS: noun, common, plural
    undergraduates scotches bric-a-brac products bodyguards facets coasts
    divestitures storehouses designs clubs fragrances averages
    subjectivists apprehensions muses factory-jobs ...
PDT: pre-determiner
    all both half many quite such sure this
POS: genitive marker
    ' 's
PRP: pronoun, personal
    hers herself him himself hisself it itself me myself one oneself ours
    ourselves ownself self she thee theirs them themselves they thou thy us
PRP$: pronoun, possessive
    her his mine my our ours their thy your
RB: adverb
    occasionally unabatingly maddeningly adventurously professedly
    stirringly prominently technologically magisterially predominately
    swiftly fiscally pitilessly ...
RBR: adverb, comparative
    further gloomier grander graver greater grimmer harder harsher
    healthier heavier higher however larger later leaner lengthier less-
    perfectly lesser lonelier longer louder lower more ...
RBS: adverb, superlative
    best biggest bluntest earliest farthest first furthest hardest
    heartiest highest largest least less most nearest second tightest worst
RP: particle
    aboard about across along apart around aside at away back before behind
    by crop down ever fast for forth from go high i.e. in into just later
    low more off on open out over per pie raising start teeth that through
    under unto up up-pp upon whole with you
SYM: symbol
    % & ' '' ''. ) ). * + ,. < = > @ A[fj] U.S U.S.S.R * ** ***
TO: "to" as preposition or infinitive marker
    to
UH: interjection
    Goodbye Goody Gosh Wow Jeepers Jee-sus Hubba Hey Kee-reist Oops amen
    huh howdy uh dammit whammo shucks heck anyways whodunnit honey golly
    man baby diddle hush sonuvabitch ...
VB: verb, base form
    ask assemble assess assign assume atone attention avoid bake balkanize
    bank begin behold believe bend benefit bevel beware bless boil bomb
    boost brace break bring broil brush build ...
VBD: verb, past tense
    dipped pleaded swiped regummed soaked tidied convened halted registered
    cushioned exacted snubbed strode aimed adopted belied figgered
    speculated wore appreciated contemplated ...
VBG: verb, present participle or gerund
    telegraphing stirring focusing angering judging stalling lactating
    hankerin' alleging veering capping approaching traveling besieging
    encrypting interrupting erasing wincing ...
VBN: verb, past participle
    multihulled dilapidated aerosolized chaired languished panelized used
    experimented flourished imitated reunifed factored condensed sheared
    unsettled primed dubbed desired ...
VBP: verb, present tense, not 3rd person singular
    predominate wrap resort sue twist spill cure lengthen brush terminate
    appear tend stray glisten obtain comprise detest tease attract
    emphasize mold postpone sever return wag ...
VBZ: verb, present tense, 3rd person singular
    bases reconstructs marks mixes displeases seals carps weaves snatches
    slumps stretches authorizes smolders pictures emerges stockpiles
    seduces fizzes uses bolsters slaps speaks pleads ...
WDT: WH-determiner
    that what whatever which whichever
WP: WH-pronoun
    that what whatever whatsoever which who whom whosoever
WP$: WH-pronoun, possessive
    whose
WRB: Wh-adverb
    how however whence whenever where whereby whereever wherein whereof why
``: opening quotation mark
    ` ``

pos_df = pos_df['PoS'].value_counts()
pos_df

NN	23
DT	15
JJ	13
CD	11
PRP	8
RB	6
VBZ	5
VBP	5
IN	5
CC	4
NNP	4
VBD	4
TO	3
NNS	2
VB	2
PDT	1
Name: PoS, dtype: int64

pos_df.iplot(
    kind='bar',
    theme='polar',
    title='Parts-of-Speech',
    yTitle='Count',
    xTitle='Tags'
)

Bivariant Analysis

sns.pairplot(
    data=reviews_df_expanded,
    hue='Rating',
    palette='winter'
)

plt.rcParams['figure.figsize'] = [10,5]

sns.catplot(
    data=reviews_df_expanded,
    x='Department Name',
    y='Polarity',
    hue='Rating',
    palette='winter',
    height=5,
    aspect=2
)

sns.catplot(
    kind='box',
    data=reviews_df_expanded,
    x='Department Name',
    y='Polarity',
    hue='Rating',
    palette='winter',
    height=5,
    aspect=2
)

sns.catplot(
    kind='box',
    data=reviews_df_expanded,
    x='Department Name',
    y='Review Length',
    hue='Rating',
    palette='winter',
    height=5,
    aspect=2
)

Recommendation Sentiment Polarity Distribution

recommended_ps = reviews_df_expanded[reviews_df_expanded['Recommended IND']==1]['Polarity']
recommended_ps.head(5)

	Polarity
0	0.633333
1	0.339583
3	0.550000
4	0.512891
6	0.133750

not_recommended_ps = reviews_df_expanded[reviews_df_expanded['Recommended IND']==0]['Polarity']
not_recommended_ps.head(5)

	Polarity
2	0.073675
5	0.178750
10	-0.097149
22	-0.045595
25	0.261508

trace_pos = go.Histogram(x=recommended_ps, name='Recommended', opacity=0.8)
trace_neg = go.Histogram(x=not_recommended_ps, name='Not Recommended', opacity=0.8)

data = [trace_pos, trace_neg]
layout = go.Layout(barmode='overlay', title='Recommendation Sentiment Polarity Distribution')

fig=go.Figure(data=data, layout=layout)
fig.data[0].marker.color = ('mediumspringgreen')
fig.data[1].marker.color = ('dodgerblue')
iplot(fig)

Recommendation Rating Distribution

recommended_rating_ps = reviews_df_expanded[reviews_df_expanded['Recommended IND']==1]['Rating']
not_recommended_rating_ps = reviews_df_expanded[reviews_df_expanded['Recommended IND']==0]['Rating']

trace_pos_rating = go.Histogram(x=recommended_rating_ps, name='Recommended', opacity=0.3)
trace_neg_rating = go.Histogram(x=not_recommended_rating_ps, name='Not Recommended', opacity=0.3)

data2 = [trace_pos_rating, trace_neg_rating]
layout2 = go.Layout(barmode='overlay', title='Recommendation Rating Distribution')

fig=go.Figure(data=data2, layout=layout2)
fig.data[0].marker.color = ('mediumspringgreen')
fig.data[1].marker.color = ('dodgerblue')
iplot(fig)

Joint Plot

sns.jointplot(
    data=reviews_df_expanded,
    y='Positive Feedback Count',
    x='Polarity',
    hue='Recommended IND',
    palette='winter',
    kind='kde',
    fill=True
)