[EDA & Visualization] Netflix Dataset

https://www.kaggle.com/code/emilyjiminroh/netflix-dataset-eda-visualization-collabo-ver

# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

/kaggle/input/netflix-icon-word-cloud/732082.png
/kaggle/input/netflix-data/netflix_titles.csv

1. Import Library

In [2]:

import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans, AffinityPropagation
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
import plotly as py
import plotly.graph_objs as go
import os
py.offline.init_notebook_mode(connected = True)
import datetime as dt
import missingno as msno
plt.rcParams['figure.dpi'] = 140
import matplotlib.lines as lines
from sklearn.preprocessing import MultiLabelBinarizer 
import matplotlib.colors
from wordcloud import WordCloud
import random
from PIL import Image
import matplotlib

 

2. EDA

In [3]:

df = pd.read_csv('../input/netflix-data/netflix_titles.csv')

df.head(3)

Out[3]:

show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription012

s1	Movie	Dick Johnson Is Dead	Kirsten Johnson	NaN	United States	September 25, 2021	2020	PG-13	90 min	Documentaries	As her father nears the end of his life, filmm...
s2	TV Show	Blood & Water	NaN	Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...	South Africa	September 24, 2021	2021	TV-MA	2 Seasons	International TV Shows, TV Dramas, TV Mysteries	After crossing paths at a party, a Cape Town t...
s3	TV Show	Ganglands	Julien Leclercq	Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...	NaN	September 24, 2021	2021	TV-MA	1 Season	Crime TV Shows, International TV Shows, TV Act...	To protect his family from a powerful drug lor...

In [4]:

for i in df.columns:
    null_rate = df[i].isna().sum() / len(df) * 100 
    if null_rate > 0 :
        print("{} null rate: {}%".format(i,round(null_rate,2)))

director null rate: 29.91%
cast null rate: 9.37%
country null rate: 9.44%
date_added null rate: 0.11%
rating null rate: 0.05%
duration null rate: 0.03%

1) Dealing with the missing data

• replace blank countries with the mode (most common) country
• The director's value is maintained because the box office rate may vary depending on the director.
• The cast's value is maintained because the box office rate may vary depending on the cast.

In [5]:

# Replacments
df['country'] = df['country'].fillna(df['country'].mode()[0])


df['cast'].replace(np.nan, 'No Data',inplace  = True)
df['director'].replace(np.nan, 'No Data',inplace  = True)

# Drops
df.dropna(inplace=True)

# Drop Duplicates
df.drop_duplicates(inplace= True)

In [6]:

df.isnull().sum()

Out[6]:

show_id         0
type            0
title           0
director        0
cast            0
country         0
date_added      0
release_year    0
rating          0
duration        0
listed_in       0
description     0
dtype: int64

In [7]:

df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 8790 entries, 0 to 8806
Data columns (total 12 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   show_id       8790 non-null   object
 1   type          8790 non-null   object
 2   title         8790 non-null   object
 3   director      8790 non-null   object
 4   cast          8790 non-null   object
 5   country       8790 non-null   object
 6   date_added    8790 non-null   object
 7   release_year  8790 non-null   int64 
 8   rating        8790 non-null   object
 9   duration      8790 non-null   object
 10  listed_in     8790 non-null   object
 11  description   8790 non-null   object
dtypes: int64(1), object(11)
memory usage: 892.7+ KB

2) Dealing with Date Value

In [8]:

df["date_added"] = pd.to_datetime(df['date_added'])

df['month_added']=df['date_added'].dt.month
df['month_name_added']=df['date_added'].dt.month_name()
df['year_added'] = df['date_added'].dt.year

df.head(3)

Out[8]:

show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescriptionmonth_addedmonth_name_addedyear_added012

s1

Movie

Dick Johnson Is Dead

Kirsten Johnson

No Data

United States

2021-09-25

2020

PG-13

90 min

Documentaries

As her father nears the end of his life, filmm...

9

September

2021

s2

TV Show

Blood & Water

No Data

Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...

South Africa

2021-09-24

2021

TV-MA

2 Seasons

International TV Shows, TV Dramas, TV Mysteries

After crossing paths at a party, a Cape Town t...

9

September

2021

s3

TV Show

Ganglands

Julien Leclercq

Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...

United States

2021-09-24

2021

TV-MA

1 Season

Crime TV Shows, International TV Shows, TV Act...

To protect his family from a powerful drug lor...

9

September

2021

3) Feature Engineering

In [9]:

df['count'] = 1
df['first_country'] = df['country'].apply(lambda x: x.split(",")[0])
df['first_country'].head()

# Rating age
ratings_ages = {
    'TV-PG': 'Older Kids',
    'TV-MA': 'Adults',
    'TV-Y7-FV': 'Older Kids',
    'TV-Y7': 'Older Kids',
    'TV-14': 'Teens',
    'R': 'Adults',
    'TV-Y': 'Kids',
    'NR': 'Adults',
    'PG-13': 'Teens',
    'TV-G': 'Kids',
    'PG': 'Older Kids',
    'G': 'Kids',
    'UR': 'Adults',
    'NC-17': 'Adults'
}

df['target_ages'] = df['rating'].replace(ratings_ages)
df['target_ages'].unique()

# Genre
df['genre'] = df['listed_in'].apply(lambda x :  x.replace(' ,',',').replace(', ',',').split(',')) 

# Reducing name length
df['first_country'].replace('United States', 'USA', inplace=True)
df['first_country'].replace('United Kingdom', 'UK',inplace=True)
df['first_country'].replace('South Korea', 'S. Korea',inplace=True)
data = df.groupby('first_country')['count'].sum().sort_values(ascending=False)[:10]

3. Visualization

1) Neflix colours Palette

• Using a consistent color palette is a great way to give your work credibility. It looks professional, and keeps the reader engaged.

In [10]:

# Palette
sns.palplot(['#221f1f', '#b20710', '#e50914','#f5f5f1'])

plt.title("Netflix brand palette ",loc='left',fontfamily='serif',fontsize=15,y=1.2)
plt.show()

2) Content

• Let's find out which content makes up more, Movie or TV show

In [11]:

x=df.groupby(['type'])['type'].count()
y=len(df)
r=((x/y)).round(2)

mf_ratio = pd.DataFrame(r).T

fig, ax = plt.subplots(1,1,figsize=(6.5, 2.5))

ax.barh(mf_ratio.index, mf_ratio['Movie'], 
        color='#b20710', alpha=0.9, label='Male')
ax.barh(mf_ratio.index, mf_ratio['TV Show'], left=mf_ratio['Movie'], 
        color='#221f1f', alpha=0.9, label='Female')

ax.set_xlim(0, 1)
ax.set_xticks([])
ax.set_yticks([])


# movie percentage
for i in mf_ratio.index:
    ax.annotate(f"{int(mf_ratio['Movie'][i]*100)}%", 
                   xy=(mf_ratio['Movie'][i]/2, i),
                   va = 'center', ha='center',fontsize=40, fontweight='light', fontfamily='serif',
                   color='white')

    ax.annotate("Movie", 
                   xy=(mf_ratio['Movie'][i]/2, -0.25),
                   va = 'center', ha='center',fontsize=15, fontweight='light', fontfamily='serif',
                   color='white')
        
for i in mf_ratio.index:
    ax.annotate(f"{int(mf_ratio['TV Show'][i]*100)}%", 
                   xy=(mf_ratio['Movie'][i]+mf_ratio['TV Show'][i]/2, i),
                   va = 'center', ha='center',fontsize=40, fontweight='light', fontfamily='serif',
                   color='white')
    ax.annotate("TV Show", 
                   xy=(mf_ratio['Movie'][i]+mf_ratio['TV Show'][i]/2, -0.25),
                   va = 'center', ha='center',fontsize=15, fontweight='light', fontfamily='serif',
                   color='white')



# Title & Subtitle
fig.text(0.125,1.03,'Movie & TV Show distribution', fontfamily='serif',fontsize=15, fontweight='bold')
fig.text(0.125,0.92,'We see vastly more movies than TV shows on Netflix.',fontfamily='serif',fontsize=12)  

for s in ['top', 'left', 'right', 'bottom']:
    ax.spines[s].set_visible(False)


# Removing legend due to labelled plot
ax.legend().set_visible(False)
plt.show()

3) By Country

a) Let's look at the 10 countries that produced the most content

In [12]:

color_map = ['#f5f5f1' for _ in range(10)]
color_map[0] = color_map[1] = color_map[2] =  '#b20710' # color highlight

fig, ax = plt.subplots(1,1, figsize=(12, 6))
ax.bar(data.index, data, width=0.5, 
       edgecolor='darkgray',
       linewidth=0.6,color=color_map)

for i in data.index:
    ax.annotate(f"{data[i]}", 
                   xy=(i, data[i] + 150), #i like to change this to roughly 5% of the highest cat
                   va = 'center', ha='center',fontweight='light', fontfamily='serif')



for s in ['top', 'left', 'right']:
    ax.spines[s].set_visible(False)
    

ax.set_xticklabels(data.index, fontfamily='serif', rotation=0)


fig.text(0.09, 1, 'Top 10 countries on Netflix', fontsize=15, fontweight='bold', fontfamily='serif')
fig.text(0.09, 0.95, 'The three most frequent countries have been highlighted.', fontsize=12, fontweight='light', fontfamily='serif')

fig.text(1.1, 1.01, 'Insight', fontsize=15, fontweight='bold', fontfamily='serif')

fig.text(1.1, 0.67, '''
The most prolific producers of
content for Netflix are, primarily,
the USA, with India and the UK
a significant distance behind.

It makes sense that the USA produces 
the most content as, afterall, 
Netflix is a US company.
'''
         , fontsize=12, fontweight='light', fontfamily='serif')

ax.grid(axis='y', linestyle='-', alpha=0.4)   

grid_y_ticks = np.arange(0, 4000, 500) # y ticks, min, max, then step
ax.set_yticks(grid_y_ticks)
ax.set_axisbelow(True)

plt.axhline(y = 0, color = 'black', linewidth = 1.3, alpha = .7)

ax.tick_params(axis='both', which='major', labelsize=12)


import matplotlib.lines as lines
l1 = lines.Line2D([1, 1], [0, 1], transform=fig.transFigure, figure=fig,color='black',lw=0.2)
fig.lines.extend([l1])

ax.tick_params(axis=u'both', which=u'both',length=0)

plt.show()

b) Let's look at the proportion of Movie and TV shows among the contents of the above 10 countries

In [13]:

country_order = df['first_country'].value_counts()[:11].index
data_q2q3 = df[['type', 'first_country']].groupby('first_country')['type'].value_counts().unstack().loc[country_order]
data_q2q3['sum'] = data_q2q3.sum(axis=1)
data_q2q3_ratio = (data_q2q3.T / data_q2q3['sum']).T[['Movie', 'TV Show']].sort_values(by='Movie',ascending=False)[::-1]


fig, ax = plt.subplots(1,1,figsize=(15, 8),)

ax.barh(data_q2q3_ratio.index, data_q2q3_ratio['Movie'], 
        color='#b20710', alpha=0.8, label='Movie')
ax.barh(data_q2q3_ratio.index, data_q2q3_ratio['TV Show'], left=data_q2q3_ratio['Movie'], 
        color='#221f1f', alpha=0.8, label='TV Show')


ax.set_xlim(0, 1)
ax.set_xticks([])
ax.set_yticklabels(data_q2q3_ratio.index, fontfamily='serif', fontsize=11)

# male percentage
for i in data_q2q3_ratio.index:
    ax.annotate(f"{data_q2q3_ratio['Movie'][i]*100:.3}%", 
                   xy=(data_q2q3_ratio['Movie'][i]/2, i),
                   va = 'center', ha='center',fontsize=12, fontweight='light', fontfamily='serif',
                   color='white')

for i in data_q2q3_ratio.index:
    ax.annotate(f"{data_q2q3_ratio['TV Show'][i]*100:.3}%", 
                   xy=(data_q2q3_ratio['Movie'][i]+data_q2q3_ratio['TV Show'][i]/2, i),
                   va = 'center', ha='center',fontsize=12, fontweight='light', fontfamily='serif',
                   color='white')
    

fig.text(0.13, 0.93, 'Top 10 countries Movie & TV Show split', fontsize=15, fontweight='bold', fontfamily='serif')   
fig.text(0.131, 0.89, 'Percent Stacked Bar Chart', fontsize=12,fontfamily='serif')   

for s in ['top', 'left', 'right', 'bottom']:
    ax.spines[s].set_visible(False)
    

fig.text(0.75,0.9,"Movie", fontweight="bold", fontfamily='serif', fontsize=15, color='#b20710')
fig.text(0.81,0.9,"|", fontweight="bold", fontfamily='serif', fontsize=15, color='black')
fig.text(0.82,0.9,"TV Show", fontweight="bold", fontfamily='serif', fontsize=15, color='#221f1f')


fig.text(1.1, 0.93, 'Insight', fontsize=15, fontweight='bold', fontfamily='serif')

fig.text(1.1, 0.44, '''
Interestingly, Netflix in India
is made up nearly entirely of Movies. 

Bollywood is big business, and perhaps
the main focus of this industry is Movies
and not TV Shows.

South Korean Netflix on the other hand is 
almost entirely TV Shows.

The underlying resons for the difference 
in content must be due to market research
conducted by Netflix.
'''
         , fontsize=12, fontweight='light', fontfamily='serif')


import matplotlib.lines as lines
l1 = lines.Line2D([1, 1], [0, 1], transform=fig.transFigure, figure=fig,color='black',lw=0.2)
fig.lines.extend([l1])


ax.tick_params(axis='both', which='major', labelsize=12)
ax.tick_params(axis=u'both', which=u'both',length=0)

plt.show()

4) Ratings

Let's briefly check out how ratings are distributed

In [14]:

order = pd.DataFrame(df.groupby('rating')['count'].sum().sort_values(ascending=False).reset_index())
rating_order = list(order['rating'])

mf = df.groupby('type')['rating'].value_counts().unstack().sort_index().fillna(0).astype(int)[rating_order]

movie = mf.loc['Movie']
tv = - mf.loc['TV Show']

In [15]:

fig, ax = plt.subplots(1,1, figsize=(12, 6))
ax.bar(movie.index, movie, width=0.5, color='#b20710', alpha=0.8, label='Movie')
ax.bar(tv.index, tv, width=0.5, color='#221f1f', alpha=0.8, label='TV Show')

# Annotations
for i in tv.index:
    ax.annotate(f"{-tv[i]}", 
                   xy=(i, tv[i] - 60),
                   va = 'center', ha='center',fontweight='light', fontfamily='serif',
                   color='#4a4a4a')   

for i in movie.index:
    ax.annotate(f"{movie[i]}", 
                   xy=(i, movie[i] + 60),
                   va = 'center', ha='center',fontweight='light', fontfamily='serif',
                   color='#4a4a4a')
    
 

for s in ['top', 'left', 'right', 'bottom']:
    ax.spines[s].set_visible(False)

ax.set_xticklabels(mf.columns, fontfamily='serif')
ax.set_yticks([])    

ax.legend().set_visible(False)
fig.text(0.16, 1, 'Rating distribution by Film & TV Show', fontsize=15, fontweight='bold', fontfamily='serif')
fig.text(0.16, 0.89, 
'''We observe that some ratings are only applicable to Movies. 
The most common for both Movies & TV Shows are TV-MA and TV-14.
'''

, fontsize=12, fontweight='light', fontfamily='serif')


fig.text(0.755,0.924,"Movie", fontweight="bold", fontfamily='serif', fontsize=15, color='#b20710')
fig.text(0.815,0.924,"|", fontweight="bold", fontfamily='serif', fontsize=15, color='black')
fig.text(0.825,0.924,"TV Show", fontweight="bold", fontfamily='serif', fontsize=15, color='#221f1f')

plt.show()

5) Movies & TV shows added over time

Let's look at the amount of content added by year

In [16]:

fig, ax = plt.subplots(1, 1, figsize=(12, 6))
color = ["#b20710", "#221f1f"]

for i, mtv in enumerate(df['type'].value_counts().index):
    mtv_rel = df[df['type']==mtv]['year_added'].value_counts().sort_index()
    ax.plot(mtv_rel.index, mtv_rel, color=color[i], label=mtv)
    ax.fill_between(mtv_rel.index, 0, mtv_rel, color=color[i], alpha=0.9)
    
ax.yaxis.tick_right()
    
ax.axhline(y = 0, color = 'black', linewidth = 1.3, alpha = .7)

for s in ['top', 'right','bottom','left']:
    ax.spines[s].set_visible(False)

ax.grid(False)

ax.set_xlim(2008,2020)
plt.xticks(np.arange(2008, 2021, 1))

fig.text(0.13, 0.85, 'Movies & TV Shows added over time', fontsize=15, fontweight='bold', fontfamily='serif')
fig.text(0.13, 0.59, 
'''We see a slow start for Netflix over several years. 
Things begin to pick up in 2015 and then there is a 
rapid increase from 2016.

It looks like content additions have slowed down in 2020, 
likely due to the COVID-19 pandemic.
'''

, fontsize=12, fontweight='light', fontfamily='serif')


fig.text(0.13,0.2,"Movie", fontweight="bold", fontfamily='serif', fontsize=15, color='#b20710')
fig.text(0.19,0.2,"|", fontweight="bold", fontfamily='serif', fontsize=15, color='black')
fig.text(0.2,0.2,"TV Show", fontweight="bold", fontfamily='serif', fontsize=15, color='#221f1f')

ax.tick_params(axis=u'both', which=u'both',length=0)

plt.show()

6) Month-by-Month

Let's take a monthly look at the amount of content added during the year

In [17]:

month_order = ['January',
 'February',
 'March',
 'April',
 'May',
 'June',
 'July',
 'August',
 'September',
 'October',
 'November',
 'December']

df['month_name_added'] = pd.Categorical(df['month_name_added'], categories=month_order, ordered=True)

In [18]:

data_sub = df.groupby('type')['month_name_added'].value_counts().unstack().fillna(0).loc[['TV Show','Movie']].cumsum(axis=0).T

fig, ax = plt.subplots(1, 1, figsize=(12, 6))
color = ["#b20710", "#221f1f"]

for i, mtv in enumerate(df['type'].value_counts().index):
    mtv_rel = data_sub[mtv]
    ax.fill_between(mtv_rel.index, 0, mtv_rel, color=color[i], label=mtv,alpha=0.9)
    
    
ax.yaxis.tick_right()
    
ax.axhline(y = 0, color = 'black', linewidth = 1.3, alpha = .4)


for s in ['top', 'right','bottom','left']:
    ax.spines[s].set_visible(False)

ax.grid(False)
ax.set_xticklabels(data_sub.index, fontfamily='serif', rotation=0)
ax.margins(x=0) # remove white spaces next to margins


fig.text(0.13, 0.95, 'Content added by month [Cumulative Total]', fontsize=15, fontweight='bold', fontfamily='serif')
fig.text(0.13, 0.905, 
"The end & beginnings of each year seem to be Netflix's preference for adding content."

, fontsize=12, fontweight='light', fontfamily='serif')



fig.text(0.13,0.855,"Movie", fontweight="bold", fontfamily='serif', fontsize=15, color='#b20710')
fig.text(0.19,0.855,"|", fontweight="bold", fontfamily='serif', fontsize=15, color='black')
fig.text(0.2,0.855,"TV Show", fontweight="bold", fontfamily='serif', fontsize=15, color='#221f1f')


ax.tick_params(axis=u'both', which=u'both',length=0)

plt.show()

7) Movie Genres

In [19]:

cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#221f1f', '#b20710','#f5f5f1'])

def genre_heatmap(df, title):
    df['genre'] = df['listed_in'].apply(lambda x :  x.replace(' ,',',').replace(', ',',').split(',')) 
    Types = []
    for i in df['genre']: Types += i
    Types = set(Types)
    print("There are {} types in the Netflix {} Dataset".format(len(Types),title))    
    test = df['genre']
    mlb = MultiLabelBinarizer()
    res = pd.DataFrame(mlb.fit_transform(test), columns=mlb.classes_, index=test.index)
    corr = res.corr()
    mask = np.zeros_like(corr, dtype=np.bool)
    mask[np.triu_indices_from(mask)] = True
    fig, ax = plt.subplots(figsize=(10, 7))
    fig.text(.54,.88,'Genre correlation', fontfamily='serif',fontweight='bold',fontsize=15)
    fig.text(.75,.665,
            '''
             It is interesting that Independant Movies
             tend to be Dramas. 
             
             Another observation is that 
             Internatinal Movies are rarely
             in the Children's genre.
             ''', fontfamily='serif',fontsize=12,ha='right')
    pl = sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, vmin=-.3, center=0, square=True, linewidths=2.5)
    
    plt.show()

In [20]:

df_tv = df[df["type"] == "TV Show"]
df_movies = df[df["type"] == "Movie"]

genre_heatmap(df_movies, 'Movie')
plt.show()

There are 20 types in the Netflix Movie Dataset

In [21]:

data = df.groupby('first_country')[['first_country','count']].sum().sort_values(by='count',ascending=False).reset_index()[:10]
data = data['first_country']


df_heatmap = df.loc[df['first_country'].isin(data)]
df_heatmap = pd.crosstab(df_heatmap['first_country'],df_heatmap['target_ages'],normalize = "index").T

8) Target Ages

Does Netflix uniformly target certain demographics? Or does this vary by country?

In [22]:

fig, ax = plt.subplots(1, 1, figsize=(12, 12))

country_order2 = ['USA', 'India', 'UK', 'Canada', 'Japan', 'France', 'S. Korea', 'Spain',
       'Mexico', 'Australia']

age_order = ['Kids','Older Kids','Teens','Adults']

sns.heatmap(df_heatmap.loc[age_order,country_order2],cmap=cmap,square=True, linewidth=2.5,cbar=False,
            annot=True,fmt='1.0%',vmax=.6,vmin=0.05,ax=ax,annot_kws={"fontsize":12})

ax.spines['top'].set_visible(True)


fig.text(.99, .725, 'Target ages proportion of total content by country', fontweight='bold', fontfamily='serif', fontsize=15,ha='right')   
fig.text(0.99, 0.7, 'Here we see interesting differences between countries. Most shows in India are targeted to teens, for instance.',ha='right', fontsize=12,fontfamily='serif') 

ax.set_yticklabels(ax.get_yticklabels(), fontfamily='serif', rotation = 0, fontsize=11)
ax.set_xticklabels(ax.get_xticklabels(), fontfamily='serif', rotation=90, fontsize=11)

ax.set_ylabel('')    
ax.set_xlabel('')
ax.tick_params(axis=u'both', which=u'both',length=0)
plt.tight_layout()
plt.show()

9) WordCloud of Netflix Title

Let's take a look at the words that are often used in content titles through WordCloud

In [23]:

cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#221f1f', '#b20710'])

text = str(list(df['title'])).replace(',', '').replace('[', '').replace("'", '').replace(']', '').replace('.', '')

mask = np.array(Image.open('../input/netflix-icon-word-cloud/732082.png'))

wordcloud = WordCloud(background_color = 'white', width = 500,  height = 200,colormap=cmap, max_words = 150, mask = mask).generate(text)

plt.figure( figsize=(5,5))
plt.imshow(wordcloud, interpolation = 'bilinear')
plt.axis('off')
plt.tight_layout(pad=0)
plt.show()