可视化 | Netflix 数据可视化最佳实践

- 作者JOSH
- 原文: https://www.kaggle.com/code/joshuaswords/netflix-data-visualization/notebook?scriptVersionId=58425238&cellId=17

import pandas as pd
import warnings
warnings.filterwarnings("ignore")

df = pd.read_csv('netflix_titles.csv')
df.head(3)

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

import numpy as np

# Replacments替换
#将空白国家/地区替换为模式（最常见）国家/地区
df['country'] = df['country'].fillna(df['country'].mode()[0])
#我想保留演员阵容，因为看某个演员的电影可能会很有趣
df['cast'].replace(np.nan, 'No Data', inplace=True)
#我想保留导演，因为看某个导演的电影可能会很有趣
df['director'].replace(np.nan, 'No Data', inplace=True)


# Drops删除
df.dropna(inplace=True)

# Drop Duplicates剔除重复
df.drop_duplicates(inplace= True)


#查看各字段null数量
df.isnull().sum()

df['date_added'] = pd.to_datetime(df['date_added'], format='mixed')

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)

import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
plt.rcParams['figure.dpi'] = 140

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

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

# Timeline code from Subin An's awesome notebook
# https://www.kaggle.com/subinium/awesome-visualization-with-titanic-dataset


from datetime import datetime

## these go on the numbers below
tl_dates = [
    "1997\nFounded",
    "1998\nMail Service",
    "2003\nGoes Public",
    "2007\nStreaming service",
    "2016\nGoes Global",
    "2021\nNetflix & Chill"
]

tl_x = [1, 2, 4, 5.3, 8, 9]

## these go on the numbers
tl_sub_x = [1.5, 3, 5, 6.5, 7]


tl_sub_times = [
    "1998","2000","2006","2010","2012"
]

tl_text = [
    "Netflix.com launched",
    "Starts\nPersonal\nRecommendations","Billionth DVD Delivery","Canadian\nLaunch","UK Launch\n(my birthplace)"]



# Set figure & Axes
fig, ax = plt.subplots(figsize=(15, 4), constrained_layout=True)
ax.set_ylim(-2, 1.75)
ax.set_xlim(0, 10)


# Timeline : line
ax.axhline(0, xmin=0.1, xmax=0.9, c='#4a4a4a', zorder=1)


# Timeline : Date Points
ax.scatter(tl_x, np.zeros(len(tl_x)), s=120, c='#4a4a4a', zorder=2)
ax.scatter(tl_x, np.zeros(len(tl_x)), s=30, c='#fafafa', zorder=3)
# Timeline : Time Points
ax.scatter(tl_sub_x, np.zeros(len(tl_sub_x)), s=50, c='#4a4a4a',zorder=4)

# Date Text
for x, date in zip(tl_x, tl_dates):
    ax.text(x, -0.55, date, ha='center', 
            fontfamily='serif', fontweight='bold',
            color='#4a4a4a',fontsize=12)
    

# Stemplot : vertical line
levels = np.zeros(len(tl_sub_x))    
levels[::2] = 0.3
levels[1::2] = -0.3
markerline, stemline, baseline = ax.stem(tl_sub_x, levels, use_line_collection=True)    
plt.setp(baseline, zorder=0)
plt.setp(markerline, marker=',', color='#4a4a4a')
plt.setp(stemline, color='#4a4a4a')

# Text
for idx, x, time, txt in zip(range(1, len(tl_sub_x)+1), tl_sub_x, tl_sub_times, tl_text):
    ax.text(x, 1.3*(idx%2)-0.5, time, ha='center', 
            fontfamily='serif', fontweight='bold',
            color='#4a4a4a' if idx!=len(tl_sub_x) else '#b20710', fontsize=11)
    
    ax.text(x, 1.3*(idx%2)-0.6, txt, va='top', ha='center', 
        fontfamily='serif',color='#4a4a4a' if idx!=len(tl_sub_x) else '#b20710')



# Spine
for spine in ["left", "top", "right", "bottom"]:
    ax.spines[spine].set_visible(False)

# Ticks    
ax.set_xticks([]) 
ax.set_yticks([]) 

# Title
ax.set_title("Netflix through the years", fontweight="bold", fontfamily='serif', fontsize=16, color='#4a4a4a')
ax.text(2.4,1.57,"From DVD rentals to a global audience of over 150m people - is it time for Netflix to Chill?", fontfamily='serif', fontsize=12, color='#4a4a4a')

plt.show()

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([])
#ax.set_yticklabels(mf_ratio.index, fontfamily='serif', fontsize=11)


# 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)
    


#ax.legend(loc='lower center', ncol=3, bbox_to_anchor=(0.5, -0.06))

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

# Quick feature engineering

# Helper column for various plots
df['count'] = 1

# Many productions have several countries listed - this will skew our results , we'll grab the first one mentioned

# Lets retrieve just the first country
df['first_country'] = df['country'].apply(lambda x: x.split(",")[0])
df['first_country'].head()

# Rating ages from this notebook: https://www.kaggle.com/andreshg/eda-beginner-to-expert-plotly (thank you!)

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]

# Plot

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)

#annotations
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')



# Remove border from plot

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

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

# Title and sub-title

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)

#Axis labels

#plt.xlabel("Country", fontsize=12, fontweight='light', fontfamily='serif',loc='left',y=-1.5)
#plt.ylabel("Count", fontsize=12, fontweight='light', fontfamily='serif')
 #plt.legend(loc='upper right')
    
# thicken the bottom line if you want to
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()

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)
    
#ax.legend(loc='lower center', ncol=3, bbox_to_anchor=(0.5, -0.06))

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()

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']


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')
#ax.set_ylim(-35, 50)

# 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()

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)

#ax.set_ylim(0, 50)
#ax.legend(loc='upper left')
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()

data_sub = df.groupby('type')['year_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 = .7)

#ax.set_ylim(0, 50)
#ax.legend(loc='upper left')
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 [Cumulative Total]', fontsize=15, fontweight='bold', fontfamily='serif')
fig.text(0.13, 0.58, 
'''Netflix peak global content amount was in 2019.

It appears that Netflix has focused more attention
on increasing Movie content that TV Shows. 
Movies have increased much more dramatically
than TV shows.
'''

, 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()

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)


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)

#ax.set_ylim(0, 50)
#ax.legend(loc='upper left')
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

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

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()

data_sub2 = data_sub

data_sub2['Value'] = data_sub2['Movie'] + data_sub2['TV Show']
data_sub2 = data_sub2.reset_index()

df_polar = data_sub2.sort_values(by='month_name_added',ascending=False)


color_map = ['#221f1f' for _ in range(12)]
color_map[0] = color_map[11] =  '#b20710' # color highlight


# initialize the figure
plt.figure(figsize=(8,8))
ax = plt.subplot(111, polar=True)
plt.axis('off')

# Constants = parameters controling the plot layout:
upperLimit = 30
lowerLimit = 1
labelPadding = 30

# Compute max and min in the dataset
max = df_polar['Value'].max()

# Let's compute heights: they are a conversion of each item value in those new coordinates
# In our example, 0 in the dataset will be converted to the lowerLimit (10)
# The maximum will be converted to the upperLimit (100)
slope = (max - lowerLimit) / max
heights = slope * df_polar.Value + lowerLimit

# Compute the width of each bar. In total we have 2*Pi = 360°
width = 2*np.pi / len(df_polar.index)

# Compute the angle each bar is centered on:
indexes = list(range(1, len(df_polar.index)+1))
angles = [element * width for element in indexes]
angles

# Draw bars
bars = ax.bar(
    x=angles, 
    height=heights, 
    width=width, 
    bottom=lowerLimit,
    linewidth=2, 
    edgecolor="white",
    color=color_map,alpha=0.8
)

# Add labels
for bar, angle, height, label in zip(bars,angles, heights, df_polar["month_name_added"]):

    # Labels are rotated. Rotation must be specified in degrees :(
    rotation = np.rad2deg(angle)

    # Flip some labels upside down
    alignment = ""
    if angle >= np.pi/2 and angle < 3*np.pi/2:
        alignment = "right"
        rotation = rotation + 180
    else: 
        alignment = "left"

    # Finally add the labels
    ax.text(
        x=angle, 
        y=lowerLimit + bar.get_height() + labelPadding, 
        s=label, 
        ha=alignment, fontsize=10,fontfamily='serif',
        va='center', 
        rotation=rotation, 
        rotation_mode="anchor") 

# Genres
from sklearn.preprocessing import MultiLabelBinarizer 

import matplotlib.colors


# Custom colour map based on Netflix palette
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=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()
    
    
df_tv = df[df["type"] == "TV Show"]
df_movies = df[df["type"] == "Movie"]


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