Highest Hollywood Grossing Movies¶

---

This nootebook aims to answer the following questions based on the dataset highest holywood grossing movies:

1. What is the highest-grossing film of all time? What was the gross in dollars ($)?
2. What is the average runtime of Warner Bros.’s movies?
3. If you were a great executive responsible for approving projects, which genre would you choose to maximize the most your profit?

In [159]:

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

data = pd.read_csv('highest_holywood_grossing_movies.csv')
data.info()

import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) import matplotlib.pyplot as plt import seaborn as sns data = pd.read_csv('highest_holywood_grossing_movies.csv') data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 918 entries, 0 to 917
Data columns (total 11 columns):
 #   Column                      Non-Null Count  Dtype 
---  ------                      --------------  ----- 
 0   Unnamed: 0                  918 non-null    int64 
 1   Title                       918 non-null    object
 2   Movie Info                  918 non-null    object
 3   Distributor                 918 non-null    object
 4   Release Date                800 non-null    object
 5   Domestic Sales (in $)       918 non-null    int64 
 6   International Sales (in $)  918 non-null    int64 
 7   World Sales (in $)          918 non-null    int64 
 8   Genre                       918 non-null    object
 9   Movie Runtime               918 non-null    object
 10  License                     744 non-null    object
dtypes: int64(4), object(7)
memory usage: 79.0+ KB

What is the highest-grossing film of all time? What was the gross in dollars ($)?¶

---

In [160]:

def currency(currency: float):
    formatted_float = "${:,.2f}".format(currency)
    return formatted_float

def currency(currency: float): formatted_float = "${:,.2f}".format(currency) return formatted_float

In [161]:

sort_by_grossing = data.sort_values(['World Sales (in $)'],ascending=False)
sort_by_grossing['World Sales (in $)'] = pd.to_numeric(sort_by_grossing['World Sales (in $)'])
sort_by_grossing['Domestic Sales (in $)'] = pd.to_numeric(sort_by_grossing['Domestic Sales (in $)'])
sort_by_grossing['International Sales (in $)'] = pd.to_numeric(sort_by_grossing['International Sales (in $)'])

sort_by_grossing['Domestic Sales (in $)'] = sort_by_grossing['Domestic Sales (in $)'].apply(currency)
sort_by_grossing['International Sales (in $)'] = sort_by_grossing['International Sales (in $)'].apply(currency)
sort_by_grossing['World Sales (in $)'] = sort_by_grossing['World Sales (in $)'].apply(currency)

sort_by_grossing[['Title', 'Domestic Sales (in $)','International Sales (in $)','World Sales (in $)']].head(3)

sort_by_grossing = data.sort_values(['World Sales (in $)'],ascending=False) sort_by_grossing['World Sales (in $)'] = pd.to_numeric(sort_by_grossing['World Sales (in $)']) sort_by_grossing['Domestic Sales (in $)'] = pd.to_numeric(sort_by_grossing['Domestic Sales (in $)']) sort_by_grossing['International Sales (in $)'] = pd.to_numeric(sort_by_grossing['International Sales (in $)']) sort_by_grossing['Domestic Sales (in $)'] = sort_by_grossing['Domestic Sales (in $)'].apply(currency) sort_by_grossing['International Sales (in $)'] = sort_by_grossing['International Sales (in $)'].apply(currency) sort_by_grossing['World Sales (in $)'] = sort_by_grossing['World Sales (in $)'].apply(currency) sort_by_grossing[['Title', 'Domestic Sales (in $)','International Sales (in $)','World Sales (in $)']].head(3)

Out[161]:

	Title	Domestic Sales (in $)	International Sales (in $)	World Sales (in $)
2	Avatar (2009)	$760,507,625.00	$2,086,738,578.00	$2,847,246,203.00
1	Avengers: Endgame (2019)	$858,373,000.00	$1,939,128,328.00	$2,797,501,328.00
6	Titanic (1997)	$659,363,944.00	$1,542,283,320.00	$2,201,647,264.00

What is the average runtime of Warner Bros.’s movies?¶

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In [162]:

#data treatment -> total minutes
def time_conv(time: str):
    # Convert runtimes to minute
    time = time.split(' ')
    length = len(time)

    if length == 4:
        hour, minute = int(time[0]), int(time[2])
        return hour*60 + minute
    elif length == 2:
        minute = int(time[0])
        return minute*60

#data treatment -> total minutes def time_conv(time: str): # Convert runtimes to minute time = time.split(' ') length = len(time) if length == 4: hour, minute = int(time[0]), int(time[2]) return hour*60 + minute elif length == 2: minute = int(time[0]) return minute*60

In [163]:

#Converting string runtime into minutes
data["Movie Runtime"] = data["Movie Runtime"].apply(time_conv)
data[['Title','Distributor','Movie Runtime']].head(3)

#Converting string runtime into minutes data["Movie Runtime"] = data["Movie Runtime"].apply(time_conv) data[['Title','Distributor','Movie Runtime']].head(3)

Out[163]:

	Title	Distributor	Movie Runtime
0	Star Wars: Episode VII - The Force Awakens (2015)	Walt Disney Studios Motion Pictures	138
1	Avengers: Endgame (2019)	Walt Disney Studios Motion Pictures	181
2	Avatar (2009)	Twentieth Century Fox	162

In [164]:

#Counting warner bros movies 
warner_bros_movies = data[data['Distributor']=="Warner Bros."]
print("Total movies:\n")
print(warner_bros_movies["Distributor"].count())
total_movies = warner_bros_movies["Distributor"].count()

#Counting warner bros movies warner_bros_movies = data[data['Distributor']=="Warner Bros."] print("Total movies:\n") print(warner_bros_movies["Distributor"].count()) total_movies = warner_bros_movies["Distributor"].count()

Total movies:

158

In [165]:

#Sum duration time in minutes from warner bros movies
total_duration_minutes = warner_bros_movies["Movie Runtime"].sum()
print("Total time(in minutes):\n")
print(total_duration_minutes)

#Sum duration time in minutes from warner bros movies total_duration_minutes = warner_bros_movies["Movie Runtime"].sum() print("Total time(in minutes):\n") print(total_duration_minutes)

Total time(in minutes):

19360

In [166]:

#Calculating average time in minutes
time_average = total_duration_minutes/total_movies
#print(time_average)
hours = time_average//60
minutes = time_average % 60
# seconds = time_average * 60

print("Warner Bros.'s movies average time:\n")
print("%d Hours %02d minutes" % (hours, minutes))

#Calculating average time in minutes time_average = total_duration_minutes/total_movies #print(time_average) hours = time_average//60 minutes = time_average % 60 # seconds = time_average * 60 print("Warner Bros.'s movies average time:\n") print("%d Hours %02d minutes" % (hours, minutes))

Warner Bros.'s movies average time:

2 Hours 02 minutes

If you were a great executive responsible for approving projects, which genre would you choose to maximize the most your profit?¶

---

In [167]:

gb_genre = data.groupby('Genre')

gb_genre = data.groupby('Genre')

In [168]:

most_sales_genre = gb_genre.sum().sort_values('World Sales (in $)', ascending=False)[:10]

most_sales_genre = gb_genre.sum().sort_values('World Sales (in $)', ascending=False)[:10]

In [169]:

fig, ax = plt.subplots(figsize=(10, 11))

sns.set_theme(palette='Set2')
sns.barplot(ax=ax, x=most_sales_genre['World Sales (in $)'], y=most_sales_genre.index)

ax.set_title('Top 10 Genres by World Sales', fontsize=20, pad=10)
ax.set_xlabel('World Sales', fontdict={'fontsize': 13}, labelpad=10)
ax.set_ylabel('Genres', fontdict={'fontsize': 13}, labelpad=10)
ax.xaxis.set_major_formatter(lambda x, pos: f'{int(x)/1_000_000_000}B')

plt.show()

fig, ax = plt.subplots(figsize=(10, 11)) sns.set_theme(palette='Set2') sns.barplot(ax=ax, x=most_sales_genre['World Sales (in $)'], y=most_sales_genre.index) ax.set_title('Top 10 Genres by World Sales', fontsize=20, pad=10) ax.set_xlabel('World Sales', fontdict={'fontsize': 13}, labelpad=10) ax.set_ylabel('Genres', fontdict={'fontsize': 13}, labelpad=10) ax.xaxis.set_major_formatter(lambda x, pos: f'{int(x)/1_000_000_000}B') plt.show()