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Top Car Brands YouTube Chanel Statistics

1 Toyota

Toyota Motor Corporation is a Japanese multinational automotive manufacturer headquartered in Toyota City, Aichi, Japan. It was founded by Kiichiro Toyoda and incorporated on August 28, 1937.

2 Nissan

Nissan Motor Co. Ltd is a Japanese multinational automobile manufacturer headquartered in Yokohama, Kanagawa, Japan

3 Land Rover

Land Rover is a British brand of predominantly four-wheel drive, off-road capable vehicles, owned by multinational car manufacturer Jaguar Land Rover, since 2008 a subsidiary of India's Tata Motors. JLR currently builds Land Rovers in Brazil, China, India, Slovakia, and the United Kingdom

4 Mercedes-Benz

Mercedes-Benz, commonly referred to as Mercedes and sometimes as Benz, is a German luxury and commercial vehicle automotive brand established in 1926. Mercedes-Benz AG is headquartered in Stuttgart, Baden-Württemberg, Germany

5 BMW

Bayerische Motoren Werke AG, commonly abbreviated to BMW is a German multinational manufacturer of luxury vehicles and motorcycles headquartered in Munich, Bavaria, Germany

# lets import libraries
from googleapiclient.discovery import build
import pandas as pd
import seaborn as sns

1 Toyota

Function to get Toyota channel statistics

api_key = 'AIzaSyB24Kop04L1GlTgRCm1XtQ4KB2a4gaBOwA'
Toyota_channel_id = ['UC1pOTJteEef10zJM0cHs4iQ' # Toyota
     
              ]
youtube = build('youtube','v3',developerKey = api_key)

from googleapiclient.errors import HttpError

def get_channel_stats(youtube, Toyota_channel_id):
    all_data = []
    try:
        request = youtube.channels().list(
            part='snippet,contentDetails,statistics',
            id=','.join(Toyota_channel_id)
        )
        response = request.execute()

        for item in response.get('items', []):
            snippet = item.get('snippet', {})
            statistics = item.get('statistics', {})

            data = {
                'Channel_name': snippet.get('title', ''),
                'Subscribers': statistics.get('subscriberCount', ''),
                'Views': statistics.get('viewCount', ''),
                'Total_videos': statistics.get('videoCount', '')
            }

            all_data.append(data)

    except HttpError as e:
        print(f"HTTP error occurred: {e}")
        print(f"Request URL: {e.resp.request.url}")
        print(f"Request body: {e.resp.request.body}")

    return all_data

get_channel_stats(youtube,Toyota_channel_id)

[{'Channel_name': 'Toyota USA',
  'Subscribers': '741000',
  'Views': '194176666',
  'Total_videos': '2837'}]

channel_statistics = get_channel_stats(youtube, Toyota_channel_id)

Toyota_channel_data =pd.DataFrame(channel_statistics) 

Toyota_channel_data

	Channel_name	Subscribers	Views	Total_videos
0	Toyota USA	741000	194176666	2837

2 Nissan

api_key = 'AIzaSyB24Kop04L1GlTgRCm1XtQ4KB2a4gaBOwA'
Nissan_channel_id = ['UCIpK0Bh0wFnC-QqgJs6hx5w' # Nissan
     
              ]
youtube = build('youtube','v3',developerKey = api_key)

from googleapiclient.errors import HttpError

def get_channel_stats(youtube, Nissan_channel_id):
    all_data = []
    try:
        request = youtube.channels().list(
            part='snippet,contentDetails,statistics',
            id=','.join(Nissan_channel_id)
        )
        response = request.execute()

        for item in response.get('items', []):
            snippet = item.get('snippet', {})
            statistics = item.get('statistics', {})

            data = {
                'Channel_name': snippet.get('title', ''),
                'Subscribers': statistics.get('subscriberCount', ''),
                'Views': statistics.get('viewCount', ''),
                'Total_videos': statistics.get('videoCount', '')
            }

            all_data.append(data)

    except HttpError as e:
        print(f"HTTP error occurred: {e}")
        print(f"Request URL: {e.resp.request.url}")
        print(f"Request body: {e.resp.request.body}")

    return all_data

get_channel_stats(youtube,Nissan_channel_id)

[{'Channel_name': 'Nissan',
  'Subscribers': '224000',
  'Views': '109822455',
  'Total_videos': '873'}]

channel_statistics = get_channel_stats(youtube, Nissan_channel_id)

Nissan_channel_data =pd.DataFrame(channel_statistics) 

Nissan_channel_data

	Channel_name	Subscribers	Views	Total_videos
0	Nissan	224000	109822455	873

3 Land Rover

api_key = 'AIzaSyB24Kop04L1GlTgRCm1XtQ4KB2a4gaBOwA'
Land_Rover_channel_id = ['UCZANLEnWKjMbuIkCtDUOqlA' #  Land Rover
     
              ]
youtube = build('youtube','v3',developerKey = api_key)

from googleapiclient.errors import HttpError

def get_channel_stats(youtube, Land_Rover_channel_id):
    all_data = []
    try:
        request = youtube.channels().list(
            part='snippet,contentDetails,statistics',
            id=','.join(Land_Rover_channel_id)
        )
        response = request.execute()

        for item in response.get('items', []):
            snippet = item.get('snippet', {})
            statistics = item.get('statistics', {})

            data = {
                'Channel_name': snippet.get('title', ''),
                'Subscribers': statistics.get('subscriberCount', ''),
                'Views': statistics.get('viewCount', ''),
                'Total_videos': statistics.get('videoCount', '')
            }

            all_data.append(data)

    except HttpError as e:
        print(f"HTTP error occurred: {e}")
        print(f"Request URL: {e.resp.request.url}")
        print(f"Request body: {e.resp.request.body}")

    return all_data

get_channel_stats(youtube,Land_Rover_channel_id)

[{'Channel_name': 'Land Rover',
  'Subscribers': '877000',
  'Views': '189172542',
  'Total_videos': '285'}]

channel_statistics = get_channel_stats(youtube, Land_Rover_channel_id)

Land_Rover_channel_data =pd.DataFrame(channel_statistics) 

Land_Rover_channel_data 

	Channel_name	Subscribers	Views	Total_videos
0	Land Rover	877000	189172542	285

4 Mercedes-Benz

api_key = 'AIzaSyB24Kop04L1GlTgRCm1XtQ4KB2a4gaBOwA'
Mercedes_Benz_channel_id = ['UClj0L8WZrVydk5xKOscI6-A' # Mercedes-Benz
     
              ]
youtube = build('youtube','v3',developerKey = api_key)

from googleapiclient.errors import HttpError

def get_channel_stats(youtube, Mercedes_Benz_channel_id):
    all_data = []
    try:
        request = youtube.channels().list(
            part='snippet,contentDetails,statistics',
            id=','.join(Mercedes_Benz_channel_id)
        )
        response = request.execute()

        for item in response.get('items', []):
            snippet = item.get('snippet', {})
            statistics = item.get('statistics', {})

            data = {
                'Channel_name': snippet.get('title', ''),
                'Subscribers': statistics.get('subscriberCount', ''),
                'Views': statistics.get('viewCount', ''),
                'Total_videos': statistics.get('videoCount', '')
            }

            all_data.append(data)

    except HttpError as e:
        print(f"HTTP error occurred: {e}")
        print(f"Request URL: {e.resp.request.url}")
        print(f"Request body: {e.resp.request.body}")

    return all_data

get_channel_stats(youtube,Mercedes_Benz_channel_id)

[{'Channel_name': 'Mercedes-Benz',
  'Subscribers': '1860000',
  'Views': '313315470',
  'Total_videos': '2188'}]

channel_statistics = get_channel_stats(youtube, Mercedes_Benz_channel_id)

Mercedes_Benz_channel_data =pd.DataFrame(channel_statistics) 

	Channel_name	Subscribers	Views	Total_videos
0	Mercedes-Benz	1860000	313315470	2188

5 BMW

api_key = 'AIzaSyB24Kop04L1GlTgRCm1XtQ4KB2a4gaBOwA'
BMW_channel_id = ['UCYwrS5QvBY_JbSdbINLey6Q' # BMW
     
              ]
youtube = build('youtube','v3',developerKey = api_key)

from googleapiclient.errors import HttpError

def get_channel_stats(youtube, BMW_channel_id):
    all_data = []
    try:
        request = youtube.channels().list(
            part='snippet,contentDetails,statistics',
            id=','.join(BMW_channel_id)
        )
        response = request.execute()

        for item in response.get('items', []):
            snippet = item.get('snippet', {})
            statistics = item.get('statistics', {})

            data = {
                'Channel_name': snippet.get('title', ''),
                'Subscribers': statistics.get('subscriberCount', ''),
                'Views': statistics.get('viewCount', ''),
                'Total_videos': statistics.get('videoCount', '')
            }

            all_data.append(data)

    except HttpError as e:
        print(f"HTTP error occurred: {e}")
        print(f"Request URL: {e.resp.request.url}")
        print(f"Request body: {e.resp.request.body}")

    return all_data

get_channel_stats(youtube,BMW_channel_id)

[{'Channel_name': 'BMW',
  'Subscribers': '1460000',
  'Views': '241472898',
  'Total_videos': '829'}]

channel_statistics = get_channel_stats(youtube, BMW_channel_id)

BMW_channel_data =pd.DataFrame(channel_statistics) 

BMW_channel_data

	Channel_name	Subscribers	Views	Total_videos
0	BMW	1460000	241472898	829

# Lets combine the data frames


# Combine DataFrames using concat
combined_car_brands_channels_df = pd.concat([Toyota_channel_data ,
                                       Nissan_channel_data , 
                                       BMW_channel_data ,
                                       Land_Rover_channel_data ,
                                      Mercedes_Benz_channel_data
                                      
                                      ], ignore_index=True)

# Display the result
print(combined_car_brands_channels_df)

    Channel_name Subscribers      Views Total_videos
0     Toyota USA      741000  194176666         2837
1         Nissan      224000  109822455          873
2            BMW     1460000  241472898          829
3     Land Rover      877000  189172542          285
4  Mercedes-Benz     1860000  313315470         2188

# lets have  a look at the datatypes
combined_car_brands_channels_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 4 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   Channel_name  5 non-null      object
 1   Subscribers   5 non-null      object
 2   Views         5 non-null      object
 3   Total_videos  5 non-null      object
dtypes: object(4)
memory usage: 292.0+ bytes

# lets change the datatypes to perform  visualisations
combined_car_brands_channels_df['Subscribers'] = pd.to_numeric(combined_car_brands_channels_df['Subscribers'])
combined_car_brands_channels_df['Views'] = pd.to_numeric(combined_car_brands_channels_df['Views'])
combined_car_brands_channels_df['Total_videos'] = pd.to_numeric(combined_car_brands_channels_df['Total_videos'])
# letsconirm if the datatypes has changed
combined_car_brands_channels_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 4 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   Channel_name  5 non-null      object
 1   Subscribers   5 non-null      int64 
 2   Views         5 non-null      int64 
 3   Total_videos  5 non-null      int64 
dtypes: int64(3), object(1)
memory usage: 292.0+ bytes

combined_car_brands_channels_df

	Channel_name	Subscribers	Views	Total_videos
0	Toyota USA	741000	194176666	2837
1	Nissan	224000	109822455	873
2	BMW	1460000	241472898	829
3	Land Rover	877000	189172542	285
4	Mercedes-Benz	1860000	313315470	2188

import pandas as pd
import matplotlib.pyplot as plt

# Sort the DataFrame by 'Subscribers' in descending order
combined_car_brands_channels_df_subscribers_sorted = combined_car_brands_channels_df.sort_values(by='Subscribers', ascending=False)

# Shorten channel names
combined_car_brands_channels_df_subscribers_sorted['Channel_name'] = combined_car_brands_channels_df_subscribers_sorted['Channel_name'].apply(lambda x: x[:8])

# Plotting Subscribers in descending order
fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(10, 15))

axes[0].bar(combined_car_brands_channels_df_subscribers_sorted['Channel_name'], combined_car_brands_channels_df_subscribers_sorted['Subscribers'], color='blue')
axes[0].set_title('Subscribers')

# Sort the DataFrame by 'Views' in descending order
combined_car_brands_channels_df_views_sorted = combined_car_brands_channels_df.sort_values(by='Views', ascending=False)

# Shorten channel names
combined_car_brands_channels_df_views_sorted['Channel_name'] = combined_car_brands_channels_df_views_sorted['Channel_name'].apply(lambda x: x[:8])

# Plotting Views in descending order
axes[1].bar(combined_car_brands_channels_df_views_sorted['Channel_name'], combined_car_brands_channels_df_views_sorted['Views'], color='green')
axes[1].set_title('Views')

# Sort the DataFrame by 'Total_videos' in descending order
combined_car_brands_channels_df_total_videos_sorted =combined_car_brands_channels_df.sort_values(by='Total_videos', ascending=False)

# Shorten channel names
combined_car_brands_channels_df_total_videos_sorted['Channel_name'] = combined_car_brands_channels_df_total_videos_sorted['Channel_name'].apply(lambda x: x[:8])

# Plotting Total Videos in descending order
axes[2].bar(combined_car_brands_channels_df_total_videos_sorted['Channel_name'], combined_car_brands_channels_df_total_videos_sorted['Total_videos'], color='orange')
axes[2].set_title('Total Videos')

# Adjust layout for better visibility
plt.tight_layout()

# Show the plot
plt.show()