TikTok Project

Inspect and analyze data

The purpose of this project is to investigate and understand the data provided.

Imports and data loading

# Import packages
import pandas as pd
import numpy as np

Loading the dataset into a dataframe

data = pd.read_csv("tiktok_dataset.csv")

Inspect the data

# Display and examine the first 10 rows of the dataframe

data.head(10)

# Get summary info

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 19382 entries, 0 to 19381
Data columns (total 12 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   #                         19382 non-null  int64  
 1   claim_status              19084 non-null  object 
 2   video_id                  19382 non-null  int64  
 3   video_duration_sec        19382 non-null  int64  
 4   video_transcription_text  19084 non-null  object 
 5   verified_status           19382 non-null  object 
 6   author_ban_status         19382 non-null  object 
 7   video_view_count          19084 non-null  float64
 8   video_like_count          19084 non-null  float64
 9   video_share_count         19084 non-null  float64
 10  video_download_count      19084 non-null  float64
 11  video_comment_count       19084 non-null  float64
dtypes: float64(5), int64(3), object(4)
memory usage: 1.8+ MB

# Get summary statistics

data.describe()

Investigate the variables

# What are the different values for claim status and how many of each are in the data?

data['claim_status'].value_counts()

claim      9608
opinion    9476
Name: claim_status, dtype: int64

# What is the average view count of videos with "claim" status?

claims = data[data['claim_status'] == 'claim']
print('Mean view count claims:', claims['video_view_count'].mean())
print('Median view count claims:', claims['video_view_count'].median())

Mean view count claims: 501029.4527477102
Median view count claims: 501555.0

# What is the average view count of videos with "opinion" status?

opinions = data[data['claim_status'] == 'opinion']
print('Mean view count opinions:', opinions['video_view_count'].mean())
print('Median view count opinions:', opinions['video_view_count'].median())

Mean view count opinions: 4956.43224989447
Median view count opinions: 4953.0

# Get counts for each group combination of claim status and author ban status

data.groupby(['claim_status', 'author_ban_status']).count()[['#']]

data.groupby(['author_ban_status']).agg(
    {'video_view_count': ['mean', 'median'],
     'video_like_count': ['mean', 'median'],
     'video_share_count': ['mean', 'median']})

# What's the median video share count of each author ban status?

data.groupby(['author_ban_status']).median(numeric_only=True)[
    ['video_share_count']]

data.groupby(['author_ban_status']).agg(
    {'video_view_count': ['count', 'mean', 'median'],
     'video_like_count': ['count', 'mean', 'median'],
     'video_share_count': ['count', 'mean', 'median']
     })

# Create a likes_per_view column
data['likes_per_view'] = data['video_like_count'] / data['video_view_count']

# Create a comments_per_view column
data['comments_per_view'] = data['video_comment_count'] / data['video_view_count']

# Create a shares_per_view column
data['shares_per_view'] = data['video_share_count'] / data['video_view_count']

data.groupby(['claim_status', 'author_ban_status']).agg(
    {'likes_per_view': ['count', 'mean', 'median'],
     'comments_per_view': ['count', 'mean', 'median'],
     'shares_per_view': ['count', 'mean', 'median']})

• Around 50% are claims, 9,608 total
• Engagement has a high level of correlation with claim status
• Videos with banned authors have higher engagement than videos with active authors