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plot_function.py
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plot_function.py
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####################################################################################################
# IMPORTS
####################################################################################################
# Standard libraries
import calendar
# Third-party libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
# Plotly imports
import plotly.express as px
import plotly.graph_objs as go
from plotly.subplots import make_subplots
# Statsmodels imports
from statsmodels.tsa.stattools import acf
from statsmodels.graphics.tsaplots import plot_acf
from statsmodels.tsa.seasonal import seasonal_decompose
from plotly.offline import plot
#####
# To save as html
#####
def to_html(fig, html_file_path):
# Generate the HTML elements to embed the plot
div = plot(fig, output_type='div', include_plotlyjs=True)
# Create the full HTML content
html_content = f"""
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Interactive Plot</title>
</head>
<body>
{div}
</body>
</html>
"""
# Write the HTML content to a file
with open(html_file_path, 'w') as html_file:
html_file.write(html_content)
####################################################################################################
# Plot for Preprocessing Part
####################################################################################################*
def plot_month_release_availability(df):
"""
Plot a pie chart showing the proportion of movies with available and unavailable release months in a DataFrame.
Args:
df (pandas.DataFrame): The input DataFrame containing movie data.
Returns:
None
"""
# Calculate the number of NaN and non-NaN values
non_available = df[df['Release Month'] == -1].shape[0]
available = df[df['Release Month'] != -1].shape[0]
# Print the number of movies with missing release month and the total number of movies in the dataset
print("In this dataset, there are {} movies with missing release month out of a total of {} movies.".format(non_available, non_available + available))
# Create a list with the values
data_counts = [non_available, available]
# Create a list with the labels
labels = ['Month release unavailable', 'Month release available']
# Create the pie chart using Seaborn
sns.set(style="whitegrid")
plt.figure(figsize=(6, 6))
sns.set_palette("pastel")
plt.pie(data_counts, labels=labels, autopct='%1.1f%%', startangle=140)
# Add a title
plt.title('Proportion of Month Release Availability')
# Show the pie chart
plt.show()
####################################################################################################
# Research Questions 1
####################################################################################################
#########################################
# Exploratory functions
#########################################
def filter_movies_by_genres(df, selected_genres):
"""
Filter a DataFrame of movies based on selected genres.
Args:
df (pandas.DataFrame): The DataFrame containing the movie data.
selected_genres (list): A list of genres to filter by.
Returns:
pandas.DataFrame: A filtered DataFrame containing only rows with selected genres.
"""
# Use the .isin() method to filter rows where 'Movie genres' match the selected genres
df_filter = df[df['Movie genres'].isin(selected_genres)]
return df_filter
def plot_monthly_movie_counts(df, selected_genres = None):
"""
Plot the number of movies released over the months for selected genres.
Args:
df (pd.DataFrame): DataFrame containing the movie data with a "Release Month" column.
selected_genres (list): List of selected genres to filter the data.
Returns:
None
"""
if selected_genres is None:
# Count the number of movies released in each month and sort by month index
month_counts = df["Release Month"].value_counts().sort_index()
# Create a list of month names using the calendar library
month_names = [calendar.month_name[i] for i in range(1, 13)]
# Create a bar plot for the months
plt.figure(figsize=(10, 6))
sns.barplot(x=month_counts.index, y=month_counts.values)
plt.title(f"Number of movies released over the months")
plt.xlabel("Month")
plt.ylabel("Number of movies")
plt.xticks(range(12), month_names, rotation=45) # You can adjust the rotation angle as per your preference
plt.show()
else:
for genre in selected_genres:
# Filter the DataFrame to include only rows where movies are available and belong to the selected genre
df_filtered = filter_movies_by_genres(df, [genre])
# Count the number of movies released in each month and sort by month index
month_counts = df_filtered["Release Month"].value_counts().sort_index()
# Create a list of month names using the calendar library
month_names = [calendar.month_name[i] for i in range(1, 13)]
# Create a bar plot for the months
plt.figure(figsize=(10, 6))
sns.barplot(x=month_counts.index, y=month_counts.values)
plt.title(f"Number of {genre} movies released over the months")
plt.xlabel("Month")
plt.ylabel("Number of movies")
plt.xticks(range(12), month_names, rotation=45) # You can adjust the rotation angle as per your preference
plt.show()
def create_genre_pie_chart(df_filter):
"""
Create a pie chart to visualize the distribution of movie genres in a DataFrame.
Args:
df (pandas.DataFrame): The DataFrame containing movie genre data.
Returns:
None
This function takes a DataFrame with a 'Movie genres' column and creates a pie chart to visualize
the distribution of movie genres in the dataset.
"""
labels = df_filter['Movie genres'].value_counts().index.tolist()
data_counts = df_filter['Movie genres'].value_counts().values.tolist()
# Create the pie chart using Seaborn
sns.set(style="whitegrid")
plt.figure(figsize=(6, 6))
sns.set_palette("pastel")
plt.pie(data_counts, labels=labels, autopct='%1.1f%%', startangle=140)
plt.title("Movie Genres Distribution")
plt.show()
def plot_movie_continent_distribution(df):
"""
Plot a pie chart to visualize the distribution of movies by continent in a DataFrame.
Args:
df (pandas.DataFrame): The DataFrame containing movie data.
Returns:
None
"""
# Extract labels and data counts for the movie continents
labels = df['Movie Continent'].value_counts().index.tolist()
data_counts = df['Movie Continent'].value_counts().values.tolist()
# Set the style for the plot
sns.set(style="whitegrid")
# Create a figure with a specified size and pastel color palette
plt.figure(figsize=(8, 6))
sns.set_palette("pastel")
# Create a pie chart without exploded segments and set the starting angle
plt.pie(data_counts, startangle=140)
# Set the aspect to 'equal' for a circular pie chart
plt.gca().set_aspect('equal')
# Add a title to the chart
plt.title("Movie Continent Distribution")
# Calculate percentages for the legend
total = sum(data_counts)
percentages = [f"{count / total * 100:.1f}%" for count in data_counts]
# Create a legend with labels and percentages, placed to the side of the chart
plt.legend(
loc='center left',
bbox_to_anchor=(1, 0.5),
labels=[f"{label} ({percentage})" for label, percentage in zip(labels, percentages)]
)
# Show the plot
plt.show()
def filter_dataframe_by_threshold(df, threshold=200):
"""
Filter a DataFrame based on a specified threshold for the count ('Counts') per release year ('Release Year').
Args:
df (pd.DataFrame): The input DataFrame containing the data.
threshold (int): The threshold for the count below which years will be excluded.
Returns:
pd.DataFrame: The filtered DataFrame containing only the rows where the count is greater than or equal to the threshold.
"""
# Group the data by release year and count occurrences
grouped = df.groupby(['Release Year']).size().reset_index(name='Counts')
# Add a 'Keep' column to mark whether the count is above or equal to the threshold
grouped['Keep'] = grouped['Counts'] >= threshold
# Find the index of the last year below the threshold
last_false_index = grouped[~grouped['Keep']]['Release Year'].values[-1]
# Filter the original DataFrame, keeping only years above the threshold
filtered_df = df[df['Release Year'] > int(last_false_index)]
return filtered_df
def plot_histogram_by_release_year(df):
"""
Plot a histogram of counts by release year.
Args:
df (pd.DataFrame): The input DataFrame containing the data.
Returns:
None
"""
grouped = df.groupby(['Release Year']).size().reset_index(name='Counts')
plt.figure(figsize=(10, 6))
plt.bar(grouped['Release Year'], grouped['Counts'], color='darkblue')
plt.xlabel("Release Year")
plt.ylabel("Counts")
plt.title('Histogram of Counts by Release Year')
plt.show()
####################################################################
# Comprehensive Seasonality Analysis Across All Genres and Locations
####################################################################
def plot_monthly_average(df_mean_month):
"""
Plot the monthly average of the percentage of movies released in a given month.
Parameters:
df_mean_month (DataFrame): A DataFrame containing the monthly mean percentage data.
Returns:
None
"""
# Create a list of month names using the calendar library
month_names = [calendar.month_abbr[i] for i in range(1, 13)]
# Create a bar plot for the months
plt.figure(figsize=(10, 6))
plt.bar(range(12), df_mean_month['Percentage'], color='darkblue')
plt.xlabel("Release Month")
plt.ylabel("Mean of the percentage of movies released in a given month (%)")
plt.xticks(range(12), month_names, rotation=45)
plt.show()
def plot_acf_custom(data, max_lags=240, y_min=-0.2, y_max=1.0):
"""
Generate and display an Autocorrelation Function (ACF) plot.
Parameters:
- data: The time series data for which ACF needs to be calculated and plotted.
- max_lags: The maximum number of lags to consider (default is 240).
- y_min: The minimum value for the y-axis (default is -0.2).
- y_max: The maximum value for the y-axis (default is 1.0).
Returns:
None (displays the ACF plot).
"""
# Creating the plot
fig, ax = plt.subplots(figsize=(12, 6))
# Plotting the ACF with dark blue color
plot_acf(data, lags=max_lags, ax=ax, color="darkblue")
# Custom x-axis formatting
ticks = [12 * i for i in range(1, 21)] # Multiples of 12 up to 240
ticklabels = [f"{i} year{'s' if i > 1 else ''}" for i in range(1, 21)]
ax.set_xticks(ticks)
ax.set_xticklabels(ticklabels, rotation=45, ha="right", fontsize=10, color="black")
# Set the title, x-axis label, and y-axis label
ax.set_title('Autocorrelation Function (ACF)')
ax.set_xlabel('Lags (in months)')
ax.set_ylabel('Autocorrelation')
# Set the desired y-axis limits
ax.set_ylim(y_min, y_max)
plt.tight_layout()
plt.show()
def plot_seasonal_decomposition(df_past, df_recent):
"""
Perform seasonal decomposition and plot the results for past and recent datasets.
Args:
df_past (pd.DataFrame): DataFrame containing past years' time series data.
df_recent (pd.DataFrame): DataFrame containing recent years' time series data.
Returns:
None
"""
# Perform multiplicative seasonal decomposition on the 'Counts' column with a period of 12 (monthly data) for the 'df_past' dataset
result_past = seasonal_decompose(df_past['Counts'], model='multiplicative', period=12)
# Perform multiplicative seasonal decomposition on the 'Counts' column with a period of 12 (monthly data) for the 'df_recent' dataset
result_recent = seasonal_decompose(df_recent['Counts'], model='multiplicative', period=12)
# Create subplots for 'df_past' and 'df_recent'
fig, axes = plt.subplots(4, 2, figsize=(12, 12))
fig.suptitle("Seasonal Decomposition for Past and Recent Years", fontsize=16)
# Plotting the decomposed components for 'df_past'
colors_past = ['darkblue', 'darkblue', 'darkblue', 'darkblue']
axes[0, 0].plot(df_past['Counts'], color=colors_past[0], label='Original')
axes[1, 0].plot(result_past.trend, color=colors_past[1], label='Trend')
axes[2, 0].plot(result_past.seasonal, color=colors_past[2], label='Seasonal')
axes[3, 0].plot(result_past.resid, color=colors_past[3], label='Residuals')
axes[0, 0].set_title('Past Years Original Data', fontsize=14)
axes[1, 0].set_title('Past Years Decomposition (Trend)', fontsize=14)
axes[2, 0].set_title('Past Years Decomposition (Seasonal)', fontsize=14)
axes[3, 0].set_title('Past Years Decomposition (Residuals)', fontsize=14)
# Plotting the decomposed components for 'df_recent'
colors_recent = ['darkblue', 'darkblue', 'darkblue', 'darkblue']
axes[0, 1].plot(df_recent['Counts'], color=colors_recent[0], label='Original')
axes[1, 1].plot(result_recent.trend, color=colors_recent[1], label='Trend')
axes[2, 1].plot(result_recent.seasonal, color=colors_recent[2], label='Seasonal')
axes[3, 1].plot(result_recent.resid, color=colors_recent[3], label='Residuals')
axes[0, 1].set_title('Recent Years Original Data', fontsize=14)
axes[1, 1].set_title('Recent Years Decomposition (Trend)', fontsize=14)
axes[2, 1].set_title('Recent Years Decomposition (Seasonal)', fontsize=14)
axes[3, 1].set_title('Recent Years Decomposition (Residuals)', fontsize=14)
for ax in axes.ravel():
ax.legend()
ax.grid()
plt.tight_layout()
plt.subplots_adjust(top=0.9)
plt.show()
return result_past, result_recent
def plot_seasonal_components(result_past, result_recent):
"""
Plots the seasonal components for 'Past' and 'Recent' datasets side by side.
Args:
result_past (seasonal decomposition result): Seasonal decomposition result for 'Past' dataset.
result_recent (seasonal decomposition result): Seasonal decomposition result for 'Recent' dataset.
Returns:
None
"""
# Extract the seasonal values for 'Past' dataset
seasonal_values_past = result_past.seasonal[-12:] # Replace 12 with the period of your data if different
# Extract the seasonal values for 'Recent' dataset
seasonal_values_recent = result_recent.seasonal[-12:] # Replace 12 with the period of your data if different
# Find the maximum value in the seasonal components
max_seasonal_value = max(max(seasonal_values_past), max(seasonal_values_recent)) + 0.1
# Create subplots side by side
fig, axs = plt.subplots(1, 2, figsize=(12, 6))
# Plot the 'Past' seasonal component
axs[0].bar(x=np.arange(1, 13), height=seasonal_values_past, tick_label=[calendar.month_abbr[i] for i in range(1, 13)], color='darkblue')
axs[0].set_title('Seasonal Component for Past Years [1976-1992]', fontsize=14)
axs[0].set_xlabel('Month')
axs[0].set_ylabel('Seasonal Effect')
axs[0].set_ylim(0, max_seasonal_value) # Set the same y-axis limits
axs[0].grid(True)
# Plot the 'Recent' seasonal component
axs[1].bar(x=np.arange(1, 13), height=seasonal_values_recent, tick_label=[calendar.month_abbr[i] for i in range(1, 13)], color='darkblue')
axs[1].set_title('Seasonal Component for Recent Years [1993-2009]', fontsize=14)
axs[1].set_xlabel('Month')
axs[1].set_ylabel('Seasonal Effect')
axs[1].set_ylim(0, max_seasonal_value) # Set the same y-axis limits
axs[1].grid(True)
plt.tight_layout()
plt.show()
####################################################################
# Comprehensive Seasonality Analysis Across All Genres and Locations
####################################################################
def plot_genre_month_percentage(df_year):
"""
Plots the percentage of movie releases by month for different genres.
Args:
df_year (DataFrame): The DataFrame containing movie data.
Returns:
None
"""
# Suppress user warnings
warnings.filterwarnings("ignore")
# Find unique movie genres in the DataFrame
genres = df_year['Movie genres'].unique()
# Create a figure with appropriate size for all subplots
plt.figure(figsize=(15, 5 * len(genres)))
# Loop through each genre to create a bar plot
for i, genre in enumerate(genres):
# Create a subplot for each genre
plt.subplot(len(genres), 1, i + 1)
# Filter the DataFrame for the current genre
df_genre = df_year[df_year['Movie genres'] == genre]
# Create the bar plot using seaborn
sns.barplot(data=df_genre, x='Release Month', y='Percentage', hue='Movie Continent', errorbar=None)
# Set the x-axis labels to month abbreviations
month_names = [calendar.month_abbr[i] for i in range(1, 13)]
plt.xticks(range(12), month_names)
# Add a title to the subplot
plt.title(f'Percentage by Month for {genre}')
# Add the legend in the upper right corner
plt.legend(loc='upper right')
# Adjust subplots automatically to fit within the figure
plt.tight_layout()
# Show the plots
plt.show()
def calculate_correlations(df, number_years=4):
"""
Calculate correlations for a given number of years using the provided DataFrame.
Args:
df (pd.DataFrame): The input DataFrame containing the data.
number_years (int): The number of years for which you want to calculate correlations.
Returns:
pd.DataFrame: A DataFrame containing the correlations.
"""
# Initialize an empty DataFrame to store correlations
correlations = pd.DataFrame()
for continent in df['Movie Continent'].unique():
for genre in df['Movie genres'].unique():
data = df[(df['Movie genres'] == genre) & (df['Movie Continent'] == continent)]
for lag in range(1, number_years + 1):
acf_vals = acf(data['Counts'], nlags=lag * 12)
acf_df = pd.DataFrame({
'Movie genres': genre,
'Movie Continent': continent,
'Lag': f'{lag} year(s)',
'Correlation': [acf_vals[lag * 12]]
})
correlations = pd.concat([correlations, acf_df], axis=0)
return correlations
def plot_correlation_heatmap(df, number_years=4):
"""
Plot a correlation heatmap for a given number of years using the provided DataFrame.
Args:
df (pd.DataFrame): The input DataFrame containing the data.
number_years (int): The number of years for which you want to calculate correlations.
Returns:
None (displays the heatmap).
"""
# Initialize an empty DataFrame to store correlations
correlations = calculate_correlations(df, number_years)
# Create the heatmap
plt.figure(figsize=(10, 8))
pivot_df = correlations.pivot(values='Correlation', index=['Movie genres', 'Movie Continent'], columns='Lag')
# Sort the columns chronologically
sorted_columns = [f'{i} year(s)' for i in range(1, number_years + 1)]
pivot_df = pivot_df[sorted_columns]
sns.heatmap(pivot_df, cmap='coolwarm', annot=True, fmt=".2f", vmin=-1, vmax=1)
plt.title(f'Correlation Heatmap ({number_years} Year Lag)')
plt.show()
def plot_seasonality_heatmap(decomposition_results):
"""
Plot a heatmap for seasonality factors of movies across genres and continents.
Parameters:
decomposition_results (DataFrame): A DataFrame containing the decomposed components of seasonality
with 'Movie genres', 'Movie Continent', and 'Release Month' as columns.
"""
# Pivot the data to create a matrix suitable for a heatmap
heatmap_data = decomposition_results.pivot_table(values='Seasonality', index=['Movie genres', 'Movie Continent'], columns='Release Month')
heatmap_data.columns = [calendar.month_abbr[i] for i in range(1, 13)]
# Plot heatmap
plt.figure(figsize=(14, 8))
sns.heatmap(heatmap_data, cmap='coolwarm', annot=True, fmt=".2f", vmin=0, vmax=2.2)
plt.title('Seasonality Factors Heatmap')
plt.ylabel('Genre - Continent')
plt.xlabel('Release Month')
plt.show()
####################################################################################################
# Research Questions 2
####################################################################################################
def plot_box_office_oscars(df):
"""
Plot a box plot to visualize the distribution of box office revenue for Oscar winners and non-winners.
Parameters:
df (DataFrame): A pandas DataFrame containing movie data, including the 'Winner Binary' column (1 for winners,
0 for non-winners) and 'Movie box office revenue'.
"""
df_plot = df[['Winner Binary', 'Movie box office revenue']].copy()
# Plot using seaborn
plt.figure(figsize=(10, 6))
sns.set(style="whitegrid")
sns.boxplot(x='Winner Binary', y='Movie box office revenue', data=df_plot, palette=['blue', 'red'])
plt.title('Distribution of Box Office Revenue for Oscar Winners and Non-Winners')
plt.xlabel('Oscar Winner (1: Yes, 0: No)')
plt.ylabel('Box Office Revenue')
plt.yscale('log') # Set y-axis to log scale
plt.xticks(ticks=[0, 1], labels=['No', 'Yes'])
plt.show()
def plot_ratings_oscars(df):
"""
Plot a box plot to visualize the distribution of average ratings for Oscar winners and non-winners.
Parameters:
df (DataFrame): A pandas DataFrame containing movie data, including the 'Winner' column (1 for winners,
0 for non-winners) and 'Average Vote' for average ratings.
"""
df_plot = df[['Winner', 'Average Vote ']].copy()
df_plot['Winner'] = df_plot['Winner'].astype(int)
df_plot['Average Vote '] = df_plot['Average Vote '].astype(float)
# Plot using seaborn
plt.figure(figsize=(10, 6))
sns.set(style="whitegrid")
sns.boxplot(x='Winner', y='Average Vote ', data=df_plot, palette=['blue', 'red'])
plt.title('Distribution of Average Ratings for Oscar Winners and Non-Winners')
plt.xlabel('Oscar Winner (1: Yes, 0: No)')
plt.ylabel('Average Ratings')
plt.xticks(ticks=[0, 1], labels=['No', 'Yes'])
plt.show()
def plot_column_by_oscars_category(df, column, yscale=False):
"""
Plots a box plot of the specified column for Oscar-winning movies in each category.
Parameters:
df (DataFrame): The input DataFrame containing movie data.
column (str): The name of the column to plot.
yscale (bool, optional): Whether to use a log scale for the y-axis (default is False).
Returns:
None
"""
# Filter the DataFrame for movies that have won Oscars and have non-NaN values in the specified column
df_oscar_winners = df[(df['Winner'] == 1) & (~df[column].isna())]
# Filter out categories with fewer than a certain threshold of movies
category_threshold = 5 # Adjust as needed
category_counts = df_oscar_winners['Category'].value_counts()
valid_categories = category_counts[category_counts >= category_threshold].index
df_oscar_winners_filtered = df_oscar_winners[df_oscar_winners['Category'].isin(valid_categories)]
# Calculate the mean of the specified column for each category and sort by mean in ascending order
mean_column_by_category = df_oscar_winners_filtered.groupby('Category')[column].mean().sort_values(ascending=True)
# Set up the plot
sns.set(style="whitegrid")
plt.figure(figsize=(12, 8))
if yscale:
plt.yscale('log') # Set y-axis to log scale for better visualization
# Create the box plot for each category, sorted by mean column values
sns.boxplot(x='Category', y=column, data=df_oscar_winners_filtered,
order=mean_column_by_category.index, palette='Set1')
plt.title(f'Distribution of {column} for Oscar Winners in Each Category (Filtered)')
plt.xlabel('Oscar Category')
if yscale:
plt.ylabel(f'{column} (log scale)')
else:
plt.ylabel(column)
plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability
plt.tight_layout()
plt.show()
####################################################################################################
# Research Questions 3
####################################################################################################
def plot_monthly_votes_and_ratings(df):
"""
Plot histograms of average vote and rating by month using Plotly.
Args:
df (pd.DataFrame): The input DataFrame containing the data.
Returns:
None (displays the plot).
"""
# Group by month and calculate average vote average and rating
monthly_votes = df.groupby('Release Month')['Average Vote '].mean()
monthly_ratings = df.dropna(subset=['Rating']).groupby('Release Month')["Rating"].mean()
# Create subplots with two y-axes
fig = make_subplots(specs=[[{"secondary_y": True}]])
# Vote average on the left y-axis
fig.add_trace(
go.Bar(x=monthly_votes.index, y=monthly_votes.values, name='Vote Avg', marker_color='blue'),
secondary_y=False
)
# Rating on the right y-axis
fig.add_trace(
go.Bar(x=monthly_ratings.index, y=monthly_ratings.values, name='Rating', marker_color='red'),
secondary_y=True
)
# Update x-axis and y-axes labels
fig.update_xaxes(title_text='Month', tickvals=list(range(1, 13)),
ticktext=[calendar.month_name[i] for i in range(1, 13)])
fig.update_yaxes(title_text='Vote Average', secondary_y=False)
fig.update_yaxes(title_text='Rating', secondary_y=True)
# Add layout details
fig.update_layout(
title='Average Vote Average and Rating by Month',
barmode='group'
)
# Show the plot
fig.show()
def plot_adjusted_box_office_revenue(merged_df):
"""
Plot a bar chart of the average adjusted box office revenue by month using Plotly.
Args:
merged_df (pd.DataFrame): The DataFrame containing the data.
Returns:
None (displays the plot).
"""
# Create a bar plot using Plotly Express
fig = px.bar(
merged_df,
x='Release Month',
y='Ratio Revenue',
labels={'Ratio Revenue': 'Average Adjusted Revenue'},
title='Average Adjusted Box Office Revenue by Month'
)
# Update layout for better readability
fig.update_layout(
xaxis_title='Month',
yaxis_title='Average Adjusted Revenue'
)
# Show the plot
fig.show()
def plot_movie_metrics_evolution(yearly_data):
"""
Plot a line chart showing the evolution of movie metrics over years using Plotly.
Args:
yearly_data (pd.DataFrame): The DataFrame containing the data.
Returns:
None (displays the plot).
"""
# Create a line plot using Plotly Express
fig = px.line(
yearly_data,
title='Evolution of Movie Metrics Over Years',
labels={'value': 'Average Values', 'variable': 'Metrics'}
)
# Update layout for better readability
fig.update_layout(
xaxis_title='Year',
yaxis_title='Average Values'
)
# Show the plot
fig.show()
def plot_seasonal_votes_and_ratings(df):
"""
Plot histograms of average vote average and rating by season using Plotly.
Args:
df (pd.DataFrame): The DataFrame containing the data.
Returns:
None (displays the plot).
"""
# Group by season and calculate average vote average and rating
seasonal_votes = df.groupby('season')['Average Vote '].mean()
seasonal_ratings = df.groupby('season')['Rating'].mean()
# Create subplots with two y-axes
fig = make_subplots(specs=[[{"secondary_y": True}]])
# Vote average on the left y-axis
fig.add_trace(
go.Bar(x=seasonal_votes.index, y=seasonal_votes.values, name='Vote Avg', marker_color='blue'),
secondary_y=False
)
# Rating on the right y-axis
fig.add_trace(
go.Bar(x=seasonal_ratings.index, y=seasonal_ratings.values, name='Rating', marker_color='red'),
secondary_y=True
)
# Update x-axis and y-axes labels
fig.update_xaxes(title_text='Season')
fig.update_yaxes(title_text='Vote Average', secondary_y=False)
fig.update_yaxes(title_text='Rating', secondary_y=True)
# Add layout details
fig.update_layout(
title='Average Vote Average and Rating by Season',
barmode='group'
)
# Show the plot
fig.show()
def plot_rating_and_vote_vs_box_office(df):
"""
Plot scatter plots for 'Rating' vs 'Box Office Revenue' and 'Vote Average' vs 'Box Office Revenue' using Plotly.
Args:
df (pd.DataFrame): The DataFrame containing the data.
Returns:
None (displays the plots).
"""
# Calculate correlation coefficients
corr_rating = df['Rating'].corr(df['Movie box office revenue'])
corr_vote_average = df['Average Vote '].corr(df['Movie box office revenue'])
# Print the correlation coefficients
print("Correlation between Rating and Box Office Revenue:", corr_rating)
print("Correlation between Vote Average and Box Office Revenue:", corr_vote_average)
# Scatter plot for 'Rating' vs 'Movie box office revenue'
fig_rating = px.scatter(
df,
x='Rating',
y='Movie box office revenue',
title='Rating vs Box Office Revenue',
labels={'Rating': 'Rating', 'Movie box office revenue': 'Box Office Revenue'}
)
fig_rating.show()
# Scatter plot for 'Vote Average' vs 'Movie box office revenue'
fig_vote = px.scatter(
df,
x='Average Vote ',
y='Movie box office revenue',
title='Vote Average vs Box Office Revenue',
labels={'Average Vote ': 'Vote Average', 'Movie box office revenue': 'Box Office Revenue'}
)
fig_vote.show()