import numpy as np import pandas as pd import plotly.graph_objs as go import matplotlib.colors import warnings warnings.filterwarnings("ignore") import matplotlib.pyplot as plt import seaborn as sns import plotly as py from datetime import datetime import datetime as dt 

amazon_csv = pd.read_csv('Amazon_prime_titles.csv') amazon_csv.shape 

np.random.seed(0) #seed for reproducibility amazon_csv.sample(5) 

# Operator to find null/ missing values print('Percentage of null values in the given data set:') for col in amazon_csv.columns: null_count = amazon_csv[col].isna().sum()/len(amazon_csv)*100 if null_count > 0: print(f'{col} : {round(null_count,2)}%') 

# Define valid values valid_types = {"Movie", "TV Show"} # Find invalid values invalid_types = amazon_csv[~amazon_csv['type'].isin(valid_types)] # Check if any invalid values exist if invalid_types.empty: print("Valid values") else: print("\nInvalid 'type' values:") print(invalid_types['type'].unique()) 

# Check if there are any invalid ratings (less than 0 or greater than 10) if ((amazon_csv['rating'] < 0.0) | (amazon_csv['rating'] > 10.0)).any(): print("Invalid") else: print("Valid range") 

# Amazon Prime Video brand colors sns.palplot(['#00A8E1', '#232F3E', '#FFFFFF', '#B4B4B4']) plt.title("Amazon Prime Brand Palette", loc='left', fontweight="bold", fontsize=16, color='#4a4a4a', y=1.2) plt.show() 

# summary statistics print("\nSummary Statistics:\n", amazon_csv.describe()) 

# new column 'duration_numeric' amazon_csv['duration_numeric'] = amazon_csv['duration'].str.extract(r'(\d+)').astype(float) #seaborn style and color palette sns.set(style="whitegrid", rc={"font.family": "serif"}) palette = ['#00A8E1', '#232F3E', '#FFFFFF', '#B4B4B4'] #subplots for histograms fig, axes = plt.subplots(1, 3, figsize=(18, 5)) fig.suptitle("Exploratory Data Analysis by plotting the Histogram and Box Plots", fontsize=16, fontweight='bold', family='serif') # aesthetics for ax in axes: ax.spines["top"].set_visible(False) ax.spines["right"].set_visible(False) ax.spines["left"].set_visible(False) ax.spines["bottom"].set_visible(False) # Histogram for Release Year sns.histplot(amazon_csv['release_year'], bins=30, kde=True, ax=axes[0], color=palette[0]) axes[0].set_title("Distribution of Release Years") axes[0].set_xlabel("Year") # Histogram for Rating sns.histplot(amazon_csv['rating'].dropna(), bins=30, kde=True, ax=axes[1], color=palette[1]) axes[1].set_title("Distribution of Ratings") axes[1].set_xlabel("Rating") # Histogram for Duration sns.histplot(amazon_csv['duration_numeric'].dropna(), bins=30, kde=True, ax=axes[2], color=palette[3]) axes[2].set_title("Distribution of Duration (Minutes)") axes[2].set_xlabel("Duration (Minutes)") plt.tight_layout() plt.show() # box plots fig, axes = plt.subplots(1, 3, figsize=(18, 5)) for ax in axes: ax.spines["top"].set_visible(False) ax.spines["right"].set_visible(False) ax.spines["left"].set_visible(False) ax.spines["bottom"].set_visible(False) sns.boxplot(y=amazon_csv['release_year'], ax=axes[0], color=palette[0]) axes[0].set_title("Box Plot of Release Years") sns.boxplot(y=amazon_csv['rating'], ax=axes[1], color=palette[1]) axes[1].set_title("Box Plot of Ratings") sns.boxplot(y=amazon_csv['duration_numeric'], ax=axes[2], color=palette[3]) axes[2].set_title("Box Plot of Duration (Minutes)") plt.tight_layout() plt.show() 

#Drop the column date_added amazon_csv.drop(columns=['date_added'], inplace=True) # Replace missing values in 'country' with its mode (most frequent value) amazon_csv['country'].fillna(amazon_csv['country'].mode()[0], inplace=True) # Replace missing values in 'director' and 'cast' with "Not available" amazon_csv[['director', 'cast']] = amazon_csv[['director', 'cast']].fillna("Not available") # Drop rows where 'age group' or 'rating' are missing amazon_csv.dropna(subset=['age_group', 'rating'], inplace=True) # Reset index after dropping rows amazon_csv.reset_index(drop=True, inplace=True) 

 # 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=0.68, c='#B4B4B4', zorder=1) # these values go on the numbers below tl_dates = [ "1994\nFounded", "2006\nIntroduced Prime", "2015\n100M Users", "2024\nPrime membership hits 250M" ] tl_x = [1, 2.6, 4.3,6.8] ## these go on the numbers tl_sub_x = [2.3,3.5,4.1,5.1,5.8] tl_sub_times = [ "2005","2010","2014","2016","2020" ] # these values go on Stemplot : vertical line tl_text = [ "Amazon Prime Launched","First Amazon Original","Amazon adds streaming benefits", "India Launch (My birthplace)", "Prime Video Goes Global"] # 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', fontweight='bold', color='#4a4a4a',fontfamily='serif',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) plt.setp(baseline, zorder=0) plt.setp(markerline, marker=',', color='#4a4a4a') plt.setp(stemline, color='#232F3E') # Remove the Spine around the plot for spine in ["left", "top", "right", "bottom"]: ax.spines[spine].set_visible(False) # Ticks ax.set_xticks([]) ax.set_yticks([]) # Title ax.set_title("Amazon through the years", fontweight="bold", fontsize=16,fontfamily='serif', color='#4a4a4a') ax.text(2.4,1.57,"From Fast Shipping to Global Streaming - Popular streaming service for movies and TV shows.", fontsize=12, fontfamily='serif',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', fontweight='bold', color='#4a4a4a' if idx!=len(tl_sub_x)-1 else '#00A8E1', fontfamily='serif',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)-1 else '#00A8E1') plt.show() 

amazon_csv['count'] = 1 # Extract the first country from the 'country' column amazon_csv['first_country'] = amazon_csv['country'].apply(lambda x: x.split(",")[0]) # Shorten country names amazon_csv['first_country'].replace({'United States': 'USA','United Kingdom': 'UK','South Korea': 'S. Korea'}, inplace=True) # Group by country and get total count df_country = amazon_csv.groupby('first_country')['count'].sum().sort_values(ascending=False) 

# Load the ISO code CSV data iso_df = pd.read_csv('country_iso_codes.csv') # Merge the country counts with the ISO codes merged_df = pd.merge(countriess, iso_df, left_index=True, right_on='Country') # Define Amazon Prime themed color scale for specific ranges for better visibility custom_colorscale = [ (0.0, '#00A8E1'), # Highest values (Prime Blue)  (0.1, '#0073CF'), # 700-800 range (Deep Blue)  (0.2, '#005BB5'), # 600-700 range (Darker Blue)  (0.3, '#004494'), # 500-600 range (Navy Blue)  (0.4, '#002E73'), # 400-500 range (Deep Navy)  (0.5, '#001A52'), # 300-400 range (Dark Indigo)  (0.6, '#232F3E'), # 100-300 range (Amazon Dark Blue)  (1.0, '#0F1111') # 1-100 range (Almost Black) ] # Create the choropleth map fig = go.Figure(data=go.Choropleth( locations=merged_df['ISO Code'], # Country ISO codes  z=merged_df['count'], # Data values  text=merged_df['Country'], # Hover text  colorscale=custom_colorscale, marker_line_color='darkgray', marker_line_width=0.6, colorbar_title='Count', )) # Update layout fig.update_layout( title={ 'text': "TV Shows / Movies origin country available on Amazon Prime", 'x': 0.5, 'xanchor': 'center', 'yanchor': 'top', 'font': dict( size=30, family='serif', weight='bold' ), 'pad': dict(t=20) }, geo=dict( showframe=False, showcoastlines=True, projection_type='equirectangular' ) ) # Show the figure fig.show() 

# Find the ratio of type x=amazon_csv.groupby(['type'])['type'].count() y=len(amazon_csv) r=((x/y)).round(2) mf_ratio = pd.DataFrame(r).T print(mf_ratio) 

 #calclated 'mf_ratio' mf_ratio = pd.DataFrame({'Movie': [0.8], 'TV Show': [0.2]}) fig, ax = plt.subplots(figsize=(6, 1.5)) # single horizontal stacked bar ax.barh(0, mf_ratio['Movie'], color='#00A8E1', alpha=0.9) ax.barh(0, mf_ratio['TV Show'], left=mf_ratio['Movie'], color='#232F3E', alpha=0.9) # Remove axes details ax.set_xlim(0, 1) ax.set_xticks([]) ax.set_yticks([]) ax.spines[['top', 'left', 'right', 'bottom']].set_visible(False) # Annotations for Movie & TV Show ax.annotate("80%", xy=(mf_ratio['Movie'][0] / 2, 0), va='center', ha='center', color='white', fontsize=20, fontweight='bold', fontfamily='serif') ax.annotate("20%", xy=(mf_ratio['Movie'][0] + mf_ratio['TV Show'][0] / 2, 0), va='center', ha='center', fontsize=20, color='white', fontweight='bold', fontfamily='serif') fig.text(1.1, 1, 'Insight', fontsize=15, fontweight='bold', fontfamily='serif') fig.text(1.1, 0.40, ''' The graph shows that the content available in Amazon Prime is majorly Movies with (80%) and TV Shows only add upto (20%).This could be a result because of the country distribution which we saw on the previous plot. Or Maybe because TV Shows are something which are new to the trend than movies. ''' , fontsize=8, fontweight='light', fontfamily='serif') # Creates a vertical line to seperate the insight 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]) # Title fig.text(0.125, 1.1, 'Movie & TV Show Distribution', fontsize=14, fontweight='bold', fontfamily='serif') fig.text(0.64,0.9,"Movie", fontweight="bold", fontfamily='serif', fontsize=15, color='#00A8E1') fig.text(0.77,0.9,"|", fontweight="bold", fontfamily='serif', fontsize=15, color='black') fig.text(0.8,0.9,"TV Show", fontweight="bold", fontfamily='serif', fontsize=15, color='#221f1f') plt.show() 

 # Filter out 'Not Available' values before counting occurrences filtered_directors = amazon_csv[amazon_csv['director'] != "Not available"] # Count the number of titles per director director_counts = filtered_directors['director'].value_counts() # Select top 10 directors top_directors = director_counts[:6] # Define bar colors colors = ['#232F3E'] * 10 # Default light color colors[:3] = ['#00A8E1'] * 3 # Highlight top 3 directors  # Create figure and axis for the bar chart fig, ax = plt.subplots(figsize=(12, 6)) # Plot the bar chart ax.bar(top_directors.index, top_directors.values, width=0.5, color=colors, edgecolor='darkgray', linewidth=0.6) # Get the maximum value for dynamic annotation positioning max_value = top_directors.max() # Add value labels above the bars for i, director in enumerate(top_directors.index): ax.annotate(f"{top_directors[director]} Content", xy=(i, top_directors[director] + max_value * 0.05), # 5% above the bar  ha='center', va='bottom', fontweight='light', fontfamily='serif') # Remove top, left, and right borders for side in ['top', 'left', 'right']: ax.spines[side].set_visible(False) # Set x-axis labels ax.set_xticks(range(len(top_directors.index))) # Correct tick positioning ax.set_xticklabels(top_directors.index, fontfamily='serif', rotation=0, ha='center') ax.set_yticklabels('', fontfamily='serif', rotation=0, ha='center') # Add horizontal grid lines for better readability ax.grid(axis='y', linestyle='-', alpha=0.4) ax.set_axisbelow(True) # Place grid lines behind bars  # Add a thick bottom border plt.axhline(y=0, color='black', linewidth=1.3, alpha=0.7) fig.text(0.95, 0.75, 'Insight', fontsize=15, fontweight='bold', fontfamily='serif') fig.text(0.95, 0.45, ''' I have chosen only Top 6 for better visualization because majority of the directors have single figure content under their name. Director, Mark Knight has the highest content of Movies/ TV show with 104 counts that is hosted on Amazon Prime. Followed by Cannis Holder (60), Moonbug E(36). Its interesting although India , South Korea are one of the top producers, the directors dont hold a Monopoly. ''' , fontsize=8.5, fontweight='light', fontfamily='serif') # Remove tick marks ax.tick_params(axis='both', which='major', labelsize=12, length=0) fig.text(0.65,0.75,"Top 3", fontweight="bold", fontfamily='serif', fontsize=15, color='#00A8E1') fig.text(0.71,0.75,"|", fontweight="bold", fontfamily='serif', fontsize=15, color='black') fig.text(0.73,0.75,"Rest 3", fontweight="bold", fontfamily='serif', fontsize=15, color='#221f1f') # Title styling plt.title('Top 6 Directors on Amazon Prime', fontsize=16, fontweight='bold', fontfamily='serif') # Display the plot plt.show() 

# To create a layout with length and width defined fig, ax = plt.subplots(figsize=(12, 6)) color = ["#00A8E1", "#221f1f"] # Annotation for i, mtv in enumerate(amazon_csv['type'].value_counts().index): mtv_rel = amazon_csv[amazon_csv['type']==mtv]['release_year'].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() # Creates a horizontal line below in x axis ax.axhline(y = 0, color = 'black', linewidth = 1.3, alpha = .7) # Limits for the y axis ticks ax.set_ylim(0, 1000) #To remove the box around for s in ['top', 'right','bottom','left']: ax.spines[s].set_visible(False) ax.grid(False) plt.xticks(fontfamily='serif') plt.yticks(fontfamily='serif') # Insights fig.text(0.30, 0.85, 'Chronological availability of Movies & TV Shows', fontsize=16, fontweight='bold', fontfamily='serif') fig.text(0.13, 0.75, 'Insight', fontsize=15, fontweight='bold', fontfamily='serif') fig.text(0.13, 0.55, '''It shows that majority of the content on Amazon Prime is new. We can see a steep curve in the range of 2000-2020. Definitely this will give us an insight of the age group of the consumers. Could this mean contents are mainly focused on youth or teens? ''' , fontsize=8.5, fontweight='light', fontfamily='serif') # Legends fig.text(0.13,0.2,"Movie", fontweight="bold", fontfamily='serif', fontsize=15, color='#00A8E1') 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') # removes the ticks ax.tick_params(axis=u'both', which=u'both',length=0) # Display the grapgh plt.show() 

# Get the unique countries unique_countries = set() # Get the first country if multiple are listed for entry in amazon_csv['country'].dropna(): first_country = entry.split(', ')[0] unique_countries.add(first_country) # Filter the dataset amazon_csv['first_country'] = amazon_csv['country'].apply(lambda x: x.split(', ')[0] if isinstance(x, str) else x) # Get the top 10 countries top_countries = amazon_csv['first_country'].value_counts().head(10).index # Creating a dictionary for clubbing ratings_ages = { 'TV-Y': 'Kids', 'TV-G': 'Kids', 'G': 'Kids', 'ALL': 'Kids', 'ALL_AGES': 'Kids', 'TV-Y7': 'Older Kids', '7+': 'Older Kids', 'TV-PG': 'Older Kids', 'PG': 'Older Kids', 'PG-13': 'Teens', 'TV-14': 'Teens', '13+': 'Teens', '16+': 'Teens', '16': 'Teens', 'AGES_16_': 'Teens', '18+': 'Adults', 'R': 'Adults', 'TV-MA': 'Adults', 'NC-17': 'Adults', 'NR': 'Adults', 'TV-NR': 'Adults', 'UNRATED': 'Adults', 'AGES_18_': 'Adults', 'NOT_RATE': 'Adults' } # Create 'target_age_group' column *before* filtering amazon_csv['target_age_group'] = amazon_csv['age_group'].map(ratings_ages) # Filter data for only the top 10 countries filtered_data = amazon_csv[amazon_csv['first_country'].isin(top_countries)] filtered_data['first_country'].replace({'United States': 'USA', 'United Kingdom': 'UK', 'South Korea': 'S. Korea'}, inplace=True) # Create a pivot table with counts of each age group per country heatmap_data = filtered_data.pivot_table(index='target_age_group', columns='first_country', aggfunc='size', fill_value=0) # Convert counts to percentages heatmap_data_percentage = heatmap_data.div(heatmap_data.sum(axis=0), axis=1) * 100 # custom colormap for Amazon cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#FFFFFF', '#B4B4B4', '#00A8E1']) # Plot the heatmap plt.figure(figsize=(12, 6)) sns.heatmap(heatmap_data_percentage, cmap=cmap, annot=True, fmt='.1f', linewidths=2.5, square=True, cbar=False, vmax=60, vmin=5, annot_kws={"fontsize": 12, "fontfamily": 'serif', "fontweight": 'bold', "color": 'black'}) # Annotations for t in plt.gca().texts: t.set_text(t.get_text() + "%") t.set_fontsize(10) # Title and labels plt.title("Target Age Group Distribution by Country (%)", fontsize=16, fontweight='bold', fontfamily='serif', pad=20, color='#232F3E') plt.xlabel("", fontsize=10, labelpad=10, fontfamily='serif', color='#232F3E') plt.ylabel("", fontsize=10, labelpad=10, fontfamily='serif', color='#232F3E') plt.xticks(fontsize=10, fontfamily='serif') plt.yticks(fontsize=10, fontfamily='serif') # Add labels plt.text(10.5, 0.2, "Insight", fontweight="bold", fontfamily='serif', fontsize=16) plt.text(10.5, 0.9, '''Yes, we can see that Most of the countries target Teens and Adults for their viewer engagement. Italy targets 70% of younger viewers. ''', fontfamily='serif', fontsize=8.5) # Show plot plt.show() 

amazon_csv['num_seasons'] = amazon_csv['duration'].str.extract(r'(\d+) Season[s]?') 

###Correlation between Number of Seasons Vs Ratings.  correlation = amazon_csv['num_seasons'].corr(amazon_csv['rating']) print(f"Correlation Coefficient: {correlation}") 

 # Make it numeric to extract the number of seasons amazon_csv['num_seasons'] = pd.to_numeric(amazon_csv['num_seasons'], errors='coerce') # Create the regplot / Scatter plot plt.figure(figsize=(12, 6)) sns.regplot(x=amazon_csv['num_seasons'], y=amazon_csv['rating'], scatter_kws={"s": 3, "color": "#232F3E"}, line_kws={"color": "#00A8E1", "linewidth": 2}) # Set title and labels plt.title("Number of Seasons vs Rating", fontsize=16, fontweight='bold', family='serif') plt.xlabel("Number of Seasons", fontsize=12, fontweight='normal', family='serif') plt.ylabel("Rating", fontsize=12, fontweight='normal', family='serif') # Grid customization plt.grid(True, axis='both', linestyle='-', alpha=0.4) # Make the background color white for a cleaner look plt.gca().set_facecolor('white') plt.text(22, 15.5, 'Insight', fontsize=16, fontweight='bold', fontfamily='serif') plt.text(22, 13, '''There is a strong correlation of 0.67 to show that rating might have tempted the Directors to increase the number of seasons. ''' , fontsize=8.5, fontweight='light', fontfamily='serif') # Customize the ticks plt.xticks(fontsize=8, family='serif') plt.yticks(fontsize=8, family='serif') # Remove the borders (spines) for spine in plt.gca().spines.values(): spine.set_visible(False) # Display the plot plt.tight_layout() plt.show()