analysis_viz

# Loading data into a dictionary

# Create the years and durations lists
years = [2011,2012,2013,2014,2015,2016,2017,2018,2019,2020]
durations = [103, 101, 99, 100, 100, 95, 95, 96, 93, 90]

# Create a dictionary with the two lists
movie_dict = {'years':[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020],
              'durations':[103, 101, 99, 100, 100, 95, 95, 96, 93, 90]}

# Print the dictionary
print(movie_dict)

# Creating a DataFrame from a dictionary

# Import pandas under its usual alias
import pandas as pd

# Create a DataFrame from the dictionary
durations_df = pd.DataFrame(movie_dict)
# Print the DataFrame
print(durations_df)

# visual inspection of our data 

# Import matplotlib.pyplot under its usual alias and create a figure
from matplotlib import pyplot as plt
fig = plt.figure()

# Draw a line plot of release_years and durations
plt.plot(years,durations)

# Create a title
plt.title("Netflix Movie Durations 2011-2020")

# Show the plot
plt.show()


# Loading the rest of the data from a CSV

# Read in the CSV as a DataFrame
netflix_df = pd.read_csv("datasets/netflix_data.csv")

# Print the first five rows of the DataFrame
print(netflix_df.head())


# Filtering for movies

# Subset the DataFrame for type "Movie"
netflix_df_movies_only = netflix_df[netflix_df['type']=='Movie']

# Select only the columns of interest
netflix_movies_col_subset = netflix_df_movies_only.loc[:,['title','country','genre','release_year', 'duration']]

# Print the first five rows of the new DataFrame
print(netflix_movies_col_subset.head())



# Creating a scatter plot

# Create a figure and increase the figure size
fig = plt.figure(figsize=(12,8))

# Create a scatter plot of duration versus year
plt.scatter(netflix_movies_col_subset.release_year, netflix_movies_col_subset.duration)

# Create a title
plt.title("Movie Duration by Year of Release")

# Show the plot
plt.show()



# More filtering

# Filter for durations shorter than 60 minutes
short_movies = netflix_movies_col_subset[netflix_movies_col_subset['duration']<60]

# Print the first 20 rows of short_movies
print(short_movies.head(20))



# Marking non-feature films

# Define an empty list
colors = []

# Iterate over rows of netflix_movies_col_subset
for lab,row in netflix_movies_col_subset.iterrows() :
    if row['genre'] == "Children" :
        colors.append('red')
    elif row['genre'] == "Documentaries" :
        colors.append('blue')
    elif row['genre'] == "Stand-Up":
        colors.append('green')
    else:
        colors.append('black')
        
# Inspect the first 10 values in your list        
colors[0:10]




# Plotting with color

# Set the figure style and initalize a new figure
plt.style.use('fivethirtyeight')
fig = plt.figure(figsize=(12,8))

# Create a scatter plot of duration versus release_year
plt.scatter(netflix_movies_col_subset['release_year'],netflix_movies_col_subset['duration'], c=colors)


# Create a title and axis labels
plt.title("Movie duration by year of release")
plt.xlabel("Release year")
plt.ylabel("Duration (min)")


# Show the plot
plt.show()




# Are we certain that movies are getting shorter?
are_movies_getting_shorter = 'maybe'