✨Heroes of Pymoli - Pandas-Challenge✨

Congratulations! After a lot of hard work in the data wrangling mines, you've landed a job as Lead Analyst for an independent gaming company. You've been assigned >the task of analyzing the data for their most recent fantasy game Heroes of Pymoli.

Like many others in its genre, the game is free-to-play, but players are encouraged to purchase optional items that enhance their playing experience. As a first >task, the company would like you to generate a report that breaks down the game's purchasing data into meaningful insights.

My final report includes the following:

Three observable trends based on the data:

Observable Trends

• The most popular, reported gender demographic for video game players is far and away the Male demographic, at 84.03%. (Which, based on experience - is fitting, but we definitely need more female gamers!)
• The vast majority, at 44.79% of video game players - are in the age bracket of 20-24. The second most popular age group demographic is 15-19.
• According to the "Average Player Purchase" purchasing data, <10 year olds spent ~$4.54 per player, which is more than the most popular age demographic of 20-24 year olds. (Basically, this means that the parents of 10 year olds are giving their kiddos lots of in game purchase money! haha)

Player Count:

• Total Number of Players

player = len(purchase_data["SN"].value_counts())
player_count = pd.DataFrame([player], columns = ["Total Players"])
player_count

Purchasing Analysis (Total)

• Number of Unique Items
• Average Purchase Price
• Total Number of Purchases
• Total Revenue

# Purchasing Analysis (Total)
UniqueItems = len(purchase_data["Item Name"].unique())
Averageprice = purchase_data["Price"].mean()
NumPurchases = len(purchase_data["Item Name"])
Revenue = purchase_data["Price"].sum()


Pur_Analysis_Total = pd.DataFrame({"Number of Unique Items": [UniqueItems],
                                           "Average Price": [Averageprice],
                                           "Number of Purchases": [NumPurchases],
                                           "Total Revenue": [Revenue]})


Pur_Analysis_Total["Average Price"] = Pur_Analysis_Total["Average Price"].map("${:.2f}".format)
Pur_Analysis_Total["Total Revenue"] = Pur_Analysis_Total["Total Revenue"].map("${:.2f}".format)
Pur_Analysis_Total = Pur_Analysis_Total[["Number of Unique Items", "Average Price", "Number of Purchases", "Total Revenue"]]


Pur_Analysis_Total

Gender Demographics

• Percentage and Count of Male Players
• Percentage and Count of Female Players
• Percentage and Count of Other / Non-Disclosed

# Gender Demographics

gender_groups = purchase_data.groupby("Gender")

total_count_gender = gender_groups.nunique()["SN"]

#percentages
percentage_gender = total_count_gender/player*100

gender_demographics = pd.DataFrame({
    "Percentage of Players":percentage_gender,
    "Total Count": total_count_gender
})

gender_demographics.drop_duplicates()

#float $
pd.options.display.float_format = '{:,.2f}%'.format
gender_demographics

Purchasing Analysis (Gender)

• The below each broken by gender
  • Purchase Count
  • Average Purchase Price
  • Total Purchase Value
  • Average Purchase Total per Person by Gender

Age Demographics

• The below each broken into bins of 4 years (i.e. <10, 10-14, 15-19, etc.)
  • Purchase Count
  • Average Purchase Price
  • Total Purchase Value
  • Average Purchase Total per Person by Age Group

# Purchasing Analysis (age)
# Combined with the Purchasing Analysis from above!
bins = [0, 9.9, 14.9, 19.9, 24.9, 29.9, 34.9, 39.9, 150]
group_names = ["<10", "10-14", "15-19", "20-24", "25-29", "30-34", "35-39", "40+"]

purchase_data["Age Groups"]=pd.cut(purchase_data["Age"], bins, labels=group_names)
pd.cut(purchase_data["Age"], bins, labels=group_names).head()

age_groupings=purchase_data.groupby("Age Groups")

total_age_count=age_groupings["SN"].nunique()

percentage_by_age= total_age_count / player*100

age_demographics= pd.DataFrame({
    "Percentage of Players": percentage_by_age,
    "Total Count": total_age_count
})

#float $
pd.options.display.float_format = '{:,.2f}%'.format

age_demographics

Purchasing Analysis (age)

• The below each broken into bins of 4 years (i.e. <10, 10-14, 15-19, etc.)
  • Purchase Count
  • Average Purchase Price
  • Total Purchase Value
  • Average Purchase Total per Person by Age

#using the data frame from previous exercise and use that the create the variables for this set
purchase_count_age = age_groupings["Purchase ID"].count()

average_purchase_price = age_groupings["Price"].mean()

total_purchase_value = age_groupings["Price"].sum()

average_purch_per = total_purchase_value/total_age_count

age_analysis = pd.DataFrame({
    "Purchase Count": purchase_count_age,
    "Average Purchase Price": average_purchase_price,
    "Total Purchase Value":total_purchase_value,
    "Average Player Purchase": average_purch_per})

#float $
pd.options.display.float_format = '${:,.2f}'.format

age_analysis 

Top Spenders

• Identified the the top 5 spenders in the game by total purchase value, then list (in a table):
  • SN
  • Purchase Count
  • Average Purchase Price
  • Total Purchase Value

# Top Spenders 
top_spender_stats = purchase_data.groupby("SN")

purchase_count_sn = top_spender_stats["Purchase ID"].count()

avg_spender_purch = top_spender_stats["Price"].mean()

total_spender_purchases = top_spender_stats["Price"].sum()

top_spenders = pd.DataFrame({"Purchase Count": purchase_count_sn,
                             "Average Purchase Price": avg_spender_purch,
                             "Total Purchase Value":total_spender_purchases})

ascending_purch_values = top_spenders.sort_values(["Purchase Count","Total Purchase Value"], ascending = False)

#float $
pd.options.display.float_format = '${:,.2f}'.format

ascending_purch_values.head(3)

Most Popular Items

• Identified the 5 most popular items by purchase count, then list (in a table):
  • Item ID
  • Item Name
  • Purchase Count
  • Item Price
  • Total Purchase Value

#isolate the Item ID, Item Name, and Item Price columns
items = purchase_data[["Item ID", "Item Name", "Price"]]

item_id_names = items.groupby(["Item ID", "Item Name"])

purchase_count = item_id_names["Price"].count()

total_purchase_value = item_id_names["Price"].sum()

item_price = total_purchase_value/purchase_count

most_popular_items = pd.DataFrame({
    "Purchase Count": purchase_count, 
    "Item Price": item_price,
    "Total Purchase Value": total_purchase_value
})

#sort largest values
asc_most_popular_items = most_popular_items.sort_values(["Purchase Count"], ascending = False)

#float $
pd.options.display.float_format = '${:,.2f}'.format

asc_most_popular_items.head(3)

Most Profitable Items

• Identified the 5 most profitable items by total purchase value, then list (in a table):
  • Item ID
  • Item Name
  • Purchase Count
  • Item Price
  • Total Purchase Value

# Most Profitable Items

most_profitable = most_popular_items.sort_values(["Total Purchase Value"],ascending = False).head(3)

#float $
pd.options.display.float_format = '${:,.2f}'.format

most_profitable