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#show most basic package that Vein Pack offers, and wheter it has a significant impact on subscribers. | |
#If subscribers of Vein Pack live longer than other people, then there exists a marketing goldmine. | |
#import lifespan data | |
vein_pack_lifespans = familiar.lifespans(package='vein') | |
#find out if the average lifespan of a Vein Pack subscriber is | |
#significanlty different from the average life expectancy of 71 years using a 1-Sample T-Test. | |
#perform 1-Sample T-Test |
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import codecademylib3_seaborn | |
import pandas as pd | |
from matplotlib import pyplot as plt | |
healthcare = pd.read_csv("healthcare.csv") | |
#print(healthcare.head(4)) | |
chest_pain = healthcare[healthcare['DRG Definition'] == '313 - CHEST PAIN'] | |
alabama_chest_pain = chest_pain[chest_pain['Provider State'] == "AL"] | |
costs = alabama_chest_pain[' Average Covered Charges '].values |
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#We will create wo histograms, each displaying the frequency of an occurrence each day of the year | |
#(either flights or flower blooms). | |
#You will use the in_bloom variable to find a count of the number of flowers that start blooming each day of the year. | |
#You will use the flights variable to find a count of the number of flights that occur each day of the year. | |
# import codecademylib3 | |
import codecademylib3 | |
import numpy as np | |
from matplotlib import pyplot as plt |
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import codecademylib | |
from matplotlib import pyplot as plt | |
import pandas as pd | |
orders = pd.read_csv('orders.csv') | |
customer_amount = orders.groupby('customer_id').price.sum().reset_index() | |
print customer_amount.head() |
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import codecademylib3_seaborn | |
from matplotlib import pyplot as plt | |
import numpy as np | |
import pandas as pd | |
# Bar Graph: Featured Games | |
games = ["LoL", "Dota 2", "CS:GO", "DayZ", "HOS", "Isaac", "Shows", "Hearth", "WoT", "Agar.io"] | |
viewers = [1070, 472, 302, 239, 210, 171, 170, 90, 86, 71] |
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/*getting a feel for tables */ | |
SELECT * FROM stream LIMIT 2; | |
SELECT * FROM chat LIMIT 2; | |
/*How many unique games and unique channels are there in stream table */ | |
SELECT DISTINCT game FROM stream; | |
SElECT DIStinct channel FROM stream; | |
/*What are the most popular games in the stream table? */ | |
SELECT COUNT(*), game FROM stream GROUP BY game ORDER BY 1 DESC; |
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import codecademylib3_seaborn | |
from matplotlib import pyplot as plt | |
import pandas as pd | |
import seaborn as sns | |
df = pd.read_csv('WorldCupMatches.csv') | |
print(df.head()) | |
#We want to visualize the total number of goals scored in each match | |
df['Total Goals'] = df['Home Team Goals'] + df['Away Team Goals'] |
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#Bar Chart with Error | |
import codecademylib | |
from matplotlib import pyplot as plt | |
past_years_averages = [82, 84, 83, 86, 74, 84, 90] | |
years = [2000, 2001, 2002, 2003, 2004, 2005, 2006] | |
error = [1.5, 2.1, 1.2, 3.2, 2.3, 1.7, 2.4] | |
# Make your chart here | |
plt.figure(figsize=(10, 8)) |
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#Funnel analysis is a method used to visualize and map the flow of visitors across a set of website pages or events. | |
#A website funnel gets its name because, much like a physical funnel, | |
#it narrows toward the end—so the volume of visitors at the top is larger than the volume of visitors at the bottom. | |
#Funnel process, 1. A user visits CoolTShirts.com, 2. A user adds a t-shirt to their cart, 3. A user clicks “checkout” | |
#4. A user actually purchases a t-shirt | |
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import codecademylib | |
import pandas as pd | |
df = pd.read_csv('ad_clicks.csv') | |
print(df.head(10)) | |
#which ad platform is getting you the most views. | |
views = df.groupby('utm_source').user_id.count().reset_index() | |
print(views) |
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