<jupyter_start><jupyter_text>9 defenses 1. Low Bar2. ALLIANCE selected3. Audience selected4. ALLIANCE selected5. ALLIANCE selected Data structure choices include:   - Pandas dataframes   - Numpy Arrays   - Object oriented   - Dictionary<jupyter_code># Object oriented approach, would have to feed csv data into objects

# maybe get rid of this and just use library analysis tools
class Robot(object):

	def __init__(self, name, alliance, auto_points, points):

		self.name = name
		self.alliance = alliance
		self.auto_points = auto_points
		self.points = points

	def points_per_sec(self):
		return self.points / 150

	def auto_points_per_sec(self):
		return self.auto_points / 15

	def get_name(self):
		return self.name

	def get_alliance(self):
		return self.alliance
data
def analyze(dataframe, team):
    total_points = dataframe[team]['Points'] + dataframe[team]['Auto Points']
    cumulative_success_rate = 4
    pps = dataframe[team]['Points'] / 150
    auto_pps = dataframe[team]['Auto Points'] / 15
    
    return(total_points, pps, auto_pps)

stuff = analyze(data, 'Cougar Tech')
print stuff<jupyter_output>(45.0, 0.23333333333333334, 0.66666666666666663)<jupyter_text>Analysis Functions:<jupyter_code>data = pd.read_csv("robodummy.csv")
fig, axs = plt.subplots(1, 4, sharey = True)
data.plot(kind='scatter', x = 'x', y = 'y', ax = axs[0], figsize = (16, 8))
data.plot(kind='scatter', x = 'x', y = 'y', ax = axs[1])
data.plot(kind='scatter', x = 'x', y = 'y', ax = axs[2])
a = np.array(([1, 4], [6, 5], [9, 3]))
np.sort(a)<jupyter_output><empty_output>
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