intro to data analysis

excel data analysis

• Basic calculation 

• Auto-filling cells (down and right)

• Formatting graphs 

• Adding tred lines 

• Simple macros 

• Basic stats

python calculations

• Basic math 

• Adding items to lists and dictionaries 

• Open .csv file 

• Idex series (array) from dataframe (spreadsheet)

• Basic stats 

• Indexing items from series and array 

• Plotting data and simple formatting

convert samples to time (s)

= A2+(1/208)

negate angular velocity Y

C2*(-1) where C is the column of angular velocity

find min, max, average, and average of 1000 samples

1. Max: =max(col:col)

2. Min: =min(col:col)

3. Average: =average(col:col)

4. Average-1000: average(col2:col1002)

area of eclipse

• Pi*A*B

  • A: length of trial 

  • B: angular velocity

t-test

• Ttest2-tailed: =TTEST(B2:B2566,C2:C2566,2,2)

• Ttest1-tailed: =TTEST(B2:B2566,C2:C2566,1,2)

setting up for data analysis in python

import pandas as pd

import matplotlib.pyplot as plt

import numpy as np

basic math in python

• x = 3 and y = 5

  • mult = x*y

  • div = x/y

  • add = x+y

  • sub = x-y

  • square = x**2

  • squareRoot = np.sqrt(x)

adding data to list and dictionaries in python

list1 = [mult,div,add,sub,square,squareRoot]

print(list1)

dict1 = {'multiply':mult,'divide':div,'addition':add,'subtraction':sub,'exponentiation':square,'square root':squareRoot}

print(dict1)

importing csv data into python

data = pd.read_csv('C:/Users/Drew/Documents/GitHub/MMRL/EPHE-341-labs/IMU Data.csv')  # Enter file location from your computer

calculating basic stats

maximum = data['Angular Velocity Y (rad/s)'].max()

minimum = data['Angular Velocity Y (rad/s)'].min()

average = data['Angular Velocity Y (rad/s)'].mean()

stats = {'max':maximum, 'min':minimum, 'mean':average}

index an element of a series

tenth = data['Angular Velocity Y (rad/s)'].iloc[9]

convert pandas series to lumpy array

array = data['Angular Velocity Y (rad/s)'].to_numpy()

index an element of an array

tenthArray = array[(9)]

plotting examples

#Most basic plot, using plt.plot()

plt.plot(data['Angular Velocity Y (rad/s)'])

#Since our signal is negated (upside down) we can correct this by negating the whole series

dataNegate = data['Angular Velocity Y (rad/s)']*-1

#A bit more complex, using subplots we can create abd display multiple graphs

#We will also change the color

#Now we can use the newly created negated data set

fig1, ax1 = plt.subplots()

ax1.plot(dataNegate, color='red')

#Now we can change some of the features of the graph and label the axes

fig2, ax2 = plt.subplots()

ax2.plot(dataNegate, color = 'teal', label= 'Walking Gyro Data')

ax2.set_xlabel("Samples (#)")

ax2.set_ylabel("Angular Velocity (rad/s)")

fig2.set_size_inches(12, 8, forward=True)

fig2.set_dpi = 300

print(list1)

print(dict1)

print(stats)