Python for Data Science

# Basic Strings in Python 

variable1= "This is a sample string"
print (variable1)

# Lists in Python

variable2 = ["ID", "Name", "Depth", "Latitude", "Longitude"]
depth = [0, 120, 140, 0, 150, 80, 0, 10]
variable2[3]

# Simple computation to pull the mean value from the elements of the Depth list

sum(depth)/len(depth)

# Tuples in Python (Cant modify the content after creation)

depth = (0, 120, 140, 0, 150, 80, 0, 10)

print(depth)

# Dictionaries in Python

variable4 = {"ID":1, "Name": "Valley City", "Depth":0, "Latitude":49.6, "Longitude":-98.01}
variable4["Longitude"]

# Loops in Python 
variable2 = ["ID", "Name", "Depth", "Latitude", "Longitude"]
print(variable2[3])
print(variable2[4])

for element in variable2:print(element)

# Functions & Classes 
# 'Print' is a built-in function...

print("Hello")

# Time Series Data - Snow-depth data for different locations inside three seperate Python lists - one list per month.  

december_depth=[0,120,140,0,150,80,0,10]
january_depth=[20,180,140,0,170,170,30,30]
february_depth=[0,100,100,40,100,160,40,40]

# Defining 'average' function
# 'any_list' is just a variable that's later assigned the given value when the function is executed 

def average (any_list):return(sum(any_list)/len(any_list)) 

average(february_depth)

# Importing the NumPy library (generate multi-dimensional arrays) 

import numpy
array_1d=numpy.arange(8)
print(array_1d)

array_2d=numpy.arange(8) .reshape(2,4)
print(array_2d)

# Putting lists into NumPy array 

depth=numpy.array([december_depth, january_depth, february_depth])
print(depth)

# NumPy Mean Function 

numpy.mean(depth[:,0])

# Exploring SciPy Library 
import scipy

# help(scipy) returns: "IOPUB data rate exceeded error" 

print(depth)

# MatPlotLib, For Loop, PyPlot

import matplotlib.pyplot as plt
for month in depth:
    plt.plot(month)
plt.show()

# loading the modules
import pandas as pd 
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats 

# this will show the charts below each cell instead of in a seperate viewer
%matplotlib inline

# Loading a .CSV
grades = pd.read_csv('class_grades.csv')

# Prints Grades
grades

#Calculating a Weighted Average in Python
grades['grade'] = np.round((0.1 * grades.HW1 + 0.1 * grades.HW2 + 0.25 * grades.Midterm + 0.1 * grades.Participation + 0.45 * grades.Exam), 0)

grades.head()

# Using a letter_grade Function and if Command in Python 

def calc_letter(row):
    if row.grade>=90:
        letter_grade = 'A'
    elif row['grade'] > 75:
        letter_grade = 'B'
    elif row['grade'] > 60:
        letter_grade = 'C'
    else:
        letter_grade = 'F'
    return letter_grade
# In Python there are no "then" statements, no braces, and few brackets or parentheses. Flow is determined by colons and indents. 

grades['ltr'] = grades.apply(calc_letter, axis=1)
# "apply" with axis=1 applies a function to an entire column using values from the same row.

grades 

# Example Code for Creating a Trendline in Python
student = 'Bhirasri, Silpa'

y_values = [] # Create and empyt list
for column in ['HW1', 'HW2', 'Midterm', 'Participation', 'Exam']:
    y_values.append(grades[grades.Name == student][column].iloc[0])
#Append each grade in the order it appears in the dataframe
y_values 

x = np.array([1,2,3,4,5])
y = np.array(y_values)
slope, intercept, r, p, slope_std_err = stats.linregress(x,y)
# This automatically calculates the slope, intercept, Pearson correlation, coefficient (r), and two other statistics I wont use here. 

bestfit_y = intercept + slope * x 
# This calculates the best-fit line to show on the chart.

plt.plot(x, y, 'ko')
# This plots x and y values; 'k' is the standard printer's abbreviation for the color 'blacK', and '0' signifies markers to be circular.

plt.plot(x, bestfit_y, 'r-')
# This plots the best-fit regression line as a 'r'ed line (-). 

plt.ylim(0, 100)
# This sets the upper and lower limits of the y axis.
# If it were not specified, the min and max value would be used. 

# Change the Style to default parameters 
plt.rcParams.update(plt.rcParamsDefault)

# Change the chart style to xkcd 
# plt.xkcd()

plt.show() # since the plot is ready, it will be shown below the cell

'Pearson coefficient (R) = ' + str(r)