Python For Data Science! 🤘

Beginner Guide to Python for Data Science

1 Python Basics 🐍

Here the tool used to run Python is Anaconda - Spyder (Python 3.7)

Comment and Print statement

#prints Hello World!
print('Hello World!')

1.1 Types

Types	Examples	Typecast
int	1	int('1') = 1
floats	1.12	float(1) = 1.0
str	"Sachin Araballi"	str(1) = 1
bool	True (1), False (0)	bool(1) = True

We can type(value) to get the type of value

1.2 Expressions and Variables

2 * 3 + 5
11

6 / 2 
3.0 # float

6 // 2
3 #int

#variable declaration
var = 10
#assign a value
var:20

1.3 String Operations

Here we have both positive indexing and negative indexing

name = "Sachin A"

S	A	C	H	I	N		A
0	1	2	3	4	5	6	7
-8	-7	-6	-5	-4	-3	-2	-1

name[0]:S name[8]:A or name[-8]:S name[-1]:A

• String : Slicing

  name[0:4] = SAC name[4:7] = HIN

• String : Stride

  name[::2]:SCI A name[0:5:2]:SCI

• Some String Methods mame.upper()

  name.replace('SA' , 'MA)

  name.find('A') -> 8

2 Python Data Structures

2.1 Lists and Tuples

Tuples

• In Python, there are different data types: string, integer and float. These data types can all be contained in a tuple.
• Tuples are immutable

tuple1 = ("sachin", 100, 1.20)

#concatinating tuples
tuple2 = tuple1 + ("sarvesh" , 30)

#nested tuple
nested =(1, 2, ("sachin", "sarvesh") ,(3,4),("araballi",(1,2)))

#Access the element, with respect to index 2:
nested[3][1] - "sachin"

nested.index("1") - 0

Lists

Lists are mutable datastructures

L = ["sachin", 1 , 2, 3]

#We can use the method "extend" to add new elements to the list:
L.extend(["sarvesh", 2])

["sachin", 1 , 2, 3, "sarvesh", 2]

L.append(["sarvesh", 2])
["sachin", 1 , 2, 3, ["sarvesh", 2]]

L.del("sarvesh")

"sachin, a".split(',');
[sachin, a]

2.2 Sets

A set is a unique collection of objects in Python. It is denoted by curly bracket {}.

set1 = {"carnatic", "pop", "hundostani", "western"}

list1 = [1, 2, 3, 4]
set2 = set(list1)

set2.add(5)

5 in set2
True

#common elements in 2 sets
set3 = set1 & set2

#difference
set3 = set1.difference(set2)

#intersection
set1.intersection(set2)   

#other methods union, issuperset , issubset

2.3 Dictionaries

A dictionary consists of keys and values.

dict={"key1":1,"key2":"2","key3":[3,3,3],"key4":(4,4,4),('key5'):5,(0,1):6}

#Accessing 
dict["key1"] - 1

dict.keys()

dict.values()

del(dict["key1"])

"key1" in dict

3 Python Programming Fundamentals

3.1 Conditions and Branching

if not (a < 16 or a < 17 and a < 19) : 

elif a < 18:

else : 

3.2 Loops ➿

for i in range(5) : 

As = [1, 2, 3]
for a in As : 

for i, a in enumerate(As) : 

while(condition) : 

3.3 Functions

complete the function f so that it returns the product of a and b , you the next cell to test the function

def f(a,b);
    return a+b
    
f(2,4)

3.4 Objects and Classes

class Circle(object): 
    
    def __init__(self,radius=3,color='blue'): #constructor with default values
        # data attributes
        self.radius=radius
        self.color=color 
    
    def add_radius(self,r): #method 
        
        self.radius=self.radius+r
        return(self.radius)
        
RedCircle = Circle(4, red)

dir(RedCircle)
#returns all data atrributes

RedCircle.radius
RedCircle.color 

4 Working with Data in Python

4.1 Reading files with open

example1="/resources/data/Example1.txt"
file1 = open(example1,"r")

FileContent=file1.read()

#dont need to close the file
with open(example1,"r") as file1:
    FileContent=file1.read()
    print(FileContent)
    
#reading line by line 
with open(example1,"r") as file1:
    FileContent=file1.readlines()
    print(FileContent)
    
#reading character by character
with open(example1,"r") as file1:
    print(file1.read(4))
    print(file1.read(4))
    print(file1.read(7))
    print(file1.read(15))
    

4.2 Writing files with open

with open('/resources/data/Example2.txt','w') as writefile:
    writefile.write("This is line A")
    
#append the file
with open('/resources/data/Example2.txt','a') as testwritefile:
    testwritefile.write("This is line C\n")

#copy a file 
with open('Example2.txt','r') as readfile:
with open('Example3.txt','w') as writefile:
          for line in readfile:
                writefile.write(line

4.3 Loading data with Pandas 🐼🐼

import pandas as pd

csv_path = "sample.csv"
df = pd.read_csv(csv_path)

df.head() #returns first five rows of dataframe

#we can get a new data frame from a single column of a dataframe
x = df[['column_name']]

#to access specific value
df.iloc[0,0]

df.loc[1,'column_name']

#You can perform slicing using both the index and the name of the column:
df.iloc[0:2, 0:3]

df.loc[0:2, 'from_column_name':'to_column_name']

4.4 Working with and Saving data with Pandas

import pandas as pd

df=pd.DataFrame({'a':[1,2,1],'b':[1,1,1]})

df.to_csv("sample.csv")

Numpy Arrays

Numpy is a python library for scientific computing.

1 D Array

import numpy as np
a = np.array([0,1,2,3,4])

type(a)
# returns numpy.ndarray
a.size
a.ndim
a.size

Vector Addition, subtraction, multiplication can be done e.g dot product

x=np.array([0,np.pi/2 , np.pi] )
y=np.sin(x)
array([0.0000000e+00, 1.0000000e+00, 1.2246468e-16])

np.linspace(-2,2,num=5)
array([-2., -1.,  0.,  1.,  2.])

2 D Array

a = [[1,2,3][4,5,6][7,8,9]] A = np.array(a) A.T #transpose of matrix