Longest word of a given String in Python

def longest_word(str):

words = str.split()

max_len = len(max(words, key=len))

return [word for word in words if len(word) == max_len]

print(longest_word("I have an interview Scheduled today in Bangalore"))

['interview', 'Scheduled', 'Bangalore']

print(longest_word("Do you want me to do something?"))

['something?']

NamedTuple Example in Python

import collections 

Movie = collections.namedtuple('Movie',['MovieID','Title','Genres'])  #NamedTuple Example

M = Movie('1','Toy Story (1995)',"Animation|Children's|Comedy") 

print(M[0])

print(M[1])

print(M.MovieID)

print(M.Title)

print(M.Genres)

1

Toy Story (1995)

1

Toy Story (1995)

Animation|Children's|Comedy

#NamedTuple Example : #1

import collections 

Movie = collections.namedtuple('Movie',['MovieID','Title','Genres']) 

def parseMovie(_row):

fields = _row.split("::")

movieid = (int) (fields[0])

title = fields[1]

genres = fields[2]

M = Movie(movieid,title,genres)

return M

print(parseMovie("1::Toy Story (1995)::Animation|Children's|Comedy"))

Answer:

Movie(MovieID=1, Title='Toy Story (1995)', Genres="Animation|Children's|Comedy")

#NamedTuple Example : #2

import collections 

from datetime import datetime

Rating = collections.namedtuple('Rating',['UserID','MovieID','Rating','Timestamp']) 

def parseRatingRecord(_row):

fields = _row.split("::")

userid = (int) (fields[0])

movieid = (int) (fields[1])

rating = (int) (fields[2])

_timestamp = datetime.fromtimestamp((int) (fields[3]))

_rating = Rating(userid,movieid,rating,_timestamp)

return _rating

print(parseRatingRecord("1::1193::5::978300760"))

Answer:

Rating(UserID=1, MovieID=1193, Rating=5, Timestamp=datetime.datetime(2001, 1, 1, 3, 42, 40))

#Example 3

import collections

User = collections.namedtuple("Users",["UserID","Gender","Age","Occupation","ZipCode"])

def parseUserRecord(_row):

fields = _row.split("::")

userid = (int)(fields[0])

gender= fields[1]

age = (int) (fields[2])

occupation = (int) (fields[3])

zipcode = (int) (fields[4])

_user = User(userid,gender,age,occupation,zipcode) 

return _user

print(parseUserRecord("3::M::25::15::55117"))            

                               

                               

StructField, StructType Example in PySpark. Create Dataframe using in memory object in PySpark

#Create in memory object in Pyspark and create dataframe using it and export into MySQL.

import pyspark.sql.types as st

data = [

    (101,'Sankaranarayanan','1976-04-20','M','Bangalore','Married'),

    (102,'Anbusudha','1979-07-22','F','Bangalore','Married'),

(103,'Rahul','1989-07-15','M','Manachai','Bachelor')

]

print(data)

[(101, 'Sankaranarayanan', '1976-04-20', 'M', 'Bangalore', 'Married'), (102, 'Anbusudha', '1979-07-22', 'F', 'Bangalore', 'Married'), (103, 'Rahul', '1989-07-15', 'M', 'Manachai', 'Bachelor')]

for i in data:

    print(i)

(101, 'Sankaranarayanan', '1976-04-20', 'M', 'Bangalore', 'Married')

(102, 'Anbusudha', '1979-07-22', 'F', 'Bangalore', 'Married')

(103, 'Rahul', '1989-07-15', 'M', 'Manachai', 'Bachelor')

person_schema = st.StructType([

st.StructField('SNo', st.IntegerType(), True),

    st.StructField('Name', st.StringType(), True),

    st.StructField('DOB', st.StringType(), True),

    st.StructField('Gender', st.StringType(), True),

    st.StructField('City', st.StringType(), True),

    st.StructField('MarritalStatus', st.StringType(), True)

])

print(person_schema)

StructType(List(StructField(SNo,IntegerType,true),StructField(Name,StringType,true),StructField(DOB,StringType,true),StructField(Gender,StringType,true),StructField(City,StringType,true),StructField(MarritalStatus,StringType,true)))

user_df = spark.createDataFrame(data, person_schema)

 

user_df.printSchema()

root

 |-- SNo: integer (nullable = true)

 |-- Name: string (nullable = true)

 |-- DOB: string (nullable = true)

 |-- Gender: string (nullable = true)

 |-- City: string (nullable = true)

 |-- MarritalStatus: string (nullable = true)

user_df.show(5)

+---+----------------+----------+------+---------+--------------+

|SNo|            Name|       DOB|Gender|     City|MarritalStatus|

+---+----------------+----------+------+---------+--------------+

|101|Sankaranarayanan|1976-04-20|     M|Bangalore|       Married|

|102|       Anbusudha|1979-07-22|     F|Bangalore|       Married|

|103|           Rahul|1989-07-15|     M| Manachai|      Bachelor|

+---+----------------+----------+------+---------+--------------+ 

#Write the dataframe content into MySQL table

user_df.write.format("jdbc").\

option("url", "jdbc:mysql://localhost:3306/school").\

option("driver", "com.mysql.jdbc.Driver").\

option("dbtable", "user_table").\

option("user", "root").\

option("password", "Studi0Plus").save()

Goto Mysql :

use school;

show tables;

select * from user_table;

# SNo, Name, DOB, Gender, City, MarritalStatus

'103', 'Rahul', '1989-07-15', 'M', 'Manachai', 'Bachelor'

'102', 'Anbusudha', '1979-07-22', 'F', 'Bangalore', 'Married'

'101', 'Sankaranarayanan', '1976-04-20', 'M', 'Bangalore', 'Married'

MatPlotLib - Chart Examples

import numpy as np

from matplotlib import pyplot as plt

x = np.arange(1,11)

x

array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])

y=2*x

y

array([ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20])

plt.plot(x,y)

plt.show()

plt.plot(x,y)

plt.title("Line Plot Example")

plt.xlabel("x-label")

plt.ylabel("y-label")

plt.show()

plt.plot(x,y,color="g",linestyle=":",linewidth=2)

plt.title("Line Plot Example")

plt.xlabel("Sales")

plt.ylabel("Transactions")

plt.show()

x = np.arange(1,11)

y1 = 2 * x

y2 = 3 * x

plt.plot(x,y1,color="g",linestyle=":",linewidth=2)

plt.plot(x,y2,color="r",linestyle="-.",linewidth=3)

plt.title("Line Plot Example")

plt.xlabel("Sales")

plt.ylabel("Transactions")

plt.grid(True)

plt.show()

plt.subplot(1,2,1)

plt.plot(x,y1,color="g")

plt.subplot(1,2,2)

plt.plot(x,y2,color="r")

plt.show()

#Bar chart example

transDict = {"Jan":33000,"Feb":23800,"March":40000,"April":57900}

months = list(transDict.keys())

sales = list(transDict.values())

plt.bar(months,sales)

plt.show()

transDict = {"Jan":33000,"Feb":23800,"March":40000,"April":57900}

months = list(transDict.keys())

sales = list(transDict.values())

plt.barh(months,sales,color="g")

plt.xlabel("Month")

plt.ylabel("Sales")

plt.grid(True)

plt.show()

transDict = {"Jan":33000,"Feb":23800,"March":40000,"April":57900}

months = list(transDict.keys())

sales = list(transDict.values())

plt.bar(months,sales,color="r")

plt.xlabel("Month")

plt.ylabel("Sales")

plt.grid(True)

plt.show()

#Scatter plot

x = [10,20,30,40,50,60,70,80,90]

y = [8,1,7,2,0,3,7,3,2]

plt.scatter(x,y)

plt.show()

x = [10,20,30,40,50,60,70,80,90]

y = [8,1,7,2,0,3,7,3,2]

plt.scatter(x,y,marker="*",c="orange",s=200)

plt.show()

x = [10,20,30,40,50,60,70,80,90]

y1 = [8,3,7,1,4,9,3,6,0]

y2 = [1,2,3,8,6,5,3,9,6]

plt.scatter(x,y1,marker="*",c="orange",s=200)

plt.scatter(x,y2,marker=".",c="red",s=300)

plt.show()

x = [10,20,30,40,50,60,70,80,90]

y1 = [8,3,7,1,4,9,3,6,0]

y2 = [1,2,3,8,6,5,3,9,6]

plt.subplot(1,2,1) #single row and 2 columns and left side

plt.scatter(x,y1,marker="*",c="orange",s=200)

plt.subplot(1,2,2) #single row and 2 columns and right side

plt.scatter(x,y2,marker=".",c="red",s=300)

plt.show()

x = [10,20,30,40,50,60,70,80,90]

y1 = [8,3,7,1,4,9,3,6,0]

y2 = [1,2,3,8,6,5,3,9,6]

plt.subplot(2,1,1) #2 rows single column top side

plt.scatter(x,y1,marker="*",c="orange",s=200)

plt.subplot(2,1,2) #2 rows single column bottom side

plt.scatter(x,y2,marker=".",c="red",s=300)

plt.show()

#histogram example

data = [1,3,3,3,3,9,9,5,4,4,8,8,8,6,7]

plt.hist(data)

plt.show()

#load data from csv and display histogram chart

import pandas as pd

df = pd.read_csv("E:\\PyExa\\iris.csv")

plt.hist(df["sepal.length"],bins=30,color="r")

plt.show()

import pandas as pd

df = pd.read_csv("E:\\PyExa\\iris.csv")

plt.hist(df["sepal.length"],bins=30,color="r")

plt.hist(df["petal.width"],color="green")

plt.show()

Introduction to Pandas - Sample Programs

#Series Example with 0,1,2,3,. are indexes

import pandas as pd

s1 = pd.Series([1,2,3,4,5,6,7,8,9,10])

print(s1)

0     1

1     2

2     3

3     4

4     5

5     6

6     7

7     8

8     9

9    10

dtype: int64

type(s1)

pandas.core.series.Series

#Series example with Custom indexes

import pandas as pd

s1 = pd.Series([86,63,85,81,90],index=["Tamil","English","Maths","Science","Social"])

print(s1)

Tamil      86

English    63

Maths      85

Science    81

Social     90

dtype: int64

#Passing dictionary object to the Series

#Keys of a given Dictionary will become Indexes

import pandas as pd

subjectDict = {"Tamil":85, "English":63, "Maths":85, "Science":81, "Social":90}

s1 = pd.Series(subjectDict)

print(s1)

Tamil      85

English    63

Maths      85

Science    81

Social     90

dtype: int64

# 'b' and 'd' are not there in the given indexes, so, NaN as the value assigned to them

import pandas as pd

s1 = pd.Series({"a":10,"c":30,"e":40},index=["b","c","d","a"])

print(s1)

b     NaN

c    30.0

d     NaN

a    10.0

dtype: float64

s1 = pd.Series([5,7,3,2,88,22,-1,0,33])

print(s1[3])

2

print(s1[:2])

0    5

1    7

dtype: int64

print(s1[-1:])

8    33

dtype: int64

print(s1[:6])

0     5

1     7

2     3

3     2

4    88

5    22

dtype: int64

#Arithmetic operations

s1 = pd.Series([10,20,30,40])

s2 = pd.Series([11,22,33,44])

s3 = s1 + s2

print(s3)

0    21

1    42

2    63

3    84

dtype: int64

s1 = pd.Series([11,66,77,55])

s2 = pd.Series([5,22,22,44])

s3 = s1 - s2

print(s3)

0     6

1    44

2    55

3    11

dtype: int64

print(s1+15)

0    26

1    81

2    92

3    70

dtype: int64

print(s2 ** 1.3)

0      8.103283

1     55.609563

2     55.609563

3    136.926807

dtype: float64

s1 = pd.Series([1,2,3])

s2 = pd.Series([6,7,8])

print(s1,s2)

0    1

1    2

2    3

dtype: int64 0    6

1    7

2    8

dtype: int64

print(s1+s2)

0     7

1     9

2    11

dtype: int64

print(s1*s2)

0     6

1    14

2    24

dtype: int64

print(s1-s2, s2-s1)

0   -5

1   -5

2   -5

dtype: int64 0    5

1    5

2    5

dtype: int64

#DataFrame Example

import pandas as pd

subjectDict = {"Subjects":["Tamil","English","Maths","Science","Social"],"Marks":[86,63,85,81,90]}

df = pd.DataFrame(subjectDict)

print(df)

  Subjects  Marks

0    Tamil     86

1  English     63

2    Maths     85

3  Science     81

4   Social     90

import pandas as pd

subjectDict = {"Names":["Arjun","Ram","Biswa","Kalai","Nila"],"Age":[78,37,88,43,93]}

df = pd.DataFrame(subjectDict)

print(df)

   Names  Age

0  Arjun   78

1    Ram   37

2  Biswa   88

3  Kalai   43

4   Nila   93

df = pd.read_csv("https://gist.githubusercontent.com/netj/8836201/raw/6f9306ad21398ea43cba4f7d537619d0e07d5ae3/iris.csv")

df.head()

df = pd.read_csv("E:\\PyExa\\iris.csv")

df.head()

sepal.length sepal.width petal.length petal.width variety

0 5.1 3.5 1.4 0.2 Setosa

1 4.9 3.0 1.4 0.2 Setosa

2 4.7 3.2 1.3 0.2 Setosa

3 4.6 3.1 1.5 0.2 Setosa

4 5.0 3.6 1.4 0.2 Setosa

df.tail()

sepal.length sepal.width petal.length petal.width variety

145 6.7 3.0 5.2 2.3 Virginica

146 6.3 2.5 5.0 1.9 Virginica

147 6.5 3.0 5.2 2.0 Virginica

148 6.2 3.4 5.4 2.3 Virginica

149 5.9 3.0 5.1 1.8 Virginica

print(df.shape)

(150, 5)  # 150 X 5 ==> 150 Rows X 5 columns

df.describe()

sepal.length sepal.width petal.length petal.width

count 150.000000 150.000000 150.000000 150.000000

mean 5.843333 3.057333 3.758000 1.199333

std 0.828066 0.435866 1.765298 0.762238

min 4.300000 2.000000 1.000000 0.100000

25% 5.100000 2.800000 1.600000 0.300000

50% 5.800000 3.000000 4.350000 1.300000

75% 6.400000 3.300000 5.100000 1.800000

max 7.900000 4.400000 6.900000 2.500000

df.iloc[0:3,0:2]

#1st 3 Rows and 1st 2 Columns

sepal.length sepal.width

0 5.1 3.5

1 4.9 3.0

2 4.7 3.2

df.iloc[0:4,0:4]

#1st 4 Rows and 1st 4 Columns

  sepal.length sepal.width petal.length petal.width

0 5.1 3.5 1.4 0.2

1 4.9 3.0 1.4 0.2

2 4.7 3.2 1.3 0.2

3 4.6 3.1 1.5 0.2

df.loc[0:7,("petal.length","petal.width","variety")]

#1st 7 rows and specified columns

petal.length petal.width variety

0 1.4 0.2 Setosa

1 1.4 0.2 Setosa

2 1.3 0.2 Setosa

3 1.5 0.2 Setosa

4 1.4 0.2 Setosa

5 1.7 0.4 Setosa

6 1.4 0.3 Setosa

7 1.5 0.2 Setosa

#Drop variety column in the dataframe

s1 = df.drop("variety",axis=1)

print (s1.head())

sepal.length  sepal.width  petal.length  petal.width

0           5.1          3.5           1.4          0.2

1           4.9          3.0           1.4          0.2

2           4.7          3.2           1.3          0.2

3           4.6          3.1           1.5          0.2

4           5.0          3.6           1.4          0.2

#Drop  3 rows

s1 = df.drop([1,2,3],axis=0)

print(s1.head())

sepal.length  sepal.width  petal.length  petal.width variety

0           5.1          3.5           1.4          0.2  Setosa

4           5.0          3.6           1.4          0.2  Setosa

5           5.4          3.9           1.7          0.4  Setosa

6           4.6          3.4           1.4          0.3  Setosa

7           5.0          3.4           1.5          0.2  Setosa

df.mean()

sepal.length    5.843333

sepal.width     3.057333

petal.length    3.758000

petal.width     1.199333

dtype: float64

df.median()

sepal.length    5.80

sepal.width     3.00

petal.length    4.35

petal.width     1.30

dtype: float64

df.min()

sepal.length       4.3

sepal.width          2

petal.length         1

petal.width        0.1

variety         Setosa

dtype: object

df.max()

sepal.length          7.9

sepal.width           4.4

petal.length          6.9

petal.width           2.5

variety         Virginica

dtype: object

#applying user defined function

def half(s):

    return s*0.5

s1 = df[["sepal.length","petal.length"]].apply(half) #half is the udf

print(df[["sepal.length","petal.length"]].head())

print(s1.head())

   sepal.length  petal.length

0           5.1           1.4

1           4.9           1.4

2           4.7           1.3

3           4.6           1.5

4           5.0           1.4

   sepal.length  petal.length

0          2.55          0.70

1          2.45          0.70

2          2.35          0.65

3          2.30          0.75

4          2.50          0.70

#user defined function to double the dataframe values

def double_make(s):

    return s*2

print(df[["sepal.width","petal.width"]].head(5))

s1 = df[["sepal.width","petal.width"]].apply(double_make)

print(s1.head())

  sepal.width  petal.width

0          3.5          0.2

1          3.0          0.2

2          3.2          0.2

3          3.1          0.2

4          3.6          0.2

   sepal.width  petal.width

0          7.0          0.4

1          6.0          0.4

2          6.4          0.4

3          6.2          0.4

4          7.2          0.4

#grouping and counting of particular column

s1 = df["variety"].value_counts()

print(s1)

Virginica     50

Setosa        50

Versicolor    50

Name: variety, dtype: int64

#Sort order

s

df.sort_values(by="sepal.length").head()

sepal.length sepal.width petal.length petal.width variety

13 4.3 3.0 1.1 0.1 Setosa

42 4.4 3.2 1.3 0.2 Setosa

38 4.4 3.0 1.3 0.2 Setosa

8 4.4 2.9 1.4 0.2 Setosa

41 4.5 2.3 1.3 0.3 Setosa

Numpy Basics

#1 Dimensional Array

import numpy as np

n1 = np.array([1,2,3,4,5,6])

print(n1)

print(type(n1))

[1 2 3 4 5 6]

<class 'numpy.ndarray'>

#2 Dimensional Array

import numpy as np

n2 = np.array([[5,6,7,8,9,10],[50,51,52,53,54,55]])

print(n2)

print(type(n2))

[[ 5  6  7  8  9 10]

 [50 51 52 53 54 55]]

<class 'numpy.ndarray'>

#Initialize with Zeros - fill the array with zeros

import numpy as np

n3 = np.zeros(6)

print(n3)

[0. 0. 0. 0. 0. 0.]

import numpy as np

n4 = np.zeros((4,4))

print(n4)

[[0. 0. 0. 0.]

 [0. 0. 0. 0.]

 [0. 0. 0. 0.]

 [0. 0. 0. 0.]]

 

 

import numpy as np

n5 = np.zeros((5,3,2))

print(n5)

[[[0. 0.]

  [0. 0.]

  [0. 0.]]

 [[0. 0.]

  [0. 0.]

  [0. 0.]]

 [[0. 0.]

  [0. 0.]

  [0. 0.]]

 [[0. 0.]

  [0. 0.]

  [0. 0.]]

 [[0. 0.]

  [0. 0.]

  [0. 0.]]]

  

  

#Fill the array with a constant value

n5 = np.full((2,3),5)

print(n5)

[[5 5 5]

 [5 5 5]]

 

n6 = np.full((2,6),3)

print(n6)

[[3 3 3 3 3 3]

 [3 3 3 3 3 3]]

 

 

n7 = np.full((5),6)

print(n7)

[6 6 6 6 6]

#fill with a range (arange) 

n1 = np.arange(10,50,2.5)

print(n1)

[10 11 12 13 14 15 16 17 18 19]

n1 = np.arange(10,50,2.5)

print(n1)

[10.  12.5 15.  17.5 20.  22.5 25.  27.5 30.  32.5 35.  37.5 40.  42.5

 45.  47.5]

 

 

n1 = np.arange(10,50,7)

print(n1)

[10 17 24 31 38 45]

#fill with random integer values

n1 = np.random.randint(1,100,10)

print(n1)

[55 65 10 38 86 97 35 55 11 99]

n1 = np.random.randint(5,55,5)

print(n1)

[37 27 28 11 53]

#shape of the array

n1 = np.array([ [1,2,3],[4,3,2]  ])

print(n1)

(2,3)

n1 = np.array([ [1,2,3],[4,3,2] ,[53,2,3] ])

print(n1.shape)

(3, 3)

#stacking examples - vstack, hstack,column_stack

#vstack

n1 = np.array([2,6,3])

n2 = np.array([6,3,8])

np.vstack((n1,n2))

array([[2, 6, 3],

       [6, 3, 8]])

   

n1 = np.array([2,6,3])

n2 = np.array([6,3,8])

n3 = np.array([10,20,33])

np.vstack((n1,n2,n3))

array([[ 2,  6,  3],

       [ 6,  3,  8],

       [10, 20, 33]])

#hstack

n1 = np.array([2,6,3])

n2 = np.array([6,3,8])

np.hstack((n1,n2))

array([2, 6, 3, 6, 3, 8])

n1 = np.array([2,6,3])

n2 = np.array([6,3,8])

n3 = np.array([10,20,33])

np.vstack((n1,n2,n3))

array([[ 2,  6,  3],

       [ 6,  3,  8],

       [10, 20, 33]])

   

#column_stack

n1 = np.array([2,6,3])

n2 = np.array([6,3,8])

np.column_stack((n1,n2))

array([[2, 6],

       [6, 3],

       [3, 8]])

   

n1 = np.array([2,6,3])

n2 = np.array([6,3,8])

n3 = np.array([10,20,33])

np.column_stack((n1,n2,n3))

array([[ 2,  6, 10],

       [ 6,  3, 20],

       [ 3,  8, 33]])

   

n1 = np.array([2,6,3])

n2 = np.array([6,3,8])

n3 = np.array([10,20,33])

n4 = np.array([55,33,66])

np.column_stack((n1,n2,n3,n4))

array([[ 2,  6, 10, 55],

       [ 6,  3, 20, 33],

       [ 3,  8, 33, 66]])

   

   

#intersection

import numpy as np

n1  = np.array([1,2,3,4,5,6])

n2 = np.array([5,6,7,8,9])

np.intersect1d(n1,n2)

array([5, 6])

import numpy as np

n1  = np.array([1,2,3,4,5,6])

n2 = np.array([5,6,7,8,9])

np.intersect1d(n2,n1)

array([5, 6])

#difference

import numpy as np

n1  = np.array([1,2,3,4,5,6])

n2 = np.array([5,6,7,8,9])

np.setdiff1d(n1,n2)

array([1, 2, 3, 4])

import numpy as np

n1  = np.array([1,2,3,4,5,6])

n2 = np.array([5,6,7,8,9])

np.setdiff1d(n2,n1)

array([7, 8, 9])

import numpy as np

n1  = np.array([1,2,3,4,5,6])

n2 = np.array([1,2,3,4,5,6])

np.setdiff1d(n1,n2)

array([], dtype=int32)

#Sum

import numpy as np

n1  = np.array([10,20,30])

n2 = np.array([40,50,60])

np.sum([n1,n2])

210

np.sum([n1,n2],axis=0) #axis 0 : x axis - horizontal

array([50, 70, 90])

np.sum([n1,n2],axis=1) #axis 1 : y axis - vertical

array([ 60, 150])

import numpy as np

n1 = np.array([1,2,3,4,5,6])

n2 = np.array([7,8,9,10,11,12])

n3 = np.array([10,20,30,40,50,60])

n4 = np.array([100,200,300,400,500,600])

np.sum([n1,n2,n3,n4])

2388

np.sum([n1,n2,n3,n4],axis=0)

array([118, 230, 342, 454, 566, 678])

np.sum([n1,n2,n3,n4],axis=1)

array([  21,   57,  210, 2100])

Numpy Array Mathemetics

#Addition

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

n1 = n1 + 1

print(n1)

[56 34 45 67 23 12 78]

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

n1 = n1 + 10

print(n1)

[65 43 54 76 32 21 87]

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

n1 = n1 - 1

print(n1)

[54 32 43 65 21 10 76]

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

n1 = n1 - 10

print(n1)

[45 23 34 56 12  1 67]

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

n1 = n1 * 5

print(n1)

[275 165 220 330 110  55 385]

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

n1 = n1 * 55

print(n1)

[3025 1815 2420 3630 1210  605 4235]

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

n1 = n1 / 2.0

print(n1)

[27.5 16.5 22.  33.  11.   5.5 38.5]

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

n1 = n1 / 5

print(n1)

[11.   6.6  8.8 13.2  4.4  2.2 15.4]

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

n1 = n1 ** 3

print(n1)

[166375  35937  85184 287496  10648   1331 456533]

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

n1 = n1 / (3 * 3)

print(n1)

[6.11111111 3.66666667 4.88888889 7.33333333 2.44444444 1.22222222 8.55555556]

#statistics functions

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

np.mean(n1)

np.sum(n1) / len(n1)  # mean is nothing but average

44.0

#middle element 

import numpy as np

n1 = np.array([11,77,33,44,66,22])

np.median(n1)

38.5

#standard deviation

import numpy as np

n1 = np.array([11,77,33,44,66,22])

np.std(n1)

23.262392157490787

#saving and Loading

import numpy as np

n1 = np.array([1,2,3,5,4,6,7,9,8,10,22])

np.save('result',n1)  #saving

 

n2 = np.load('result.npy') #loading

print(n2*10)

[ 10  20  30  50  40  60  70  90  80 100 220]

   

Python OOPs - Class, Parameters, Constructor, Inheritance (Multi Level, Multiple)

#Create the first class in python

class Phone:

    def make_call(self):

        print("Let's make a phone call")

    

    def play_game(self):

        print("Let's play a game")

        

p1 = Phone()

p1.make_call()

p1.play_game()

#Adding parameters to a class method

class Phone:

    def set_color(self,color):

        self.color=color

        

    def set_cost(self,cost):

        self.cost=cost

        

    def show_color(self):

        return self.color

    

    def show_cost(self):

        return self.cost

    

    def play_game(self):

        print("Playing a game")

        

    def make_call(self):

        print("Making a call")

        

p1=Phone()

p1.set_color("Blue")

p1.set_cost(500)

p1.show_cost()

500

p1.show_color()

'Blue'

p1.play_game()

p1.make_call()

Playing a game

Making a call

#creating a class with constructor

class Employee:

    def __init__(self,name,age,gender,city,salary,pin):

        self.name=name

        self.age=age

        self.gender=gender

        self.city=city

        self.salary=salary

        self.pin=pin

        

    def show_details(self):

        print("Name : {}, Age : {}, Gender: {}, City: {}, Salary: {}, Pin: {}".format(self.name,self.age,self.gender,self.city,self.salary,self.pin))

        

        

e1 = Employee("Sankara",43,'M','Bangalore',67000,560093)

e1.show_details()

Name : Sankara, Age : 43, Gender: M, City: Bangalore, Salary: 67000, Pin: 560093

#Inheritance example:

#Multiple Inheritance

class Parent1:

    def assign_str1(self,str1):

        self.str1 = str1

        

    def show_str1(self):

        print(self.str1)

        

class Parent2:

    def assign_str2(self,str2):

        self.str2 = str2

    

    def show_str2(self):

        print(self.str2)

        

class Child(Parent1, Parent2):

    def assign_str3(self,str3):

        self.str3 = str3

        

    def show_str3(self):

        print(self.str3)

c1 = Child()

c1.assign_str1("I")

c1.assign_str2("Love")

c1.assign_str3("India")

c1.show_str1()

I

c1.show_str2()

Love

c1.show_str3()

India

#Multi Level inheritance

class Parent:

    def assign_name(self,name):

        self.name = name

        

    def show_name(self):

        return self.name

    

class Child(Parent):

    def assign_age(self,age):

        self.age=age

        

    def show_age(self):

        return self.age

    

class GrandChild(Child):

    def assign_gender(self,gender):

        self.gender=gender

        

    def show_gender(self):

        return self.gender

g1 = GrandChild()

g1.assign_name("Anbu")

g1.assign_age(5)

g1.assign_gender("F")

name = g1.show_name()

age = g1.show_age()

gender = g1.show_gender()

print("Name : {}, Age : {}, Gender : {}".format(name,age,gender))

Name : Anbu, Age : 5, Gender : F

Python with Mongo db - Sample Programs

python -m pip install pymongo

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

print(myclient.list_database_names())

['admin', 'config', 'local']

dblist = myclient.list_database_names()

if "school" in dblist:

  print("The database exists.")

  

mydb = myclient["school"]

for p in dblist:

    print(p)

    

admin

config

local

 

 

#Display all the databases.

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

for p in myclient.list_database_names():

    print(p)

    

admin

config

local

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

dblist = myclient.list_database_names()

if "school" in dblist:

  print("The database : {} exists.".format("school"))

    

The database : school exists.

#Display all the collections (tables) in a database:

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

collist = mydb.list_collection_names()

for collectionName in collist:

    print(collectionName)

    

student

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

collist = mydb.list_collection_names()

if "student"  in collist:

        print("Collection student exists in school database")

Collection student exists in school database

#insert a document (Row) in a collection (table)

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

personDict = { "firstname":"Priya","lastname":"Balakumaran","city":"Kandanur" }

x = mycol.insert_one(personDict)

 

print(x.inserted_id) 

#insert multiple documents - Multi row insert

#autogenerated ids

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

mydocs = [

  { "firstname":"Ravi","lastname":"Rahul","city":"Pallathur"},

  { "firstname":"Siva","lastname":"Prasad","city":"Vadagudi"},

  { "firstname":"Arun","lastname":"Kumar","city":"Kanadukathan"},

  { "firstname":"Awesome","lastname":"Nator","city":"London"}

]

x = mycol.insert_many(mydocs)

#print list of the _id values of the inserted documents:

print(x.inserted_ids)

#insert multiple document with id specified

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

mydocs = [

  {"_id": 101,"firstname":"Kalai","lastname":"Selvi","city":"Aathalur"},

  {"_id": 102,"firstname":"Anna","lastname":"Malai","city":"Singapore"},

  {"_id": 103,"firstname":"Vanakkam","lastname":"da mappla","city":"Theni"},

  {"_id": 104 ,"firstname":"Anbu","lastname":"Sudha","city":"Bangalore"}

]

x = mycol.insert_many(mydocs)

#print list of the _id values of the inserted documents:

print(x.inserted_ids)

[101, 102, 103, 104]

#Search document in a collection

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

x = mycol.find_one()

print(x)

{'_id': ObjectId('5eba5eff2a9545fa868c9aa8'), 'firstname': 'Raja', 'lastname': 'Raman', 'city': 'Kottaiyur'}

#find : Selct all : Select * from doc

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

docs = mycol.find()

for d in docs:

    print (d)

{'_id': ObjectId('5eba5eff2a9545fa868c9aa8'), 'firstname': 'Raja', 'lastname': 'Raman', 'city': 'Kottaiyur'}

{'_id': ObjectId('5eba5f522a9545fa868c9aac'), 'firstname': 'Priya', 'lastname': 'Balakumaran', 'city': 'Kandanur'}

{'_id': ObjectId('5eba60012a9545fa868c9aae'), 'firstname': 'Ravi', 'lastname': 'Rahul', 'city': 'Pallathur'}

{'_id': ObjectId('5eba60012a9545fa868c9aaf'), 'firstname': 'Siva', 'lastname': 'Prasad', 'city': 'Vadagudi'}

{'_id': ObjectId('5eba60012a9545fa868c9ab0'), 'firstname': 'Arun', 'lastname': 'Kumar', 'city': 'Kanadukathan'}

{'_id': ObjectId('5eba60012a9545fa868c9ab1'), 'firstname': 'Awesome', 'lastname': 'Nator', 'city': 'London'}

{'_id': 101, 'firstname': 'Kalai', 'lastname': 'Selvi', 'city': 'Aathalur'}

{'_id': 102, 'firstname': 'Anna', 'lastname': 'Malai', 'city': 'Singapore'}

{'_id': 103, 'firstname': 'Vanakkam', 'lastname': 'da mappla', 'city': 'Theni'}

{'_id': 104, 'firstname': 'Anbu', 'lastname': 'Sudha', 'city': 'Bangalore'}

#display specific columns

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

for x in mycol.find({},{ "_id": 0, "firstname": 1, "lastname": 1, "city":1 }):

  print(x)

  

  

  {'firstname': 'Raja', 'lastname': 'Raman', 'city': 'Kottaiyur'}

{'firstname': 'Priya', 'lastname': 'Balakumaran', 'city': 'Kandanur'}

{'firstname': 'Ravi', 'lastname': 'Rahul', 'city': 'Pallathur'}

{'firstname': 'Siva', 'lastname': 'Prasad', 'city': 'Vadagudi'}

{'firstname': 'Arun', 'lastname': 'Kumar', 'city': 'Kanadukathan'}

{'firstname': 'Awesome', 'lastname': 'Nator', 'city': 'London'}

{'firstname': 'Kalai', 'lastname': 'Selvi', 'city': 'Aathalur'}

{'firstname': 'Anna', 'lastname': 'Malai', 'city': 'Singapore'}

{'firstname': 'Vanakkam', 'lastname': 'da mappla', 'city': 'Theni'}

{'firstname': 'Anbu', 'lastname': 'Sudha', 'city': 'Bangalore'}

#display only the last name:

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

for x in mycol.find({},{ "_id": 0,"city":0,"firstname":0  }):

  print(x)

  

  {'lastname': 'Raman'}

{'lastname': 'Balakumaran'}

{'lastname': 'Rahul'}

{'lastname': 'Prasad'}

{'lastname': 'Kumar'}

{'lastname': 'Nator'}

{'lastname': 'Selvi'}

{'lastname': 'Malai'}

{'lastname': 'da mappla'}

{'lastname': 'Sudha'}

#Except id and City:

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

for x in mycol.find({},{ "_id":0,"city":0 }):

  print(x)

  

  {'firstname': 'Raja', 'lastname': 'Raman'}

{'firstname': 'Priya', 'lastname': 'Balakumaran'}

{'firstname': 'Ravi', 'lastname': 'Rahul'}

{'firstname': 'Siva', 'lastname': 'Prasad'}

{'firstname': 'Arun', 'lastname': 'Kumar'}

{'firstname': 'Awesome', 'lastname': 'Nator'}

{'firstname': 'Kalai', 'lastname': 'Selvi'}

{'firstname': 'Anna', 'lastname': 'Malai'}

{'firstname': 'Vanakkam', 'lastname': 'da mappla'}

{'firstname': 'Anbu', 'lastname': 'Sudha'}

#Search specific city

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

myquery = { "city": "London" }

mydoc = mycol.find(myquery)

for x in mydoc:

  print(x)

  

  {'_id': ObjectId('5eba60012a9545fa868c9ab1'), 'firstname': 'Awesome', 'lastname': 'Nator', 'city': 'London'}

#Search city >= "S"

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

myquery = { "city": { "$gt": "S" } }

mydoc = mycol.find(myquery)

for x in mydoc:

  print(x)

  

  

{'_id': ObjectId('5eba60012a9545fa868c9aaf'), 'firstname': 'Siva', 'lastname': 'Prasad', 'city': 'Vadagudi'}

{'_id': 102, 'firstname': 'Anna', 'lastname': 'Malai', 'city': 'Singapore'}

{'_id': 103, 'firstname': 'Vanakkam', 'lastname': 'da mappla', 'city': 'Theni'}

#Regular Explression. city starts with 'S'

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

myquery = { "city": { "$regex": "S" } }

mydoc = mycol.find(myquery)

for x in mydoc:

  print(x)

  

  {'_id': 102, 'firstname': 'Anna', 'lastname': 'Malai', 'city': 'Singapore'}

#Order by City Ascending

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

myquery = { "city": { "$regex": "S" } }

mydoc = mycol.find().sort("city")

for x in mydoc:

  print(x)

  

  

  {'_id': 101, 'firstname': 'Kalai', 'lastname': 'Selvi', 'city': 'Aathalur'}

{'_id': 104, 'firstname': 'Anbu', 'lastname': 'Sudha', 'city': 'Bangalore'}

{'_id': ObjectId('5eba60012a9545fa868c9ab0'), 'firstname': 'Arun', 'lastname': 'Kumar', 'city': 'Kanadukathan'}

{'_id': ObjectId('5eba5f522a9545fa868c9aac'), 'firstname': 'Priya', 'lastname': 'Balakumaran', 'city': 'Kandanur'}

{'_id': ObjectId('5eba5eff2a9545fa868c9aa8'), 'firstname': 'Raja', 'lastname': 'Raman', 'city': 'Kottaiyur'}

{'_id': ObjectId('5eba60012a9545fa868c9ab1'), 'firstname': 'Awesome', 'lastname': 'Nator', 'city': 'London'}

{'_id': ObjectId('5eba60012a9545fa868c9aae'), 'firstname': 'Ravi', 'lastname': 'Rahul', 'city': 'Pallathur'}

{'_id': 102, 'firstname': 'Anna', 'lastname': 'Malai', 'city': 'Singapore'}

{'_id': 103, 'firstname': 'Vanakkam', 'lastname': 'da mappla', 'city': 'Theni'}

{'_id': ObjectId('5eba60012a9545fa868c9aaf'), 'firstname': 'Siva', 'lastname': 'Prasad', 'city': 'Vadagudi'}

#Order by City descending

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

myquery = { "city": { "$regex": "S" } }

mydoc = mycol.find().sort("city",-1)

for x in mydoc:

  print(x)

{'_id': ObjectId('5eba60012a9545fa868c9aaf'), 'firstname': 'Siva', 'lastname': 'Prasad', 'city': 'Vadagudi'}

{'_id': 103, 'firstname': 'Vanakkam', 'lastname': 'da mappla', 'city': 'Theni'}

{'_id': 102, 'firstname': 'Anna', 'lastname': 'Malai', 'city': 'Singapore'}

{'_id': ObjectId('5eba60012a9545fa868c9aae'), 'firstname': 'Ravi', 'lastname': 'Rahul', 'city': 'Pallathur'}

{'_id': ObjectId('5eba60012a9545fa868c9ab1'), 'firstname': 'Awesome', 'lastname': 'Nator', 'city': 'London'}

{'_id': ObjectId('5eba5eff2a9545fa868c9aa8'), 'firstname': 'Raja', 'lastname': 'Raman', 'city': 'Kottaiyur'}

{'_id': ObjectId('5eba5f522a9545fa868c9aac'), 'firstname': 'Priya', 'lastname': 'Balakumaran', 'city': 'Kandanur'}

{'_id': ObjectId('5eba60012a9545fa868c9ab0'), 'firstname': 'Arun', 'lastname': 'Kumar', 'city': 'Kanadukathan'}

{'_id': 104, 'firstname': 'Anbu', 'lastname': 'Sudha', 'city': 'Bangalore'}

{'_id': 101, 'firstname': 'Kalai', 'lastname': 'Selvi', 'city': 'Aathalur'}

#Delete single doc

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

myquery = { "city": "London" }

mycol.delete_one(myquery)

#Delete multiple docs

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

myquery = { "city": { "$gt": "S" } }  # city >= "S"

x = mycol.delete_many(myquery)

print(x.deleted_count, " documents deleted.")

3  documents deleted.

#Delete all docs / rows in a collection

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

myquery = { "city": { "$gt": "S" } }

x = mycol.delete_many({})

print(x.deleted_count, " documents deleted.")

6  documents deleted.

#Drop a collection

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

mycol.drop()

#Adding documents again

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

mydocs = [

  {"_id": 101,"firstname":"Kalai","lastname":"Selvi","city":"Aathalur"},

  {"_id": 102,"firstname":"Anna","lastname":"Malai","city":"Singapore"},

  {"_id": 103,"firstname":"Vanakkam","lastname":"da mappla","city":"Theni"},

  {"_id": 104 ,"firstname":"Anbu","lastname":"Sudha","city":"Bangalore"}

]

x = mycol.insert_many(mydocs)

#print list of the _id values of the inserted documents:

print(x.inserted_ids)

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

mydocs = [

  { "firstname":"Ravi","lastname":"Rahul","city":"Pallathur"},

  { "firstname":"Siva","lastname":"Prasad","city":"Vadagudi"},

  { "firstname":"Arun","lastname":"Kumar","city":"Kanadukathan"},

  { "firstname":"Awesome","lastname":"Nator","city":"London"}

]

x = mycol.insert_many(mydocs)

#print list of the _id values of the inserted documents:

print(x.inserted_ids)

#limit example.. display 3 records

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

myresult = mycol.find().limit(3)

#print the result:

for x in myresult:

  print(x)

  

  {'_id': ObjectId('5eba65732a9545fa868c9acf'), 'firstname': 'Ravi', 'lastname': 'Rahul', 'city': 'Pallathur'}

{'_id': ObjectId('5eba65732a9545fa868c9ad0'), 'firstname': 'Siva', 'lastname': 'Prasad', 'city': 'Vadagudi'}

{'_id': ObjectId('5eba65732a9545fa868c9ad1'), 'firstname': 'Arun', 'lastname': 'Kumar', 'city': 'Kanadukathan'}

#Update one record - Single record update

#London to Pudukkottai

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

myquery = { "city": "London" }

newvalues = { "$set": { "city": "Pudukkottai" } }

mycol.update_one(myquery, newvalues)

for x in mycol.find():

  print(x)

{'_id': ObjectId('5eba65732a9545fa868c9acf'), 'firstname': 'Ravi', 'lastname': 'Rahul', 'city': 'Pallathur'}

{'_id': ObjectId('5eba65732a9545fa868c9ad0'), 'firstname': 'Siva', 'lastname': 'Prasad', 'city': 'Vadagudi'}

{'_id': ObjectId('5eba65732a9545fa868c9ad1'), 'firstname': 'Arun', 'lastname': 'Kumar', 'city': 'Kanadukathan'}

{'_id': ObjectId('5eba65732a9545fa868c9ad2'), 'firstname': 'Awesome', 'lastname': 'Nator', 'city': 'Pudukkottai'}

{'_id': 101, 'firstname': 'Kalai', 'lastname': 'Selvi', 'city': 'Aathalur'}

{'_id': 102, 'firstname': 'Anna', 'lastname': 'Malai', 'city': 'Singapore'}

{'_id': 103, 'firstname': 'Vanakkam', 'lastname': 'da mappla', 'city': 'Theni'}

{'_id': 104, 'firstname': 'Anbu', 'lastname': 'Sudha', 'city': 'Bangalore'}

#Update multiple documents

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

mydb = myclient["school"]

mycol = mydb["student"]

myquery = { "city": { "$gt": "S" } }

newvalues = { "$set": { "city": "Pillaiyarpatti" } }

x = mycol.update_many(myquery, newvalues)

print(x.modified_count, "documents updated.")

2 documents updated.

Python with MySQL database programs

install python connector

python -m pip install mysql-connector

#verify the mysql connectivity

import mysql.connector

mydb = mysql.connector.connect(

  host="localhost",

  user="root",

  passwd="p@ssw0rd"

)

print(mydb)

<mysql.connector.connection_cext.CMySQLConnection object at 0x000000E2CA88AB70>

#create a database

#display the databases

mycursor = mydb.cursor()

mycursor.execute("CREATE DATABASE School")

mycursor.execute("SHOW DATABASES")

for x in mycursor:

  print(x)

  

('employee',)

('information_schema',)

('mysql',)

('performance_schema',)

('sakila',)

('school',)

('sys',)

('world',)

import socket

socket.getaddrinfo('127.0.0.1', 3306)

ALTER USER 'root'@'localhost' IDENTIFIED BY 'p@ssw0rd' PASSWORD EXPIRE NEVER;

ALTER USER 'root'@'localhost' IDENTIFIED WITH mysql_native_password BY 'p@ssw0rd';

#connect with School database 

#create a table

import mysql.connector

mydb = mysql.connector.connect(

  host="localhost",

  user="root",

  passwd="p@ssw0rd",

  database="school"

)

mycursor = mydb.cursor()

mycursor.execute("CREATE TABLE Student (rollnumber int, firstname varchar(50), lastname varchar(50), city varchar(50))")

mycursor.execute("SHOW TABLES")

for x in mycursor:

  print(x)

  

('student',)

#Drop table

mycursor.execute("DROP TABLE Student")

for x in mycursor:

  print(x)

  

  

  

#create a table with auto increment column

import mysql.connector

mydb = mysql.connector.connect(

  host="localhost",

  user="root",

  passwd="p@ssw0rd",

  database="school"

)

mycursor = mydb.cursor()

mycursor.execute("CREATE TABLE Student (rollnumber INT AUTO_INCREMENT PRIMARY KEY, firstname varchar(50), lastname varchar(50), city varchar(50))")

mycursor.execute("show tables")

for x in mycursor:

  print(x)

('student',)

mydb = mysql.connector.connect(

  host="localhost",

  user="root",

  passwd="p@ssw0rd",

  database="school"

)

mycursor = mydb.cursor()

sql = "INSERT INTO student (firstname, lastname,city) VALUES (%s, %s, %s)"

val = ("Sankara","Narayanan","Pallathur")

mycursor.execute(sql, val)

mydb.commit()

print(mycursor.rowcount, "record inserted.")

1 record inserted.

mycursor = mydb.cursor()

sql = "INSERT INTO student (firstname, lastname,city) VALUES (%s, %s, %s)"

val = [

  ('Prathap','Pothan','திருவனந்தப்புரம்'),

  ('Rajini','Kanth','Bangalore'),

  ('Vijay', 'Balani','Bangalore'),

  ('Anbu','Sudha','Melasivapuri')

]

mycursor.executemany(sql, val)

mydb.commit()

print(mycursor.rowcount, "was inserted.")

4 was inserted.

Get Inserted identity ids:

sql = "INSERT INTO student (firstname, lastname,city) VALUES (%s, %s, %s)"

val = ("Arun","Vijay","Chennai")

mycursor.execute(sql, val)

mydb.commit()

print("1 record inserted, ID:", mycursor.lastrowid)

1 record inserted, ID: 6

select from table:

mycursor.execute("SELECT * FROM student")

myresult = mycursor.fetchall()

for x in myresult:

  print(x)

  

  

(1, 'Sankara', 'Narayanan', 'Pallathur')

(2, 'Prathap', 'Pothan', 'திருவனந்தப்புரம்')

(3, 'Rajini', 'Kanth', 'Bangalore')

(4, 'Vijay', 'Balani', 'Bangalore')

(5, 'Anbu', 'Sudha', 'Melasivapuri')

(6, 'Arun', 'Vijay', 'Chennai')

#select columns

mycursor.execute("SELECT rollnumber as RollNumber, concat(firstname, ' ' ,lastname) as Name, city as City FROM student")

myresult = mycursor.fetchall()

for x in myresult:

  print(x)

  

(1, 'Sankara Narayanan', 'Pallathur')

(2, 'Prathap Pothan', 'திருவனந்தப்புரம்')

(3, 'Rajini Kanth', 'Bangalore')

(4, 'Vijay Balani', 'Bangalore')

(5, 'Anbu Sudha', 'Melasivapuri')

(6, 'Arun Vijay', 'Chennai')

Fetch one record:

import mysql.connector

mydb = mysql.connector.connect(

  host="localhost",

  user="root",

  passwd="p@ssw0rd",

  database="school"

)

mycursor = mydb.cursor()

mycursor.execute("SELECT * FROM Student")

myresult = mycursor.fetchone()

print(myresult)

myresult = mycursor.fetchone()

print(myresult)

myresult = mycursor.fetchone()

print(myresult)

myresult = mycursor.fetchone()

print(myresult)

(1, 'Sankara', 'Narayanan', 'Pallathur')

(2, 'Prathap', 'Pothan', 'திருவனந்தப்புரம்')

(3, 'Rajini', 'Kanth', 'Bangalore')

(4, 'Vijay', 'Balani', 'Bangalore')

#where condition

import mysql.connector

mydb = mysql.connector.connect(

  host="localhost",

  user="root",

  passwd="p@ssw0rd",

  database="school"

)

mycursor = mydb.cursor()

sql = "SELECT * FROM student WHERE city ='Bangalore'"

mycursor.execute(sql)

myresult = mycursor.fetchall()

for x in myresult:

  print(x)

  

  

(3, 'Rajini', 'Kanth', 'Bangalore')

(4, 'Vijay', 'Balani', 'Bangalore')

Like Condition:

import mysql.connector

mydb = mysql.connector.connect(

  host="localhost",

  user="root",

  passwd="p@ssw0rd",

  database="school"

)

mycursor = mydb.cursor()

sql = "SELECT * FROM student WHERE lastname LIKE '%an%'"

mycursor.execute(sql)

myresult = mycursor.fetchall()

for x in myresult:

  print(x)

  

    (1, 'Sankara', 'Narayanan', 'Pallathur')

(2, 'Prathap', 'Pothan', 'திருவனந்தப்புரம்')

(3, 'Rajini', 'Kanth', 'Bangalore')

(4, 'Vijay', 'Balani', 'Bangalore')

  

  

  

import mysql.connector

mydb = mysql.connector.connect(

  host="localhost",

  user="root",

  passwd="p@ssw0rd",

  database="school"

)

mycursor = mydb.cursor()

sql = "SELECT * FROM student WHERE lastname LIKE '%an'"

mycursor.execute(sql)

myresult = mycursor.fetchall()

for x in myresult:

  print(x)

  

  (1, 'Sankara', 'Narayanan', 'Pallathur')

  (2, 'Prathap', 'Pothan', 'திருவனந்தப்புரம்')

  

  

 Prevent SQL Injection:

 

import mysql.connector

mydb = mysql.connector.connect(

  host="localhost",

  user="root",

  passwd="p@ssw0rd",

  database="school"

)

mycursor = mydb.cursor()

sql = "SELECT * FROM student WHERE city = %s"

city = ("Bangalore", )

mycursor.execute(sql, city)

myresult = mycursor.fetchall()

for x in myresult:

  print(x)

  

  

  (3, 'Rajini', 'Kanth', 'Bangalore')

  (4, 'Vijay', 'Balani', 'Bangalore')

  

  

Order by example (ascending : default):

 

import mysql.connector

mydb = mysql.connector.connect(

  host="localhost",

  user="root",

  passwd="p@ssw0rd",

  database="school"

)

mycursor = mydb.cursor()

sql = "SELECT * FROM student order by city"

mycursor.execute(sql)

myresult = mycursor.fetchall()

for x in myresult:

  print(x)

  

(3, 'Rajini', 'Kanth', 'Bangalore')

(4, 'Vijay', 'Balani', 'Bangalore')

(6, 'Arun', 'Vijay', 'Chennai')

(5, 'Anbu', 'Sudha', 'Melasivapuri')

(1, 'Sankara', 'Narayanan', 'Pallathur')

(2, 'Prathap', 'Pothan', 'திருவனந்தப்புரம்')

Order by descending example:

import mysql.connector

mydb = mysql.connector.connect(

  host="localhost",

  user="root",

  passwd="p@ssw0rd",

  database="school"

)

mycursor = mydb.cursor()

sql = "SELECT * FROM student order by lastname desc"

mycursor.execute(sql)

myresult = mycursor.fetchall()

for x in myresult:

  print(x)

  

  

(6, 'Arun', 'Vijay', 'Chennai')

(5, 'Anbu', 'Sudha', 'Melasivapuri')

(2, 'Prathap', 'Pothan', 'திருவனந்தப்புரம்')

(1, 'Sankara', 'Narayanan', 'Pallathur')

(3, 'Rajini', 'Kanth', 'Bangalore')

(4, 'Vijay', 'Balani', 'Bangalore')

Delete a record:

import mysql.connector

mydb = mysql.connector.connect(

  host="localhost",

  user="root",

  passwd="p@ssw0rd",

  database="school"

)

mycursor = mydb.cursor()

sql = "DELETE FROM student WHERE city = %s"

adr = ("திருவனந்தப்புரம்", )

mycursor.execute(sql, adr)

mydb.commit()

print(mycursor.rowcount, "record(s) deleted")

1 record(s) deleted

#drop a table

mycursor = mydb.cursor()

sql = "DROP TABLE student"

mycursor.execute(sql)

#Drop Only if Exist

mycursor = mydb.cursor()

sql = "DROP TABLE IF EXISTS student"

mycursor.execute(sql)

#update record with condition

#avoid sql injection

import mysql.connector

mydb = mysql.connector.connect(

  host="localhost",

  user="root",

  passwd="p@ssw0rd",

  database="school"

)

mycursor = mydb.cursor()

sql = "UPDATE Student SET lastname = %s WHERE lastname = %s"

val = ("Balaji", "Balani")

mycursor.execute(sql, val)

mydb.commit()

print(mycursor.rowcount, "record(s) affected")

1 record(s) affected

#limit example

import mysql.connector

mydb = mysql.connector.connect(

  host="localhost",

  user="root",

  passwd="p@ssw0rd",

  database="school"

)

mycursor = mydb.cursor()

mycursor.execute("SELECT * FROM Student LIMIT 5")

myresult = mycursor.fetchall()

for x in myresult:

  print(x)

  

(1, 'Sankara', 'Narayanan', 'Pallathur')

(3, 'Rajini', 'Kanth', 'Bangalore')

(4, 'Vijay', 'Balaji', 'Bangalore')

(5, 'Anbu', 'Sudha', 'Melasivapuri')

(6, 'Arun', 'Vijay', 'Chennai')

  

  

#limit with offset example

import mysql.connector

mydb = mysql.connector.connect(

  host="localhost",

  user="root",

  passwd="p@ssw0rd",

  database="school"

)

mycursor = mydb.cursor()

mycursor.execute("SELECT * FROM Student LIMIT 3 offset 2")

myresult = mycursor.fetchall()

for x in myresult:

  print(x)

  

  

(4, 'Vijay', 'Balaji', 'Bangalore')

(5, 'Anbu', 'Sudha', 'Melasivapuri')

(6, 'Arun', 'Vijay', 'Chennai')

Numpy Sample Programs

import numpy

import numpy as np

print(np.__version__)

1.15.4

import numpy as np

arr = np.array([1,2,3,4,5,6,7,8,9,10])

print(type(arr))

print(arr)

for a in arr:

    print("a : {}".format(a))

<class 'numpy.ndarray'>

[ 1  2  3  4  5  6  7  8  9 10]

a : 1

a : 2

a : 3

a : 4

a : 5

a : 6

a : 7

a : 8

a : 9

a : 10

import numpy as np

arr = np.array([range(1,10,2)])

print(type(arr))

<class 'numpy.ndarray'>

print(arr)

[[1 3 5 7 9]]

for a in arr:

    print(a)

[1 3 5 7 9]

#Tuple to ndarray

myTuple = tuple((1,6,3,2,6,7,2,2))

import numpy as np

arr = np.array(myTuple)

print(arr)

[1 6 3 2 6 7 2 2]

1Dimension Arrray:

import numpy as np

arr1D = np.array(7)

print(arr1D.shape)

print(arr1D)

7

print(arr1D.ndim)

0

2D array:

arr = np.array([1,2,3,4,5,6])

print(arr.shape)

(6,)

print(arr2D.ndim)

2

arr2D = np.array([[1,2,3], [10,11,12]])

print(arr2D)

[[ 1  2  3]

 [10 11 12]]

(2, 3)

 

import numpy as np

arr3D=np.array([[  [1,2,3],[6,7,8]], [[5,3,2],[6,2,1] ] ])

print(arr3D)

print(arr3D.shape)

              

  

[[[1 2 3]

  [6 7 8]]

 [[5 3 2]

  [6 2 1]]]

(2, 2, 3)

print(arr3D.ndim)

3

import numpy as np

arr = np.array([5,7,3,2,6,33,2,1,2,3,4,5,6,7,8,9,2,3,5,6,2,12,1,3,5,3])

print(len(arr))

print(arr[0])

print(arr[1])

print(arr[-1])

print(arr[1:2])

print(arr[3:20])

print(arr[3:20:2])

print(arr[3:-1])

print(arr[3:-1:3])

26

5

7

3

[7]

[ 2  6 33  2  1  2  3  4  5  6  7  8  9  2  3  5  6]

[ 2 33  1  3  5  7  9  3  6]

[ 2  6 33  2  1  2  3  4  5  6  7  8  9  2  3  5  6  2 12  1  3  5]

[ 2  2  3  6  9  5 12  5]

import numpy as np

arr = np.array([1, 2, 3, 4])

print(arr[0] + arr[-1])

5

import numpy as np

arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])

print('3rd element on 2st dim: ', arr[1, 2])

8

import numpy as np

arr = np.array([[1,2,3,4,5,3,2,3,4], [6,7,8,9,10,3,2,5,3]])

print('5th element on 1st dim: ', arr[0, 4])

5th element on 1st dim:  5

import numpy as np

arr = np.array([[[3,2,1], [6,-5,-4]], [[9,8,7], [5,4,3]]])

print(arr[0, 1, 2])

print(arr[1,0,1])

-4

8

import numpy as np

arr = np.array([[1,2,22,453,5], [3,7,8,32,3233]])

print('Last element from 2nd dim: ', arr[1, -1])

print('Last element from 1st dim: ', arr[0, -1])

Last element from 2nd dim:  3233

Last element from 1st dim:  5

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6, 7])

print(arr[1:5])

print(arr[:3])

print(arr[:7:2])

print(arr[:-1:2])

print(arr[::2])

[2 3 4 5]

[1 2 3]

[1 3 5 7]

[1 3 5]

[1 3 5 7]

import numpy as np

arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])

print(arr[1, 1:4])

print(arr[0,2:-1])

print(arr[0,:4])

print(arr[1,-1])

print(arr[1,1:-1:2])

[7 8 9]

[3 4]

[1 2 3 4]

10

[7 9]

import numpy as np

arr = np.array([6,34,3,2])

print(arr.dtype)  #data type 

int64

arr = np.array(['silva', 'kalai', 'Raj'])

print(arr.dtype)

<U5

arr = np.array([1, 2, 3, 4], dtype='S') #defined data type

print(arr)

print(arr.dtype)

[b'1' b'2' b'3' b'4']

|S1

arr = np.array([1.3, 2.31, 3.12])

print(arr.dtype)  #float

float64

newarr = arr.astype('i') #float into integer

print(newarr)

print(newarr.dtype)

[1 2 3]

int32

arr = np.array([12, 0, 34])

print(arr)

[12  0 34]

newarr = arr.astype(bool) #integer into bool

print(newarr)

print(newarr.dtype)

[ True False  True]

bool

Copy Example:

import numpy as np

arr = np.array([6, 2, 7, 4, 5])

x = arr.copy()

arr[0] = 3333 #making changes in the source

print(arr)

[6, 2, 7, 4, 5]

print(x) #source changes not reflected here

[333, 2, 7, 4, 5]

import numpy as np

arr = np.array([1, 2, 3, 4, 5])

x = arr.view()

arr[0] = 42 #source element got changed

print(arr)

print(x)  #target will reflect the source 

[42  2  3  4  5]

[42  2  3  4  5]

import numpy as np

arr = np.array(['a','e','i','o','u'])

x = arr.copy()

y = arr.view()

print(x.base)

print(y.base)

#The copy returns None.

#The view returns the original array.

import numpy as np

arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])

print(arr.shape)  # 2 D with 4 elements

(2, 4)

import numpy as np

arr = np.array([['a','b','c','d','e'], ['f','g','h','i','j'], ['k','l','m','n','o']  ])

print(arr.shape) # 3 D with 5 elements

(3, 5)

Reshape exa:

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16])

newarr = arr.reshape(8, 2) #8 arrays 2 elements

print(newarr)

[[ 1  2]

 [ 3  4]

 [ 5  6]

 [ 7  8]

 [ 9 10]

 [11 12]

 [13 14]

 [15 16]]

 

 

newarr = arr.reshape(2, 8) #2 arrays 8 elements

print(newarr)

[[ 1  2  3  4  5  6  7  8]

 [ 9 10 11 12 13 14 15 16]]

newarr = arr.reshape(4, 4) #4 arrays 4 elements

print(newarr)

[[ 1  2  3  4]

 [ 5  6  7  8]

 [ 9 10 11 12]

 [13 14 15 16]]

 

 

1 D to 3 D:

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])

newarr = arr.reshape(2, 3, 2) #2 arrays that contains 3 arrays, each with 2 elements:

print(newarr)

[[[ 1  2]

  [ 3  4]

  [ 5  6]]

 [[ 7  8]

  [ 9 10]

  [11 12]]]

  

  

#flattening array

  

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])

print(arr)

[[1 2 3]

 [4 5 6]]

 

 

newarr = arr.reshape(-1) #flattening

print(newarr)

[1 2 3 4 5 6]

Iteration - Looping:

--------------------

import numpy as np

arr = np.array(['a','b','c','d','e','f'])

for x in arr:

  print(x)

a

b

c

d

e

f

#iteration loop through 

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6], [7,8,9]])

for x in arr:

  print(x)

[1 2 3]

[4 5 6]

[7 8 9]

#flattening

for x in arr:

for y in x:

    print(y)

            

1

2

3

4

5

6

7

8

9

import numpy as np

arr = np.array([ [[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]], [[13,14, 15], [16, 17, 18]]  ])

for x in arr:

  print("x represents the 2-D array:")

  print(x)

x represents the 2-D array:

[[1 2 3]

 [4 5 6]]

 

x represents the 2-D array:

[[ 7  8  9]

 [10 11 12]]

 

x represents the 2-D array:

[[13 14 15]

 [16 17 18]]

 

 

 

import numpy as np

arr = np.array([ [[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]], [[13,14, 15], [16, 17, 18]]  ])

for x in arr:

  for y in x:

    for z in y:

      print(z)

  

#flattening

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

#nd iteration : nditer

import numpy as np

arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])

for x in arr:

print(x)

[[1 2]

 [3 4]]

[[5 6]

 [7 8]]

 

for x in np.nditer(arr):

  print(x)

1

2

3

4

5

6

7

8

#join / Concatenate

import numpy as np

arr1 = np.array([1, 2, 3,7,8,9])

arr2 = np.array([4, 5, 6,10,11,12])

arr = np.concatenate((arr1, arr2))

print(arr)

[ 1  2  3  7  8  9  4  5  6 10 11 12]

import numpy as np

arr1 = np.array([ [1, 2], [3,7],[8,9]])

arr2 = np.array([[4, 5], [6,10],[11,12]])

arr = np.concatenate((arr1, arr2), axis=1)

print(arr)

[[ 1  2  4  5]

 [ 3  7  6 10]

 [ 8  9 11 12]]

 

 

 

import numpy as np

arr1 = np.array([1, 2, 3,4,5,6,7,8])

arr2 = np.array([9,10,11,12,13,14,15,16])

arr = np.stack((arr1, arr2), axis=1)

print(arr)

[[ 1  9]

 [ 2 10]

 [ 3 11]

 [ 4 12]

 [ 5 13]

 [ 6 14]

 [ 7 15]

 [ 8 16]]

 

 

import numpy as np

arr1 = np.array([1, 2, 3,4,5,6,7,8])

arr2 = np.array([9,10,11,12,13,14,15,16])

arr3 = np.array([17,18,19,20,21,22,23,24])

arr = np.stack((arr1, arr2,arr3), axis=1)

print(arr)

[[ 1  9 17]

 [ 2 10 18]

 [ 3 11 19]

 [ 4 12 20]

 [ 5 13 21]

 [ 6 14 22]

 [ 7 15 23]

 [ 8 16 24]]

split example:

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6,7,8,9,10,11,12,13,14,15,16])

newarr = np.array_split(arr, 3)

print(newarr)

[array([1, 2, 3, 4, 5, 6]), array([ 7,  8,  9, 10, 11]), array([12, 13, 14, 15, 16])]

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6,7,8,9,10,11,12,13,14,15,16])

newarr = np.array_split(arr, 4)

print(newarr)

[array([1, 2, 3, 4]), array([5, 6, 7, 8]), array([ 9, 10, 11, 12]), array([13, 14, 15, 16])]

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6,7,8,9,10,11,12,13,14,15,16])

newarr = np.array_split(arr,8)

print(newarr)

[array([1, 2]), array([3, 4]), array([5, 6]), array([7, 8]), array([ 9, 10]), array([11, 12]), array([13, 14]), array([15, 16])] 

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6,7,8,9,10,11,12,13,14,15,16])

newarr = np.array_split(arr,2)

print(newarr)

print(newarr[0])

print(newarr[1])

[array([1, 2, 3, 4, 5, 6, 7, 8]), array([ 9, 10, 11, 12, 13, 14, 15, 16])]

#split and slice 

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6,7,8,9,10,11,12,13,14,15,16])

newarr = np.array_split(arr,2)

print(newarr)

print(newarr[0])

print(newarr[1]) 

print(newarr[0][3:5])

print(newarr[1][3:5])

[array([1, 2, 3, 4, 5, 6, 7, 8]), array([ 9, 10, 11, 12, 13, 14, 15, 16])]

[1 2 3 4 5 6 7 8]

[ 9 10 11 12 13 14 15 16]

[4 5]

[12 13]

import numpy as np

arr = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13,14],[15,16],[17,18]])

newarr = np.array_split(arr, 3)

print(newarr)

[array([[1, 2],

       [3, 4],

       [5, 6]]), 

 array([[ 7,  8],

       [ 9, 10],

       [11, 12]]), 

 array([[13, 14],

       [15, 16],

       [17, 18]])]

   

#find the index position start with 0

   

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 4, 4])  #0,1,2,3,4,5,6

x = np.where(arr == 4)

print(x)

(array([3, 5, 6]),)

#Even number's position index starts from 0

import numpy as np

arr = np.array([2222,1111,3333,4444,8888,223,224,999,1000])

x = np.where(arr%2 == 0)

print(x)

(array([0, 3, 4, 6, 8]),)

#Odd number's position index starts from 0

import numpy as np

arr = np.array([2222,1111,3333,4444,8888,223,224,999,1000])

x = np.where(arr%2 == 1)

print(x)

(array([1, 2, 5, 7]),)

#Find the indexes where the value 'n' should be inserted:

import numpy as np

arr = np.array([636, 173,285, 492])

x = np.searchsorted(arr, 177)

print(x)

2

x = np.searchsorted(arr, 77)

print(x)

0

x = np.searchsorted(arr, 377)

print(x)

3

x = np.searchsorted(arr, 500)

print(x)

4

x = np.searchsorted(arr, 1000)

print(x)

4

import numpy as np

arr = np.array([66, 17, 38, 69])

x = np.searchsorted(arr, 27, side='left')

print(x)

2

x = np.searchsorted(arr, 27, side='right')

print(x)

2

import numpy as np

arr = np.array([400, 200, 350, 100])

x = np.searchsorted(arr, [22, 444, 266,133,277,342,479])

print(x)

#sort

import numpy as np

arr = np.array([6,3,66,22,33,100,10,-1,-2,-3,0,34,1000,98])

print(np.sort(arr))

[  -3   -2   -1    0    3    6   10   22   33   34   66   98  100 1000]

import numpy as np

arr = np.array(["zeebra","elephant","lion","tiger","deer","cheetah","fox"])

print(np.sort(arr))

['cheetah' 'deer' 'elephant' 'fox' 'lion' 'tiger' 'zeebra']

import numpy as np

arr = np.array([True, False,False,True,True,True,False])

print(np.sort(arr))

[False False False  True  True  True  True]

#sort an array

import numpy as np

arr = np.array([[3, 2, 4], [5, 0, 1],[6,3,2], [5,2,1], [6,-1,-2],[0,1,-33] ])

print(np.sort(arr))

[[  2   3   4]

 [  0   1   5]

 [  2   3   6]

 [  1   2   5]

 [ -2  -1   6]

 [-33   0   1]]

 

 

Filter: 

import numpy as np

arr = np.array([41, 42, 43, 44])

x = arr[[True, True, True, True]]

print(x)

[41 42 43 44]

x = arr[[False,False,False,False]]

print(x)

[]

x = arr[[False,False,True,True]]

print(x)

[43 44]

x = arr[[True,True,False,False]]

print(x)

[41 42]

import numpy as np

arr = np.array([100,1000,800,200,700,500,600,300,200,0,900])

filter_arr = arr > 550

newarr = arr[filter_arr]

print(filter_arr)

print(newarr)

[False  True  True False  True False  True False False False  True]

[1000  800  700  600  900]

Even number filtering:

import numpy as np

arr = np.array([100,1000,800,200,700,500,600,300,200,0,900])

filter_arr = arr %2 == 0

newarr = arr[filter_arr]

print(filter_arr)

print(newarr)

[ True  True  True  True  True  True  True  True  True  True  True]

[ 100 1000  800  200  700  500  600  300  200    0  900]

Odd number filtering:

import numpy as np

arr = np.array([100,1000,800,200,700,500,600,300,200,0,900])

filter_arr = arr %2 == 1

newarr = arr[filter_arr]

print(filter_arr)

print(newarr)

[False False False False False False False False False False False]

[]

#odd number filtering

import numpy as np

arr = np.array([100,55,77,200,700,399,600,241,200,0,887])

filter_arr = arr %2 == 1

newarr = arr[filter_arr]

print(filter_arr)

print(newarr) 

[False  True  True False False  True False  True False False  True]

[ 55  77 399 241 887]

import numpy as np

arr = np.array([100,55,77,200,700,399,600,241,200,0,887])

filter_arr = arr %2 == 0

newarr = arr[filter_arr]

print(filter_arr)

print(newarr)

#even number filtering

[ True False False  True  True False  True False  True  True False]

[100 200 700 600 200   0]