# Progarm of find Greatest value
a=int(input(" enter the value"))
b = int(input(" enter the value"))
c=int(input("enter the value "))
print("value of a=",a)
print("value of b=",b)
print("value of c=",c)
if a > b and a>c:
print("a is greatest value",a)
elif b>a and b>c:
print(" b is greatest value",b)
else:
print("c is greatest value",c)
OUTPUT:-
# Python program to print diamond star pattern using for
loop
# take input
n =int(input("enter value "))
# printing pyramid
for i in range(n):
for j in range(n-i-1):
# print spaces
print("", end=" ")
for j in range(2*i+1):
# print stars
print("*", end="")
print()
# printing downward pyramid
for i in range(n-1):
for j in range(i+1):
# print spaces
print("", end=" ")
for j in range(2*(n-i-1)-1):
# print stars
print("*", end="")
print()
OUTPUT:-
# Program of Function Recursion
def tri_recursion(k):
if(k > 0):
result = k + tri_recursion(k - 1)
print(result)
else:
result = 0
return result
print("\n\nRecursion Results")
tri_recursion(6)
OUTPUT:-
# Program to create module
Data = {
"name": "John",
"age": 36,
"country": "Norway"
}
print("The result is =",Data)
OUTPUT:-
# Program of call mod module which is created by user
import mod
a = mod.Data["age"]
print(“value after calling module in this program”,a)
OUTPUT:-
# Python program to find duplicate items in list
# take list
my_list = [1, 3, 7, 1, 2, 7, 5, 3, 8, 1]
# printing original list
print('List:', my_list)
# find duplicate items using set()
seen = set()
duplicate_item = [x for x in my_list if x in seen or (seen.add(x) or False)]
# printing duplicate elements
print('Duplicate Elements:', duplicate_item)
OUTPUT:-
# Program to read data from text file
f = open("lk.txt", "r")
print(f.read())
f = open("kl.txt", "a")
f.write("add new content in file")
f.close()
#open and read the file after the appending:
f = open("kl.txt", "r")
print(f.read())
OUTPUT:-
# Program of one dimension array by nympy
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print("creation of one dimension array",arr)
print("type of array",type(arr))
OUTPUT:-
# Program of two dimension array by numpy
import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
print(" presentation of 2d array \n",arr)
OUTPUT:-
# Program Addition of two array
import numpy as np
m1 = np.array([[3, 4], [5, 6]])
m2 = np.array([[8, 9], [0, 6]])
print(" 1st array= \n",m1)
print(" 2nd array= \n ",m2)
r = np.add(m1, m2)
print("The result after addition of arr m1 and arrm2 \n",r)
OUTPUT:-
# Program of slicing of one demission array
import numpy as np
arr = np.array([3,4,3,2,4,5,34,5,4,6,78])
print(arr)
print('values of array after slicing',arr[1:5])
OUTPUT:-
# Program of slicing on two Dimension array
import numpy as np
arr = np.array([[11, 25, 37, 44, 35], [16, 27, 18, 93, 10]])
print(" Array before Slicing \n",arr)
print('vales of after slicing\n',arr[0:2, 1:4])
OUTPUT:_
# Program of indexing one demission array
import numpy as np
arr = np.array([1, 2, 3, 4])
print("print values according to indexing",arr[0])
print(arr[2] + arr[3])
OUTPUT:-
# Program of Indexing on Two Dimension array
import numpy as np
arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])
print( "simple array\n",arr)
print('5th element on 2nd row: ', arr[1, 4],"\n 4th element of first row",arr[0,3])
OUTPUT:-
# Program of Negative indexing of 2d array
import numpy as np
arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])
print("array is = \n",arr)
print('Last element from 2nd dim: ', arr[1, -1])
OUTPUT:-
# Program of create simple data frame
import pandas as pd
data = {
"calories": [420, 380, 390],
"duration": [50, 40, 45]
}
#load data into a DataFrame object:
df = pd.DataFrame(data)
print(df)
OUTPUT:-
# program to create Data frame by csv file
import pandas as pd
df = pd.read_csv('data.csv')
print(df)
OUTPUT:-
# Program of data frame on given data
import pandas as pd
data = {
"Duration":{
"0":60,
"1":60,
"2":60,
"3":45,
"4":45,
"5":60
},
"Pulse":{
"0":110,
"1":117,
"2":103,
"3":109,
"4":117,
"5":102
},
"Maxpulse":{
"0":130,
"1":145,
"2":135,
"3":175,
"4":148,
"5":127
},
"Calories":{
"0":409,
"1":479,
"2":340,
"3":282,
"4":406,
"5":300
}
}
df = pd.DataFrame(data)
print(df)
OUTPUT:-
#program of printing the first 10 rows of the Data Frame
import pandas as pd
df = pd.read_csv('data.csv')
print(df.head(10))
OUTPUT:-
# Program of a new Data Frame with no empty cells
import pandas as pd
df = pd.read_csv('data.csv')
new_df = df.dropna()
print(new_df.to_string())
#Notice in the result that some rows have been removed (row 18, 22 and 28).
#These rows had cells with empty values.
OUTPUT:-
# Program handle wrong data where value is greater than 120 of duration set
that value = 120
import pandas as pd
df = pd.read_csv('data.csv')
for x in df.index:
if df.loc[x, "Duration"] > 120:
df.loc[x, "Duration"] = 120
print(df.to_string())
OUTPUT:-
# Program of handling duplicate values in data frame
import pandas as pd
df = pd.read_csv('data.csv')
df.drop_duplicates(inplace = True)
print(df.to_string())
#Notice that row 12 has been removed from the result
OUTPUT:-
# Program to find correlation between columns
import pandas as pd
df = pd.read_csv('data.csv')
print(df.corr())
OUTPUT:-
# Program to remove all rows with NULL values
import pandas as pd
df = pd.read_csv('data.csv')
df.dropna(inplace = True)
print(df.to_string())
OUTPUT:-
# Program of slicing by row and column
import pandas as pd
data = [[50, True], [40, False], [30, False]]
label_rows = ["Sally", "Mary", "John"]
label_cols = ["age", "qualified"]
df = pd.DataFrame(data, label_rows, label_cols)
print(df)
print("values after slicing",df.loc["Mary", "age"])
OUTPUT:-
# Program of selection condition
pandas as pd
data = {
"age": [50, 40, 30, 40, 20, 10, 30],
"qualified": [True, False, False, False, False, True, True]
}
df = pd.DataFrame(data)
print(df)
newdf = df.where(df["age"] > 30)
print("new data frame after condition true")
print(newdf)
OUTPUT:-
import sys
# Program use of matplotlib by creating a chart
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
x = np.array([2007,2008,2009,2010,2011,2012,2013])
y = np.array([450,540,1000,1050,700,1200,1000])
plt.plot(x, y)
plt.xlabel("years")
plt.ylabel("No. of students")
plt.show()
#Two lines to make our compiler able to draw:
plt.savefig(sys.stdout.buffer)
sys.stdout.flush()
OUTPUT:-
# Program of creating subplots
import matplotlib.pyplot as plt
import numpy as np
#plot 1:
x = np.array([0, 1, 2, 3])
y = np.array([3, 8, 1, 10])
plt.subplot(1, 2, 1)
plt.plot(x,y)
#plot 2:
x = np.array([0, 1, 2, 3])
y = np.array([10, 20, 30, 40])
plt.subplot(1, 2, 2)
plt.plot(x,y)
plt.show()
OUTPUT:-
# Program of Scatter plots
import matplotlib.pyplot as plt
import numpy as np
x = np.array([55,75,85,75,200,117,200,95,59,110,120,90,60])
y = np.array([99,86,87,88,89,86,91,87,94,78,77,85,86])
colors = np.array([0, 10, 20, 30, 40, 45, 50, 55, 60, 70, 80, 90, 100])
plt.scatter(x, y, c=colors, cmap='viridis')
plt.xlabel("no of student")
plt.ylabel("pass percentage")
plt.show()
OUTPUT:-
# Program of creating data visualization by bar chart
import sys
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
x = ["APPLES", "BANANAS","ORANGE","MANGO","GRAPS"]
y = [67, 78,45,89,85]
plt.bar(x, y,color="pink",width=0.3)
plt.xlabel("Name Fruits")
plt.ylabel("Percentage of people like ")
plt.show()
plt.savefig(sys.stdout.buffer)
sys.stdout.flush()
OUTPUT:-
# Program of draw Histogram
import sys
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
x = np.random.normal(150, 30, 250)
plt.hist(x,color="green")
plt.xlabel("Marks")
plt.ylabel("Percentage")
plt.show()
#Two lines to make our compiler able to draw:
plt.savefig(sys.stdout.buffer)
sys.stdout.flush()
OUTPUT:-
# Program of box plot
# Import libraries
import matplotlib.pyplot as plt
import numpy as np
# Creating dataset
np.random.seed(10)
data = np.random.normal(100, 20, 200)
fig = plt.figure(figsize =(7, 5))
# Creating plot
plt.boxplot(data)
plt.xlabel("Data")
plt.ylabel("Result")
# show plot
plt.show()
OUTPUT:-
# Create More than one Boxplot on different datasets
import matplotlib.pyplot as plt
import numpy as np
# Creating dataset
np.random.seed(10)
data_1 = np.random.normal(100, 10, 200)
data_2 = np.random.normal(90, 20, 200)
data_3 = np.random.normal(80, 30, 200)
data_4 = np.random.normal(70, 40, 200)
data = [data_1, data_2, data_3, data_4]
fig = plt.figure(figsize =(7, 5))
ax = fig.add_subplot(111)
# Creating axes instance
bp = ax.boxplot(data, patch_artist = True,
notch ='True', vert = 0)
colors = ['#0000FF', '#00FF00',
'#FFFF00', '#FF00FF']
for patch, color in zip(bp['boxes'], colors):
patch.set_facecolor(color)
# changing color and linewidth of
# whiskers
for whisker in bp['whiskers']:
whisker.set(color ='#8B008B',
linewidth = 1.5,
linestyle =":")
# changing color and linewidth of
# caps
for cap in bp['caps']:
cap.set(color ='#8B008B',
linewidth = 2)
# changing color and linewidth of
# medians
for median in bp['medians']:
median.set(color ='red',
linewidth = 3)
# changing style of fliers
for flier in bp['fliers']:
flier.set(marker ='D',
color ='#e7298a',
alpha = 0.5)
# x-axis labels
ax.set_yticklabels(['data_1', 'data_2',
'data_3', 'data_4'])
# Adding title
plt.title("Malwa college Data")
plt.xlabel("student marks")
plt.ylabel("Percentage")
# Removing top axes and right axes
# ticks
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
# show plot
plt.show()
OUTPUT:-
