Python Project [An application to save any random article from Wikipedia to a text file].

Program that converts arrays into a list, loops through the list to form a string using the chr() function

Input :

#Program that converts arrays into a list, loops through the list to form a string using the chr() function

#clcoding.com

import numpy as np

def ord_to_character(words = ""):

x = np.array([112,121,134,123,32,96,34,56,67,111,98,97,119,105,113])

y = np.array([78,90,104,123,132,53,34,56,97,116,98,27,119,77,117,12])

z = np.array([111,112,134,123,21,32,108,106,89,70,80,103,103,120,121])

letters = x.tolist() + y.tolist() + z.tolist()

for letter in letters:

words += chr(letter)

return words

sentence = ord_to_character()

print(sentence)

Output :

py†{ `"8CobawiqNZh{„5"8atbwMuop†{ ljYFPggxy

Plotting a colourful Scatter Plot using Matplotlib

from matplotlib import pyplot as plt

x = [10,20,30,40,50]

y = [200,300,100,400,500]

colors=[70,20,80,10,50]

sizes=[100,50,300,250,150]

plt.scatter(x,y,c=colors,s=sizes,cmap="Accent",alpha=1.0)

plt.colorbar()

plt.show

Plotting Histogram using Pyplot

from matplotlib import pyplot as plt

marks=[90,50,40,60,55,44,30,10,34,84]

grade_intervals=[0,35,70,100]

plt.title("Student Grades")

plt.hist(marks,grade_intervals,histtype="bar",rwidth=0.5,facecolor="blue")

plt.xticks([0,35,70,100])

plt.xlabel("Percentage")

plt.ylabel("No. of Students")

plt.show()

Plotting Colourful Pie Chart In MatPlotlib

from matplotlib import pyplot as plt

student_performance = ["Excellent","Good","Average","Poor"]

student_values = [15,25,12,8]

plt.figure(figsize=(7,10))

plt.pie(student_values,labels=student_performance,startangle=90,

explode=[0.2,0,0,0],shadow=True,colors=["black","blue","yellow","red"],autopct="%2.1f%%")

plt.legend(title="Perfomances")

plt.show

oin us: https://t.me/jupyter_python

HANDLING MISSING DATA (FillNa) IN PANDAS

Firstly, we are importing an excel sheet as a DataFrame,

import pandas as pd

ds = pd.read_excel("C:\\Users\\mahes\\OneDrive\\Documents\\Students.xlsx")

dt = pd.DataFrame(ds)

print(dt)

Now, Let's perform some operations related to FillNa()

HANDLING MISSING DATA (dropna) IN PANDAS

Firstly, we are importing an excel sheet as a DataFrame,

import pandas as pd

ds = pd.read_excel("C:\\Users\\mahes\\OneDrive\\Documents\\Students.xlsx")

dt = pd.DataFrame(ds)

print(dt)

Now, Let's perform some operations related to DROPNA

ILOC[ ] IN PANDAS - PYTHON

Firstly, we are importing an excel sheet as a DataFrame,

import pandas as pd

ds = pd.read_excel("C:\\Users\\mahes\\OneDrive\\Documents\\Students.xlsx")

dt = pd.DataFrame(ds)

Now, Let's perform some operations related to ILOC

LOC[ ] IN PANDAS - PYTHON

LOC[ ] IN PANDAS

Firstly, we are importing an excel sheet as a DataFrame,

import pandas as pd

ds = pd.read_excel("C:\\Users\\mahes\\OneDrive\\Documents\\Students.xlsx")

dt = pd.DataFrame(ds)

Now, Let's perform some operations related to LOC and ILOC

EXPORT DATAFRAME TO EXCEL, CSV & TEXT FILE IN PANDAS || SAVE DATAFRAME IN PANDAS

EXPORT DATAFRAME TO EXCEL, CSV & TEXT FILE IN PANDAS

Firstly, we are importing an excel sheet as a DataFrame,

import pandas as pd

st = pd.read_excel("C:\\Users\\mahes\\OneDrive\\Documents\\Students.xlsx")

sd = pd.DataFrame(st)

print(st)

Making some changes in the dataframe

st['Total']=st['Hindi']+st['English'] +st['Science'] +st['History']+st['Mathematics']

Now, exporting the DataFrame as Excel, CSV and Text File

sd.to_excel("C:\\Users\\mahes\\OneDrive\\Desktop\\StudentsNew.xlsx")

sd.to_csv("C:\\Users\\mahes\\OneDrive\\Desktop\\StudentsNew.csv")

sd.to_csv("C:\\Users\\mahes\\OneDrive\\Desktop\\StudentsNew.txt" ,sep = "\t")

As you can see we have created three new files.

SORTING DATAFRAME (BY COLUMN) IN PANDAS (Part-2) - PYTHON PROGRAMMING

#SORTING DATAFRAME (BY COLUMN) IN PANDAS (Part-2) - PYTHON PROGRAMMING

Listing Two Rows

df[['Name','Total_Marks']]

df = df.drop(columns = "Total_Marks")

print(df)

MANIPULATING DATAFRAME IN PANDAS (ADD COLUMN , DROP COLUMN) || DATAFRAME MANIPULATIONS

import pandas as pd

dt = pd.read_excel("C:\\Users\\mahes\\OneDrive\\Documents\\Students.xlsx")

df = pd.DataFrame(dt)

print(df)

df['Total_Marks'] = 0

print(df)

df['Total_Marks'] = df['Hindi']+df['English']

SORTING DATAFRAME (BY COLUMN) IN PANDAS - PYTHON PROGRAMMING

#SORTING DATAFRAME (BY COLUMN) IN PANDAS

#clcoding

import pandas as pd

dt = pd.read_excel("FILE_NAME")

print(dt)

df = pd.DataFrame(dt)

df.sort_values("MAXMTEMPERATURE", ascending = False)

How do you generate random Password?

Author June 21, 2021 Python No comments

#How do you generate 8 random character?

#clcoding

from string import printable

from random import choice

print(*[choice(printable) for _ in range(8)], sep = '')

How do you reverse the string without take another string?

Author June 21, 2021 Python No comments

#How do you reverse the string without take another string?

#clcoding

s = 'hello'

n = -1

for x in s:

n += 1

while n >= 0:

print(s[n], end = '')

n -= 1

INDEXING & SLICING DATAFRAMES IN PANDAS [PART 3]- PYTHON PROGRAMMING

INDEXING & SLICING DATAFRAMES IN PANDAS

data_frame(start:step:stop)

data_frame(col_name)

data_frame(col_name)[index_value]

INDEXING & SLICING DATAFRAMES IN PANDAS [PART 2]- PYTHON PROGRAMMING

INDEXING & SLICING DATAFRAMES IN PANDAS

Shape()

loc()

Columns()

INDEXING & SLICING DATAFRAMES IN PANDAS [PART 1]- PYTHON PROGRAMMING

INDEXING & SLICING DATAFRAMES IN PANDAS

head( )

tail( )

describe( )

MATHEMATICAL FUNCTIONS ON SERIES IN PANDAS [Part 1] - PYTHON PROGRAMMING

1] Addition

#clcoding

import pandas as pd

s1 = pd.Series([10,20,30,40,50])

s2 = pd.Series([70,80,90,100,110])

s1.add(s2)

2] Subtraction

import pandas as pd

s1 = pd.Series([10,20,30,40,50])

s2 = pd.Series([70,80,90,100,110])

s1.subtract(s2)

3] Multiplication

import pandas as pd

s1 = pd.Series([10,20,30,40,50])

s2 = pd.Series([70,80,90,100,110])

s1.multiply(s2)

4] Division

import pandas as pd

s1 = pd.Series([10,20,30,40,50])

s2 = pd.Series([70,80,90,100,110])

s1.divide(s2)

OPERATIONS ON SERIES - PYTHON

Author June 19, 2021 Python No comments

ATTRIBUTES OF SERIES IN PANDAS

1. index 2. array 3. values 4. name 5. shape 6. size 7. nbytes 8. dtype 9. memory_usage( ) 10. empty 11. ndim