MV's Code Guide

Name Numerology

2024-05-17T15:06:00.002+05:30

Explanation:

Letter Value Mapping: The same dictionary letter_values is used to map each letter to its corresponding value.
Convert to Lowercase: The input word is converted to lowercase.
Initialize Total: A variable total is initialized to store the sum of the values.
Iterate Over Characters: The program iterates over each character in the input word.
Check for Valid Characters: It checks if the character exists in the letter_values dictionary.
Calculate Value: For valid characters, it adds the corresponding value from the dictionary to the total.
Sum Digits Function: A nested function sum_digits is defined to sum the digits of a number repeatedly until a single-digit number is obtained.
Calculate Final Value: The sum_digits function is called with the total sum to get the final single-digit value.
Return Final Value: The function returns the final single-digit value.

This code will prompt the user to enter a word, calculate the sum of its letter values, and then reduce the sum to a single-digit number by repeatedly summing its digits

Password Generator

2023-11-20T12:40:00.006+05:30

import random
passlen = int(input("Enter the length of password"))
s="abcdefghijklmnopqrstuvwxyz01234567890ABCDEFGHIJKLMNOPQRSTUVWXYZ!@#$%^&*()?"
p = "".join(random.sample(s,passlen ))
print(p)

Output 1:

Enter the length of password
8
I^GOo9)2

Output 2:

Enter the length of password
12
pLfdoThK2eny

Value Convertor

2023-11-20T12:27:00.003+05:30

def convert_temperature():
    print("\n Which conversion do you want to choose:-")
    print("1. Celsius to Faranheit")
    print("2. Faranheit to Celsius")
    choice = int(input("Enter your choice: "))
    if choice == 1:
        temp = float(input("Enter temperature in celsius: "))
        print(f"{temp} degree celsius is equal to {(temp*9/5)+32} degree faranheit.\n")
    elif choice == 2:
        temp = float(input("Enter temperature in faranheit: "))
        print(f"{temp} degree faranheit is equal to {(temp-32)*5/9} degree celsius.\n")
    else:
        print("Invalid input...please try again\n")

def convert_currency():

    print("\n Which conversion do you want to choose:-")
    print("1. Dollar to pound")
    print("2. Pound to Dollar")
    print("3. Dollar to Rupees")
    print("4. Rupees to Dollar")
    choice = int(input("Enter your choice: "))

    if choice == 1:
        value = float(input("Enter currency in dollars: "))
        print(f"{value} dollars in pounds will be {value*0.73}\n")
    elif choice == 2:
        value = float(input("Enter currency in pounds: "))
        print(f"{value} pounds in dollars will be {value/0.73}\n")
    elif choice == 3:
        value = float(input("Enter currency in dollar: "))
        print(f"{value} dollars in rupees will be {value*83.32}\n")
    elif choice == 4:
        value = float(input("Enter currency in Rupees: "))
        print(f"{value} rupees in dollars will be {value/83.32}\n")

def convert_lengths():

    print("\n Which conversion do you want to choose:-")
    print("1. Centimeters to foot and inches")
    print("2. Foot and inches to centimeter")
    choice = int(input("Enter your choice: "))
    if choice == 1:
        value = float(input("Enter length in cm: "))
        inches = value/2.54
        feet = inches/12
        print(f"{value} centimeters in equal to {feet} feet and {inches} inch\n")
    elif choice == 2:
        feet = float(input("Enter length in feet: "))
        inches = float(input("Enter length in inches: "))
        length = (feet*12 + inches)*2.54
        print(f"{feet} feet and {inches} inches in centimeters will be {length}\n")

print("===== Welcome to Value Converter =====")
while 1:
    print("Which option would you like to choose:-")
    print("1. Convert temperature")
    print("2. Convert currency")
    print("3. Convert lengths")
    print("4. Exit")
    choice = int(input("Enter your choice: "))
    if choice == 1:
        convert_temperature()
    elif choice == 2:
        convert_currency()
    elif choice == 3:
        convert_lengths()
    elif choice == 4:
        print('Exiting...')
        exit(0)

===== Welcome to Value Converter =====
Which option would you like to choose:-
1. Convert temperature
2. Convert currency
3. Convert lengths
4. Exit
Enter your choice:
2

Which conversion do you want to choose:-
1. Dollar to pound
2. Pound to Dollar
3. Dollar to Rupees
4. Rupees to Dollar
Enter your choice:
3
Enter currency in dollar:
20
20.0 dollars in rupees will be 1666.3999999999999

Which option would you like to choose:-
1. Convert temperature
2. Convert currency
3. Convert lengths
4. Exit
Enter your choice:
2

Which conversion do you want to choose:-
1. Dollar to pound
2. Pound to Dollar
3. Dollar to Rupees
4. Rupees to Dollar
Enter your choice:
4
Enter currency in Rupees:
80
80.0 rupees in dollars will be 0.9601536245799329

Which option would you like to choose:-
1. Convert temperature
2. Convert currency
3. Convert lengths
4. Exit
Enter your choice:
3

Which conversion do you want to choose:-
1. Centimeters to foot and inches
2. Foot and inches to centimeter
Enter your choice:
1
Enter length in cm:
156
156.0 centimeters in equal to 5.118110236220472 feet and 61.41732283464567 inch

Which option would you like to choose:-
1. Convert temperature
2. Convert currency
3. Convert lengths
4. Exit
Enter your choice:
1

Which conversion do you want to choose:-
1. Celsius to Faranheit
2. Faranheit to Celsius
Enter your choice:
1
Enter temperature in celsius:
32
32.0 degree celsius is equal to 89.6 degree faranheit.

Which option would you like to choose:-
1. Convert temperature
2. Convert currency
3. Convert lengths
4. Exit
Enter your choice:
4
Exiting...

User Management System

2023-11-20T12:26:00.000+05:30

database = {'entries': []}

SRNO = 'srno'
NAME = 'name'
AGE = 'age'
GENDER = 'gender'
OCCUPATION = 'occupation'

def get_serial_no():
    return len(database['entries']) + 1

def add_entry(entry):
    entry = {
        'srno': get_serial_no(),
        'name': entry['name'],
        'age': entry['age'],
        'gender': entry['gender'],
        'occupation': entry['occupation']
    }
    database['entries'].append(entry)

def check_entry_presence(value):
    for num, entry in enumerate(database['entries']):
        if entry[value[0]] == value[1]:
            return 1
    return 0

def search_entry(value):
    for num, entry in enumerate(database['entries']):
        if entry[value[0]] == value[1]:
            return entry

def update_entry(value, updated_entry):
    for num, entry in enumerate(database['entries']):
        if entry[value[0]] == value[1]:
            database['entries'][num] == updated_entry

def delete_entry(value):
    for num, entry in enumerate(database['entries']):
        if entry[value[0]] == value[1]:
            database['entries'].remove(entry)

def display_entry(entry):
    print(f"SRNO: {entry['srno']}")
    print(f"Name: {entry['name']}")
    print(f"Age: {entry['age']}")
    print(f"Gender: {entry['gender']}")
    print(f"Occupation: {entry['occupation']}\n")

def display_all_entries():
    for entry in database["entries"]:
        display_entry(entry)

def select_entry_and_value():
    value_type = ''
    value = ''
    while 1:
        print('Choose an entry based on which to search entries in database: ')
        print("1. srno")
        print("2. name")
        print("3. age")
        print("4. gender")
        print("5. occupation")
        choice = int(input("Enter your choice: "))
        if choice < 1 or choice > 5:
            print("Invalid input...please try again")
        else:
            if choice == 1:
                value_type = SRNO
                value = input("Enter serial number to search: ")
                return (value_type, value)
            elif choice == 2:
                value_type = NAME
                value = input("Enter name to search: ")
                return (value_type, value)
            elif choice == 3:
                value_type = AGE
                value = input("Enter age to search: ")
                return (value_type, value)
            elif choice == 4:
                value_type = GENDER
                value = input("Enter gender to search: ")
                return (value_type, value)
            elif choice == 5:
                value_type = OCCUPATION
                value = input("Enter occupation to search: ")
                return (value_type, value)

def get_entry_details():
    output = {}
    output[NAME] = input("Enter name: ")
    output[AGE] = input("Enter age: ")
    output[GENDER] = input("Enter gender: ")
    output[OCCUPATION] = input("Enter occupation: ")
    return output

print("===== Welcome To User Management System =====")
while 1:
    print("\n What would you like to do:-")
    print("1. Add an entry")
    print("2. Update an entry")
    print("3. Delete an entry")
    print("4. Search an entry")
    print("5. Display all entries")
    print("6. Exit")
    choice = int(input("Enter your choice: "))
    if choice > 7 or choice < 1:
        print("Invalid input...please try again")
    else:
        if choice == 1:
            print("Enter details for the new entry:-")
            entry = get_entry_details()
            add_entry(entry)
            print("Entry successfully created...")
        elif choice == 2:
            value = select_entry_and_value()
            print('Enter the details of the updated entry:-')
            entry = get_entry_details()
            update_entry(value, entry)
            print("Entry successfully updated...")
        elif choice == 3:
            value = select_entry_and_value()
            delete_entry(value)
            print("Entry successfully deleted...")
        elif choice == 4:
            value = select_entry_and_value()
            entry = search_entry(value)
            display_entry(entry)
        elif choice == 5:
            display_all_entries()
        elif choice == 6:
            print('Exiting')
            break

Rock Paper Scissors Game Using Python

2023-11-20T11:58:00.000+05:30

import random
player1=input('Select Rock, Paper, or Scissors :').lower()
print("Player 1 selected:",player1)
player2=random.choice(['Rock','Paper','Scissors']).lower()
print("Player 2 selected:",player2)

if player1=='rock' and player2=='paper':
	print("Player 2 Won !")
elif player1=='paper' and player2=='scissors':
	print("Player 2 Won!")
elif player1=='scissors' and player2=='rock':
	print("Player 2 Won !")
elif player1==player2:
	print("It's A Tie !")
else:
	print("Player 1 Won !")

Output 1:

Select Rock, Paper, or Scissors :
rock
Player 1 selected: rock
Player 2 selected: paper
Player 2 Won !

Output 2:

Select Rock, Paper, or Scissors :
Paper
Player 1 selected: paper
Player 2 selected: scissors
Player 2 Won!

Output 3:

Select Rock, Paper, or Scissors :
Scissors
Player 1 selected: scissors
Player 2 selected: rock
Player 2 Won !

Output 4:

Select Rock, Paper, or Scissors :
paper
Player 1 selected: paper
Player 2 selected: paper
It's A Tie !

Story Generator in Python

2023-11-20T10:46:00.002+05:30

Using Random Module You Can Generate a random story

import random
when=['A few years ago','Yesterday','Last night','A long time ago','On 20th Jan']
who=['a rabbit','an elephant','a mouse','a turtle','a cat']
namee=['Ali','Mariam','Daniel','Houck','Starwalker']
residence=['Barcelona','India','Germany','London','England']
went=['cinema','university','seminar','school','laundary']
happened=['made a lot of friends','eats a burger','found a secret key','solved a mystery','wrote a book']
print(random.choice(when)+', '+random.choice(namee)+' that lived in '+ random.choice(residence)+', went to the '+random.choice(went)+' and '+random.choice(happened))

Output 1:

A long time ago, Mariam that lived in Germany, went to the cinema and eats a burger

Output 2:

Last night, Ali that lived in Germany, went to the seminar and solved a mystery

Pandas

2023-11-11T15:23:00.002+05:30

This tutorial will explore the most potent Python library pandas, and we will discuss the most important functions of this library that are important for data analysis. Beginners can also follow this tutorial due to its simplicity and efficiency. If you don’t have python installed in your system, you can use Google Colaboratory.

Importing Data

You can download the dataset from that link.

import pandas as pd

df = pd.read_csv("kaggle_sales_data.csv", encoding="Latin-1") # Load the data

df.head() # Show first five rows

Output:

Data Exploration

In this section, we will discuss various functions that help you to get more about your data. Like viewing it or getting the mean, average, min/max, or getting information about the dataframe.

1. Data Viewing

df.head(): It displays the first five rows of the sample data

2. df.tail(): It displays the last five rows of the sample data

3. df.sample(n): It displays the random n number of rows in the sample data

df.sample(6)

4. df.shape: It displays the sample data's rows and columns (dimensions).

(2823, 25)

It signifies that our dataset has 2823 rows, each containing 25 columns.

2. Statistics

This section contains the functions that help you perform statistics like average, min/max, and quartiles on your data.

1. df.describe(): Get the basic statistics of each column of the sample data

df.info(): Get the information about the various data types used and the non-null count of each column.

2. df.corr(): This can give you the correlation matrix between all the integer columns in the data frame.

4. df.memory_usage(): It will tell you how much memory is being consumed by each column.

3. Data Selection

You can also select the data of any specific row, column, or even multiple columns.

1. df.iloc[row_num]: It will select a particular row based on its index

For ex-,

df.iloc[0]

df[col_name]: It will select the particular column

For ex-,

df["SALES"]

Output:

3. df[[‘col1’, ‘col2’]]: It will select multiple columns given

For ex-,

df[["SALES", "PRICEEACH"]]

Output:

4. Data Cleaning

These functions are used to handle the missing data. Some rows in the data contain some null and garbage values, which can hamper the performance of our trained model. So, it is always better to correct or remove these missing values.

1. df.isnull(): This will identify the missing values in your dataframe.

2. df.dropna(): This will remove the rows containing missing values in any column.

3. df.fillna(val): This will fill the missing values with val given in the argument.

4. df[‘col’].astype(new_data_type): It can convert the data type of the selected columns to a different data type.

For ex-,

df["SALES"].astype(int)

We are converting the data type of the SALES column from float to int.

5. Data Analysis

Here, we will use some helpful functions in data analysis, like grouping, sorting, and filtering.

1. Aggregation Functions:

You can group a column by its name and then apply some aggregation functions like sum, min/max, mean, etc.

df.groupby("col_name_1").agg({"col_name_2": "sum"})

For ex-,

df.groupby("CITY").agg({"SALES": "sum"})

It will give you the total sales of each city.

If you want to apply multiple aggregations at a single time, you can write them like that.

For ex-,

aggregation = df.agg({"SALES": "sum", "QUANTITYORDERED": "mean"})

Output:

SALES 1.003263e+07

QUANTITYORDERED 3.509281e+01

dtype: float64

2. Filtering Data:

We can filter the data in rows based on a specific value or a condition.

For ex-,

df[df["SALES"] > 5000]

Displays the rows where the value of sales is greater than 5000

You can also filter the dataframe using the query() function. It will also generate a similar output as above.

For ex,

df.query("SALES" > 5000)

3. Sorting Data:

You can sort the data based on a specific column, either in the ascending order or in the descending order.

For ex-,

df.sort_values("SALES", ascending=False) # Sorts the data in descending order

4. Pivot Tables:

We can create pivot tables that summarize the data using specific columns. This is very useful in analyzing the data when you only want to consider the effect of particular columns.

For ex-,

pd.pivot_table(df, values="SALES", index="CITY", columns="YEAR_ID", aggfunc="sum")

Let me break this for you.

1. values: It contains the column for which you want to populate the table's cells.

2. index: The column used in it will become the row index of the pivot table, and each unique category of this column will become a row in the pivot table.

3. columns: It contains the headers of the pivot table, and each unique element will become the column in the pivot table.

4. aggfunc: This is the same aggregator function we discussed earlier.

Output:

This output shows a chart which depicts the total sales in a particular city for a specific year.

6. Combining Data Frames

We can combine and merge several data frames either horizontally or vertically. It will concatenate two data frames and return a single merged data frame.

For ex-,

combined_df = pd.concat([df1, df2])

You can merge two data frames based on a common column. It is useful when you want to combine two data frames that share a common identifier.

For ex,

merged_df = pd.merge(df1, df2, on="common_col")

7. Applying Custom Functions

You can apply custom functions according to your needs in either a row or a column.

For ex-,

def cus_fun(x):

return x * 3

df["Sales_Tripled"] = df["SALES"].apply(cus_fun, axis=0)

We have written a custom function that will triple the sales value for each row. axis=0 means that we want to apply the custom function on a column, and axis=1 implies that we want to apply the function on a row.

In the earlier method you have to write a separate function and then to call it from the apply() method. Lambda function helps you to use the custom function inside the apply() method itself. Let’s see how we can do that.

df["Sales_Tripled"] = df["SALES"].apply(lambda x: x * 3)

Applymap:

We can also apply a custom function to every element of the dataframe in a single line of code. But a point to remember is that it is applicable to all the elements in the dataframe.

For ex-,

df = df.applymap(lambda x: str(x))

It will convert the data type to a string of all the elements in the dataframe.

8. Time Series Analysis

In mathematics, time series analysis means analyzing the data collected over a specific time interval, and pandas have functions to perform this type of analysis.

Conversion to DateTime Object Model:

We can convert the date column into a datetime format for easier data manipulation.

For ex-,

df["ORDERDATE"] = pd.to_datetime(df["ORDERDATE"])

Output:

Calculate Rolling Average:

Using this method, we can create a rolling window to view data. We can specify a rolling window of any size. If the window size is 5, then it means a 5-day data window at that time. It can help you remove fluctuations in your data and help identify patterns over time.

For ex-

rolling_avg = df["SALES"].rolling(window=5).mean()

Output:

9. Cross Tabulation

We can perform cross-tabulation between two columns of a table. It is generally a frequency table that shows the frequency of occurrences of various categories. It can help you to understand the distribution of categories across different regions.

For ex-,

Getting a cross-tabulation between the COUNTRY and DEALSIZE.

cross_tab = pd.crosstab(df["COUNTRY"], df["DEALSIZE"])

It can show you the order size (‘DEALSIZE’) ordered by different countries.

10. Handling Outliers

Outliers in data means that a particular point goes far beyond the average range. Let’s understand it through an example. Suppose you have 5 points, say 3, 5, 6, 46, 8. Then we can clearly say that the number 46 is an outlier because it is far beyond the average of the rest of the points. These outliers can lead to wrong statistics and should be removed from the dataset.

Here pandas come to the rescue to find these potential outliers. We can use a method called Interquartile Range(IQR), which is a common method for finding and handling these outliers. You can also read about this method if you want information on it. You can read more about them here.

Let’s see how we can do that using pandas.

Q1 = df["SALES"].quantile(0.25)

Q3 = df["SALES"].quantile(0.75)

IQR = Q3 - Q1

lower_bound = Q1 - 1.5 * IQR

upper_bound = Q3 + 1.5 * IQR

outliers = df[(df["SALES"] < lower_bound) | (df["SALES"] > upper_bound)]

Q1 is the first quartile representing the 25th percentile of the data and Q3 is the third quartile representing the 75th percentile of the data.

lower_bound variable stores the lower bound that is used for finding potential outliers. Its value is set to 1.5 times the IQR below Q1. Similarly, upper_bound calculates the upper bound, 1.5 times the IQR above Q3.

After which, you filter out the outliers that are less than the lower or greater than the upper bound.

Wrapping it Up

Python pandas library enables us to perform advanced data analysis and manipulations. These are only a few of them. You can find some more tools in this pandas documentation. One important thing to remember is that the selection of techniques can be specific which caters to your needs and the dataset you are using.Top of Form

Diwali Sales Analysis

2023-11-06T21:35:00.002+05:30

# import python libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt # visualizing data
# %matplotlib inline
import seaborn as sns

# import csv file
df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Diwali Sales Data.csv', encoding= 'unicode_escape')

from google.colab import drive
drive.mount('/content/drive')

df.shape

df.head()

df.info()

#drop unrelated/blank columns
df.drop(['Status', 'unnamed1'], axis=1, inplace=True)

#check for null values
pd.isnull(df).sum()

# drop null values
df.dropna(inplace=True)

# change data type
df['Amount'] = df['Amount'].astype('int')

df['Amount'].dtypes

df.columns

#rename column
df.rename(columns= {'Marital_Status':'Shaadi'})

# describe() method returns description of the data in the DataFrame (i.e. count, mean, std, etc)
df.describe()

# use describe() for specific columns
df[['Age', 'Orders', 'Amount']].describe()

"""# Exploratory Data Analysis

### Gender
"""

# plotting a bar chart for Gender and it's count

ax = sns.countplot(x = 'Gender',data = df)

for bars in ax.containers:
    ax.bar_label(bars)

# plotting a bar chart for gender vs total amount

sales_gen = df.groupby(['Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)

sns.barplot(x = 'Gender',y= 'Amount' ,data = sales_gen)

"""*From above graphs we can see that most of the buyers are females and even the purchasing power of females are greater than men*

### Age
"""

ax = sns.countplot(data = df, x = 'Age Group', hue = 'Gender')

for bars in ax.containers:
    ax.bar_label(bars)

# Total Amount vs Age Group
sales_age = df.groupby(['Age Group'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)

sns.barplot(x = 'Age Group',y= 'Amount' ,data = sales_age)

"""*From above graphs we can see that most of the buyers are of age group between 26-35 yrs female*

### State
"""

# total number of orders from top 10 states

sales_state = df.groupby(['State'], as_index=False)['Orders'].sum().sort_values(by='Orders', ascending=False).head(10)

sns.set(rc={'figure.figsize':(15,5)})
sns.barplot(data = sales_state, x = 'State',y= 'Orders')

# total amount/sales from top 10 states

sales_state = df.groupby(['State'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False).head(10)

sns.set(rc={'figure.figsize':(15,5)})
sns.barplot(data = sales_state, x = 'State',y= 'Amount')

"""*From above graphs we can see that most of the orders & total sales/amount are from Uttar Pradesh, Maharashtra and Karnataka respectively*

### Marital Status
"""

ax = sns.countplot(data = df, x = 'Marital_Status')

sns.set(rc={'figure.figsize':(7,5)})
for bars in ax.containers:
    ax.bar_label(bars)

sales_state = df.groupby(['Marital_Status', 'Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)

sns.set(rc={'figure.figsize':(6,5)})
sns.barplot(data = sales_state, x = 'Marital_Status',y= 'Amount', hue='Gender')

"""*From above graphs we can see that most of the buyers are married (women) and they have high purchasing power*

### Occupation
"""

sns.set(rc={'figure.figsize':(20,5)})
ax = sns.countplot(data = df, x = 'Occupation')

for bars in ax.containers:
    ax.bar_label(bars)

sales_state = df.groupby(['Occupation'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)

sns.set(rc={'figure.figsize':(20,5)})
sns.barplot(data = sales_state, x = 'Occupation',y= 'Amount')

"""*From above graphs we can see that most of the buyers are working in IT, Healthcare and Aviation sector*

### Product Category
"""

sns.set(rc={'figure.figsize':(20,5)})
ax = sns.countplot(data = df, x = 'Product_Category')

for bars in ax.containers:
    ax.bar_label(bars)

sales_state = df.groupby(['Product_Category'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False).head(10)

sns.set(rc={'figure.figsize':(20,5)})
sns.barplot(data = sales_state, x = 'Product_Category',y= 'Amount')

"""*From above graphs we can see that most of the sold products are from Food, Clothing and Electronics category*"""

sales_state = df.groupby(['Product_ID'], as_index=False)['Orders'].sum().sort_values(by='Orders', ascending=False).head(10)

sns.set(rc={'figure.figsize':(20,5)})
sns.barplot(data = sales_state, x = 'Product_ID',y= 'Orders')

# top 10 most sold products (same thing as above)

fig1, ax1 = plt.subplots(figsize=(12,7))
df.groupby('Product_ID')['Orders'].sum().nlargest(10).sort_values(ascending=False).plot(kind='bar')

"""## Conclusion:

###

*Married women age group 26-35 yrs from UP,  Maharastra and Karnataka working in IT, Healthcare and Aviation are more likely to buy products from Food, Clothing and Electronics category*
"""

Link for Google Collaboratry: Click Here

House Price Prediction

2023-11-06T20:33:00.000+05:30

# -*- coding: utf-8 -*-
"""
Importing the Dependencies
"""

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn.datasets
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
from sklearn import metrics

"""Importing the Boston House Price Dataset"""

house_price_dataset = sklearn.datasets.load_boston()

print(house_price_dataset)

# Loading the dataset to a Pandas DataFrame
house_price_dataframe = pd.DataFrame(house_price_dataset.data, columns = house_price_dataset.feature_names)

# Print First 5 rows of our DataFrame
house_price_dataframe.head()

# add the target (price) column to the DataFrame
house_price_dataframe['price'] = house_price_dataset.target

house_price_dataframe.head()

# checking the number of rows and Columns in the data frame
house_price_dataframe.shape

# check for missing values
house_price_dataframe.isnull().sum()

# statistical measures of the dataset
house_price_dataframe.describe()

"""Understanding the correlation between various features in the dataset

1. Positive Correlation

2. Negative Correlation
"""

correlation = house_price_dataframe.corr()

# constructing a heatmap to nderstand the correlation
plt.figure(figsize=(10,10))
sns.heatmap(correlation, cbar=True, square=True, fmt='.1f', annot=True, annot_kws={'size':8}, cmap='Blues')

"""Splitting the data and Target"""

X = house_price_dataframe.drop(['price'], axis=1)
Y = house_price_dataframe['price']

print(X)
print(Y)

"""Splitting the data into Training data and Test data"""

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 2)

print(X.shape, X_train.shape, X_test.shape)

"""Model Training

XGBoost Regressor
"""

# loading the model
model = XGBRegressor()

# training the model with X_train
model.fit(X_train, Y_train)

"""Evaluation

Prediction on training data
"""

# accuracy for prediction on training data
training_data_prediction = model.predict(X_train)

print(training_data_prediction)

# R squared error
score_1 = metrics.r2_score(Y_train, training_data_prediction)

# Mean Absolute Error
score_2 = metrics.mean_absolute_error(Y_train, training_data_prediction)

print("R squared error : ", score_1)
print('Mean Absolute Error : ', score_2)

"""Visualizing the actual Prices and predicted prices"""

plt.scatter(Y_train, training_data_prediction)
plt.xlabel("Actual Prices")
plt.ylabel("Predicted Prices")
plt.title("Actual Price vs Preicted Price")
plt.show()

"""Prediction on Test Data """

# accuracy for prediction on test data
test_data_prediction = model.predict(X_test)

# R squared error
score_1 = metrics.r2_score(Y_test, test_data_prediction)

# Mean Absolute Error
score_2 = metrics.mean_absolute_error(Y_test, test_data_prediction)

print("R squared error : ", score_1)
print('Mean Absolute Error : ', score_2)

Cars Price Prediction

2023-11-06T20:31:00.000+05:30

"""

Importing the Dependencies
"""

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Lasso
from sklearn import metrics

"""Data Collection and Processing"""

# loading the data from csv file to pandas dataframe
car_dataset = pd.read_csv('/content/car data.csv')

# inspecting the first 5 rows of the dataframe
car_dataset.head()

# checking the number of rows and columns
car_dataset.shape

# getting some information about the dataset
car_dataset.info()

# checking the number of missing values
car_dataset.isnull().sum()

# checking the distribution of categorical data
print(car_dataset.Fuel_Type.value_counts())
print(car_dataset.Seller_Type.value_counts())
print(car_dataset.Transmission.value_counts())

"""Encoding the Categorical Data"""

# encoding "Fuel_Type" Column
car_dataset.replace({'Fuel_Type':{'Petrol':0,'Diesel':1,'CNG':2}},inplace=True)

# encoding "Seller_Type" Column
car_dataset.replace({'Seller_Type':{'Dealer':0,'Individual':1}},inplace=True)

# encoding "Transmission" Column
car_dataset.replace({'Transmission':{'Manual':0,'Automatic':1}},inplace=True)

car_dataset.head()

"""Splitting the data and Target"""

X = car_dataset.drop(['Car_Name','Selling_Price'],axis=1)
Y = car_dataset['Selling_Price']

print(X)

print(Y)

"""Splitting Training and Test data"""

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.1, random_state=2)

"""Model Training

1. Linear Regression
"""

# loading the linear regression model
lin_reg_model = LinearRegression()

lin_reg_model.fit(X_train,Y_train)

"""Model Evaluation"""

# prediction on Training data
training_data_prediction = lin_reg_model.predict(X_train)

# R squared Error
error_score = metrics.r2_score(Y_train, training_data_prediction)
print("R squared Error : ", error_score)

"""Visualize the actual prices and Predicted prices"""

plt.scatter(Y_train, training_data_prediction)
plt.xlabel("Actual Price")
plt.ylabel("Predicted Price")
plt.title(" Actual Prices vs Predicted Prices")
plt.show()

# prediction on Training data
test_data_prediction = lin_reg_model.predict(X_test)

# R squared Error
error_score = metrics.r2_score(Y_test, test_data_prediction)
print("R squared Error : ", error_score)

plt.scatter(Y_test, test_data_prediction)
plt.xlabel("Actual Price")
plt.ylabel("Predicted Price")
plt.title(" Actual Prices vs Predicted Prices")
plt.show()

"""2. Lasso Regression"""

# loading the linear regression model
lass_reg_model = Lasso()

lass_reg_model.fit(X_train,Y_train)

"""Model Evaluation"""

# prediction on Training data
training_data_prediction = lass_reg_model.predict(X_train)

# R squared Error
error_score = metrics.r2_score(Y_train, training_data_prediction)
print("R squared Error : ", error_score)

"""Visualize the actual prices and Predicted prices"""

plt.scatter(Y_train, training_data_prediction)
plt.xlabel("Actual Price")
plt.ylabel("Predicted Price")
plt.title(" Actual Prices vs Predicted Prices")
plt.show()

# prediction on Training data
test_data_prediction = lass_reg_model.predict(X_test)

# R squared Error
error_score = metrics.r2_score(Y_test, test_data_prediction)
print("R squared Error : ", error_score)

plt.scatter(Y_test, test_data_prediction)
plt.xlabel("Actual Price")
plt.ylabel("Predicted Price")
plt.title(" Actual Prices vs Predicted Prices")
plt.show()

Diabetes Prediction Using Python

2023-11-06T20:28:00.001+05:30

"""Importing the Dependencies
"""

import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn import svm
from sklearn.metrics import accuracy_score

"""Data Collection and Analysis

PIMA Diabetes Dataset
"""

# loading the diabetes dataset to a pandas DataFrame
diabetes_dataset = pd.read_csv('.../diabetes.csv')

pd.read_csv?

# printing the first 5 rows of the dataset
diabetes_dataset.head()

# number of rows and Columns in this dataset
diabetes_dataset.shape

# getting the statistical measures of the data
diabetes_dataset.describe()

diabetes_dataset['Outcome'].value_counts()

"""0 --> Non-Diabetic

1 --> Diabetic
"""

diabetes_dataset.groupby('Outcome').mean()

# separating the data and labels
X = diabetes_dataset.drop(columns = 'Outcome', axis=1)
Y = diabetes_dataset['Outcome']

print(X)

print(Y)

"""Data Standardization"""

scaler = StandardScaler()

scaler.fit(X)

standardized_data = scaler.transform(X)

print(standardized_data)

X = standardized_data
Y = diabetes_dataset['Outcome']

print(X)
print(Y)

"""Train Test Split"""

X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.2, stratify=Y, random_state=2)

print(X.shape, X_train.shape, X_test.shape)

"""Training the Model"""

classifier = svm.SVC(kernel='linear')

#training the support vector Machine Classifier
classifier.fit(X_train, Y_train)

"""Model Evaluation

Accuracy Score
"""

# accuracy score on the training data
X_train_prediction = classifier.predict(X_train)
training_data_accuracy = accuracy_score(X_train_prediction, Y_train)

print('Accuracy score of the training data : ', training_data_accuracy)

# accuracy score on the test data
X_test_prediction = classifier.predict(X_test)
test_data_accuracy = accuracy_score(X_test_prediction, Y_test)

print('Accuracy score of the test data : ', test_data_accuracy)

"""Making a Predictive System"""

input_data = (5,166,72,19,175,25.8,0.587,51)

# changing the input_data to numpy array
input_data_as_numpy_array = np.asarray(input_data)

# reshape the array as we are predicting for one instance
input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)

# standardize the input data
std_data = scaler.transform(input_data_reshaped)
print(std_data)

prediction = classifier.predict(std_data)
print(prediction)

if (prediction[0] == 0):
  print('The person is not diabetic')
else:
  print('The person is diabetic')

Extract audio from video file

2020-10-04T19:49:00.003+05:30

Audio to Video Convertor:

import moviepy.editor
import tkinter.filedialog
print("press Y to choose the video file to enter:")
if input().casefold()=='y':
    video_path=tkinter.filedialog.askopenfilename()
    video=moviepy.editor.VideoFileClip(video_path)
    audio=video.audio
    audio.write_audiofile('Audio1.mp3')
else:
    print("You need to choose a file to move ahead")

Read more about moviepy: https://pypi.org/project/moviepy/

Read about tkinter: https://docs.python.org/3/library/tkinter.html

Writing text to an Image

2020-09-15T14:27:00.003+05:30

In this project we are going to learn how we can add text to an image.

This can be used

to create an Image quote in Python.
to create watermark in Python

We will be using PIL (pillow) Library in this project

To create Image we will use Image and ImageDraw module

To set fonts we will use ImageFont module

and

To write text on an image we will use multiline_text() method if ImageDraw module

To Create an Image quote we require a background Image of our own choice on which we can write our quote

Lets define the tasks to be be performed for this project and work accordingly

Task 0: Import libraries:

from PIL import Image, ImageDraw, ImageFont

Task 1: Read an Image

imgObject=Image.open('full or relative path of your image')

Image.open() reads the image and returns an Image object

Task 2: Set the font of the text you want to add to the image

font_object = ImageFont.truetype("E:\\Font\\sunday-spring\\Sunday Spring.ttf", 350)

Download any font file of your own choice from google and specify the path inside truetype() method of ImageFont class

If you font file is with extension .otf then you can call opentype() method of ImageFont class

font_object = ImageFont.opentype("E:\\Font\\sunday-spring\\Sunday Spring.otf", 350)

Task 3 : Creating ImageDraw object to draw text on an Image

drawing_object = ImageDraw.Draw(imgObject)

Inside the ImageDraw.Draw we need to specify image object so that we can bind the drawing pen to the image

Task 4: Write the text

drawing_object.multiline_text((400,800), "MV's Code Guide", font=font_object, fill=(0, 0, 0))

With the drawing object call multiline_text() method to add multiline text or else you can call text() method to add single line text

Inside multiline_text() method :

First parameter is xy coordinate of Top left corner of the text.

Second Parameter is- Text to be drawn.

Third parameter is : font – An ImageFont instance

Fourth parameter is : fill – Color to use for the text.

You an also specify other parameter like

spacing – The number of pixels between lines.

align – "left", "center" or "right".

direction – Direction of the text. It can be "rtl" (right to left), "ltr" (left to right) or "ttb" (top to bottom). Requires libraqm.

Task 5: Save the new Image with text:

drawing_object.save('new_image.jpg')

Full code:

#Example: Create Image Quote

from PIL import Image, ImageDraw, ImageFont

# create an image object
#imgObject = Image.new("RGB", (500, 500), (255, 255, 255))

imgObject=Image.open('Path of Image')

# get a font object

font_object = ImageFont.truetype("E:\\Font\\sunday-spring\\Sunday Spring.ttf", 350)

# get a drawing context
drawing_object = ImageDraw.Draw(imgObject)

# draw multiline text
drawing_object.multiline_text((400,800), "MV's Code Guide", font=font_object, fill=(0, 0, 0))

# save the image
drawing_object.save('new_image.jpg')

Extract Text From Image Using Python

2020-09-13T17:24:00.004+05:30

We are going to use pytesseract and pillow library to work on this project!

Before you start coding, you need to complete three tasks:

1. Click on the link below and install tesseract-OCR

https://github.com/UB-Mannheim/tesseract/wiki

After you install the setup successfully, take a note of where you are saving the file because we need that path in our code.

2. Install pytesseract by using command : pip install pytesseract

3. Install pillow by using command : pip install pillow

Source Code:

import pytesseract
from PIL import Image
pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'
value=Image.open('logo.png')
text=pytesseract.image_to_string(value)
print("Extracted Data is: \n ", text)

Notice that if you want to add full path, you need to add double slash (\\) instead of single slash (\) in you path

For Example: If your path looks like this: D:\Python_Program\my_Image.jpg

Then replace \ with \\

something like : D:\\Python_Program\\my_Image.jpg

Add alphabet 'r' before your string
example: r'C:\Program Files\Tesseract-OCR\tesseract.exe'

and then you are good to go!

Spell Checker with Python

2020-09-10T22:15:00.006+05:30

We can use two different packages to create spell checker

1. Using textblob

2. Using pyspellchecker

Video Explanation:

Source Code:

Using textblob:

from textblob import TextBlob
misspelled_word= "Incorrct"
corrected_word= TextBlob(misspelled_word).correct()
print("Misspelled Word:",misspelled_word)
print("Corrected Word: ",corrected_word)

# you can also use list of misspelled words
print("========================================================")
misspelled_word_list=['Incorrct','Mang','Summay','Watr','Appl']
for word in misspelled_word_list:
    correct_word=TextBlob(word).correct()
    print("Misspelled Word:",misspelled_word_list)
    print("Corrected Word: ",correct_word)

Using pyspellchecker:

from spellchecker import SpellChecker
spell=SpellChecker()
misspelled_word=spell.unknown(['Incorrct', 'Summry','Mondy','Spellng','Pyhon'])
print(type(misspelled_word))
for word in misspelled_word:
    print("Corrected word is ", spell.correction(word))
    print("Candidate words:",spell.candidates(word))

You can play around this code and make changes according to your need!

You can also read a text file and use this code to check the spellings in the file

from textblob import TextBlob
from spellchecker import SpellChecker

f=open('demo.txt','r')
data=str(f.read())
print(data)
# you can also use list of misspelled words
print("\n\n**************************Using textblob**************************************")
misspelled_word_list= data.split()
for word in misspelled_word_list:
    correct_word=TextBlob(word).correct()
    print("\nWord: ",word)
    print('------------------------------')
    print("Corrected Word: ",correct_word)
    print("\n================================")

print('\n***************************Using pyspellchecker***********************************')
spell=SpellChecker()
misspelled_word=spell.unknown(misspelled_word_list)
print(type(misspelled_word))
for word in misspelled_word:
    print("\nword: ",word)
    print('--------------------------------------------')
    print("Corrected word is ", spell.correction(word))
    print('--------------------------------------------')
    print("Candidate words:",spell.candidates(word))
    print("\n================================")

f.close()

output:

Artifcial intelligence (AI), someties called macine intelligence, is intellience demonstated by machines, unlike the
naural intelligence dislayed by humans and animls.



**************************Using textblob**************************************

Word:  Artifcial
------------------------------
Corrected Word:  Artificial

================================

Word:  intelligence
------------------------------
Corrected Word:  intelligence

================================

Word:  (AI),
------------------------------
Corrected Word:  (of),

================================

Word:  someties
------------------------------
Corrected Word:  sometimes

================================

Word:  called
------------------------------
Corrected Word:  called

================================

Word:  macine
------------------------------
Corrected Word:  machine

================================

Word:  intelligence,
------------------------------
Corrected Word:  intelligence,

================================

Word:  is
------------------------------
Corrected Word:  is

================================

Word:  intellience
------------------------------
Corrected Word:  intelligence

================================

Word:  demonstated
------------------------------
Corrected Word:  demonstrated

================================

Word:  by
------------------------------
Corrected Word:  by

================================

Word:  machines,
------------------------------
Corrected Word:  machines,

================================

Word:  unlike
------------------------------
Corrected Word:  unlike

================================

Word:  the
------------------------------
Corrected Word:  the

================================

Word:  naural
------------------------------
Corrected Word:  natural

================================

Word:  intelligence
------------------------------
Corrected Word:  intelligence

================================

Word:  dislayed
------------------------------
Corrected Word:  displayed

================================

Word:  by
------------------------------
Corrected Word:  by

================================

Word:  humans
------------------------------
Corrected Word:  humans

================================

Word:  and
------------------------------
Corrected Word:  and

================================

Word:  animls.
------------------------------
Corrected Word:  animals.

================================

***************************Using pyspellchecker***********************************
<class 'set'>

word:  (ai),
--------------------------------------------
Corrected word is  (ai),
--------------------------------------------
Candidate words: {'(ai),'}

================================

word:  someties
--------------------------------------------
Corrected word is  sometimes
--------------------------------------------
Candidate words: {'sometimes'}

================================

word:  machines,
--------------------------------------------
Corrected word is  machines
--------------------------------------------
Candidate words: {'machines'}

================================

word:  intelligence,
--------------------------------------------
Corrected word is  intelligence
--------------------------------------------
Candidate words: {'intelligences', 'intelligence'}

================================

word:  demonstated
--------------------------------------------
Corrected word is  demonstrated
--------------------------------------------
Candidate words: {'demonstrated'}

================================

word:  naural
--------------------------------------------
Corrected word is  natural
--------------------------------------------
Candidate words: {'natural', 'aural', 'neural'}

================================

word:  dislayed
--------------------------------------------
Corrected word is  displayed
--------------------------------------------
Candidate words: {'displayed', 'dismayed'}

================================

word:  animls.
--------------------------------------------
Corrected word is  animals
--------------------------------------------
Candidate words: {'animist', 'animus', 'animals', 'animism'}

================================

word:  macine
--------------------------------------------
Corrected word is  machine
--------------------------------------------
Candidate words: {'machine', 'maine', 'racine', 'maxine', 'marine'}

================================

word:  intellience
--------------------------------------------
Corrected word is  intelligence
--------------------------------------------
Candidate words: {'intelligence'}

================================

word:  artifcial
--------------------------------------------
Corrected word is  artificial
--------------------------------------------
Candidate words: {'artifical', 'artificial'}

================================

Counting vowels in a string

2020-08-22T17:04:00.004+05:30

Using count() method:

string_data=input("Enter a string:")
vowels={'a','e','i','o','u'}
# Using Count() Method
list1=[]
for ch in string_data:
	if ch in vowels:
		if ch not in list1:
			list1.append(ch)
			print("{} occurred {} times".format(ch,string_data.count(ch)))

Without using count() method:

string_data=input("Enter a string:")
vowels={'a','e','i','o','u'}
d={}
for ch in string_data:
	if ch in vowels:
		d[ch]=d.get(ch,0)+1

print(d)
for key,value in sorted(d.items()):
	print(key,"occurred",value,"times")

Counting number of occurrences of each character in a string

2020-08-21T20:30:00.009+05:30

Using count() method:

string_data=input("Enter string: ")
l=[]
for x in string_data:
	if x not in l:
		l.append(x)
		print(x,"occurred",string_data.count(x),"times")

Without using count() method:

string_data=input("Enter string: ")
d={}
for x in string_data:
	d[x]=d.get(x,0) + 1
print(d)
print(d.items())
# for meaningful output
for key,value in sorted(d.items()):
	print(key,"occurred",value,"times")

Question by Subscriber:

How to ignore special characters like ? , . etc

If you want to ignore special characters and focus on the alphabets and digits only, then you can do that by some extra work

You can either import string class to get a set of all characters that you need to focus on, or you can manually add the set of characters you particularly want to focus on.

we will name them as valid_characters
Here goes the code:

import string
string_data=input("Enter string: ")
l=[]
valid_characters=set(string.ascii_letters+string.digits)
for x in string_data:
	if x in valid_characters:
		if x not in l:
			l.append(x)
			print(x,"occurred",string_data.count(x),"times")

Output:

string_data=input("Enter string: ")
l=[]
valid_characters=set("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"+'0123456789')
for x in string_data:
	if x in valid_characters:
		if x not in l:
			l.append(x)
			print(x,"occurred",string_data.count(x),"times")

Hope you like it ..!

Removing duplicate characters from the string

2020-08-13T20:26:00.006+05:30

Sample Input: sssssssssssssssddddeeeeggggggghhhhh

Output: sdegh

1st way:

string_data=input("Enter string: ")
result=''
for character in string_data:
	if character not in result:
		result=result+character
print("You entered: ",string_data)
print("Final result after removing duplicate characters: \n", result )

2nd way:

string_data=input("Enter string: ")
list_data=[]

for character in string_data:
	if character not in list_data:
		list_data.append(character)
result="".join(list_data)		
print("You entered: ",string_data)
print("Final result after removing duplicate characters: \n", result )

Python Program for specific requirement-2

2020-08-13T20:15:00.003+05:30

Sample Input: d8s2b5r6

Output: dlsubgrx

string_data=input("Enter string: ")
result=''
for character in string_data:
	if character.isalpha():
		result=result+character
		previous=character
	else:
		digit=int(character)
		new_character=chr(ord(previous)+digit)
		result=result+new_character

print("You entered: ",string_data)
print("Final result : \n", result )

Python Program for specific requirements

2020-08-13T19:47:00.006+05:30

Requirement:
Sample Input: s4b6c5a2d9

Output: ssssbbbbbbcccccaaddddddddd

string_data=input("Enter string: ")
result=''
for character in string_data:
	if character.isalpha():
		alphabet=character
	else:
		digit=int(character)
		result=result+alphabet*digit
print("You entered: ",string_data)
print("Final result: \n", result )

Requirement:
Sample Input:ssssbbbbbbcccccaaddddddddd

Output: 4s6b5c2a9d

string_data=input("Enter string: ")
result=''
previous=string_data[0]
character_count=1
i=1
while i<len(string_data):
	if string_data[i]==previous:
		character_count=character_count+1
	else:
		result=result+str(character_count)+previous
		previous=string_data[i]
		character_count=1
	if i==len(string_data)-1:
		result=result+str(character_count)+previous
	i=i+1
print("You entered: ",string_data)
print("Final result: \n", result )

Sort characters in a string

2020-08-13T19:38:00.000+05:30

Assume input string contains only alphabet symbols and digits.

We need to write a program to sort characters of the string were we will sort alphabets first and then digits will follow

Sample Input: C8N9A2U6R5B7

Output: ABCNRU256789

string_data=input("Enter some alphanumeric string to sort: ")
alphabets=[]
digits=[]
for ch in string_data:
	if ch.isalpha():
		alphabets.append(ch)
	else:
		digits.append(ch)
result=''.join(sorted(alphabets)+sorted(digits))
print("You entered: ",string_data)

print("Final result after Sorting: \n", result )

Merge two strings

2020-08-13T19:14:00.000+05:30

Merge two strings by taking characters alternatively from each string

Sample string 1 : PYTHON
Sample string 2 : python
Output: PpYyTtHhOoNn

Let us understand the two scenarios of this problem

Scenario1: If both the strings are of same length:

string1=input("Enter first string: ")
string2=input("Enter second string: ")
i,j=0,0 # object initialization using Tuple Unpacking
result=""
while i<len(string1) or j<len(string2):
	result=result+string1[i]+string2[j]
	i=i+1
	j=j+1
print("You entered first string : ",string1)
print("You entered second string : ",string2)
print("Final result after alternate merge: \n", result )

Scenario 2: If the strings have different length:

string1=input("Enter first string: ")
string2=input("Enter second string: ")
i,j=0,0 # object initialization using Tuple Unpacking
result=""
while i<len(string1) or j<len(string2):
	if i<len(string1):
		result=result+string1[i]
		i=i+1
	if j<len(string2):
		result=result+string2[j]
		j=j+1
print("You entered first string: ",string1)
print("You entered second string: ",string2)
print("Final result after alternate merge: \n", result )

Print characters present at even and odd index separately

2020-08-13T16:52:00.004+05:30

1st Way :

string_data=input("Enter a string: ")
print("Characters present at even index position: ")
i=0
while i<len(string_data):
	print(string_data[i])
	i=i+2
print("Characters present at odd index position: ")
i=1
while i<len(string_data):
	print(string_data[i])
	i=i+2

2nd Way:

string_data=input("Enter a string: ")
print("Characters present at even index position: ")
print(string_data[::2]) # or
print(string_data[0::2])
print("Characters present at odd index position: ")
print(string_data[1::2])

Reverse internal content of every second word

2020-08-13T15:09:00.002+05:30

Sample Input: one two three four five six seven eight

Output: one owt three ruof five xis seven thgie

string_data=input("Enter a string: ")
list1=string_data.split()
print(list1)
list2=[]
i=0
while i<len(list1):
	if i%2==0:
		list2.append(list1[i])
	else:
		list2.append(list1[i][::-1])# Reversing word and then adding to list2
	i=i+1
print(list2)
result=" ".join(list2)
print("The string you entered:", string_data, sep="\n")
print("The result after reversing order of words in the string: ", result, sep="\n")

Reverse the internal content of each word

2020-08-13T14:58:00.005+05:30

Sample Input: Hello Python

Output: olleH nohtyP

string_data=input("Enter a string: ")
list1=string_data.split()
print(list1)
list2=[]
for word in list1:
	list2.append(word[::-1]) # Reversing word and then adding to list2
print(list2)
result=" ".join(list2)
print("The string you entered:", string_data, sep="\n")
print("The result after reversing order of words in the string: ", result, sep="\n")

MV's Code Guide

Name Numerology

Letter Value Mapping: The same dictionary `letter_values` is used to map each letter to its corresponding value.

Convert to Lowercase: The input word is converted to lowercase.

Initialize Total: A variable `total` is initialized to store the sum of the values.

Iterate Over Characters: The program iterates over each character in the input word.

Check for Valid Characters: It checks if the character exists in the `letter_values` dictionary.

Calculate Value: For valid characters, it adds the corresponding value from the dictionary to the `total`.

Sum Digits Function: A nested function `sum_digits` is defined to sum the digits of a number repeatedly until a single-digit number is obtained.

Calculate Final Value: The `sum_digits` function is called with the total sum to get the final single-digit value.

Return Final Value: The function returns the final single-digit value.

This code will prompt the user to enter a word, calculate the sum of its letter values, and then reduce the sum to a single-digit number by repeatedly summing its digits

Password Generator

Value Convertor

User Management System

Rock Paper Scissors Game Using Python

Story Generator in Python

Using Random Module You Can Generate a random story

Pandas

Diwali Sales Analysis

House Price Prediction

Cars Price Prediction

Diabetes Prediction Using Python

Extract audio from video file

Writing text to an Image

Extract Text From Image Using Python

Spell Checker with Python

Counting vowels in a string

Counting number of occurrences of each character in a string

Removing duplicate characters from the string

Python Program for specific requirement-2

Python Program for specific requirements

Sort characters in a string

Merge two strings

Merge two strings by taking characters alternatively from each string

Print characters present at even and odd index separately

Reverse internal content of every second word

Reverse the internal content of each word

MV's Code Guide

Name Numerology

Letter Value Mapping: The same dictionary letter_values is used to map each letter to its corresponding value.

Convert to Lowercase: The input word is converted to lowercase.

Initialize Total: A variable total is initialized to store the sum of the values.

Iterate Over Characters: The program iterates over each character in the input word.

Check for Valid Characters: It checks if the character exists in the letter_values dictionary.

Calculate Value: For valid characters, it adds the corresponding value from the dictionary to the total.

Sum Digits Function: A nested function sum_digits is defined to sum the digits of a number repeatedly until a single-digit number is obtained.

Calculate Final Value: The sum_digits function is called with the total sum to get the final single-digit value.

Return Final Value: The function returns the final single-digit value.

This code will prompt the user to enter a word, calculate the sum of its letter values, and then reduce the sum to a single-digit number by repeatedly summing its digits

Password Generator

Value Convertor

User Management System

Rock Paper Scissors Game Using Python

Story Generator in Python

Using Random Module You Can Generate a random story

Pandas

Diwali Sales Analysis

House Price Prediction

Cars Price Prediction

Diabetes Prediction Using Python

Extract audio from video file

Writing text to an Image

Extract Text From Image Using Python

Spell Checker with Python

Counting vowels in a string

Counting number of occurrences of each character in a string

Removing duplicate characters from the string

Python Program for specific requirement-2

Python Program for specific requirements

Sort characters in a string

Merge two strings

Merge two strings by taking characters alternatively from each string

Print characters present at even and odd index separately

Reverse internal content of every second word

Reverse the internal content of each word

Letter Value Mapping: The same dictionary `letter_values` is used to map each letter to its corresponding value.

Initialize Total: A variable `total` is initialized to store the sum of the values.

Check for Valid Characters: It checks if the character exists in the `letter_values` dictionary.

Calculate Value: For valid characters, it adds the corresponding value from the dictionary to the `total`.

Sum Digits Function: A nested function `sum_digits` is defined to sum the digits of a number repeatedly until a single-digit number is obtained.

Calculate Final Value: The `sum_digits` function is called with the total sum to get the final single-digit value.