daily-coding-log

class Solution { public boolean isPalindrome(int x) { if(x<0){ return false; } int rev=0; int num=x;

    while(num!=0){
        rev=rev*10 + num%10;
        num=num/10;
    }

    return (rev==x);
    
}

}

class Solution { public void reverseString(char[] s) { int l=0; int r=s.length-1; while(l<r){ char temp=s[l]; s[l]=s[r]; s[r]=temp;

        l++;
        r--;
    }
    
}

}

print('hello world')

int a if (a%2==0): print('even') else: print ('false')

a = 5 b = 3 sum = a + b print("Sum:", sum)

arithmetic operator in python hacker rank :- if name == 'main': a = int(input()) b = int(input())

print(a+b)
print(a-b)
print(a*b)

print ("hello world") print ("hello world")

a = int(input("Enter first number: ")) b = int(input("Enter second number: "))

print("1.Add 2.Subtract 3.Multiply 4.Divide") choice = int(input("Enter choice: "))

if choice == 1: print("Result:", a + b) elif choice == 2: print("Result:", a - b) elif choice == 3: print("Result:", a * b) elif choice == 4: print("Result:", a / b) else: print("Invalid choice")

import pandas as pd

employees=pd.DataFrame({ 'employees_id':[501,502,503,504], 'name':['A','B','C','D'], 'department_id':[1,2,3,4] })

employees.to_excel('employees.xlsx', index=False)

salaries=pd.DataFrame({ 'employees_id':[501,502,503,504], 'salaries':[5000,6000,7000,8000] })

salaries.to_csv('salaries.csv',index=False)

employees.shape

employees.columns

employees.isnull().sum()

salaries.columns

employees.tail()

employees.info()

salaries.describe()

import pandas as pd import json

departments = [ {"department_id": 1, "department_name": "HR"}, {"department_id": 2, "department_name": "Finance"}, {"department_id": 3, "department_name": "IT"} ]

with open('departments.json', 'w') as f: json.dump(departments, f)

print("department.json created successfully.")

dept_df=pd.read_json('departments.json')

print(dept_df)

print(set(employees['employees_id']).intersection(set(salaries['employees_id']))) print("common employe ID's bw employees & salaries")

merged_df=pd.merge(employees,salaries,on='employees_id', how='inner') print("after merging employees & salaries") print(merged_df)

final_df=pd.merge(merged_df,dept_df,on='department_id',how='inner')

print('final merged dataframe') print(final_df)

final_df.to_csv('final_df.csv',index=False)

print('final_df.csv created successfully')

import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_iris

#need to load datasets

iris = load_iris() df= pd.DataFrame(iris.data, columns=iris.feature_names)

df.shape

df['species']=iris.target

Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)', 'species'], dtype='object')

descriptive statistics we r going to do here

print("mean :",df.mean(numeric_only=True))

print("median :" , df.median(numeric_only=True))

print("dt_deviation :", df.std(numeric_only=True))

print("kurtosis :" , df.kurtosis(numeric_only=True))

print("skewness :", df.skew(numeric_only=True))

boxplot :

plt.figure(figsize=(8,5)) df.drop('species', axis =1).boxplot() plt.title("boxplot of iris features") plt.show()

#histogram for saple length

plt.figure(figsize=(6,4)) plt.hist(df['sepal length (cm)'], bins=10) plt.title("histogram of sepal length") plt.xlabel("sepal length") plt.ylabel("frequency") plt.show()

plt.figure(figsize=(6,4)) plt.hist(df['petal width (cm)'], bins=10) plt.title("histogram of petal width") plt.xlabel("petal width") plt.ylabel("frequency") plt.show()

#Import required libraries #SEMMA - Sample/Access, Explore, Modify, Model, Assessment #Data management libraries (Sample, Modify) import pandas as pd import numpy as np from sklearn.feature_extraction.text import CountVectorizer from sklearn.model_selection import train_test_split

#Visualization import seaborn as sns import matplotlib.pyplot as plt

#Modeling from sklearn.naive_bayes import MultinomialNB from sklearn.svm import LinearSVC

#Assessment/Evaluation from sklearn.metrics import classification_report, confusion_matrix

#Accessing/sampling the data

Replace 'spam_dataset.csv' with the path to your actual file

file_path = 'C:/Users/hansi/Downloads/4th sem minor project/spam.csv'

Read the CSV file

df = pd.read_csv(file_path, encoding='latin-1')

#Explore df.sample(10)

#Modify the data df.columns

df = df[['Label', 'tmessage']]

df1.sample()

Tokenize using lambda function (splitting by whitespace)

df['Tokens'] = df['Message'].apply(lambda x: x.split())

Target variable

y = df['Label']

Initialize CountVectorizer

vectorizer = CountVectorizer()

Feature matrix from 'Message' column

X = vectorizer.fit_transform(df['Message'])

Show shape of feature matrix

print(f"Feature matrix shape: {X.shape}") print(f"Target variable shape: {y.shape}")

print("hello world")

print ("hello world")

import seaborn as sns import matplotlib.pyplot as plt

scatter plot

plt.figure(figsize=(6,4)) plt.scatter(df['sepal width (cm)'], df['petal width (cm)']) plt.xlabel("sepal width (cm)") plt.ylabel('petal width (cm)') plt.title('scatter plot : sepal width vs petal width') plt.show()

#heatmap for correlation : it means correlation of feature plt.figure(figsize=(7,5)) correlation = df.drop('species', axis =1).corr() sns.heatmap(correlation, annot=True, cmap='coolwarm') plt.title('heatmap of feature correlation') plt.show()

print(" u can fo this")

print ('hello world ')