Data Analysis Using Python(EDA, Univariate, bivariate)

 Collection of COmmonly used codes for Exploratory Analysis, Focusing on Univariate, bivariate  and general university analysis tasks. 

These examples used Python with Popular Libraries like Pandas, Numpy, Matplotlib, seaborn.

Lets get started with the EDA:

1. Exploratory Data Analysis (EDA):

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

#LOAD DATA SET

df= pd.read_csv)'data.csv')

#Display Information

df.head()                  #First 5 records

df.tail()                    #Last 5 records

df.info()                   #Summary of the dataset

df.describe               #Statistical summary of the data)

#CHecking for missing Values

df.isnull().sum()     #Give the details of the null values

#VIsualise the missing Values:

sns.heatmap(df.isnull(), cbar=False, cmap="virdis")

plt.title("Missing Vales HeatMap")

plt.show()

#Handling Missing Values:

#Filling the missing Value with Median

df.['column_name'].fillna(df['column_name'].median(), inplace=True)

#Drop rows with missing values

df.dropna(inplace=True)

#Data Distribution

#Histogram

sns.histplot(df['column_name'], bins=30, kde= True)

plt.title("DIstribution if Column Name")

plt.show()

#Boxplot

sns.boxplot(x=df['column_name']

plt.title("Box plot of column Name")

plt.show()

#Outlier detection

#using IQR method

Q1 =  df['column_name'].quantile(0.25)

Q3= df['Column_name'].quantile(0.75)

IQR= Q3-Q1

outliers =  df[(df['column_name] < Q1-1.5 *IQR) | (df['column_name'] > Q3 +1.5 *IQR)]

print("No of outliers:", len(outliers))

2. University Analysis commonly used codes

# Analysing Student Demographics

#coountplot for categorical variables (gender, course)

sns.countplot(x='gender', data=df)

plt.title("Gender Distribution")

plt.show()

sns.countplot(x= 'course', data =df,order=df['course'].value_counts().index)

plt.title("COurse populatory")

plt.xticke(rotation=45)

plt.show()

#Analyzing scores

#Distrubution of scores:

sns.histplot(df['score'], bins=20, kde=True)

plt.title("Score Distribution")

plt.show()

#top 10 students based on scores

top_students = df.nlargest(10,'score')

print(top_students[['name','score']])

#Exam performance Analysis:

#Average score by course:

avg_score = df.groupby('cpurse)['score'].mean().sort_values(ascending=False)

print(avg_score)

#visualization:

avg_score.plot(kind='bar',figsize=(10,6))

plt.title("Average scores by course")

plt.ylabel("AVerage score")
plt.show()

3.Bivariate Analysis COmmonly codes:

#Correlation Analysis:

#correlation heatmap

correlation= df.corr()

sns.heatmap(correlation, annot=True,cmap='coolwarm")

plt.title("Correlation Heatmap")
plt.show()

#Scatter Plots

 sns.scatterplot(x='hours_studies', y='score;, data=df)

plt.title("hours studies Vs Score")
plt.show()

, kde=True