pip install seaborn

Collecting seaborn
  Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)
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Downloading seaborn-0.13.2-py3-none-any.whl (294 kB)
Installing collected packages: seaborn
Successfully installed seaborn-0.13.2
Note: you may need to restart the kernel to use updated packages.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Enable inline plotting
%matplotlib inline

data = {
    "Name": ["Alice", "Bob", "Charlie", "David", "Eva", "Frank", "Grace", "Henry"],
    "Age": [25, 30, np.nan, 45, 28, 33, 38, np.nan],
    "Salary": [50000, 60000, 55000, 65000, np.nan, 70000, 62000, 58000],
    "Department": ["HR", "Finance", "IT", "Finance", "HR", "IT", "IT", "HR"]
}
df = pd.DataFrame(data)
df

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8 entries, 0 to 7
Data columns (total 4 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   Name        8 non-null      object 
 1   Age         6 non-null      float64
 2   Salary      7 non-null      float64
 3   Department  8 non-null      object 
dtypes: float64(2), object(2)
memory usage: 388.0+ bytes

df.isnull().sum()

Name          0
Age           2
Salary        1
Department    0
dtype: int64

df['Age'] = df['Age'].fillna(df['Age'].median())
df

df['Salary'] = df['Salary'].fillna(df['Salary'].mean())
df

df.describe()

sns.histplot(df['Age'], kde=True, bins=5, color='skyblue')
plt.title("Age Distribution")
plt.xlabel("Age")
plt.ylabel("Count")
plt.show()

sns.countplot(x="Department", data=df, palette="viridis")
plt.title("Department Count")
plt.show()

C:\Users\HP\AppData\Local\Temp\ipykernel_1960\1748937494.py:1: FutureWarning:

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x="Department", data=df, palette="viridis")

sns.boxplot(x="Department", y="Salary", data=df)
plt.title("Salary Distribution by Department")
plt.show()

df['Experience'] = [2, 5, 3, 10, 4, 8, 6, 7]
df

df.corr(numeric_only=True)

df.corr(numeric_only=True)

sns.heatmap(df.corr(numeric_only=True), annot=True, cmap='coolwarm')
plt.title("Correlation Heatmap")
plt.show()

df[df['Salary'] > 60000]

df.groupby('Department')['Salary'].mean()

Department
Finance    62500.000000
HR         56000.000000
IT         62333.333333
Name: Salary, dtype: float64

plt.scatter(df['Age'], df['Salary'], color='green')
plt.xlabel('Age')
plt.ylabel('Salary')
plt.title('Age vs Salary')
plt.grid(True)
plt.show()

df['Department'].value_counts().plot.pie(autopct='%1.1f%%', startangle=90)
plt.title('Department Distribution')
plt.ylabel('')
plt.show()

sns.lineplot(x='Experience', y='Salary', data=df, marker='o')
plt.title("Salary vs Experience")
plt.show()

df['Bonus'] = df['Salary'] * 0.1
df

df.head()

df.to_csv("cleaned_employee_data.csv", index=False)
print("Saved to cleaned_employee_data.csv")

Saved to cleaned_employee_data.csv