> ## Documentation Index > Fetch the complete documentation index at: https://ai.tharung.in/llms.txt > Use this file to discover all available pages before exploring further. # Matplotlib # Introduction to Matplotlib ## What is Data Visualization? Data Visualization is the process of converting raw data into charts, graphs, and plots to: * Understand patterns * Detect trends * Find outliers * Analyze relationships *** # What is Matplotlib? Matplotlib is a Python library used for creating: * Line charts * Histograms * Boxplots * Pie charts * Scatter plots * Bar charts * 3D plots Widely used in: * Data Science * Machine Learning * Analytics *** # Installing Matplotlib ## Installation ```bash theme={null} pip install matplotlib ``` *** # Importing Libraries ```python theme={null} import matplotlib.pyplot as plt import pandas as pd ``` ### Explanation * `matplotlib.pyplot` → plotting functions * `pandas` → data handling *** # Basic Plot Function ## `plt.plot()` Creates a line plot. ```python theme={null} x = [1,2,3] y = [4,5,6] plt.plot(x,y) plt.grid() plt.show() ``` ### Output A line graph connecting points: * (1,4) * (2,5) * (3,6) ### Explanation * `plot()` → creates line graph * `grid()` → adds background grid * `show()` → displays graph Basicline

*** # Components of a Figure | Component | Purpose | | :-------- | :-------------------- | | Figure | Entire canvas | | Axes | X and Y plotting area | | Title | Graph heading | | Labels | Axis descriptions | | Legend | Explains colors/lines | | Grid | Improves readability | | Ticks | Scale markings | *** # Univariate Analysis Analyzing one variable. *** # Line Plot Shows trends or changes in numerical data. ```python theme={null} plt.plot(df['Salary'], color='red', marker='o', linestyle=':', linewidth=2) plt.grid() plt.show() ``` ### Explanation * `color='red'` → line color * `marker='o'` → circle markers * `linestyle=':'` → dotted line * `linewidth=2` → line thickness ### Output Salary trend line graph. Lineplot

*** # Histogram ## `plt.hist()` Shows frequency distribution. ```python theme={null} plt.hist(df['Salary'], bins=5, color='green') plt.show() ``` ### Explanation * `bins=5` → divides data into 5 ranges * Shows how many values fall into each range ### Output Bar-like histogram distribution.

*** # Box Plot ## `plt.boxplot()` Used for: * detecting outliers * understanding spread * quartile analysis ```python theme={null} plt.boxplot(df['Salary']) plt.show() ``` ### Output Boxplot showing median and quartiles. Boxplot

*** # Outlier Detection ```python theme={null} df.loc[15] = [0] plt.boxplot(df['Salary']) plt.show() ``` ### Explanation Adding `0` creates an outlier visible in boxplot. *** # Categorical Analysis *** # Pie Chart Shows percentage contribution. ```python theme={null} count = df['dept'].value_counts() plt.pie(count, labels=count.index, autopct='%1.2f%%', explode=[0,0.1,0]) plt.axis('equal') plt.show() ``` ### Explanation * `labels` → category names * `autopct` → percentage display * `explode` → separates slice * `axis('equal')` → perfect circle ### Output Department percentage pie chart. Pie

*** # Count Plot / Bar Chart ## `plt.bar()` Shows category frequencies. ```python theme={null} plt.bar(count.index, count) plt.show() ``` ### Output Bar chart of department counts. Countplot25

*** # Bivariate Analysis Analyzing relationship between two variables. *** # Scatter Plot ## `plt.scatter()` Shows relationship between two numerical variables. ```python theme={null} plt.scatter(df['Salary'], df['age']) plt.show() ``` ### Explanation Each dot represents: * X → Salary * Y → Age ### Output Scatter plot showing salary vs age. Scatterplot26

*** # Sorted Line Plot ```python theme={null} sort_salary = df.sort_values('Salary') plt.plot(sort_salary['Salary'], df['age'], color='red', marker='o', linestyle=':') plt.grid() plt.show() ``` ### Explanation Sorting improves line continuity. Lineplot

*** # Bar Chart ```python theme={null} plt.bar(df['age'], df['Salary'], color='green') plt.show() ``` ### Explanation Compares salary for each age. Bar1

*** # Numerical vs Categorical Analysis *** # Multiple Boxplots ```python theme={null} plt.boxplot([hr_sal, it_sal, finance_sal], labels=['HR', 'IT', 'Finance']) plt.show() ``` ### Explanation Compares salary distributions across departments. ### Output Three side-by-side boxplots. Multibox

*** # Department Salary Pie Chart ```python theme={null} salary_by_dept = df.groupby('dept')['Salary'].sum() plt.pie(salary_by_dept, labels=salary_by_dept.index, autopct='%1.2f%%', explode=[0,0.1,0], shadow=True) plt.axis('equal') plt.show() ``` ### Explanation Shows total salary contribution by department. Pie2

*** # Mean Salary Bar Chart ```python theme={null} plt.bar(['HR','IT','Finance'], [hr_mean,it_mean,finance_mean]) plt.show() ``` ### Explanation Displays average salary per department. Meanbar

*** # Multivariate Analysis Analyzing 3 or more variables. *** # Bubble Plot ```python theme={null} plt.scatter(df['Salary'], df['age'], s=df['experience']*100) plt.title('Salary vs Age vs Experience') plt.xlabel('Salary') plt.ylabel('Age') plt.show() ``` ### Explanation * X → Salary * Y → Age * Bubble Size → Experience ### Output Bubble plot with varying circle sizes. Bubble

*** # Color-Based Scatter Plot ```python theme={null} plt.scatter(df['Salary'], df['age'], c=df['dept'].map({ 'HR':'red', 'IT':'green', 'Finance':'blue' })) plt.show() ``` ### Explanation Different colors represent departments. Colorscatter

*** # Scatter Plot with Legend ```python theme={null} color = {'HR':'red', 'IT':'green', 'Finance':'blue'} for dept in color: dept_data = df[df['dept'] == dept] plt.scatter(dept_data['Salary'], dept_data['age'], label=dept, color=color[dept]) plt.legend() plt.show() ``` ### Explanation Adds department-wise legend. Scatterlegend

*** # Object Oriented API Provides more control over plots. *** # `plt.subplots()` Creates multiple plots. ```python theme={null} fig, axs = plt.subplots(2,2, figsize=(10,10)) ``` ### Explanation * 2 rows * 2 columns * Figure size = 10x10 Download

*** # Multiple Plots ## Line Plot ```python theme={null} axs[0,0].plot(df['Salary'], color='red', marker='o', linestyle=':') axs[0,0].grid() ``` *** ## Histogram ```python theme={null} axs[0,1].hist(df['Salary'], bins=5, color='green') ``` Multiplot2

*** ## Boxplot ```python theme={null} axs[1,0].boxplot(df['Salary']) ``` *** # Saving Figures ## `savefig()` Saves plot locally. ```python theme={null} plt.savefig('plot.png') ``` ### Explanation Saves graph as PNG image. *** # Multiple Line Plots ```python theme={null} plt.plot(df2['Year'], df2['Sales'], label='Sales') plt.plot(df2['Year'], df2['Profit'], label='Profit') plt.plot(df2['Year'], df2['Expenses'], label='Expenses') plt.legend() plt.show() ``` ### Explanation Displays multiple lines in same graph. ### Output Sales, Profit, and Expenses comparison graph. Multilineplot

*** # 3D Plot ```python theme={null} ax = plt.figure().add_subplot(projection='3d') ax.scatter(df2['Year'], df2['Sales'], df2['Profit']) plt.show() ``` ### Explanation Creates 3D scatter plot. Axes: * X → Year * Y → Sales * Z → Profit

*** # Plotly 3D Plot ```python theme={null} import plotly.express as px fig = px.scatter_3d(df2, x='Year', y='Sales', z='Profit') fig.show() ``` ### Explanation Interactive 3D visualization using Plotly. I3d