# Matplotlib

## Introduction

1. All other data visualization libraries are built on matplotlib
    
2. Types Of Data
    
    1. Categorical Data: Data that can be categorized / groups can be made
        
    2. Numerical Data: Data that cannot be categorized / cannot be grouped
        
3. Types Of Analysis
    
    1\. Univariate Analysis ---- analyzing single columns
    
    2\. Bivariate analysis --- analyzing two columns
    
    3\. Multivariate Analysis --analyzing multiple columns ---- more than 2
    

## 2-d Line plot

1. Used for bivariate Analysis
    
2. Type Of Data
    
    1\. Numerical - Numerical --- In most case , we do this
    
    2\. Categorical - Numerical
    
3. Used in time series data ---- data measure with respect to time ---most popular use case
    
    example:
    
    ```python-repl
    plt.plot()
    ```
    
4. Giving title and x-axis name and y-axis name
    
    ```python-repl
    plt.title('Rohit Sharma Vs Virat Kohli Career Comparison')
    ## will add the title to entire table
    plt.xlabel('Season')
    plt.ylabel('Runs Scored')
    ```
    
5. Adding Color
    
    ```python-repl
    # colors(hex) and line(width and style) and marker(size)
    plt.plot(batsman['index'],batsman['V Kohli'],color='#D9F10F')
    plt.plot(batsman['index'],batsman['RG Sharma'],color='#FC00D6')
    
    plt.title('Rohit Sharma Vs Virat Kohli Career Comparison')
    plt.xlabel('Season')
    plt.ylabel('Runs Scored')
    ```
    
6. Change Line style
    
    ```python-repl
    plt.plot(batsman['index'],batsman['V Kohli'],color='#D9F10F',linestyle='solid',linewidth=3)
    plt.plot(batsman['index'],batsman['RG Sharma'],color='#FC00D6',linestyle='dashdot',linewidth=2)
    
    plt.title('Rohit Sharma Vs Virat Kohli Career Comparison')
    plt.xlabel('Season')
    plt.ylabel('Runs Scored')
    ```
    
7. Marker
    
    A 2d plot is drawn by marking the x & y co-ordinate and then join using a line
    
8. Changing Marker and line-Width & controlling marker size
    
    ```python-repl
    plt.plot(batsman['index'],batsman['V Kohli'],color='#D9F10F',linestyle='solid',linewidth=3,marker='D',markersize=10)
    plt.plot(batsman['index'],batsman['RG Sharma'],color='#FC00D6',linestyle='dashdot',linewidth=2,marker='o')
    
    plt.title('Rohit Sharma Vs Virat Kohli Career Comparison')
    plt.xlabel('Season')
    plt.ylabel('Runs Scored')
    ```
    
9. Giving a label to each line
    
    ```python-repl
    # legend -> location
    plt.plot(batsman['index'],batsman['V Kohli'],color='#D9F10F',linestyle='solid',linewidth=3,marker='D',markersize=10,label='Virat')
    plt.plot(batsman['index'],batsman['RG Sharma'],color='#FC00D6',linestyle='dashdot',linewidth=2,marker='o',label='Rohit')
    
    plt.title('Rohit Sharma Vs Virat Kohli Career Comparison')
    plt.xlabel('Season')
    plt.ylabel('Runs Scored')
    
    plt.legend(loc='upper right')## this will prin the label and loc can used to change the position of the legend
    ```
    
10. Changing the limit of values
    
    ```python-repl
    # limiting axes
    price = [48000,54000,57000,49000,47000,45000,4500000]
    year = [2015,2016,2017,2018,2019,2020,2021]
    
    plt.plot(year,price)
    plt.ylim(0,75000)
    plt.xlim(2017,2019)
    ```
    
11. Switching On Grids
    
    ```python-repl
    plt.grid() # grids get activated 
    plt.show()
    ```
    

## Scatter Plots

1. Bivariate Analysis
    
2. numerical vs numerical
    
3. Used for finding the co-relation between two quantities
    
    ```python-repl
    plt.scatter()
    ```
    
4. 3-d plot using scatter
    
    ```python-repl
    # size
    tips = sns.load_dataset('tips')
    
    
    # slower
    plt.scatter(tips['total_bill'],tips['tip'],s=tips['size']*20)
    ```
    

## Bar Chart

1. x-axisCategorical vs y-axis aggregate function
    
2. horizontal bars-- more number of categories (greater than 5 )
    
    ```python-repl
    # horizontal bar chart
    plt.barh(colors,children,color='black')
    ## does not work well for multiple bar chart
    ```
    

## Histogram

1. Used for univariate analysis
    
2. Numerical Column
    
3. Used for frequency count