MM - Data Visualization using Python Line plot, Scatter plot, Histogram, Density plot, Box- plot

Creating a mind map involves identifying key concepts and connecting them in a way that visually represents their relationships. Here are the keywords and short sentences for each of the visualization techniques:

Data Visualization Using Python Mind Map

Central Theme: Data Visualization Using Python

1. Line Plot

• Purpose: Trends over time
• Key Features: Time-series data, Continuous data, Trend identification
• Implementation: plt.plot(), marker='o'
• Example: Sales over years

2. Scatter Plot

• Purpose: Relationship between variables
• Key Features: Correlation, Patterns, Outliers
• Implementation: plt.scatter()
• Example: Height vs Weight

3. Histogram

• Purpose: Distribution of data
• Key Features: Frequency, Bins, Distribution shape
• Implementation: plt.hist(), bins=5
• Example: Age distribution

4. Density Plot

• Purpose: Probability density function
• Key Features: Smooth curve, Distribution shape, Kernel Density Estimation (KDE)
• Implementation: sns.kdeplot(), shade=True
• Example: Age density plot

5. Box Plot

• Purpose: Distribution summary
• Key Features: Five-number summary, Outliers, IQR (Interquartile Range)
• Implementation: sns.boxplot()
• Example: Values by category

Example Keywords and Short Sentences for Mind Map

Line Plot

• Trends: Show trends over time.
• Time-series: Use for time-series data.
• Trend Identification: Identify trends, fluctuations.
• Matplotlib: plt.plot(), markers.

Scatter Plot

• Relationship: Show relationships between variables.
• Correlation: Identify correlations and patterns.
• Outliers: Detect outliers.
• Matplotlib: plt.scatter().

Histogram

• Distribution: Display data distribution.
• Frequency: Frequency of data within bins.
• Bins: Choice of bin width.
• Matplotlib: plt.hist(), edgecolor.

Density Plot

• Probability Density: Estimate probability density function.
• Smooth Curve: Visualize data distribution.
• KDE: Kernel Density Estimation.
• Seaborn: sns.kdeplot(), shading.

Box Plot

• Distribution Summary: Summarize data distribution.
• Five-number Summary: Min, Q1, Median, Q3, Max.
• Outliers: Identify outliers.
• Seaborn: sns.boxplot(), categories.

Mind Map Structure

To create a mind map, start with the central theme "Data Visualization Using Python" at the center. Branch out to each of the five visualization techniques, then further branch out to their purpose, key features, implementation details, and examples.

Central Node:

• Data Visualization Using Python

Branches:

1. Line Plot

   • Purpose: Trends over time
   • Key Features: Time-series data, Continuous data, Trend identification
   • Implementation: plt.plot(), marker='o'
   • Example: Sales over years

2. Scatter Plot

   • Purpose: Relationship between variables
   • Key Features: Correlation, Patterns, Outliers
   • Implementation: plt.scatter()
   • Example: Height vs Weight

3. Histogram

   • Purpose: Distribution of data
   • Key Features: Frequency, Bins, Distribution shape
   • Implementation: plt.hist(), bins=5
   • Example: Age distribution

4. Density Plot

   • Purpose: Probability density function
   • Key Features: Smooth curve, Distribution shape, Kernel Density Estimation (KDE)
   • Implementation: sns.kdeplot(), shade=True
   • Example: Age density plot

5. Box Plot

   • Purpose: Distribution summary
   • Key Features: Five-number summary, Outliers, IQR (Interquartile Range)
   • Implementation: sns.boxplot()
   • Example: Values by category

This structure will help in recalling and organizing the information effectively through visual representation.