matplotlib.md

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Matplotlib

Matplotlib is a popular Python library for creating various types of plots and charts, which is essential for visualizing data during the Exploratory Data Analysis (EDA) process. Here are some common Matplotlib syntax and functions suitable for EDA:

1. Importing Matplotlib:

   • Import Matplotlib's pyplot module:

     import matplotlib.pyplot as plt

2. Basic Plot:

   • Create a simple line plot:

     plt.plot(x, y)

3. Scatter Plot:

   • Create a scatter plot to visualize relationships:

     plt.scatter(x, y)

4. Bar Chart:

   • Generate a bar chart for categorical data:

     plt.bar(categories, values)

5. Histogram:

   • Create a histogram to visualize data distribution:

     plt.hist(data, bins=10)

6. Box Plot:

   • Generate a box plot to display data quartiles and outliers:

     plt.boxplot(data)

7. Pie Chart:

   • Create a pie chart to represent data proportions:

     plt.pie(sizes, labels=labels, autopct='%1.1f%%')

8. Heatmap:

   • Display a heatmap for correlation matrices:

     plt.imshow(correlation_matrix, cmap='coolwarm')
     plt.colorbar()

9. Customizing Plots:

   • Customize plot labels, titles, legends, and colors:

     plt.xlabel('X-axis label')
     plt.ylabel('Y-axis label')
     plt.title('Plot Title')
     plt.legend(['Legend 1', 'Legend 2'])
     plt.color='blue'

10. Subplots:

    • Create multiple subplots within a single figure:

      plt.subplot(2, 2, 1)  # 2x2 grid, first subplot
      plt.plot(x1, y1)
      plt.subplot(2, 2, 2)  # 2x2 grid, second subplot
      plt.plot(x2, y2)

11. Saving Plots:

    • Save plots as image files (e.g., PNG, SVG):

      plt.savefig('plot.png')

12. Displaying Plots:

    • Display the plots in the Jupyter Notebook or Python script:

      plt.show()

Example

import pandas as pd
import matplotlib.pyplot as plt

# URL of the Titanic dataset
url = 'https://raw.githubusercontent.com/drshahizan/dataset/main/titanic/train.csv'

# Load the dataset
titanic_data = pd.read_csv(url)

# Display the first 5 rows
print(titanic_data.head())

# Plotting the distribution of passengers' ages
plt.figure(figsize=(10, 6))
plt.hist(titanic_data['Age'].dropna(), bins=30, edgecolor='k', alpha=0.7)
plt.title('Distribution of Passengers\' Ages')
plt.xlabel('Age')
plt.ylabel('Number of Passengers')
plt.grid(True)
plt.show()

# Plotting the survival rate by gender
survival_by_gender = titanic_data.groupby('Sex')['Survived'].mean()
survival_by_gender.plot(kind='bar', color=['blue', 'pink'], alpha=0.7)
plt.title('Survival Rate by Gender')
plt.xlabel('Gender')
plt.ylabel('Survival Rate')
plt.xticks(rotation=0)
plt.grid(True)
plt.show()

# Plotting the survival rate by class
survival_by_class = titanic_data.groupby('Pclass')['Survived'].mean()
survival_by_class.plot(kind='bar', color='green', alpha=0.7)
plt.title('Survival Rate by Class')
plt.xlabel('Class')
plt.ylabel('Survival Rate')
plt.xticks(rotation=0)
plt.grid(True)
plt.show()

Matplotlib offers a wide range of customization and plot types, making it a versatile tool for visualizing and exploring data during the EDA process. You can use these Matplotlib syntax and functions to create various types of plots and gain insights into your dataset.

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