Article all countries dataset visualizations and analysis with python:

# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Load your dataset
# You can load your dataset into a pandas DataFrame. Replace 'your_data.csv' with your actual data file.
countries = pd.read_csv('/content/drive/MyDrive/All Countries.csv')

# Data Overview
# Display basic information about the dataset
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Load your dataset
# Replace 'your_data.csv' with the actual path or URL of your dataset.
countries = pd.read_csv('/content/drive/MyDrive/All Countries.csv')

# Data Overview
# Display basic information about the dataset
countries = pd.read_csv('/content/drive/MyDrive/All Countries.csv')


# Summary statistics
# Get summary statistics for numerical columns
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Load your dataset
# Replace 'your_data.csv' with the correct file path or URL of your dataset.
try:
    data = pd.read_csv('your_data.csv')

    # Data Overview
    # Display basic information about the dataset
    print(data.info())

    # Summary statistics
    # Get summary statistics for numerical columns
    print(data.describe())

    # Missing Value Analysis
    # Check for missing values in the dataset
    missing_values = data.isnull().sum()
    print("Missing Values:\n", missing_values)

    # Visualization
    # Example: Plotting a histogram of agricultural land distribution
    plt.figure(figsize=(10, 6))
    sns.histplot(data['agricultural_land'], bins=20, kde=True)
    plt.title('Agricultural Land Distribution')
    plt.xlabel('Agricultural Land')
    plt.ylabel('Frequency')
    plt.show()

    # Additional visualization and analysis can be done for other columns as needed.

    # Export Data
    # If you want to save your cleaned and processed data to a new CSV file
    # data.to_csv('cleaned_data.csv', index=False)

    # Further analysis and visualization can be done based on your specific requirements.

except FileNotFoundError:
    print("File not found. Please provide the correct file path.")
except Exception as e:
    print(f"An error occurred: {str(e)}")


# Missing Value Analysis
# Check for missing values in the dataset
# Import necessary libraries
import pandas as pd

# Load your dataset
# Replace 'your_data.csv' with the correct file path or URL of your dataset.
try:
    countries = pd.read_csv('/content/drive/MyDrive/All Countries.csv')

    # Data Overview
    # Display basic information about the dataset
    print(data.info())

except FileNotFoundError:
    print("File not found. Please provide the correct file path.")
except Exception as e:
    print(f"An error occurred: {str(e)}")

# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Load your dataset
# Replace 'your_data.csv' with the correct file path or URL of your dataset.
try:
    data = pd.read_csv('your_data.csv')

    # Data Overview
    # Display basic information about the dataset
    print(data.info())

    # Summary statistics
    # Get summary statistics for numerical columns
    summary_stats = data.describe()
    print("Summary Statistics:\n", summary_stats)

    # Missing Value Analysis
    # Check for missing values in the dataset
    missing_values = data.isnull().sum()
    print("Missing Values:\n", missing_values)

    # Visualization
    # Example: Plotting a histogram of agricultural land distribution
    plt.figure(figsize=(10, 6))
    sns.histplot(data['agricultural_land'], bins=20, kde=True)
    plt.title('Agricultural Land Distribution')
    plt.xlabel('Agricultural Land')
    plt.ylabel('Frequency')
    plt.show()

    # Additional visualization and analysis can be done for other columns as needed.

    # Export Data
    # If you want to save your cleaned and processed data to a new CSV file
    # data.to_csv('cleaned_data.csv', index=False)

    # Further analysis and visualization can be done based on your specific requirements.

except FileNotFoundError:
    print("File not found. Please provide the correct file path.")
except Exception as e:
    print(f"An error occurred: {str(e)}")


# Visualization
# Example: Plotting a histogram of agricultural land distribution
plt.figure(figsize=(10, 6))
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

try:
    # Load your dataset
    # Replace 'your_data.csv' with the correct file path or URL of your dataset.
    data = pd.read_csv('your_data.csv')

    # Data Overview
    # Display basic information about the dataset
    print(data.info())

    # Summary statistics
    # Get summary statistics for numerical columns
    summary_stats = data.describe()
    print("Summary Statistics:\n", summary_stats)

    # Missing Value Analysis
    # Check for missing values in the dataset
    missing_values = data.isnull().sum()
    print("Missing Values:\n", missing_values)

    # Visualization
    # Example: Plotting a histogram of agricultural land distribution
    plt.figure(figsize=(10, 6))
    sns.histplot(data['agricultural_land'], bins=20, kde=True)
    plt.title('Agricultural Land Distribution')
    plt.xlabel('Agricultural Land')
    plt.ylabel('Frequency')
    plt.show()

    # Additional visualization and analysis can be done for other columns as needed.

    # Export Data
    # If you want to save your cleaned and processed data to a new CSV file
    # data.to_csv('cleaned_data.csv', index=False)

    # Further analysis and visualization can be done based on your specific requirements.

except FileNotFoundError:
    print("File not found. Please provide the correct file path.")
except Exception as e:
    print(f"An error occurred: {str(e)}")

plt.title('Agricultural Land Distribution')
plt.xlabel('Agricultural Land')
plt.ylabel('Frequency')
plt.show()

# Additional visualization and analysis can be done for other columns as needed.

# Export Data
# If you want to save your cleaned and processed data to a new CSV file
# data.to_csv('cleaned_data.csv', index=False)

# Further analysis and visualization can be done based on your specific requirements.

# Don't forget to install the required libraries if you haven't already:
# pip install pandas numpy matplotlib seaborn


# Summary statistics
# Get summary statistics for numerical columns
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Load your dataset
# Replace 'your_data.csv' with the correct file path or URL of your dataset.
try:
    data = pd.read_csv('your_data.csv')

    # Data Overview
    # Display basic information about the dataset
    print(data.info())

    # Summary statistics
    # Get summary statistics for numerical columns
    summary_stats = data.describe()
    print("Summary Statistics:\n", summary_stats)

    # Missing Value Analysis
    # Check for missing values in the dataset
    missing_values = data.isnull().sum()
    print("Missing Values:\n", missing_values)

    # Visualization
    # Example: Plotting a histogram of agricultural land distribution
    plt.figure(figsize=(10, 6))
    sns.histplot(data['agricultural_land'], bins=20, kde=True)
    plt.title('Agricultural Land Distribution')
    plt.xlabel('Agricultural Land')
    plt.ylabel('Frequency')
    plt.show()

    # Additional visualization and analysis can be done for other columns as needed.

    # Export Data
    # If you want to save your cleaned and processed data to a new CSV file
    # data.to_csv('cleaned_data.csv', index=False)

    # Further analysis and visualization can be done based on your specific requirements.

except FileNotFoundError:
    print("File not found. Please provide the correct file path.")
except Exception as e:
    print(f"An error occurred: {str(e)}")


# Missing Value Analysis
# Check for missing values in the dataset
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Load your dataset
# Replace 'your_data.csv' with the correct file path or URL of your dataset.
try:
    data = pd.read_csv('your_data.csv')

    # Data Overview
    # Display basic information about the dataset
    print(data.info())

    # Summary statistics
    # Get summary statistics for numerical columns
    summary_stats = data.describe()
    print("Summary Statistics:\n", summary_stats)

    # Missing Value Analysis
    # Check for missing values in the dataset
    missing_values = data.isnull().sum()
    print("Missing Values:\n", missing_values)

    # Visualization
    # Example: Plotting a histogram of agricultural land distribution
    plt.figure(figsize=(10, 6))
    sns.histplot(data['agricultural_land'], bins=20, kde=True)
    plt.title('Agricultural Land Distribution')
    plt.xlabel('Agricultural Land')
    plt.ylabel('Frequency')
    plt.show()

    # Additional visualization and analysis can be done for other columns as needed.

    # Export Data
    # If you want to save your cleaned and processed data to a new CSV file
    # data.to_csv('cleaned_data.csv', index=False)

    # Further analysis and visualization can be done based on your specific requirements.

except FileNotFoundError:
    print("File not found. Please provide the correct file path.")
except Exception as e:
    print(f"An error occurred: {str(e)}")

# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Load your dataset
# Replace 'your_data.csv' with the correct file path or URL of your dataset.
try:
    data = pd.read_csv('your_data.csv')

    # Data Overview
    # Display basic information about the dataset
    print(data.info())

    # Summary statistics
    # Get summary statistics for numerical columns
    summary_stats = data.describe()
    print("Summary Statistics:\n", summary_stats)

    # Missing Value Analysis
    # Check for missing values in the dataset
    missing_values = data.isnull().sum()
    print("Missing Values:\n", missing_values)

    # Visualization
    # Example: Plotting a histogram of agricultural land distribution
    plt.figure(figsize=(10, 6))
    sns.histplot(data['agricultural_land'], bins=20, kde=True)
    plt.title('Agricultural Land Distribution')
    plt.xlabel('Agricultural Land')
    plt.ylabel('Frequency')
    plt.show()

    # Additional visualization and analysis can be done for other columns as needed.

    # Export Data
    # If you want to save your cleaned and processed data to a new CSV file
    # data.to_csv('cleaned_data.csv', index=False)

    # Further analysis and visualization can be done based on your specific requirements.

except FileNotFoundError:
    print("File not found. Please provide the correct file path.")
except Exception as e:
    print(f"An error occurred: {str(e)}")


# Visualization
# Let's assume you want to visualize some aspects of the data.
# Example: Plotting a histogram of agricultural land distribution
plt.figure(figsize=(10, 6))
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

try:
    # Load your dataset
    # Replace 'your_data.csv' with the correct file path or URL of your dataset.
    data = pd.read_csv('your_data.csv')

    # Data Overview
    # Display basic information about the dataset
    print(data.info())

    # Summary statistics
    # Get summary statistics for numerical columns
    summary_stats = data.describe()
    print("Summary Statistics:\n", summary_stats)

    # Missing Value Analysis
    # Check for missing values in the dataset
    missing_values = data.isnull().sum()
    print("Missing Values:\n", missing_values)

    # Additional analysis and visualization
    # ...

except FileNotFoundError:
    print("File not found. Please provide the correct file path.")
except Exception as e:
    print(f"An error occurred: {str(e)}")

# Visualization (outside the try-except block)
# Example: Plotting a histogram of agricultural land distribution
plt.figure(figsize=(10, 6))
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

try:
    # Load your dataset
    # Replace 'your_data.csv' with the correct file path or URL of your dataset.
    data = pd.read_csv('your_data.csv')

    # Data Overview
    # Display basic information about the dataset
    print(data.info())

    # Summary statistics
    # Get summary statistics for numerical columns
    summary_stats = data.describe()
    print("Summary Statistics:\n", summary_stats)

    # Missing Value Analysis
    # Check for missing values in the dataset
    missing_values = data.isnull().sum()
    print("Missing Values:\n", missing_values)

    # Additional analysis and visualization
    # ...

except FileNotFoundError:
    print("File not found. Please provide the correct file path.")
except Exception as e:
    print(f"An error occurred: {str(e)}")

# Visualization (outside the try-except block)
# Example: Plotting a histogram of agricultural land distribution
plt.figure(figsize=(10, 6))
import seaborn as sns
import matplotlib.pyplot as plt

# Example: Plotting a histogram of agricultural land distribution
plt.figure(figsize=(10, 6))
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Load your dataset
# Replace 'your_data.csv' with the correct file path or URL of your dataset.
try:
    data = pd.read_csv('your_data.csv')

    # Data Overview
    # Display basic information about the dataset
    print(data.info())

    # Summary statistics
    # Get summary statistics for numerical columns
    summary_stats = data.describe()
    print("Summary Statistics:\n", summary_stats)

    # Missing Value Analysis
    # Check for missing values in the dataset
    missing_values = data.isnull().sum()
    print("Missing Values:\n", missing_values)

except FileNotFoundError:
    print("File not found. Please provide the correct file path.")
except Exception as e:
    print(f"An error occurred: {str(e)}")

# Visualization (moved outside the try-except block)
if 'data' in locals():
    # Example: Plotting a histogram of agricultural land distribution
    plt.figure(figsize=(10, 6))
    sns.histplot(data['agricultural_land'], bins=20, kde=True)
    plt.title('Agricultural Land Distribution')
    plt.xlabel('Agricultural Land')
    plt.ylabel('Frequency')
    plt.show()

    # Additional visualization and analysis can be done here.
else:
    print("Data not loaded. Please check the data loading process.")

plt.title('Agricultural Land Distribution')
plt.xlabel('Agricultural Land')
plt.ylabel('Frequency')
plt.show()

plt.title('Agricultural Land Distribution')
plt.xlabel('Agricultural Land')
plt.ylabel('Frequency')
plt.show()

# Additional visualization and analysis can be done here.

plt.title('Agricultural Land Distribution')
plt.xlabel('Agricultural Land')
plt.ylabel('Frequency')
plt.show()

# Additional visualization and analysis can be done here.

plt.title('Agricultural Land Distribution')
plt.xlabel('Agricultural Land')
plt.ylabel('Frequency')
plt.show()

# Additional visualization and analysis can be done for other columns as needed.

# Export Data
# If you want to save your cleaned and processed data to a new CSV file
# data.to_csv('cleaned_data.csv', index=False)

# Further analysis and visualization can be done based on your specific requirements.

# Don't forget to install the required libraries if you haven't already:
# pip install pandas numpy matplotlib s