Graph distribution of resources

draw.py

import pandas as pd
import matplotlib.pyplot as plt

# Load the CSV file
file_path = '/mnt/data/resources(2).csv'
data = pd.read_csv(file_path)

# Calculate the number of resources per region
resources_per_region = data['Region'].value_counts().reset_index()
resources_per_region.columns = ['Region', 'Number of Resources']

# Add the Resources column with the link
resources_per_region['Resources'] = resources_per_region['Region'].apply(
    lambda region: f'https://eu-west-1.console.aws.amazon.com/resource-groups/tag-editor/find-resources?region=eu-west-1#query=regions:!%28{region}%29,resourceTypes:!%28%27AWS::AllSupported%27%29,tagFilters:!%28%29,type:TAG_EDITOR_1_0'
)

# Display the dataframe to the user (this step is done using ace_tools, which might not be available in standard Python environments)
import ace_tools as tools; tools.display_dataframe_to_user(name="Resources per Region with Updated Links", dataframe=resources_per_region)

# Filter data for eu-west-1 and us-east-1
eu_west_1_data = data[data['Region'] == 'eu-west-1']
us_east_1_data = data[data['Region'] == 'us-east-1']

# Calculate the distribution of resource types for each region
eu_west_1_distribution = eu_west_1_data['Type'].value_counts()
us_east_1_distribution = us_east_1_data['Type'].value_counts()

# Plot horizontal bar chart for eu-west-1 with numbers next to bars
fig, ax = plt.subplots(figsize=(12, 16))
bars = ax.barh(eu_west_1_distribution.index, eu_west_1_distribution.values, color='skyblue')
ax.set_title('Distribution of Resource Types in eu-west-1')
ax.set_xlabel('Number of Resources')
ax.set_ylabel('Resource Type')
for bar in bars:
    width = bar.get_width()
    ax.text(width + 5, bar.get_y() + bar.get_height()/2, f'{int(width)}', va='center')
plt.tight_layout()
plt.show()

# Plot horizontal bar chart for us-east-1 with numbers next to bars
fig, ax = plt.subplots(figsize=(12, 16))
bars = ax.barh(us_east_1_distribution.index, us_east_1_distribution.values, color='lightgreen')
ax.set_title('Distribution of Resource Types in us-east-1')
ax.set_xlabel('Number of Resources')
ax.set_ylabel('Resource Type')
for bar in bars:
    width = bar.get_width()
    ax.text(width + 5, bar.get_y() + bar.get_height()/2, f'{int(width)}', va='center')
plt.tight_layout()
plt.show()

# Filter data for regions other than eu-west-1 and us-east-1
other_regions_data = data[~data['Region'].isin(['eu-west-1', 'us-east-1'])]

# Get unique regions from the filtered data
other_regions = other_regions_data['Region'].unique()

# Create a subplot for each region
fig, axes = plt.subplots(nrows=len(other_regions), ncols=1, figsize=(12, 5 * len(other_regions)))

# Plot for each region
for ax, region in zip(axes, other_regions):
    region_data = other_regions_data[other_regions_data['Region'] == region]
    region_distribution = region_data['Type'].value_counts()
    bars = ax.barh(region_distribution.index, region_distribution.values, color='skyblue')
    ax.set_title(f'Distribution of Resource Types in {region}')
    ax.set_xlabel('Number of Resources')
    ax.set_ylabel('Resource Type')
    for bar in bars:
        width = bar.get_width()
        ax.text(width + 5, bar.get_y() + bar.get_height()/2, f'{int(width)}', va='center')

plt.tight_layout()
plt.show()