Energy data analysis extracts statistical production information from Excel files using various charts and visualization tools for examination and presentation.

Data Analysis and Visualization

Line Chart Analysis

Line charts display energy metric values across different months, showing trends over time. This visualization helps identify seasonal patterns or growth trends in production data. The x-axis represents time, while the y-axis shows numerical values, with each line corresponding to a specific energy metric.

import matplotlib.pyplot as plt

# Generate line chart
energy_df.plot(kind='line', title='Energy Metric Trends Over Time', figsize=(12, 7))
plt.xlabel('Time Period')
plt.ylabel('Metric Value')
plt.grid(True)
plt.show()

Bar Chart Analysis

Bar charts emphasize specific values for each time period, enabling quick comparisons between months and identification of high and low production periods.

# Generate bar chart
energy_df.plot(kind='bar', title='Monthly Energy Metric Values', figsize=(12, 7))
plt.xlabel('Time Period')
plt.ylabel('Metric Value')
plt.xticks(rotation=45)
plt.show()

Pie Chart Analysis

Pie charts illustrate the proportion of crude oil production for specific months relative to total production, showing contribution percantages for different time periods.

# Generate pie chart
energy_df['Crude Production Current (10k tons)'].plot(
    kind='pie', 
    autopct='%1.1f%%', 
    title='Monthly Crude Production Distribution', 
    figsize=(8, 8)
)
plt.ylabel('')
plt.show()

Scater Plot Analysis

Scatter plots examine relationships between current and cumulative crude production values, revealing correlations and identifying potential outliers.

# Generate scatter plot
energy_df.plot(
    kind='scatter', 
    x='Crude Production Current (10k tons)', 
    y='Crude Production Cumulative (10k tons)', 
    title='Current vs Cumulative Production Correlation',
    figsize=(10, 6)
)
plt.xlabel('Current Production')
plt.ylabel('Cumulative Production')
plt.show()

Box Plot Analysis

Box plots compare distribution characteristics between current and cumulative production values, showing median, quartiles, and potential outliers.

import seaborn as sns

# Generate box plot
plt.figure(figsize=(10, 6))
sns.boxplot(data=energy_df[['Crude Production Current (10k tons)', 'Crude Production Cumulative (10k tons)']])
plt.title('Production Distribution Comparison')
plt.ylabel('Production Value')
plt.show()

Heatmap Analysis

Heatmaps viusalize correlation strength between different energy production metrics, with color intensity indicating relationship strength.

# Generate correlation heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(
    energy_df[['Crude Production Current (10k tons)', 'Crude Production Cumulative (10k tons)']].corr(), 
    annot=True, 
    cmap='viridis', 
    fmt='.2f',
    center=0
)
plt.title('Production Metric Correlations')
plt.show()