c02 intensity (2)

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Kenyatta University *

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Nov 24, 2024

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1 | P a g e C02 Intensity Institutional Affiliation Student’s Name Date

2 | P a g e C02 Intensity ( Draft ) • 1.1. Descriptive Statistics: Calculate and print the mean and standard deviation of carbon (actual) intensity values over the specified date range. Round the output numbers to three decimal places. import pandas as pd # Assuming your dataset is named 'df' # Replace 'df' with the actual variable name if it's different # Convert 'CO2_emission' column to numeric, treating errors as NaN df['CO2_emission'] = pd.to_numeric(df['CO2_emission'], errors='coerce') # Calculate mean and standard deviation mean_co2_emission = df['CO2_emission'].mean() std_dev_co2_emission = df['CO2_emission'].std() # Print the results rounded to three decimal places print(f"Mean CO2 Emission: {mean_co2_emission:.3f}") print(f"Standard Deviation of CO2 Emission: {std_dev_co2_emission:.3f}")  1.2. Time Period Analysis: Calculate and print the duration of the data collection period (i.e., the time between the earliest and latest timestamps). import pandas as pd # Create a DataFrame with the provided data data = { 'Country': ['World', 'World', 'World', 'World', 'World', 'World', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Albania', 'Albania', 'Albania', 'Albania', 'Albania', 'Albania', 'Algeria', 'Algeria', 'Algeria', 'Algeria', 'Algeria', 'Algeria', 'American Samoa', 'American Samoa', 'American Samoa', 'American Samoa', 'American Samoa', 'American Samoa'], 'Energy_type': ['all_energy_types', 'coal', 'natural_gas', 'petroleum_n_other_liquids', 'nuclear', 'renewables_n_other', 'all_energy_types', 'coal', 'natural_gas', 'petroleum_n_other_liquids', 'nuclear', 'renewables_n_other', 'all_energy_types', 'coal', 'natural_gas', 'petroleum_n_other_liquids', 'nuclear', 'renewables_n_other', 'all_energy_types', 'coal', 'natural_gas', 'petroleum_n_other_liquids', 'nuclear', 'renewables_n_other',

3 | P a g e 'all_energy_types', 'coal', 'natural_gas', 'petroleum_n_other_liquids', 'nuclear', 'renewables_n_other'], 'Year': [1980] * 30, 'Energy_consumption': [292.8997896, 78.65613403, 53.8652233, 132.0640194, 7.575700462, 20.70234415, 0.026583217, 0.002479248, 0.002094, 0.014624098, None, 0.00738587, 0.162981822, 0.024317315, 0.01047, 0.099297277, None, 0.02889723, 0.780695167, 0.002547398, 0.5428, 0.232740836, None, 0.002606933, 0.005893112, 0, 0, 0.005893112, None, 0], 'Energy_production': [296.3372276, 80.11419429, 54.76104559, 133.1111089, 7.575700462, 20.77517837, 0.072561156, 0.002355286, 0.06282, 0, None, 0.00738587, 0.15556162, 0.013229039, 0.01047, 0.10154, None, 0.030322582, 2.803017355, 7.59E-05, 0.48498, 2.31538521, None, 0.002576225, 0, 0, 0, 0, 0, None, 0], 'GDP': [27770.91028] * 30 + [13356.5] * 6 + [2682.7] * 6 + [19221.7] * 6 + [32.646] * 6, 'Population': [4298126.522] * 30 + [1.990283134] * 6 + [60.75290633] * 6 + [40.61530287] * 6 + [180.5156037] * 6, 'Energy_intensity_per_capita': [68.14592081] * 30 + [0] * 24, 'Energy_intensity_by_GDP': [10.54699996] * 30 + [0] * 24, 'CO2_emission': [4946.62713, 1409.790188, 1081.593377, 2455.243565, 0, 0, None, None, None, None, 0, 0, None, None, None, None, 0, 0, None, None, None, None, 0, 0, None, 0, 0, None, 0, None, 0] } df = pd.DataFrame(data) # Convert 'Year' column to numeric, treating errors as NaN df['Year'] = pd.to_numeric(df['Year'], errors='coerce') # Find the minimum and maximum years min_year = df['Year'].min() max_year = df['Year'].max() # Calculate the duration duration = max_year - min_year # Print the result print(f"Data Collection Period Duration: {duration} years")

4 | P a g e  Peak Intensity Detection: Identify and print the timestamp and value associated with the highest carbon (actual) intensity. import pandas as pd # Create a DataFrame with the provided data data = { 'Country': ['World', 'World', 'World', 'World', 'World', 'World', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Albania', 'Albania', 'Albania', 'Albania', 'Albania', 'Albania', 'Algeria', 'Algeria', 'Algeria', 'Algeria', 'Algeria', 'Algeria', 'American Samoa', 'American Samoa', 'American Samoa', 'American Samoa', 'American Samoa', 'American Samoa'], 'Energy_type': ['all_energy_types', 'coal', 'natural_gas', 'petroleum_n_other_liquids', 'nuclear', 'renewables_n_other', 'all_energy_types', 'coal', 'natural_gas', 'petroleum_n_other_liquids', 'nuclear', 'renewables_n_other', 'all_energy_types', 'coal', 'natural_gas', 'petroleum_n_other_liquids', 'nuclear', 'renewables_n_other', 'all_energy_types', 'coal', 'natural_gas', 'petroleum_n_other_liquids', 'nuclear', 'renewables_n_other', 'all_energy_types', 'coal', 'natural_gas', 'petroleum_n_other_liquids', 'nuclear', 'renewables_n_other'], 'Year': [1980] * 30, 'Energy_consumption': [292.8997896, 78.65613403, 53.8652233, 132.0640194, 7.575700462, 20.70234415, 0.026583217, 0.002479248, 0.002094, 0.014624098, None, 0.00738587, 0.162981822, 0.024317315, 0.01047, 0.099297277, None, 0.02889723, 0.780695167, 0.002547398, 0.5428, 0.232740836, None, 0.002606933, 0.005893112, 0, 0, 0.005893112, None, 0], 'Energy_production': [296.3372276, 80.11419429, 54.76104559, 133.1111089, 7.575700462, 20.77517837, 0.072561156, 0.002355286, 0.06282, 0, None, 0.00738587, 0.15556162, 0.013229039, 0.01047, 0.10154, None, 0.030322582, 2.803017355, 7.59E-05, 0.48498, 2.31538521, None, 0.002576225, 0, 0, 0, 0, 0, None, 0], 'GDP': [27770.91028] * 30 + [13356.5] * 6 + [2682.7] * 6 + [19221.7] * 6 + [32.646] * 6, 'Population': [4298126.522] * 30 + [1.990283134] * 6 + [60.75290633] * 6 + [40.61530287] * 6 + [180.5156037] * 6, 'Energy_intensity_per_capita': [68.14592081] * 30 + [0] * 24, 'Energy_intensity_by_GDP': [10.54699996] * 30 + [0] * 24, 'CO2_emission': [4946.62713, 1409.790188, 1081.593377, 2455.243565, 0, 0, None, None, None, None, 0, 0, None, None, None, None, 0, 0, None, None, None, None, 0, 0, None, 0, 0, None, 0, None, 0] }

5 | P a g e df = pd.DataFrame(data) # Convert 'CO2_emission' column to numeric, treating errors as NaN df['CO2_emission'] = pd.to_numeric(df['CO2_emission'], errors='coerce') # Find the row with the highest CO2 emission max_co2_row = df.loc[df['CO2_emission'].idxmax()] # Extract timestamp and value timestamp_max_co2 = max_co2_row['Year'] value_max_co2 = max_co2_row['CO2_emission'] # Print the result print(f"Highest Carbon Intensity (CO2 Emission):") Print(f"Timestamp: {timestamp_max_co2}") print(f"Value: {value_max_co2}")  1.4. Data Filtering: Filter the data to include only entries with (actual) carbon intensity values above 200, and then calculate and print the mean and standard deviation of the filtered data. Round the output numbers to three decimal places. import pandas as pd # Create a DataFrame with the provided data data = { 'Country': ['World', 'World', 'World', 'World', 'World', 'World', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Albania', 'Albania', 'Albania', 'Albania', 'Albania', 'Albania', 'Algeria', 'Algeria', 'Algeria', 'Algeria', 'Algeria', 'Algeria', 'American Samoa', 'American Samoa', 'American Samoa', 'American Samoa', 'American Samoa', 'American Samoa'], 'Energy_type': ['all_energy_types', 'coal', 'natural_gas', 'petroleum_n_other_liquids', 'nuclear', 'renewables_n_other', 'all_energy_types', 'coal', 'natural_gas', 'petroleum_n_other_liquids',

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