x̄ -> Exo Planetary dataset and graph using python

Python program that creates a planetary dataset for exoplanets, inspired by the image details of star types (A, B, F, G, K, M) and their characteristics:

```python

import pandas as pd

# Step 1: Define star and planet data

data = {

    "StarType": ["A", "B", "F", "G", "K", "M"],

    "Temperature_K": [10000, 15000, 7000, 5778, 4000, 3500],

    "StellarRadius_SolarRadii": [2.0, 5.0, 1.3, 1.0, 0.7, 0.5],

    "PlanetCount": [2, 1, 4, 8, 5, 3],  # Number of exoplanets discovered

    "AveragePlanetMass_JupiterMass": [3.0, 5.0, 0.8, 1.0, 0.5, 0.3],

    "AverageOrbitPeriod_Days": [300, 800, 200, 365, 100, 50]

}

# Step 2: Create the dataset

exoplanet_df = pd.DataFrame(data)

# Step 3: Save the dataset to a CSV file

exoplanet_df.to_csv("exoplanet_dataset.csv", index=False)

# Step 4: Display the dataset

print("Exoplanet Dataset:")

print(exoplanet_df)

```

### Dataset Description:

This program generates a dataset with the following columns:

- StarType: The type of star (A, B, F, G, K, M).

- Temperature_K: The average surface temperature of the star (Kelvin).

- StellarRadius_SolarRadii: The radius of the star in solar radii (relative to the sun's radius).

- PlanetCount: Number of exoplanets orbiting each star type.

- AveragePlanetMass_JupiterMass: Average mass of the planets (in Jupiter masses).

- AverageOrbitPeriod_Days: Average orbital period of planets (in Earth days).

### How It Works:

1. Creates a Python dictionary containing star and planet data.

2. Converts the dictionary into a Pandas DataFrame.

3. Saves the dataset as a CSV file (`exoplanet_dataset.csv`) for further analysis.

4. Prints the dataset for immediate viewing.  😊

# Import necessary libraries

import pandas as pd

import matplotlib.pyplot as plt

# Step 1: Load the dataset

# Replace 'exoplanet_data.csv' with the path to your dataset

try:

    data = pd.read_csv('exoplanet_data.csv')

except FileNotFoundError:

    print("Dataset not found. Please ensure 'exoplanet_data.csv' is in the same directory.")

    exit()

# Step 2: Explore the dataset

print(data.head())  # Display the first 5 rows of the dataset

print(data.info())  # Show information about the columns

# Step 3: Filter relevant columns (e.g., Star Type, Planet Mass, Orbit Period)

relevant_columns = ['StarType', 'PlanetMass', 'OrbitPeriod']

data = data[relevant_columns]

# Step 4: Data cleaning (remove rows with missing values)

data.dropna(inplace=True)

# Step 5: Visualize the data

# Scatter plot: Planet Mass vs Orbit Period

plt.figure(figsize=(10, 6))

scatter = plt.scatter(data['OrbitPeriod'], data['PlanetMass'], c=data['StarType'].astype('category').cat.codes, cmap='viridis')

plt.colorbar(scatter, label='Star Type (Encoded)')

plt.xlabel('Orbit Period (days)')

plt.ylabel('Planet Mass (Jupiter Mass)')

plt.title('Exoplanet Characteristics by Star Type')

plt.grid()

plt.show()

# Step 6: Basic Statistics

print("\nBasic Statistics:")

print(data.describe())

# Step 7: Grouping data by star type

grouped_data = data.groupby('StarType').mean()

print("\nAverage Planet Mass and Orbit Period by Star Type:")

print(grouped_data)

This work is licensed under a Creative Commons Attribution 4.0 International License.