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import pandas as pd

import matplotlib.pyplot as plt

# Load the space mission data from a CSV file

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

# Convert the date column to datetime format

data['Date'] = pd.to_datetime(data['Date'])

# Extract year from the date column

data['Year'] = data['Date'].dt.year

# Group the data by year and count the number of missions

missions_per_year = data.groupby('Year').size()

# Plot the number of missions per year

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

missions_per_year.plot(kind='line', marker='o')

plt.title('Number of Space Missions per Year')

plt.xlabel('Year')

plt.ylabel('Number of Missions')

plt.grid(True)

plt.show()

# with this exercise , I created a template for charting, its a bit long cut process but it teaches me and let me memorize the methods by repition:

name_frequency = clean_data['Organisation'].value_counts().reset_index()
name_frequency.columns = ['name', 'frequency']
name_frequency.shape
name_frequency.head

plt.figure(figsize=(14,8))
plt.xticks(fontsize=14, rotation=90)
plt.yticks(fontsize=14)
plt.xlabel('Organisation', fontsize=14)
plt.ylabel('Number of Launch', fontsize=14)

plt.title('Number of Launches per Company', fontsize=24)
plt.bar(name_frequency.name[:24], name_frequency.frequency[:24])

# I also learned how to use plotly and matplotlib, and differenciate their pros and cons when using it