Automate time series plot with ChatGPT

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np

# Load the dataset into a pandas DataFrame
file_path = '/mnt/data/06_logins_per_day.csv'
data = pd.read_csv(file_path)

# Convert the 'date' column to datetime format
data['date'] = pd.to_datetime(data['date'])

# Create a complete date range from the minimum to the maximum date in the data
date_range = pd.date_range(start=data['date'].min(), end=data['date'].max())

# Reindex the DataFrame to the full date range, filling missing dates with zero logins
data = data.set_index('date').reindex(date_range, fill_value=0).reset_index()
data.rename(columns={'index': 'date'}, inplace=True)

# Convert the 'logins' column back to integers if necessary
data['logins'] = data['logins'].astype(int)

# Calculate the 7-day moving average of the logins
data['7d_moving_avg'] = data['logins'].rolling(window=7).mean()

# Prepare for plotting
plt.figure(figsize=(15, 8))

# Plot daily logins using blue line chart with markers
plt.plot(data['date'], data['logins'], label='Daily Logins', color='blue', marker='o')

# Plot the 7-day moving average with an orange dashed line
plt.plot(data['date'], data['7d_moving_avg'], label='7-Day Moving Average', color='orange', linestyle='--')

# Highlight weekends with a specific background color
for i in range(len(data)):
    if data['date'][i].weekday() >= 5:  # Saturday and Sunday
        plt.axvspan(data['date'][i], data['date'][i] + pd.Timedelta(days=1), facecolor='grey', alpha=0.3)

# Annotate each data point with its corresponding login value, and adjust text y-position to above the line
for i, txt in enumerate(data['logins']):
    plt.annotate(txt, (data['date'][i], data['logins'][i]), textcoords="offset points", xytext=(0,10), ha='center')

# Custom legend indicating the number of days above and below the moving average
above_avg = np.sum(data['logins'] > data['7d_moving_avg'])
below_avg = np.sum(data['logins'] < data['7d_moving_avg'])
plt.legend(title=f"Above Avg: {above_avg} days\nBelow Avg: {below_avg} days")

# Format the x-axis to show the date with the day of the week
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d (%a)'))
plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=1))
plt.xticks(rotation=45)

# Add a title, axis labels, and a grid to the plot
plt.title('Daily Logins and 7-Day Moving Average')
plt.xlabel('Date')
plt.ylabel('Number of Logins')
plt.grid(True)

# Use a tight layout for the plot to ensure that labels and titles fit well within the display
plt.tight_layout()

# Display the plot
plt.show()