如何绘制多折线图：以月份为X轴、观影量为Y轴展示各电影类型日数据

Hey Jack, let's work through getting that multi-line chart you need. The issue with your current code is that it outputs a multi-indexed Series, which isn't structured properly for direct plotting. Let's break this down step by step:

Step 1: Reshape Your Data into a Plot-Friendly Format #

First, we need to group your data by Date and Genre, then pivot it so each genre becomes a column (with dates as the index). This structure makes it trivial to plot each genre as a separate line.

# Group by date + genre, count daily views, then pivot genres to columns
daily_genre_counts = df_movies_2018.groupby(['Date', 'Genre']).size().unstack(fill_value=0)

• groupby(['Date', 'Genre']).size() counts how many times each genre was watched per day.
• unstack(fill_value=0) converts the genre index into columns, filling days with no views for a genre with 0 (so we don't have gaps in our lines).

Step 2: Plot the Daily Multi-Line Chart #

Now we can use matplotlib to plot each genre's daily view count, with the X-axis formatted to show months (while retaining daily precision in the data):

import matplotlib.pyplot as plt
import matplotlib.dates as mdates

# Set up the plot canvas
fig, ax = plt.subplots(figsize=(12, 6))

# Loop through each genre column and plot its daily counts
for genre in daily_genre_counts.columns:
    ax.plot(daily_genre_counts.index, daily_genre_counts[genre], label=genre)

# Format X-axis to show month labels (e.g., Jan, Feb)
ax.xaxis.set_major_locator(mdates.MonthLocator())  # Place ticks at the start of each month
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b'))  # Use 3-letter month names

# Add labels and title
ax.set_title('Daily Movie View Counts by Genre (2018)')
ax.set_xlabel('Month')
ax.set_ylabel('Daily View Count')

# Add a legend to distinguish genres
ax.legend()

# Adjust layout to prevent label cutoff
plt.tight_layout()
plt.show()

Bonus: If You Want Monthly Aggregates Instead #

If you actually meant to plot total monthly views per genre (instead of daily), here's how to adjust the data and plot:

# Group by month (using dt.to_period) and genre, then pivot
monthly_genre_counts = df_movies_2018.groupby([df_movies_2018['Date'].dt.to_period('M'), 'Genre']).size().unstack(fill_value=0)
# Convert period index back to datetime for smooth plotting
monthly_genre_counts.index = monthly_genre_counts.index.to_timestamp()

# Plot monthly totals
fig, ax = plt.subplots(figsize=(10, 5))
for genre in monthly_genre_counts.columns:
    ax.plot(monthly_genre_counts.index, monthly_genre_counts[genre], marker='o', label=genre)

# Format X-axis same as before
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b'))

ax.set_title('Monthly Movie View Counts by Genre (2018)')
ax.set_xlabel('Month')
ax.set_ylabel('Total Monthly Views')
ax.legend()
plt.tight_layout()
plt.show()

The core fix here is reshaping your grouped data into a "wide" format where each genre is a column—this lets matplotlib easily map each column to a distinct line on your chart.

内容的提问来源于stack exchange，提问作者JackX