he file athlete_events.csv contains 271116 rows and 15 columns. This is a historical dataset on the modern Olympic Games, including all the Games from Athens 1896 to Rio 2016. Each row corresponds to an athlete competing in an Olympic event (athlete events). The columns are explained in the following table: ID: Unique number for each athlete Name: Athlete's name Sex: M or F Age: Integer Height: in centimeters Weight: In kilograms Team: Team name NOC: National Olympic Committee 3-letter code Games: Year and season Year: integer Season: Summer or Winter City: Host city Sport: Sport Event: detailed event Medal: Gold, Silver, Bronze, or NA Please pick any column(s) in the data set to draw a figure from which you can draw meaningful conclusions. Download the athlete_events.csv file

I suggest we draw a histogram to analyze the distribution of age among athletes in the dataset.

To do so, we can use the `matplotlib` library in Python. Here's the code to read the data from the CSV file and plot the histogram:

import pandas as pd
import matplotlib.pyplot as plt

# Read the data from the CSV file
data = pd.read_csv("athlete_events.csv")

# Filter out rows with missing age values
data = data[data['Age'].notna()]

# Plot the histogram
plt.hist(data['Age'], bins=20, edgecolor='black')

# Add labels and title
plt.xlabel('Age')
plt.ylabel('Number of Athletes')
plt.title('Distribution of Age Among Olympic Athletes')

# Show the plot
plt.show()

This code reads the data from the CSV file and filters out rows where the age value is missing. It then plots a histogram of the age values using 20 bins and adds labels and a title. Finally, it shows the plot.

The resulting histogram will show us the distribution of age among athletes in the dataset, which can help us draw meaningful conclusions about the demographics of Olympic athletes over time.