The 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
时间: 2024-02-25 18:59:22 浏览: 20
Sure, you can pick any column(s) from the dataset to draw a figure from which you can draw meaningful conclusions. Here is an example of a figure that provides insights into the age distribution of athletes who competed in the modern Olympic Games.
We can create a histogram using Python's matplotlib library to visualize the distribution of athlete ages. The histogram bin size is set to 5, and the x-axis represents the age range while the y-axis represents the number of athletes in each age range.
```python
import pandas as pd
import matplotlib.pyplot as plt
# Load the dataset
df = pd.read_csv("athlete_events.csv")
# Plot the histogram
plt.hist(df['Age'].dropna(), bins=range(10, 80, 5), edgecolor='black')
# Set the chart title and axis labels
plt.title("Age Distribution of Olympic Athletes (1896-2016)")
plt.xlabel("Age")
plt.ylabel("Number of Athletes")
# Show the chart
plt.show()
```
From the histogram, we can see that the age distribution of Olympic athletes is right-skewed, with a peak around the mid-20s. This indicates that most athletes competing in the Olympics are relatively young. Moreover, we can see that the number of athletes drops off significantly after the age of 30, indicating that it becomes increasingly difficult to compete at a high level as one ages.