practice DataAnalyzeVisu solution

中文版：数据分析与可视化练习参考答案

Data Analysis and Data Visualization

1. Import the pandas library in Python and use pd as an alias.
2. Read the nba.csv file using pandas and store it in a DataFrame named df

import pandas as pd #hint: import ... as ...
df = pd.read_csv('nba.csv') # pd.read_csv('name.csv')

3. Count the total number of unique teams in the dataset and list their names.

teams = df['Team'].unique() # hint: a['type_name'].unique()
num_teams = len(teams) # hint: len(a)
team_names = list(teams) # hint: list(a)

4. For each team, calculate the average age, average height, and average salary of its players

# hint: a.groupby('type_name').agg({'type_name':'mean'}).reset_index()
# a.groupby('type_name').[['type_name1','type_name2','type_name3']].mean().reset_index()
team_stats = df.groupby('Team')[['Age', 'Height', 'Salary']].mean().reset_index()
print(team_stats)

5. Create a bar chart to show the average age of players in each team.

import matplotlib.pyplot as plt
 
plt.figure() #plt.figure()
plt.bar(teams, team_stats['Age'])
#plt.bar(x, y)

6. Set a title for the chart.

import matplotlib.pyplot as plt
 
plt.figure() #plt.figure()
plt.bar(teams, team_stats['Age'])
#plt.bar(x, y)
 
plt.title('Average Age of NBA Teams')

Attention!

Complete all plotting operations within the same cell.

Otherwise:

import matplotlib.pyplot as plt
 
plt.figure() #plt.figure()
plt.bar(teams, team_stats['Age'])
#plt.bar(x, y)

plt.title('Average Age of NBA Teams')

7. Set labels for both axes of the chart.

import matplotlib.pyplot as plt
 
plt.figure() #plt.figure()
plt.bar(teams, team_stats['Age'])
#plt.bar(x, y)
 
plt.xlabel('Team')
plt.ylabel('Age')

8. Print the maximum and minimum height of players for each team.

# hint: a.groupby('type_name')['type_name'].agg(['max', 'min'])
height_stats = df.groupby('Team')['Height'].agg(['max', 'min'])
 
# Print the result
print("Maximum and Minimum Height per Team:")
print(height_stats)

9. Group all players by age and display the average, maximum, and minimum salary for each age group.

age_salary_stats = df.groupby('Age')['Salary'].agg(['mean', 'max', 'min']).reset_index()
 
# Print the resulting DataFrame
print("Average, Max, and Min Salary by Age:")
print(age_salary_stats)

10. Create a line chart to show the relationship between age and (average,max,min) salary. Be sure to set an appropriate title，axis labels and legend.

# Create the line chart
plt.figure(figsize=(10, 5))
# Plot mean, max, and min salary by age
plt.plot(age_salary_stats['Age'], age_salary_stats['mean'], label='Average Salary', marker='o')
plt.plot(age_salary_stats['Age'], age_salary_stats['max'], label='Max Salary', marker='^')
plt.plot(age_salary_stats['Age'], age_salary_stats['min'], label='Min Salary', marker='s')
 
# Set chart title and axis labels
plt.title('Relationship between Age and Salary (Average / Max / Min)')
plt.xlabel('Age')
plt.ylabel('Salary (USD)')
# Add legend
plt.legend()
 
# Show the chart
plt.show()