如何展示类别变量相关的DataFrame？含‘Purpose’及对应总行驶里程统计需求

Depending on what you're trying to achieve, here are a few practical ways to focus your DataFrame on the Purpose categorical variable:

• Isolate the Purpose column: If you just want to view this column alone (either as a Series or DataFrame):

  # Get as a pandas Series
  df['Purpose']
  
  # Keep it as a DataFrame (useful if you need to perform further operations)
  df[['Purpose']]
  

• Check category distribution: To see how many times each Purpose category appears in your dataset:

  # Show counts as a Series
  df['Purpose'].value_counts()
  
  # Convert to a structured DataFrame for easier reading
  df['Purpose'].value_counts().reset_index(name='Occurrences')
  

• Filter rows for a specific category: If you want to focus on all entries where Purpose matches a specific value (e.g., "Business"):

  business_df = df[df['Purpose'] == 'Business']
  print(business_df)
  

• Group data by Purpose: To organize your entire DataFrame around each Purpose category (great for analyzing subsets):

  grouped_data = df.groupby('Purpose')
  
  # Print a sample of each group to inspect
  for purpose, group in grouped_data:
      print(f"=== Purpose: {purpose} ===")
      print(group.head())
      print("\n")
  

This is a straightforward aggregation task using groupby to calculate total miles per Purpose category. Here's how to do it cleanly:

• Basic total miles per category:

  total_miles_df = df.groupby('Purpose')['Miles'].sum().reset_index(name='Total_Miles')
  print(total_miles_df)
  

  This outputs a DataFrame with two columns: Purpose (each unique category) and Total_Miles (sum of all miles for that category).

• Sort results by total miles: For better readability, sort the DataFrame from highest to lowest total miles:

  sorted_miles_df = total_miles_df.sort_values(by='Total_Miles', ascending=False)
  print(sorted_miles_df)
  

• Add extra metrics (optional): If you want more context (like average miles per trip or number of trips), use agg() to compute multiple stats at once:

  purpose_metrics_df = df.groupby('Purpose')['Miles'].agg(
      Total_Miles='sum',
      Average_Miles='mean',
      Number_of_Trips='count'
  ).reset_index()
  print(purpose_metrics_df)
  

内容的提问来源于stack exchange，提问作者Vivek Bhakar