如何修改Pandas DataFrame列名并设置从1起始的序号列及格式化输出？

Got it, let's tackle those three formatting problems you're facing with your Pandas output. Here's a step-by-step adjusted code that'll get your results matching the desired style:

Step-by-Step Code Implementation #

import pandas as pd

# 1. Load the dataset
df = pd.read_csv("past_transacted_px.csv")

# 2. Apply your filtering conditions (adjust these to match your actual input criteria)
# Example: Filter for EXECUTIVE flats in area A, resale price between 100k and 300k
filtered_data = df[
    (df["flat_type"] == "EXECUTIVE") &
    (df["town"] == "A") &
    (df["resale_price"].between(100000, 300000))
]

# 3. Fix continuous serial number (No. column)
# Reset index to drop original indices, then create a 1-based sequence
filtered_data = filtered_data.reset_index(drop=True)
filtered_data["No."] = filtered_data.index + 1

# 4. Rename columns to the required format
filtered_data = filtered_data.rename(columns={
    "block": "Block",
    "street_name": "Street Name",
    "storey_range": "Storey Range",
    "floor_area_sqm": "Floor Area Sqm",
    "resale_price": "Resale Price"
})

# 5. Format numeric values
# Convert floor area to integer to remove decimals
filtered_data["Floor Area Sqm"] = filtered_data["Floor Area Sqm"].astype(int)
# Add thousand separators to resale price
filtered_data["Resale Price"] = filtered_data["Resale Price"].apply(lambda x: f"{int(x):,}")

# 6. Print the final formatted output
print(f"The EXECUTIVE flats available in A are:")
# Emphasize the header as required
print("**No. Block Street Name Storey Range Floor Area Sqm Resale Price**")
# Iterate through rows to print with emphasized serial number and values
for _, row in filtered_data.iterrows():
    print(f"**{row['No.']}** {row['Block']} {row['Street Name']} {row['Storey Range']} **{row['Floor Area Sqm']} {row['Resale Price']}**")

Key Fixes Explained #

• Continuous Serial Number: Using reset_index(drop=True) clears the original DataFrame indices, then we create a new No. column starting from 1 with index + 1.
• Column Name Adjustment: The rename() method maps the original column names to the user-friendly, capitalized format you need.
• Numeric Formatting:
  • Convert floor_area_sqm to integer to eliminate decimal points.
  • Use a lambda function with f-string formatting to add thousand separators to resale_price.
• Output Styling: We use asterisks (**) to emphasize the header, serial numbers, and formatted numeric values, matching your expected output style.

内容的提问来源于stack exchange，提问作者Dearest