如何修改Pandas DataFrame列名并新增从1起始的序号列

Let's get your code aligned with the desired output by addressing the three key issues: custom sequential numbering, correct column names, and formatted values. Here's the step-by-step adjusted solution:

Full Corrected Code #

import pandas as pd

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

# User input variables (assuming these are defined elsewhere in your code)
# town_choice = "A"
# desired_flat = "EXECUTIVE"
# min_price = 150000
# max_price = 250000

# Step 1: Filter the data based on user criteria first
flat_list = df.loc[
    (df['town'] == town_choice) & 
    (df['flat_type'] == desired_flat) & 
    (df['resale_price'] >= min_price) & 
    (df['resale_price'] <= max_price)
].copy()  # Use .copy() to avoid SettingWithCopyWarning

# Step 2: Add sequential No. column starting from 1
flat_list['No.'] = range(1, len(flat_list) + 1)

# Step 3: Rename columns to the desired names
flat_list.rename(
    columns={
        "block": "Block",
        "street_name": "Street Name",
        "storey_range": "Storey Range",
        "floor_area_sqm": "Floor Area Sqm",
        "resale_price": "Resale Price"
    },
    inplace=True
)

# Step 4: Format values - area to integer, price with thousand separators
flat_list['Floor Area Sqm'] = flat_list['Floor Area Sqm'].astype(int)
flat_list['Resale Price'] = flat_list['Resale Price'].apply(lambda x: f"{x:,}")

# Step 5: Reorder columns to match desired output
desired_columns = ['No.', 'Block', 'Street Name', 'Storey Range', 'Floor Area Sqm', 'Resale Price']
df_final = flat_list[desired_columns]

# Print the result
print(f'The {desired_flat} flats available in {town_choice} are:')
print(df_final.to_string(index=False))  # Disable default index in output

Key Fixes Explained #

• Sequential Numbering: Instead of relying on the original DataFrame index, we generate a new No. column starting at 1 using range(1, len(flat_list)+1). This ensures numbering always starts from 1 regardless of the original index values.
• Correct Column Names: We rename columns after filtering the data (using inplace=True to apply changes directly), avoiding the mistake of overwriting the renamed DataFrame with unfiltered original columns.
• Value Formatting:
  • Convert Floor Area Sqm to integers with astype(int) to remove decimal places and match the desired whole number output.
  • Format Resale Price with thousand separators using a lambda function and Python's formatted string literals (f"{x:,}").
• Clean Output: Using to_string(index=False) when printing removes the default Pandas index, so only your custom No. column is displayed.

内容的提问来源于stack exchange，提问作者Dearest