如何使用Python的pandas库编辑.csv文件（添加行与删除行）

Hey there! Let's work through your CSV manipulation tasks with Python— I'll walk you through each requirement with practical, easy-to-use code snippets. Let's dive in!

1. Remove Top or Bottom 10 Rows #

First, we'll prompt you to choose whether to trim from the top or bottom, then modify the CSV accordingly. I'll cover two approaches: using Python's built-in csv module (no external installs needed) and pandas (great for larger files and simpler syntax).

Using the Built-in csv Module #

import csv

# Get user choice for which rows to remove
remove_choice = input("Do you want to remove the TOP 10 rows or BOTTOM 10 rows? Enter 'top' or 'bottom': ").lower()

# Read all rows from the original CSV
with open('your_file.csv', 'r', newline='') as infile:
    all_rows = list(csv.reader(infile))

# Process rows based on user choice
if remove_choice == 'top':
    modified_rows = all_rows[10:]  # Skip first 10 rows
elif remove_choice == 'bottom':
    modified_rows = all_rows[:-10]  # Exclude last 10 rows
else:
    print("Oops! Invalid choice— please enter 'top' or 'bottom'.")
    exit()

# Write the modified data to a new CSV (or overwrite the original if you prefer)
with open('trimmed_file.csv', 'w', newline='') as outfile:
    writer = csv.writer(outfile)
    writer.writerows(modified_rows)

print(f"Success! Removed the {remove_choice} 10 rows. Check out trimmed_file.csv.")

Using pandas (Shorter Syntax) #

If you have pandas installed (pip install pandas if not), this is a quicker option:

import pandas as pd

remove_choice = input("Remove TOP or BOTTOM 10 rows? Enter 'top'/'bottom': ").lower()

# Load the CSV into a DataFrame
df = pd.read_csv('your_file.csv')

# Trim rows based on choice
if remove_choice == 'top':
    df_trimmed = df.iloc[10:]
elif remove_choice == 'bottom':
    df_trimmed = df.iloc[:-10]
else:
    print("Invalid choice! Please try again.")
    exit()

# Save the trimmed DataFrame to CSV
df_trimmed.to_csv('trimmed_file.csv', index=False)
print(f"Done! {remove_choice} 10 rows removed successfully.")

2. Add 10 Rows to the Top & Input Their Data #

Next, we'll collect your input for each of the 10 new rows, then prepend them to your CSV. Again, I'll cover both csv and pandas methods, including handling cases where your CSV has column headers.

Using the Built-in csv Module #

import csv

# First, read the original CSV
with open('your_file.csv', 'r', newline='') as infile:
    reader = csv.reader(infile)
    # Check if the CSV has headers
    has_headers = input("Does your CSV have column headers? Enter 'yes' or 'no': ").lower()
    if has_headers == 'yes':
        headers = next(reader)
        original_data = list(reader)
    else:
        headers = None
        original_data = list(reader)

# Collect data for 10 new rows
new_rows = []
column_count = len(headers) if headers else len(original_data[0]) if original_data else 0
print(f"\nEnter data for each of the 10 new rows (separate values with commas— {column_count} values per row):")

for row_num in range(1, 11):
    while True:
        user_input = input(f"Row {row_num}: ")
        row_values = [val.strip() for val in user_input.split(',')]
        # Validate the number of values matches the CSV's columns
        if len(row_values) == column_count:
            new_rows.append(row_values)
            break
        else:
            print(f"Whoops! Please enter exactly {column_count} values (matching the CSV's columns).")

# Combine new rows with original data (account for headers)
if has_headers == 'yes':
    header_placement = input("\nAdd new rows ABOVE headers or BELOW headers? Enter 'above' or 'below': ").lower()
    if header_placement == 'above':
        full_data = new_rows + [headers] + original_data
    else:
        full_data = [headers] + new_rows + original_data
else:
    full_data = new_rows + original_data

# Write the combined data to a new CSV
with open('file_with_new_rows.csv', 'w', newline='') as outfile:
    writer = csv.writer(outfile)
    writer.writerows(full_data)

print("\n10 new rows added successfully! Check file_with_new_rows.csv.")

Using pandas #

import pandas as pd

# Load the original CSV
df = pd.read_csv('your_file.csv')
columns = df.columns.tolist()

# Collect data for 10 new rows
new_rows_data = []
print(f"Enter data for 10 new rows (each row needs {len(columns)} values, separated by commas):")

for row_num in range(1, 11):
    while True:
        user_input = input(f"Row {row_num}: ")
        row_values = [val.strip() for val in user_input.split(',')]
        if len(row_values) == len(columns):
            # Create a dictionary matching column names to values
            row_dict = dict(zip(columns, row_values))
            new_rows_data.append(row_dict)
            break
        else:
            print(f"Please enter exactly {len(columns)} values— one for each column.")

# Create a new DataFrame for the new rows
new_df = pd.DataFrame(new_rows_data, columns=columns)

# Prepend the new rows to the original DataFrame
combined_df = pd.concat([new_df, df], ignore_index=True)

# Save to CSV
combined_df.to_csv('file_with_new_rows.csv', index=False)

print("Success! 10 rows added to the top of your CSV.")

Quick Tips #

• Replace 'your_file.csv' with your actual file path (e.g., 'C:/data/my_csv.csv' on Windows or '/home/user/data/my_csv.csv' on Linux/macOS).
• If your CSV uses a delimiter other than commas (like tabs), add delimiter='\t' to csv.reader/csv.writer or pd.read_csv().
• Always make a backup of your original CSV before modifying it— better safe than sorry!

内容的提问来源于stack exchange，提问作者codekage