技术求助：如何将row[0]内容迁移至rows[1-9]新增列并删除原行

Got it, let's work through this data transformation task. Based on your example, you need to take each group's header row (the one starting with Grid-ref=), prepend its content to every data row in the same group, then remove the original header row. Here's a straightforward Python implementation that handles this:

Step 1: Group Your Data First #

First, we need to split your raw data into groups where each group starts with a Grid-ref= row. This works even if your groups have varying numbers of data rows (not just 10):

# Assume your raw data is stored as a list of lists
raw_data = [
    ['Grid-ref=', '1', '148'],
    ['3020', '2820', '3040', '2880', '1740', '1360', '980', '990', '1410', '1770', '2580', '2630'],
    ['3020', '2820', '3040', '2880', '1740', '1360', '980', '990', '1410', '1770', '2580', '2630'],
    # ... add the rest of your rows here
    ['Grid-ref=', '1', '311'],
    ['490', '290', '280', '230', '200', '250', '440', '530', '460', '420', '530', '450'],
    # ... add the rest of your rows here
]

# Split into groups by Grid-ref rows
groups = []
current_group = []
for row in raw_data:
    # Check if this is a header row
    if row and row[0] == 'Grid-ref=':
        # Save the current group if it's not empty
        if current_group:
            groups.append(current_group)
        # Start a new group with this header
        current_group = [row]
    else:
        # Add data row to current group
        current_group.append(row)
# Don't forget to add the last group
if current_group:
    groups.append(current_group)

Step 2: Transform Each Group #

Next, we'll process each group: take the header content, prepend it to every data row in the group, and collect all transformed rows:

processed_data = []
for group in groups:
    # Extract the Grid-ref content from the first row of the group
    grid_ref_content = group[0]
    # Iterate over all data rows in the group (skip the header row)
    for data_row in group[1:]:
        # Prepend Grid-ref content to the data row
        new_row = grid_ref_content + data_row
        processed_data.append(new_row)

Step 3: Verify the Output #

If you print processed_data, you'll see exactly the transformed format you need: each row starts with the corresponding Grid-ref= values, followed by the original data.

Handling File Input/Output #

If your data is stored in a CSV file, you can use Python's built-in csv module to read and write the data:

import csv

# Read from input CSV
with open('input_data.csv', 'r') as input_file:
    reader = csv.reader(input_file)
    raw_data = list(reader)

# ... run the grouping and processing code above ...

# Write to output CSV
with open('output_data.csv', 'w', newline='') as output_file:
    writer = csv.writer(output_file)
    writer.writerows(processed_data)

Alternative: Using Pandas (For Larger Datasets) #

If you're working with larger datasets, Pandas can make this even cleaner. Here's a quick version:

import pandas as pd

# Read data into a DataFrame (assuming no header row in the CSV)
df = pd.read_csv('input_data.csv', header=None)

# Create a group identifier: fill forward the Grid-ref rows to mark each group
df['group_id'] = df.apply(lambda row: '|'.join(row.dropna().astype(str)) if row[0] == 'Grid-ref=' else None, axis=1)
df['group_id'] = df['group_id'].ffill()

# Split the group_id into separate columns (the Grid-ref content)
grid_ref_cols = df['group_id'].str.split('|', expand=True)

# Combine Grid-ref columns with original data, then drop group_id and filter out header rows
processed_df = pd.concat([grid_ref_cols, df.drop('group_id', axis=1)], axis=1)
processed_df = processed_df[processed_df[0] != 'Grid-ref=']

# Reset index if needed
processed_df = processed_df.reset_index(drop=True)

# Save to CSV
processed_df.to_csv('output_data.csv', index=False, header=False)

Let me know if you need adjustments for specific edge cases (like missing values, different delimiters, or other data structures)!

内容的提问来源于stack exchange，提问作者Clives-online