按特定规则将两个嵌套列表映射生成DataFrame的技术需求

Got it, let's tackle this problem where we need to map two nested lists into a structured DataFrame, filling missing values with 0 (or null if preferred). Here's a straightforward approach using pandas:

Step 1: Import pandas and define your lists #

First, we'll start by importing the pandas library and declaring your input lists:

import pandas as pd

list1 = [['a','b','c'],['a','d'],['a','f','c']]
list2 = [['S1','S2','S3'],['S1','S3'],['S1','S2','S3']]

Step 2: Define target columns #

We know the columns we need are S1, S2, S3 (we can also extract them dynamically from list2 if needed, but hardcoding here keeps it simple for your use case):

target_columns = ['S1', 'S2', 'S3']

Step 3: Build row dictionaries #

We'll pair each sublist from list1 with its corresponding sublist from list2, create a dictionary mapping each S* key to its value from list1, then fill in any missing columns with 0 (or null):

rows = []
for l1_items, l2_keys in zip(list1, list2):
    # Create initial mapping from S keys to list1 values
    row_data = dict(zip(l2_keys, l1_items))
    # Fill missing columns with 0 (replace with pd.NA for nulls)
    for col in target_columns:
        if col not in row_data:
            row_data[col] = 0
    rows.append(row_data)

Step 4: Convert to DataFrame #

Finally, we'll turn our list of dictionaries into a pandas DataFrame, ensuring columns are in the order we want:

df = pd.DataFrame(rows, columns=target_columns)
print(df)

Output #

Running this code will give you exactly the structure you're looking for:

S1 S2 S3
0  a  b  c
1  a  0  d
2  a  f  c

If you prefer null instead of 0, just replace row_data[col] = 0 with row_data[col] = pd.NA in the loop. That will give you missing values marked as pandas' native null type.

内容的提问来源于stack exchange，提问作者Akshay Sachdeva