使用 Pandas 对大文件进行切片、删除重复项并合并到输出中

Question

所以，我有一个包含 12.5 亿个特征的地理包。该文件实际上并不包含几何图形，只有一个属性“id”，即唯一的 id。有很多重复项，我想删除重复的“id”并仅保留唯一值。由于存在大量数据（地理包包含 19 GB），我选择了切片。我尝试了多重处理，但这不起作用，而且会出现问题，因为我必须跟踪唯一的“id”，而多重处理不允许这样做（至少据我所知）。

我有：

import fiona
import geopandas as gpd
import pandas as pd
# import numpy as np

slice_count = 200
start = 0
end = slice_count
fname = "path/Output.gpkg"

file_gpd = gpd.read_file(fname, rows=slice(start, end))
chunk = pd.DataFrame(file_gpd)
chunks = pd.DataFrame()
only_ids = pd.DataFrame(columns=['id'])
loop = True
while loop:
    try:
        # Dropping duplicates in current dataset
        chunk = chunk.drop_duplicates(subset=['id'])

        # Extract only unique IDS from chunk variable to save memory 
        only_ids_in_chunk = pd.DataFrame()
        only_ids_in_chunk['id'] = chunk['id']

        only_ids = only_ids.append(only_ids_in_chunk)
        only_ids = only_ids.drop_duplicates(subset=['id'])

        # If we want to make another file which have all values unique
        # we must store somewhere what we have in chunk variable, to be able to load new chunk
        # Because we must not have all chunks in memory at the same time

        del chunk

        # Load next chunk

        start += slice_count
        end += slice_count
        file_gpd = gpd.read_file(fname, rows=slice(start, end))
        chunk = pd.DataFrame(file_gpd)
        if len(chunk) == 0:
            print(len(only_ids))
            loop = False
        else:
            pass
    except Exception:
        loop = False
        print("Iteration is stopped")

我陷入了无限循环。我认为使用 if 语句会发现块的长度何时等于 0 或者切片何时结束。

Answer 1

所以，这是最终的脚本。我遇到的问题是，当您使用 geopandas 切片地理包文件时，当您到达末尾时，它从头开始并且不会停止。所以我在代码末尾添加了 if 语句来覆盖这一点。

import fiona
import geopandas as gpd
import pandas as pd
import logging
import time

slice_count = 20000000
start = 0
end = slice_count
fname = "/Output.gpkg"

chunk = gpd.read_file(fname, rows=slice(start, end), ignore_geometry=True)

chunks = pd.DataFrame()
only_ids = pd.DataFrame(columns=['id'])
loop = True
chunk_num = 1
while loop:
    start_time = time.time()
    # Dropping duplicates in current dataset
    chunk = chunk.drop_duplicates(subset=['id'])
        
    only_ids = only_ids.append(chunk)
    only_ids = only_ids.drop_duplicates(subset=['id'])

    # delete chunk to save memory
    del chunk

    # Load next chunk
    start += slice_count
    end += slice_count
    chunk = gpd.read_file(fname, rows=slice(start, end), ignore_geometry=True)
    
    FORMAT = '%(asctime)s:%(name)s:%(levelname)s - %(message)s'
    logging.basicConfig(format=FORMAT, level=logging.INFO)
    logging.info(f"Chunk {chunk_num} done")
    print(f"Duration: {time.time() - start_time}")
    chunk_num += 1

    if len(chunk) != slice_count:
        chunk = chunk.drop_duplicates(subset=['id'])
        only_ids = only_ids.append(chunk)
        only_ids = only_ids.drop_duplicates(subset=['id'])
        del chunk
        break

only_ids.to_csv('output.csv')

Answer 2

借助 geopandas/pyogrio 的一些新功能，现在可以更有效地完成此类事情。

例如。像这样的事情会更容易并且会更快：

import geopandas as gpd

fname = "/Output.gpkg"
only_ids = gpd.read_file(fname, engine="pyogrio", sql="SELECT DISTINCT id FROM output")
only_ids.to_csv("output.csv")