如何从列表中拆分子列表并将数据添加到另一个列表中 [英] How to split the sublist from the list and add the data in another list

问题描述

我需要再创建一个具有列格式的数据框

I need to create one more dataframe which has the column format

df_codata["Latlong"]=  [{"__type":"GeoPoint","latitude":x,"longitude":y}]

如图所示，"Latlong"包含具有子列表的"latitude".我需要创建一个数据帧，该数据帧给我输出，例如单个"C_ID"，它具有多个纬度"子列表，我想添加以在df_codata数据帧的纬度"中添加子列表的第一个数字，并在经度"中添加子列表的第二个数字数据帧".希望在下面附加所需的输出: 请对此问题提出建议.我无法拆分子列表，并将其插入纬度"和经度"两个子列

As seen in the image the "Latlong" contains the "latitude" which has sublist. I need to create dataframe which give me output such as for the single "C_ID" it has multiple sublist of "latitude" I want to add to add sublist first number in "latitude" of df_codata dataframe and second number of the sublist in "longitude" of dataframe. Want the desired output attached below: Kindly suggest for the issue. I am unable to split the sublist and insert it two subcolumns of "latitude" and "longitude"

推荐答案

您可以进行某种数据预处理以获取所需的输出.这是解决您的任务的可能解决方案之一:

You can do some kind of data preprocessing to get your desired output. Here is one of the possible solutions to solve your task:

import pandas as pd
import json

def extend_coordinates(coordinates, c_id, geo_type):
    result = []
    for elem in coordinates:
        if len(elem) == 2:
            # normal case
            latitude, longitude = elem
        else:
            latitude, longitude = [None, None]
        result.append([c_id, geo_type, latitude, longitude])
    return result

data_json = [
    {
        'C_ID' : '1',
        'Latlong' : {
            '__type' : 'GeoPoint',
            'latitude' : [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
        }
    },
    {
        'C_ID' : '2',
        'Latlong' : {
            '__type' : 'GeoPoint',
            'latitude' : [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
        }
    },
    {
        'C_ID' : '3',
        'Latlong' : {
            '__type' : 'GeoPoint',
            'latitude' : [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
        }
    }]

data = pd.read_json(json.dumps(data_json))
data['Common'] = data.apply(lambda x: extend_coordinates(coordinates=x['Latlong'].get('latitude', [None, None]),
                                                         c_id=x['C_ID'],
                                                  geo_type=x['Latlong'].get('__type', None)), axis=1)
data_ext = pd.DataFrame(np.concatenate(data['Common']), 
                        columns=['C_ID', '__type', 'latitude', 'longitude'])

# if data_ext dataframe is not enough, you can combine data to your desired output
data_ext['Latlong'] = data_ext.apply(lambda x: {'__type': x['__type'], 
                                                'latitude': x['latitude'], 
                                                'longitude': x['longitude']}, axis=1)
result = data_ext[['C_ID', 'Latlong']]
del data_ext, data

更新:

要将纬度和经度保持为整数格式，可以执行以下操作(假设所有行的经度和纬度都为非空，并且它们正确表示了整数):

To keep latitude and longitude in integer format you can do the following (assuming that all rows have non-null longitude and latitude and they correctly represent integer numbers):

data_ext['Latlong'] = data_ext.apply(lambda x: {'__type': x['__type'], 
                                                'latitude': int(x['latitude']), 
                                                'longitude': int(x['longitude'])}, axis=1)

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