需要在循环中形成更新字典

Question

我的 CSV 文件如下：

destination_name,destination_code,destination_arrival_scheduled,destination_arrival_actual,origin_name,origin_code,origin_departure_scheduled,origin_departure_actual,train_date,train_code
Pune,Pun,11:50:00,11:52:05,Delhi,Del,10:30:00,10:30:18,17-Jan-17,D220
Mumbai,mum,11:50:00,11:52:05,Delhi,Del,10:30:00,10:30:18,17-Jan-17,D320
Delhi,del,11:50:00,11:52:05,Indore,Ind,10:30:00,10:30:18,17-Jan-17,D320

need to from Json as below
[
  {
    "origin": {
      "code": "Del", 
      "name": "Delhi", 
      "departure": {
        "scheduled": "10:30:00", 
        "diff": "0:00:18", 
        "actual": "10:30:18"
      }
    }, 
    "destination": {
      "arrival": {
        "scheduled": "11:50:00", 
        "diff": "0:02:05", 
        "actual": "11:52:05"
      }, 
      "code": "Pun", 
      "name": "Pune"
    }, 
    "train": {
      "date": "17-Jan-17", 
      "code": "D220"
    }
  }

下面是我的代码 Python 代码

import csv,itertools,json,re
csvfile =open("C:\\Users\\Amit\\train_data.csv","r")
json_file = "C:\\Users\\Amit\\file.json";

data = []
obj ={}
train={}

for row in csv.DictReader(csvfile):
    train['date']=row['train_date']
    #print(train)
    data.append(train)
print(data)

数据具有正确数量的元素，但数据已更新为最后一条记录。请帮我将 train{} 中的所有值存储在 data[] 中？

Answer 1

受pandas.io.json.json_normalize 的启发，它从嵌套的字典数组中提取数据，我认为我们可以做一些类似但又恢复的事情：

data = '''\
destination_name,destination_code,destination_arrival_scheduled,destination_arrival_actual,origin_name,origin_code,origin_departure_scheduled,origin_departure_actual,train_date,train_code
Pune,Pun,11:50:00,11:52:05,Delhi,Del,10:30:00,10:30:18,17-Jan-17,D220
Mumbai,mum,11:50:00,11:52:05,Delhi,Del,10:30:00,10:30:18,17-Jan-17,D320
Delhi,del,11:50:00,11:52:05,Indore,Ind,10:30:00,10:30:18,17-Jan-17,D320'''

import pandas as pd
import json

dates = ['destination_arrival_actual', 'destination_arrival_scheduled', 
         'origin_departure_actual', 'origin_departure_scheduled','train_date']

# Read your data to a pandas Dataframe (and parse dates)
df = pd.read_csv(pd.compat.StringIO(data),parse_dates=dates)

# Create new columns with diff in seconds
df['destination_arrival_diff'] = round((df[dates[0]]-df[dates[1]]).dt.total_seconds())
df['origin_departure_diff'] = round((df[dates[2]]-df[dates[3]]).dt.total_seconds())

# Define a merge function
# https://stackoverflow.com/a/29847323/7386332
def merge(d1, d2):
    for k in d2:
        if k in d1 and isinstance(d1[k], dict) and isinstance(d2[k], dict):
            merge(d1[k], d2[k])
        else:
            d1[k] = d2[k] 

# Loop through rows and add dictionaries to array output
# https://stackoverflow.com/a/30135649/7386332
splitkey = "_"
output = []
for ind, row in df.iterrows():
    d = {}
    for k in row.index:
        merge(d,reduce(lambda res, cur: {cur: res}, reversed(k.split(splitkey)), str(row[k])))
    output.append(d)

print(json.dumps(output,indent=2))

返回：

[
  {
    "destination": {
      "name": "Pune",
      "code": "Pun",
      "arrival": {
        "actual": "2017-12-10 11:52:05",
        "scheduled": "2017-12-10 11:50:00",
        "diff": "125.0"
      }
    },
    "origin": {
      "name": "Delhi",
      "code": "Del",
      "departure": {
        "actual": "2017-12-10 10:30:18",
        "scheduled": "2017-12-10 10:30:00",
        "diff": "18.0"
      }
    },
    "train": {
      "code": "D220",
      "date": "2017-01-17 00:00:00"
    }
  },
  {
    "destination": {
      "name": "Mumbai",
      "code": "mum",
      "arrival": {
        "actual": "2017-12-10
...
}