任务描述:当前有一份excel表格数据,里面存在缺失值,需要对缺失的数据到es数据库中进行查找并对其进行把缺失的数据进行补全。excel表格数据如下所示:
一、构建es库中的数据
1.1 创建索引:
# 创建physical_examination索引
PUT /physical_examination
{
"settings": {
"index": {
"number_of_shards": "1",
"number_of_replicas": "1"
}
},
"mappings": {
"properties": {
"nums": {
"type": "integer"
},
"name": {
"type": "text"
},
"sex": {
"type": "text"
},
"phone": {
"type": "integer"
},
"result": {
"type": "text"
}
}
}
}
1.2 插入数据:
【注意:json数据不能格式化换行,否则报错】
# 向physical_examination索引中添加数据
POST physical_examination/_bulk
{"index":{"_id":"1"}}
{"nums":1,"name":"刘一","sex":"男","phone":1234567891,"result":"优秀"}
{"index":{"_id":"2"}}
{"nums":2,"name":"陈二","sex":"男","phone":1234567892,"result":"优秀"}
{"index":{"_id":"3"}}
{"nums":3,"name":"张三","sex":"男","phone":1234567893,"result":"优秀"}
{"index":{"_id":"4"}}
{"nums":4,"name":"李四","sex":"男","phone":1234567894,"result":"优秀"}
{"index":{"_id":"5"}}
{"nums":5,"name":"王五","sex":"男","phone":1234567895,"result":"优秀"}
1.3 查询数据:
【注意:默认查询索引下的所有数据】
# 查询索引中的所有数据
GET physical_examination/_search
{
"query": {
"match_all": {}
}
}
二、对excel表格中的数据处理操作
2.1 导出es查询的数据
方法一:直接在kibana或postman查询的结果中进行复制粘贴到一个文档。
方法二:使用kibana导出数据。
方法三:使用postman导出数据保存到本地。
使用python处理数据,获取需要的数据。
示例代码:
# 读取json中体检信息
with open('./data/physical_examination.json', 'r', encoding='utf-8') as f:
data_json = f.read()
print(data_json)
# 处理json数据中的异常数据
if 'false' in data_json:
data_json = data_json.replace('false', "False")
data_json = eval(data_json)
print(data_json)
print(data_json['hits']['hits'])
print('*' * 100)
valid_data = data_json['hits']['hits']
need_data = []
for data in valid_data:
print(data['_source'])
need_data.append(data['_source'])
print(need_data)
读取缺失数据的excel表格,把缺失的数据填补进去。
# 读取需要填补数据的表格
data_xlsx = pd.read_excel('./data/体检表.xlsx', sheet_name='Sheet1')
# print(data_xlsx)
# 获取excel表格的行列
row, col = data_xlsx.shape
print(row, col)
# 修改表格中的数据
for i in range(row):
bb = data_xlsx.iloc[i]
print(bb['姓名'], bb['手机号'])
if pd.isnull(bb['手机号']):
bb['手机号'] = '666'
for cc in need_data:
if cc['name'] == bb['姓名']:
bb['手机号'] = cc['phone']
data_xlsx.iloc[i, 3] = bb['手机号']
print(bb['姓名'], bb['手机号'])
print("-" * 100)
print(data_xlsx)
将最终处理好的数据保存在新建的文件中。
# 保存数据到新文件中
data_xlsx.to_excel('./data/new_data.xlsx', sheet_name='Sheet1', index=False, header=True)
完整代码如下:
import pandas as pd
# 读取json中体检信息
with open('./data/physical_examination.json', 'r', encoding='utf-8') as f:
data_json = f.read()
print(data_json)
# 处理json数据中的异常数据
if 'false' in data_json:
data_json = data_json.replace('false', "False")
data_json = eval(data_json)
print(data_json)
print(data_json['hits']['hits'])
print('*' * 100)
valid_data = data_json['hits']['hits']
need_data = []
for data in valid_data:
print(data['_source'])
need_data.append(data['_source'])
print(need_data)
# 读取需要填补数据的表格
data_xlsx = pd.read_excel('./data/体检表.xlsx', sheet_name='Sheet1')
# print(data_xlsx)
# 获取excel表格的行列
row, col = data_xlsx.shape
print(row, col)
# 修改表格中的数据
for i in range(row):
bb = data_xlsx.iloc[i]
print(bb['姓名'], bb['手机号'])
if pd.isnull(bb['手机号']):
bb['手机号'] = '666'
for cc in need_data:
if cc['name'] == bb['姓名']:
bb['手机号'] = cc['phone']
data_xlsx.iloc[i, 3] = bb['手机号']
print(bb['姓名'], bb['手机号'])
print("-" * 100)
print(data_xlsx)
# 保存数据到新文件中
data_xlsx.to_excel('./data/new_data.xlsx', sheet_name='Sheet1', index=False, header=True)
运行效果,最终处理好的数据如下所示: