> ## Documentation Index
> Fetch the complete documentation index at: https://docs.powerdrill.ai/llms.txt
> Use this file to discover all available pages before exploring further.
> Obtains the job history retained in the specified session.
# Get job history in session
**Example request:**
```curl cURL theme={null}
curl --request GET \
--url https://ai.data.cloud/api/v2/team/sessions/{id}/history?user_id=tmm-dafasdfasdfasdf \
--header 'x-pd-api-key: '
```
```python Python theme={null}
import requests
url = "https://ai.data.cloud/api/v2/team/sessions/{id}/history?user_id=tmm-dafasdfasdfasdf"
headers = {"x-pd-api-key": ""}
response = requests.request("GET", url, headers=headers)
print(response.text)
```
**Example response:**
````json 200 theme={null}
{
"code": 0,
"data": {
"total_items": 1,
"page_number": 1,
"page_size": 10,
"records": [
{
"job_id": "job-1dsfasddfasgddsaffds",
"question": {
"blocks": [
{
"type": "MESSAGE",
"content": "Check for negative values across all sales columns"
}
]
},
"answer": {
"blocks": [
{
"type": "MESSAGE",
"content": "- Check for negative values across all sales columns.\n- Filter the DataFrame to retain only rows with no negative sales values.",
"group_id": "ba582fc9-bb36-4c5d-a8e8-d35bda6389cd",
"group_name": "Identify the channels with negative sales values by examining each day's sales data. Filter out the rows where any sales value is negative.",
"stage": "Analyze"
},
{
"type": "CODE",
"content": "```python\n\nimport pandas as pd\n\ndef invoke(input_0: pd.DataFrame) -> pd.DataFrame:\n '''\n input_0: pd.DataFrame SalesByChannelByDay_Summary_Demo.Sheet1_0_table_1.csv\n '''\n # Identify columns that represent sales data (all except the first column)\n sales_columns = input_0.columns[1:]\n \n # Filter rows where any sales value is negative\n filtered_df = input_0[~(input_0[sales_columns] < 0).any(axis=1)]\n \n # Assign the result to the output variable\n output = filtered_df\n \n return output\n\n```",
"group_id": "ba582fc9-bb36-4c5d-a8e8-d35bda6389cd",
"group_name": "Identify the channels with negative sales values by examining each day's sales data. Filter out the rows where any sales value is negative.",
"stage": "Analyze"
},
{
"type": "TABLE",
"content": "https://s3.amazonaws.com/powerdrilltest/tmp_datasource_cache/code_result/clvl4cad2001q01l1m522hxlu/baf7d6d1-fb81-4fdb-bcdd-32923d214c7b.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241018T104617Z&X-Amz-SignedHeaders=host&X-Amz-Expires=599&X-Amz-Credential=AKIARLSQLXURHEIDN4OZ%2F20241018%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=9bcb5af552793f162e35f41d62fb9306cf90888924bfbdce81ea687265fddf83",
"group_id": "ba582fc9-bb36-4c5d-a8e8-d35bda6389cd",
"group_name": "Identify the channels with negative sales values by examining each day's sales data. Filter out the rows where any sales value is negative.",
"stage": "Analyze"
},
{
"type": "MESSAGE",
"content": "- Sum the sales across all days for each channel.\n- Create a new DataFrame with the channel names and their corresponding total sales.",
"group_id": "47183fd1-307b-4408-9986-e9238d952ec1",
"group_name": "Calculate the overall sales trend for the identified channels with negative sales values. This involves summing up the sales across all days for each channel and analyzing the trend.",
"stage": "Analyze"
},
{
"type": "CODE",
"content": "```python\n\nimport pandas as pd\n\ndef invoke(negative_sales_channels: pd.DataFrame) -> pd.DataFrame:\n '''\n negative_sales_channels: pd.DataFrame negative_sales_channels.csv\n '''\n # Sum the sales across all days for each channel\n total_sales = negative_sales_channels.iloc[:, 1:].sum(axis=1)\n \n # Create a new DataFrame with the channel names and their corresponding total sales\n output = pd.DataFrame({\n 'Channel': negative_sales_channels.iloc[:, 0],\n 'Total Sales': total_sales\n })\n \n return output\n\n```",
"group_id": "47183fd1-307b-4408-9986-e9238d952ec1",
"group_name": "Calculate the overall sales trend for the identified channels with negative sales values. This involves summing up the sales across all days for each channel and analyzing the trend.",
"stage": "Analyze"
},
{
"type": "TABLE",
"content": "https://s3.amazonaws.com/powerdrilltest/tmp_datasource_cache/code_result/clvl4cad2001q01l1m522hxlu/10cffac2-8bf3-45f4-86e6-1ed8457329f2.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241018T104617Z&X-Amz-SignedHeaders=host&X-Amz-Expires=600&X-Amz-Credential=AKIARLSQLXURHEIDN4OZ%2F20241018%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=c6f5b522d2ddceea730304b86a45d5f5165f05f9fda3c1d275d11e9022c1e7ac",
"group_id": "47183fd1-307b-4408-9986-e9238d952ec1",
"group_name": "Calculate the overall sales trend for the identified channels with negative sales values. This involves summing up the sales across all days for each channel and analyzing the trend.",
"stage": "Analyze"
},
{
"type": "MESSAGE",
"content": "- Replace any negative sales values with zero in the data.\n- Sum the sales across all days for each channel.\n- Create a new data structure with the summed sales values.",
"group_id": "6b93c2b1-8908-4c2b-afb2-2a81f2d24739",
"group_name": "Calculate the overall sales trend for the same channels but excluding the negative sales values. This involves setting negative values to zero or removing them and then summing up the sales across all days for each channel.",
"stage": "Analyze"
},
{
"type": "CODE",
"content": "```python\n\nimport pandas as pd\n\ndef invoke(negative_sales_channels: pd.DataFrame) -> pd.DataFrame:\n '''\n negative_sales_channels: pd.DataFrame negative_sales_channels.csv\n '''\n # Replace negative values with zero\n negative_sales_channels.iloc[:, 1:] = negative_sales_channels.iloc[:, 1:].clip(lower=0)\n \n # Sum the sales across all days for each channel\n sales_sum = negative_sales_channels.iloc[:, 1:].sum(axis=1)\n \n # Create a new DataFrame with the summed sales values\n output = pd.DataFrame({\n 'Channel': negative_sales_channels.iloc[:, 0],\n 'Total Sales': sales_sum\n })\n \n return output\n\n```",
"group_id": "6b93c2b1-8908-4c2b-afb2-2a81f2d24739",
"group_name": "Calculate the overall sales trend for the same channels but excluding the negative sales values. This involves setting negative values to zero or removing them and then summing up the sales across all days for each channel.",
"stage": "Analyze"
},
{
"type": "TABLE",
"content": "https://s3.amazonaws.com/powerdrilltest/tmp_datasource_cache/code_result/clvl4cad2001q01l1m522hxlu/f4c99616-dd7c-48b1-8a35-d3141d732c36.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241018T104617Z&X-Amz-SignedHeaders=host&X-Amz-Expires=600&X-Amz-Credential=AKIARLSQLXURHEIDN4OZ%2F20241018%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=d21a8d939ab09547bc201754ba253ca6c0d1da5361752f2228237e3ff59be256",
"group_id": "6b93c2b1-8908-4c2b-afb2-2a81f2d24739",
"group_name": "Calculate the overall sales trend for the same channels but excluding the negative sales values. This involves setting negative values to zero or removing them and then summing up the sales across all days for each channel.",
"stage": "Analyze"
},
{
"type": "MESSAGE",
"content": "- Merge the two datasets on the 'Channel' column to align sales data for comparison.\n- Calculate the difference in 'Total Sales' between the datasets for each channel.\n- Store the results, including channel name and calculated difference, in a new dataset.",
"group_id": "3488f538-f7fc-4c0e-a265-b66e3a38d41e",
"group_name": "Compare the sales trends with and without negative sales values to determine the impact of negative sales on the overall sales trend for the affected channels.",
"stage": "Analyze"
},
{
"type": "CODE",
"content": "```python\n\nimport pandas as pd\n\ndef invoke(sales_trend_with_negatives: pd.DataFrame, sales_trend_without_negatives: pd.DataFrame) -> pd.DataFrame:\n # Merge the two DataFrames on the 'Channel' column\n merged_df = pd.merge(sales_trend_with_negatives, sales_trend_without_negatives, on='Channel', suffixes=('_with_negatives', '_without_negatives'))\n \n # Calculate the difference in 'Total Sales' between the two DataFrames\n merged_df['Sales Difference'] = merged_df['Total Sales_without_negatives'] - merged_df['Total Sales_with_negatives']\n \n # Create a new DataFrame to store the results\n output = merged_df[['Channel', 'Sales Difference']]\n \n return output\n\n```",
"group_id": "3488f538-f7fc-4c0e-a265-b66e3a38d41e",
"group_name": "Compare the sales trends with and without negative sales values to determine the impact of negative sales on the overall sales trend for the affected channels.",
"stage": "Analyze"
},
{
"type": "TABLE",
"content": "https://s3.amazonaws.com/powerdrilltest/tmp_datasource_cache/code_result/clvl4cad2001q01l1m522hxlu/aaf4f2f7-e2db-4f2e-98ae-0bdd18f42333.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241018T104617Z&X-Amz-SignedHeaders=host&X-Amz-Expires=600&X-Amz-Credential=AKIARLSQLXURHEIDN4OZ%2F20241018%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=19fcfe97c70bf40292b920ecbf0299c101e1de11e49b54fb8f62934b6e874f52",
"group_id": "3488f538-f7fc-4c0e-a265-b66e3a38d41e",
"group_name": "Compare the sales trends with and without negative sales values to determine the impact of negative sales on the overall sales trend for the affected channels.",
"stage": "Analyze"
},
{
"type": "MESSAGE",
"content": "\n\n`Analyzing Conclusions` \n\n### The impact of negative sales values on overall sales trends\n\n#### Sales variance analysis\n\n",
"group_id": "fd1a62e6-48cf-4ac1-8bac-025665444710",
"group_name": "Conclusions",
"stage": "Respond"
},
{
"type": "TABLE",
"content": "https://s3.amazonaws.com/powerdrilltest/tmp_datasource_cache/code_result/clvl4cad2001q01l1m522hxlu/aaf4f2f7-e2db-4f2e-98ae-0bdd18f42333.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241018T104617Z&X-Amz-SignedHeaders=host&X-Amz-Expires=600&X-Amz-Credential=AKIARLSQLXURHEIDN4OZ%2F20241018%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=19fcfe97c70bf40292b920ecbf0299c101e1de11e49b54fb8f62934b6e874f52",
"group_id": "fd1a62e6-48cf-4ac1-8bac-025665444710",
"group_name": "Conclusions",
"stage": "Respond"
},
{
"type": "MESSAGE",
"content": "\n\n- **Sales variance**：In all channels (including EC, JD, Tmall, WeChat, retail, corporate stores, outlets, and total), the sales difference is 0.0. This indicates that regardless of the presence of negative sales values, the sales trend has not changed.\n\n#### Conclusion and Insights\n- **The impact of negative sales values**：Based on the provided data, negative sales values have no impact on the overall sales trend of the affected channels, as the sales variance for all channels is 0.0.\n- **Data consistency**：The sales discrepancies across all channels are consistent, indicating that there are no anomalies or deviations caused by negative sales values during data processing or analysis.",
"group_id": "fd1a62e6-48cf-4ac1-8bac-025665444710",
"group_name": "Conclusions",
"stage": "Respond"
},
{
"type": "SOURCES",
"content": [
{
"source": "SalesByChannelByDay_Summary_Demo.xlsx",
"datasource_id": "cm2ej4wmo000001fcdkwbdrml",
"dataset_id": "cm2ej4vx900hp01l1o378zr9o",
"file_type": "xlsx",
"external_id": ""
}
],
"group_id": "",
"group_name": "",
"stage": "Respond"
},
{
"type": "QUESTIONS",
"content": [
"Analyze the specific channels with negative sales values on different dates and discuss whether the sales strategies of these channels might lead to negative values.",
"Study the long-term impact of negative sales values on overall sales trends and assess whether adjustments to data analysis methods are needed to more accurately reflect the actual situation.",
"Investigate the source of negative sales values, whether they are related to returns, discounts, or other factors, and propose possible solutions to reduce the occurrence of negative values."
],
"group_id": "-1",
"stage": "Respond"
}
]
}
}
]
}
}
````
## OpenAPI
````yaml get /v2/team/sessions/{id}/history
openapi: 3.0.1
info:
title: Team Open API Snake
description: ''
version: 1.0.0
servers:
- url: https://ai.data.cloud/api
description: 体验环境
security:
- apikey-header-x-pd-api-key: []
tags:
- name: Session
- name: Dataset
- name: Data source
- name: File
- name: Job
paths:
/v2/team/sessions/{id}/history:
get:
tags:
- Session
summary: Get job history in session
description: Obtains the job history contained in the specified session.
parameters:
- name: id
in: path
description: >-
The ID of the target session.
To check sessions existing in your project, call [GET
/v2/team/sessions](/api-reference/v2/list-sessions).
required: true
schema:
type: string
- name: page_number
in: query
description: >-
The page number to start listing. If not specified, the default
value `1` will be used.
required: false
schema:
type: integer
- name: page_size
in: query
description: >-
The number of items on a single page. If not specified, the default
value `10` will be used.
required: false
schema:
type: integer
- name: user_id
in: query
description: >-
Your user ID, which uniquely identifies you within your team. To
obtain your ID:
- If you're the team admin, refer to [Check user
information](/enterprise/users#check-user-information).
- If you're a system or virtual user, ask your team admin to check
your user ID by referring to [Check user
information](/enterprise/users#check-user-information).
required: true
schema:
type: string
- name: x-pd-external-trace-id
in: header
description: >-
The trace ID you set in your system to trace this request. It can be
up to 128 characters in length. If the request fails, you can
provide it to the Powerdrill team to help with troubleshooting.
required: false
example: ''
schema:
type: string
responses:
'200':
description: ''
content:
application/json:
schema:
type: object
properties:
code:
type: integer
description: >-
Status code. **0** indicates that the operation is
successful. Otherwise, the operation fails. For error
troubleshooting, refer to [Error
Codes](/api-reference/error-codes).
data:
type: object
properties:
total_items:
type: integer
description: >-
The total number of job records returned in the
response.
page_number:
type: integer
description: The page number of the current page.
page_size:
type: integer
description: >-
The number of items that are returned in a single
page.
records:
type: array
items:
description: Information about each job record.
type: object
properties:
question:
description: Your question.
type: object
properties:
blocks:
type: array
items:
type: object
properties:
type:
type: string
description: >-
The content type of the question block.
Possible values are:
- `MESSAGE`: The content is a piece of
text.
- `CODE`: The content a code snippet in
Markdown format.
enum:
- MESSAGE
- CODE
content:
type: string
description: >-
The block content. It is a piece of text
when the `type` is `MESSAGE`, and a code
snippet when the `type` is `CODE`.
description: The content of each question block.
required:
- type
- content
description: >-
A list of content blocks that make up the
entire question.
required:
- blocks
answer:
description: Powerdrill's answer to the question.
type: object
properties:
blocks:
type: array
items:
$ref: '#/components/schemas/BlockDTO'
description: >-
A list of content blocks that make up the
entire answer.
description: Powerdrill's answer.
required:
- blocks
job_id:
type: string
description: >-
The job ID, which uniquely identifies the job in
the session.
required:
- question
- answer
- job_id
description: A list of job records returned on a page.
required:
- total_items
- page_number
- page_size
- records
description: A paginated list of job records.
required:
- data
- code
example:
code: 0
data:
total_items: 1
page_number: 1
page_size: 10
records:
- job_id: job-1dsfasddfasgddsaffds
question:
blocks:
- type: MESSAGE
content: Check for negative values across all sales columns
answer:
blocks:
- type: MESSAGE
content: >-
- Check for negative values across all sales
columns.
- Filter the DataFrame to retain only rows with no
negative sales values.
group_id: ba582fc9-bb36-4c5d-a8e8-d35bda6389cd
group_name: >-
Identify the channels with negative sales values
by examining each day's sales data. Filter out the
rows where any sales value is negative.
stage: Analyze
- type: CODE
content: |-
```python
import pandas as pd
def invoke(input_0: pd.DataFrame) -> pd.DataFrame:
'''
input_0: pd.DataFrame SalesByChannelByDay_Summary_Demo.Sheet1_0_table_1.csv
'''
# Identify columns that represent sales data (all except the first column)
sales_columns = input_0.columns[1:]
# Filter rows where any sales value is negative
filtered_df = input_0[~(input_0[sales_columns] < 0).any(axis=1)]
# Assign the result to the output variable
output = filtered_df
return output
```
group_id: ba582fc9-bb36-4c5d-a8e8-d35bda6389cd
group_name: >-
Identify the channels with negative sales values
by examining each day's sales data. Filter out the
rows where any sales value is negative.
stage: Analyze
- type: TABLE
content: >-
https://s3.amazonaws.com/powerdrilltest/tmp_datasource_cache/code_result/clvl4cad2001q01l1m522hxlu/baf7d6d1-fb81-4fdb-bcdd-32923d214c7b.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241018T104617Z&X-Amz-SignedHeaders=host&X-Amz-Expires=599&X-Amz-Credential=AKIARLSQLXURHEIDN4OZ%2F20241018%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=9bcb5af552793f162e35f41d62fb9306cf90888924bfbdce81ea687265fddf83
group_id: ba582fc9-bb36-4c5d-a8e8-d35bda6389cd
group_name: >-
Identify the channels with negative sales values
by examining each day's sales data. Filter out the
rows where any sales value is negative.
stage: Analyze
- type: MESSAGE
content: >-
- Sum the sales across all days for each channel.
- Create a new DataFrame with the channel names
and their corresponding total sales.
group_id: 47183fd1-307b-4408-9986-e9238d952ec1
group_name: >-
Calculate the overall sales trend for the
identified channels with negative sales values.
This involves summing up the sales across all days
for each channel and analyzing the trend.
stage: Analyze
- type: CODE
content: >-
```python
import pandas as pd
def invoke(negative_sales_channels: pd.DataFrame)
-> pd.DataFrame:
'''
negative_sales_channels: pd.DataFrame negative_sales_channels.csv
'''
# Sum the sales across all days for each channel
total_sales = negative_sales_channels.iloc[:, 1:].sum(axis=1)
# Create a new DataFrame with the channel names and their corresponding total sales
output = pd.DataFrame({
'Channel': negative_sales_channels.iloc[:, 0],
'Total Sales': total_sales
})
return output
```
group_id: 47183fd1-307b-4408-9986-e9238d952ec1
group_name: >-
Calculate the overall sales trend for the
identified channels with negative sales values.
This involves summing up the sales across all days
for each channel and analyzing the trend.
stage: Analyze
- type: TABLE
content: >-
https://s3.amazonaws.com/powerdrilltest/tmp_datasource_cache/code_result/clvl4cad2001q01l1m522hxlu/10cffac2-8bf3-45f4-86e6-1ed8457329f2.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241018T104617Z&X-Amz-SignedHeaders=host&X-Amz-Expires=600&X-Amz-Credential=AKIARLSQLXURHEIDN4OZ%2F20241018%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=c6f5b522d2ddceea730304b86a45d5f5165f05f9fda3c1d275d11e9022c1e7ac
group_id: 47183fd1-307b-4408-9986-e9238d952ec1
group_name: >-
Calculate the overall sales trend for the
identified channels with negative sales values.
This involves summing up the sales across all days
for each channel and analyzing the trend.
stage: Analyze
- type: MESSAGE
content: >-
- Replace any negative sales values with zero in
the data.
- Sum the sales across all days for each channel.
- Create a new data structure with the summed
sales values.
group_id: 6b93c2b1-8908-4c2b-afb2-2a81f2d24739
group_name: >-
Calculate the overall sales trend for the same
channels but excluding the negative sales values.
This involves setting negative values to zero or
removing them and then summing up the sales across
all days for each channel.
stage: Analyze
- type: CODE
content: >-
```python
import pandas as pd
def invoke(negative_sales_channels: pd.DataFrame)
-> pd.DataFrame:
'''
negative_sales_channels: pd.DataFrame negative_sales_channels.csv
'''
# Replace negative values with zero
negative_sales_channels.iloc[:, 1:] = negative_sales_channels.iloc[:, 1:].clip(lower=0)
# Sum the sales across all days for each channel
sales_sum = negative_sales_channels.iloc[:, 1:].sum(axis=1)
# Create a new DataFrame with the summed sales values
output = pd.DataFrame({
'Channel': negative_sales_channels.iloc[:, 0],
'Total Sales': sales_sum
})
return output
```
group_id: 6b93c2b1-8908-4c2b-afb2-2a81f2d24739
group_name: >-
Calculate the overall sales trend for the same
channels but excluding the negative sales values.
This involves setting negative values to zero or
removing them and then summing up the sales across
all days for each channel.
stage: Analyze
- type: TABLE
content: >-
https://s3.amazonaws.com/powerdrilltest/tmp_datasource_cache/code_result/clvl4cad2001q01l1m522hxlu/f4c99616-dd7c-48b1-8a35-d3141d732c36.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241018T104617Z&X-Amz-SignedHeaders=host&X-Amz-Expires=600&X-Amz-Credential=AKIARLSQLXURHEIDN4OZ%2F20241018%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=d21a8d939ab09547bc201754ba253ca6c0d1da5361752f2228237e3ff59be256
group_id: 6b93c2b1-8908-4c2b-afb2-2a81f2d24739
group_name: >-
Calculate the overall sales trend for the same
channels but excluding the negative sales values.
This involves setting negative values to zero or
removing them and then summing up the sales across
all days for each channel.
stage: Analyze
- type: MESSAGE
content: >-
- Merge the two datasets on the 'Channel' column
to align sales data for comparison.
- Calculate the difference in 'Total Sales'
between the datasets for each channel.
- Store the results, including channel name and
calculated difference, in a new dataset.
group_id: 3488f538-f7fc-4c0e-a265-b66e3a38d41e
group_name: >-
Compare the sales trends with and without negative
sales values to determine the impact of negative
sales on the overall sales trend for the affected
channels.
stage: Analyze
- type: CODE
content: >-
```python
import pandas as pd
def invoke(sales_trend_with_negatives:
pd.DataFrame, sales_trend_without_negatives:
pd.DataFrame) -> pd.DataFrame:
# Merge the two DataFrames on the 'Channel' column
merged_df = pd.merge(sales_trend_with_negatives, sales_trend_without_negatives, on='Channel', suffixes=('_with_negatives', '_without_negatives'))
# Calculate the difference in 'Total Sales' between the two DataFrames
merged_df['Sales Difference'] = merged_df['Total Sales_without_negatives'] - merged_df['Total Sales_with_negatives']
# Create a new DataFrame to store the results
output = merged_df[['Channel', 'Sales Difference']]
return output
```
group_id: 3488f538-f7fc-4c0e-a265-b66e3a38d41e
group_name: >-
Compare the sales trends with and without negative
sales values to determine the impact of negative
sales on the overall sales trend for the affected
channels.
stage: Analyze
- type: TABLE
content: >-
https://s3.amazonaws.com/powerdrilltest/tmp_datasource_cache/code_result/clvl4cad2001q01l1m522hxlu/aaf4f2f7-e2db-4f2e-98ae-0bdd18f42333.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241018T104617Z&X-Amz-SignedHeaders=host&X-Amz-Expires=600&X-Amz-Credential=AKIARLSQLXURHEIDN4OZ%2F20241018%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=19fcfe97c70bf40292b920ecbf0299c101e1de11e49b54fb8f62934b6e874f52
group_id: 3488f538-f7fc-4c0e-a265-b66e3a38d41e
group_name: >-
Compare the sales trends with and without negative
sales values to determine the impact of negative
sales on the overall sales trend for the affected
channels.
stage: Analyze
- type: MESSAGE
content: >+
`Analyzing Conclusions`
### The impact of negative sales values on overall
sales trends
#### Sales variance analysis
group_id: fd1a62e6-48cf-4ac1-8bac-025665444710
group_name: Conclusions
stage: Respond
- type: TABLE
content: >-
https://s3.amazonaws.com/powerdrilltest/tmp_datasource_cache/code_result/clvl4cad2001q01l1m522hxlu/aaf4f2f7-e2db-4f2e-98ae-0bdd18f42333.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241018T104617Z&X-Amz-SignedHeaders=host&X-Amz-Expires=600&X-Amz-Credential=AKIARLSQLXURHEIDN4OZ%2F20241018%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=19fcfe97c70bf40292b920ecbf0299c101e1de11e49b54fb8f62934b6e874f52
group_id: fd1a62e6-48cf-4ac1-8bac-025665444710
group_name: Conclusions
stage: Respond
- type: MESSAGE
content: >-
- **Sales variance**：In all channels (including
EC, JD, Tmall, WeChat, retail, corporate stores,
outlets, and total), the sales difference is 0.0.
This indicates that regardless of the presence of
negative sales values, the sales trend has not
changed.
#### Conclusion and Insights
- **The impact of negative sales values**：Based on
the provided data, negative sales values have no
impact on the overall sales trend of the affected
channels, as the sales variance for all channels
is 0.0.
- **Data consistency**：The sales discrepancies
across all channels are consistent, indicating
that there are no anomalies or deviations caused
by negative sales values during data processing or
analysis.
group_id: fd1a62e6-48cf-4ac1-8bac-025665444710
group_name: Conclusions
stage: Respond
- type: SOURCES
content:
- source: SalesByChannelByDay_Summary_Demo.xlsx
datasource_id: cm2ej4wmo000001fcdkwbdrml
dataset_id: cm2ej4vx900hp01l1o378zr9o
file_type: xlsx
external_id: ''
group_id: ''
group_name: ''
stage: Respond
- type: QUESTIONS
content:
- >-
Analyze the specific channels with negative
sales values on different dates and discuss
whether the sales strategies of these channels
might lead to negative values.
- >-
Study the long-term impact of negative sales
values on overall sales trends and assess
whether adjustments to data analysis methods are
needed to more accurately reflect the actual
situation.
- >-
Investigate the source of negative sales values,
whether they are related to returns, discounts,
or other factors, and propose possible solutions
to reduce the occurrence of negative values.
group_id: '-1'
stage: Respond
headers:
x-pd-trace-id:
example: ''
required: true
description: >-
The trace ID returned by Powerdrill. If a failure occurs, you
can provide it to the Powerdrill team to assist with
troubleshooting.
schema:
type: string
deprecated: false
security:
- apikey-header-x-pd-api-key: []
components:
schemas:
BlockDTO:
type: object
properties:
type:
type: string
description: >-
The content type of the answer block. Possible values are:
- `MESSAGE`: The content is a piece of text.
- `CODE`: The content a code snippet.
- `TABLE`: The content represents a table, consisting of:
- `name`: The `.csv` file name.
- `url`: The S3 key or URL to the file.
- `expires_at`: The expiration time for `url`. To save the table for future use, make sure to download it before it expires.
- `IMAGE`: The content represents an image, consisting of:
- `name`: The image name.
- `url`: The S3 key or URL to the image.
- `expires_at`: The expiration time for `url`. To save the image for future use, make sure to download it before it expires.
- `SOURCES`: The content represents a reference source of the answer
block, including:
- `id`: The ID of the source.
- `content`: The chunk content.
- `page_no`: The location of the chunk in the source file.
- `source`: The file name of the data source.
- `datasource_id`: The ID of the data source.
- `dataset_id`: The ID of the dataset.
- `file_type`: The name extension of the data source file.
- `QUESTIONS`: Suggested questions generated by Powerdrill to help
guide your follow-up exploration of the data.
- `CHART_INFO`: The content is a generated chart or graph.
- When the block content type is `CHART_INFO`:
The content is a generated chart, including:
- `code`: The code executed to generate the chart.
- `code_type`: The code language, which is fixed to `PYTHON`.
- `name`: The name of the **.csv** analysis result file on which the chart is based.
- `url`: The URL of the **.csv** analysis result file.
- `image`: The static image of the chart.
- `options`: [Available chart types](https://pyecharts.org/#/en-us/) you can select.
- `columns`: The column headers from the **.csv** analysis result file.
- `chart_config`: A JSON string containing configuration settings for rendering the chart (details at [pyecharts.org](https://pyecharts.org/#/en-us/)).
- `expires_at`: The expiration timestamp for the `url`. Make sure to download the file before it expires to retain access.
enum:
- MESSAGE
- CODE
- TABLE
- SOURCES
- QUESTIONS
content:
type: string
description: >-
The block content, which varies with the value of `type`:
- When `type` is `MESSAGE`, the content is a piece of text.
- When `type` is ` CODE`, the content a code snippet in Markdown
format.
- When `type` is `TABLE`, the content represents a table, consisting
of:
- `name`: The `.csv` file name.
- `url`: The S3 key or URL to the file.
- `expires_at`: The expiration time for `url`. To save the table for future use, make sure to download it before it expires.
- When `type` is `IMAGE`, the content represents an image,
consisting of:
- `name`: The image name.
- `url`: The S3 key or URL to the image.
- `expires_at`: The expiration time for `url`.
To save the image for future use, make sure to download it before it expires.
- When `type` is `SOURCE`, the content represents a reference source
of the answer block, including:
- `source`: The file name of the data source.
- `datasource_id`: The ID of the data source.
- `dataset_id`: The ID of the dataset.
- `file_type`: The name extension of the data source file.
- When `type` is `QUESTIONS`, the content is suggested questions
generated by Powerdrill to help guide your follow-up exploration of
the data.
group_id:
type: string
description: The ID of the group containing the answer block.
group_name:
type: string
description: The name of the group containing the answer block.
stage:
type: string
enum:
- Analyze
- Respond
description: >-
There are two phases when Powerdrill generates an answer: `Analyze`
and `Respond`. Answer blocks in the `Analyze` phase are not part of
the final answer; they represent the output of the analysis process
and are used to help you understand how the answer is generated.
Answer blocks in the `Respond` phase are the final answer generated
by Powerdrill in response to your question.
required:
- type
- content
- group_id
- group_name
- stage
securitySchemes:
apikey-header-x-pd-api-key:
type: apiKey
in: header
name: x-pd-api-key
````