> ## 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. # Get job history in session > Obtains the job history contained in the specified session. ## OpenAPI ````yaml get /v1/team/sessions/{sessionId}/history openapi: 3.0.1 info: title: Team Open API description: '' version: 1.0.0 servers: - url: https://ai.data.cloud/api description: 体验环境 security: [] tags: - name: Session - name: Dataset - name: Data source - name: File - name: Job paths: /v1/team/sessions/{sessionId}/history: get: tags: - Session summary: Get job history in session description: Obtains the job history contained in the specified session. parameters: - name: sessionId in: path description: The session ID. required: true schema: type: string - name: pageNumber in: query description: >- The number of pages to display. If not specified, the default value `1` will be used. required: false schema: type: integer - name: pageSize 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: userId in: query description: >- Your user ID. How to obtain your user ID: Sign in to Powerdrill Enterprise, click **Users** in the top navigation bar, and find your user ID in the list of users. required: true schema: type: string - name: x-pd-external-trace-id in: header description: The external trace ID. The length ranges from 0 to 128 characters. 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. Possible error codes include: - 100: Bad request. - 201: Request frequency limit. - 9999: Internal error. - 100001: Invalid operation. data: type: object properties: total: type: integer description: The total number of pages displayed. pageNumber: type: integer description: The number of pages returned. pageSize: type: integer description: The number of items displayed per page. records: type: array items: description: The information about each record. type: object properties: question: description: The user's question. type: object properties: blocks: type: array items: type: object properties: type: type: string enum: - MESSAGE - CODE - TABLE - IMAGE - SOURCES - QUESTIONS description: The block type. content: type: string description: The block content. required: - type - content required: - blocks answer: description: The answer to the question. type: object properties: blocks: type: array items: $ref: '#/components/schemas/BlockDTO' required: - blocks jobId: type: string description: The job ID. required: - question - answer - jobId description: The list of returned records. required: - total - pageNumber - pageSize - records description: The returned data object. required: - data - code examples: '1': summary: Success example value: code: 0 data: total: 0 pageNumber: 1 pageSize: 10 records: - jobId: 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. groupId: ba582fc9-bb36-4c5d-a8e8-d35bda6389cd groupName: >- 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 ``` groupId: ba582fc9-bb36-4c5d-a8e8-d35bda6389cd groupName: >- 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 groupId: ba582fc9-bb36-4c5d-a8e8-d35bda6389cd groupName: >- 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. groupId: 47183fd1-307b-4408-9986-e9238d952ec1 groupName: >- 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 ``` groupId: 47183fd1-307b-4408-9986-e9238d952ec1 groupName: >- 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 groupId: 47183fd1-307b-4408-9986-e9238d952ec1 groupName: >- 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. groupId: 6b93c2b1-8908-4c2b-afb2-2a81f2d24739 groupName: >- 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 ``` groupId: 6b93c2b1-8908-4c2b-afb2-2a81f2d24739 groupName: >- 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 groupId: 6b93c2b1-8908-4c2b-afb2-2a81f2d24739 groupName: >- 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. groupId: 3488f538-f7fc-4c0e-a265-b66e3a38d41e groupName: >- 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 ``` groupId: 3488f538-f7fc-4c0e-a265-b66e3a38d41e groupName: >- 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 groupId: 3488f538-f7fc-4c0e-a265-b66e3a38d41e groupName: >- 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 groupId: fd1a62e6-48cf-4ac1-8bac-025665444710 groupName: 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 groupId: fd1a62e6-48cf-4ac1-8bac-025665444710 groupName: 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. groupId: fd1a62e6-48cf-4ac1-8bac-025665444710 groupName: Conclusions stage: Respond - type: SOURCES content: - source: SalesByChannelByDay_Summary_Demo.xlsx datasourceId: cm2ej4wmo000001fcdkwbdrml datasetId: cm2ej4vx900hp01l1o378zr9o fileType: xlsx externalId: '' groupId: '' groupName: '' 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. groupId: '-1' stage: Respond headers: x-pd-trace-id: example: '' required: true description: The trace ID of Powerdrill. schema: type: string deprecated: false security: - apikey-header-x-pd-api-key: [] components: schemas: BlockDTO: type: object properties: type: type: string enum: - MESSAGE - CODE - TABLE - IMAGE - SOURCES - QUESTIONS description: The block type. content: type: string description: The block content. groupId: type: string description: The ID of the group containing the block. groupName: type: string description: The name of the group containing the block. stage: type: string enum: - Analyze - Respond description: The stage of the block. required: - type - content - groupId - groupName - stage securitySchemes: apikey-header-x-pd-api-key: type: apiKey in: header name: x-pd-api-key ````