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1. Select the DataFrame to be reshaped into wide data.
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2. Choose the column to be set as the_**index**_ in the reshaped DataFrame.
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3. Select the column to be set as the_**columns**_ in the reshaped DataFrame.
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4. Choose the column to fill the_**values**_ in each column of the reshaped DataFrame.
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5.\[_**Pivot Table**_] Select which_**aggregation function**_ to apply for duplicate indices or columns. If multiple aggregation functions are selected, the results of each function are individually generated. For example, applying_**Count**_ and_**First**_ aggregation functions to columns A, B, and C would create six columns.
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6. Additional code beyond the functionalities provided by Visual Python can be added.
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7. Specify the variable name to assign to the result.
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8. Reset the index to assign a new default integer index.
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9. Preview the code that will be output in_**Code View**_.
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10. Preview the resulting DataFrame in_**Data View**_.
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11. Execute the code.
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1._**DataFrame**_: Select the dataframe that will be reconstructed_with_ wide data_._
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2._**Index**_: Select a column to set as the index in the reorganized dataframe.
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3._**Columns**_: Select the columns you want to set as columns in the reorganized dataframe.
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4._**Values**_: Select the columns you want to populate with values for each column in the reorganized dataframe.
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5._**Aggregate (Pivot Table)**_: Select the aggregation function you want to apply to the duplicate indexes or columns. If you select multiple aggregation functions, the results of each function are generated separately. 
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1. For example, applying the_**Count**_ and_**First**_ aggregation functions to columns A, B, and C results in a total of six columns.
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6._**User Option**_: You can add options beyond what Visual Python provides.
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7._**Allocate to**_: Specify a variable name to assign to the result.
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8._**Reset Index**_: Reset the index to specify a new default integer index.
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9._**Code View**_: Preview the code that will be output.
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10._**Data View**_: Preview the output to be printed.
1. Select the DataFrame to be reshaped into long data.
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2. Choose the column(s) to be specified as fixed variables in the reshaped DataFrame. These columns will be retained as they are in the reshaped DataFrame.
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3. The names of the selected column(s) will be filled in the '_**A**_' column of the reshaped DataFrame, and the corresponding values from the original DataFrame will be filled in the '_**B**_' column.
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4. Optionally provide additional desired options.
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5. Specify the variable name to assign to the result.
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6. Reset the index to assign a new default integer index.
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7. Preview the code that will be output in_**Code View**_.
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8. Preview the resulting DataFrame in_**Data View**_.
1._**DataFrame**_: Select the dataframe that will be reconstructed with long data_._
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2._**Id**_: Select the column(s) you want to designate as fixed variables in the reorganized dataframe. These columns will remain intact in the reorganized dataframe.
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3. The names of the column(s) selected in_**Value** are_ populated in the_**Var name**_ column of the reconstructed dataframe, and the values are populated in the_**Value name**_ column.
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4._**User Option**_: You can add options beyond what Visual Python provides.
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5._**Allocate to**_: Specify a variable name to assign to the result.
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6._**Reset Index**_: Reset the index to specify a new default integer index.
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7._**Code View**_: Preview the code that will be output.
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8._**Data View**_: Preview the output that will be printed.