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GITBOOK-49: Reshape
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‎docs/data-analysis/8.-reshape.md

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@@ -4,63 +4,53 @@ description: Reshape the Data into Long or Wide Format
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# 8. Reshape
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<figure><img src="../.gitbook/assets/image (225).png" alt="" width="290"><figcaption></figcaption></figure>
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1. Click the _**Reshape**_ under the _**Data Analysis**_ category.
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<figure><img src="../.gitbook/assets/image (55).png" alt="" width="278"><figcaption></figcaption></figure>
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<figure><img src="../.gitbook/assets/image (226).png" alt="" width="406"><figcaption></figcaption></figure>
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1. Choose the _**Reshape**_ under the Data Analysis category.
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<figure><img src="../.gitbook/assets/image (56).png" alt="" width="563"><figcaption></figcaption></figure>
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2. Select the Reshape type.
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&#x20; 2-1. _**Pivot**_: Reshape the data into a wide data format.
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&#x20; 2-2. _**Pivot Table**_: Reshape the data into a wide format and apply aggregation functions if duplicate indices or columns exist.
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&#x20; 2-3. ape the data into a long data format.
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2. **Reshape **_**type**_: Select a reshape type.
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1. _**Pivot**_: Reorganize the data into a wide data format.
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2. _**Pivot Table**_: Reorganize the data into a wide data format, and apply the _**Aggregation function**_ if there are duplicate indexes or columns.
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3. _**Melt**_: Reorganize the data into a long data format.
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***
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### Pivot & Pivot Table
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<figure><img src="../.gitbook/assets/image (227).png" alt="" width="422"><figcaption></figcaption></figure>
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<figure><img src="../.gitbook/assets/image (58).png" alt="" width="563"><figcaption></figcaption></figure>
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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.&#x20;
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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.
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11. _**Run**_: Print and run the code.
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***
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### Melt
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<figure><img src="../.gitbook/assets/image (59).png" alt="" width="563"><figcaption></figcaption></figure>
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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**_.
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9. Execute the code.
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<figure><img src="../.gitbook/assets/image (228).png" alt="" width="449"><figcaption></figcaption></figure>
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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.
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9. _**Run**_: Print and run the code.
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