Added Pandas Iloc Row Series node.
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@@ -23,6 +23,7 @@ You can right-click, select **Add Node**, go to **Data Analysis**, and look for
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| **Pandas Index** | Extract row labels (index) |
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| **Pandas Select Columns** | Select specific columns from a DataFrame |
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| **Pandas Select Rows** | Filter rows based on conditions |
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| **Pandas Iloc Row Series** | Select a row by row integer position |
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| **Pandas Loc Row Series** | Select a row by row label (index) |
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| **Pandas Loc Cell Str** | Select a cell by row label (index) and column label (index) |
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| **Pandas Join** | Join two DataFrames |
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@@ -63,14 +64,23 @@ Use **Pandas Columns** and **Pandas Index** nodes. As these nodes output JSON-s
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## Selecting columns and rows
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Use **Pandas Select Columns** for selecting columns, **Pandas Select Rows** to select rows by a filter condition.
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Use **Pandas Select Columns** for selecting columns.
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For rows, there are multiple options.
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Use **Pandas Select Rows** to select rows by a filter condition.
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You can use **Pandas Loc Row Series** to select a row by row label (index). This node returns a JSON-serialized Series object. Data type of the labels needs to be specified as shown below:
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You can use **Pandas Loc Row Series** to select a row by row label (index), or you can use **Pandas Iloc Row Series** to select a row by row integer position. Both nodes returns a JSON-serialized Series object.
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For **Pandas Loc Row Series**, data type of the labels needs to be specified as shown below:
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For **Pandas Iloc Row Series**, you only need to specify the row integer position:
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Note that in Pandas, integer position is an equivalent of an index in other software,
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while "index" means a unique label assigned to each row or column. See [pandas.DataFrame.iloc.html](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iloc.html).
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## Selecting a cell by row label (index) and column label (index)
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Use **Pandas Loc Cell Str** for selecting a cell by row label (index) and column label (index). Data type of the labels needs to be specified as shown below:
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