7. Bind

Concatenate or Merge DataFrames

1. Click Bind from the Data Analysis category.

1. Bind type: Select a bind type.

   1. Concat: Concatenates dataframes in the row or column direction.

   2. Merge: Merge two dataframes based on a common column.

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Concat

1. DataFrame: Select the dataframes you want to combine.

2. Join: Choose a join method.

   1. Outer: When concatenating dataframes, unmatched indices are filled with NaNs.

   2. Inner: Concatenate only data with matching indexes (non-matching data will be removed).

3. Axis: Select the direction of the connection.

   1. Index: Concatenates data in the row direction (vertical).

   2. Column: Concatenate data in the column direction (horizontal).

4. Sort: Choose whether you want to sort the indexes. Sorting is done in ascending order by index number, which may change the order of the data.

5. User Option: You can add options beyond what Visual Python provides.

6. Allocate to: Specify a variable name to assign to the result.

7. Reset Index: Reset the index to specify a new default integer index.

8. Code View: Preview the code that will be output.

9. Data View: Preview the output that will be printed.

10. Run: Print and run the code.

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Merge

• Merge two dataframes based on a standard column, creating two new columns for the values from each dataframe.

1. Left Data, Right Data: Select the two dataframes you want to merge.

2. How: Choose a merge method.

   1. Inner: Merge based on common values in key columns, only common values will be kept.

   2. Outer: Merge based on all rows in the key column, and values that are not common and do not exist in either dataframe will be filled with NaN.

   3. Left: Merge based on all rows in the key column in the left dataframe.

   4. Right: Merge based on all rows in the key column in the right dataframe.

   5. Cross: Outputs all combinations of data, regardless of the value in the key column.

3. On: Allows you to merge based on specific columns. The columns selected must exist in both dataframes in common.

4. Left on, Right on: You can select the columns in both dataframes that you want to base the merge on, respectively.

5. Suffixes: If you have columns with the same name other than the common key column, add a suffix to differentiate them.

6. User Option: You can add options beyond what Visual Python provides.

7. Allocate to: Specify a variable name to assign to the result.

8. Reset Index: Reset the index to specify a new default integer index.

9. Code View: Preview the code that will be output.

10. Data View: Preview the output that will be printed.

11. Run: Print and run the code.