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description Concatenate or Merge DataFrames

7. Bind

  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.

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.

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.