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diff --git a/docs/data-analysis/1.-import.md b/docs/data-analysis/1.-import.md
index 88cab545..4e7c8a58 100644
--- a/docs/data-analysis/1.-import.md
+++ b/docs/data-analysis/1.-import.md
@@ -4,24 +4,18 @@ description: Import Packages and Modules
# 1. Import
+
+1. In the _**Data Analysis**_ category, click _**Import**._ We_'_ve already prepared the packages you need for data analysis. For the packages you need for machine learning, click the **Machine Learning** tab.
-
-1. Click on _**Import**_ in the Data Analysis category.
+
-
-
-
-2. Choose _**Data Analysis**_ or _**Machine Learning**_ according to the purpose of the feature you want to import.
-3. Select the packages or modules you want to import.
-4. If the desired feature is not in the list, you can add it directly using _**+Module**_ or _**+Function**_. (The added feature is automatically saved for easy future imports.)
-
- 4-1. Use _**+Module**_ to add packages or modules.
-
- 4-2. Use _**+Function**_ to add functions.
-
-5. You can review the code that will be generated through _**Code View**_.
-6. Press _**Run**_ to execute the code.
+2. Select the packages you want to import.
+3. If the package you want isn't listed, you can add it via the **+**_**Module**_ or _**+Function**_ buttons.
+ 1. To load a package or module, use _**+Module**._
+ 2. Use _**+Function**_ to call a specific function.
+4. _**Code View**_: Preview the code that will be output.
+5. _**Run**_: Print and run the code.
diff --git a/docs/data-analysis/2.-file.md b/docs/data-analysis/2.-file.md
index 65607465..7b469fca 100644
--- a/docs/data-analysis/2.-file.md
+++ b/docs/data-analysis/2.-file.md
@@ -4,83 +4,50 @@ description: Read or Write a File
# 2. File
+
+1. In the _**Data Analysis**_ category, click _**File**._
-
-
-1. Click on the _**File**_ in the Data Analysis category.
-
-
-
-
-
-2. Choose whether to read or save a file from the top bar
-
- 2-1. You can use sample data prepared by Visual Python through _**Sample Data**_.
-
+
+2. Select the type of action _**Read File**_ / _**Write File**_
+ 1. _**Sample Data**_ brings up sample data provided by Visual Python.
### Read File
+
+1. _**File Type**_: Select the type of file you want to import.
+2. _**File Path**_: The file-shaped icon lets you find the location of the file to load.
+3. _**Allocate to**_: Give the file a variable name.
-
-
-1. Select the type of file to load.
-2. Choose the file path.
-3. Enter the variable name (Callable name, Identifier) for the file.
-
-
-
-
-
-4. _**Additional Options**_ allow various settings.
-
- 4-1. If not set separately, the default values set by Visual Python will be applied.
-
- 4-2. Any settings not found here can be entered in the _**User Option**_ for configuration.
-
-5. _**Encoding**_: Specify the encoding of the file.
-6. _**Header**_: Select _**None**_ if you do not want to use column names.
-
- 6-1. By default, the values entered in the first row are set as column names.
-
-7. _**Separator**_: Choose the delimiter that separates the data fields in the file.
-8. _**Columns**_: You can set column names by entering a pre-made list or specific values.
-9. _**Column List to Use**_: Specify specific columns to load from multiple columns in the file.
-10. _**Column to Use as Index**_: Specify the column from the file's columns to use as an index.
-11. _**Na Values**_: Represent missing values with the specified input.
-12. _**Rows to Skip**_: Specify the number of rows to ignore at the beginning of the file.
-13. _**Number of Rows**_: Read only the specified number of rows from the beginning.
-14. _**Chunksize**_: Divide the file into separate parts and read them to create separate DataFrames. Helpful in handling large files.
-
+* _**Additional Options**_: Allows you to set additional option values. If not set, the default values set in Visual Python are applied. Options not listed here can be applied by entering them directly _in **User Option**._
+1. _**Encoding**_: Select the type of encoding.
+2. _**Header**_: By default, the first row is used as the column name. If you don't want this, select '_**None**'._
+3. _**Separator**_: Choose a separator to separate the data fields in the file.
+4. _**Columns**_: Set the column names to a pre-made list or specific values.
+5. _**Column List to Use**_: Allows you to import only specific columns within the file.
+6. _**Column to Use as Index**_: Specify which column in the data should be used as the index.
+7. _**Na Values**_: Replace missing values with the values you entered.
+8. _**Rows to Skip**_: Skips and fetches as many rows as the value entered at the top of the file.
+9. _**Number of Rows**_: Fetches as many rows as the entered value from the top of the file.
+10. _**Chunksize**_: Reads the file in _chunks,_ divided by the entered value. Use this when loading large files.
### Write File
+
+1. _**File Type**_: Set the file type you want to save.
+2. _**DataFrame**_: Select the dataframe you want to save.
+3. _**File Path**_: Set the location where you want to save the file.
-
-
-1. Select the type to save.
-2. Choose the DataFrame to save.
-3. Select the location to save.
-4. _**Additional Options**_ allow various settings.
-
- 4-1. If not set separately, the default values set by Visual Python will be applied.
-
- 4-2. Any settings not found here can be entered in the _**User Options**_ for configuration.
-
-5. _**Encoding**_: Specify the encoding of the file.
-6. _**Header**_: Choose _**False**_ to exclude column names when saving.
-
- 6-1. By default, column names are saved as the first row.
-
-7. _**Index**_: Choose _**False**_ to exclude the index when saving.
-
- 7-1. By default, the index is saved as the first column.
+* _**Additional Options**_: Allows you to set additional option values. If not set, the default values set in Visual Python are applied. Options not listed here can be applied by entering them directly _in **User Option**._
-8. _**Separator**_: Choose the delimiter that separates the data fields in the file.
-9. _**Na Replacing Value**_: Replace missing values with the specified input when saving.
-10. _**Columns**_: You can save only specific columns.
+1. _**Encoding**_: Select the type of encoding.
+2. _**Header**_: Select '_**False**_' if you do not want the column names to be included in the file that will be saved.
+3. _**Index**_: Select _'_**False**_'_ if you don't want an index to be included.
+4. _**Separator**_: Choose a separator to separate the data fields in the file.
+5. _**Na Replacing Value**_: Replaces the missing value with the value you entered.
+6. _**Columns**_: Select specific columns to save.
diff --git a/docs/data-analysis/3.-data-info.md b/docs/data-analysis/3.-data-info.md
index 407675bd..7c5ed68f 100644
--- a/docs/data-analysis/3.-data-info.md
+++ b/docs/data-analysis/3.-data-info.md
@@ -4,57 +4,40 @@ description: Check the Basic Information of the Data
# 3. Data Info
+
+1. Click on _**Data Info**_ in the _**Data Analysis**_ category.
-
-
-1. Click on _**Data Info**_ in the Data Analysis category.
-
-
-
-
-
-2. Click on the _**Data tab**_ at the top left to select the DataFrame for which you want to view information.
-
- 2-1. Click on the _**funnel icon**_ on the tab's right side allows you to extract specific columns.
-
-3. In the _**Info Preview**_ on the right, information is displayed briefly.
-4. Clicking _**Run**_ will execute the code without closing the window.
-5. Use _**Code View**_ at the bottom left to check the generated code.
+
+2. _**Data**_: Select the data you want to see information about. You can select only specific columns of data via the funnel icon.
+3. _**Info Preview**_: The information is output as a preview.
+4. _**Run**_: Prints the information displayed _in the **Info Preview**._ The _**Data Info**_ window doesn't close, so you can view additional information at your convenience.
+5. _**Code View**_: Preview the code that will be output.
+* Tip: You can select one or multiple columns.
### General
-
-
-
+
1. _**Info**_ provides basic information such as Column, Non-Null Count, Dtype, etc.
2. _**Describe**_ displays basic statistics for each column.
3. _**Head**_ shows the top five rows of the DataFrame.
4. _**Tail**_ displays the bottom five rows of the DataFrame.
-
-
### Status
-
-
-
+
1. _**Null Count**_ shows the count of Null and Non-Null values for each column.
2. _**Duplicated**_ reveals the count of duplicated values.
3. _**Unique**_ works on a single column. Shows the unique values in a column.
4. _**Value Counts**_ displays the count of each value in each column. For continuous variables, it shows the count within arbitrarily defined intervals.
-
-
### Statistics
-
-
-
+
1. Check and confirm desired statistical values.
@@ -66,20 +49,14 @@ description: Check the Basic Information of the Data
### Correlation
-
-
-
+
1. _**Correlation Table**_ shows a table indicating the correlation between each column.
2. _**Correlation Matrix**_ represents the correlation table as a Heat Map.
-
-
### Distribution
-
-
-
+
1. Represents data in various forms.
diff --git a/docs/data-analysis/4.-frame/4-1.-frame-edit.md b/docs/data-analysis/4.-frame/4-1.-frame-edit.md
index 9d5f9fe6..2539bbef 100644
--- a/docs/data-analysis/4.-frame/4-1.-frame-edit.md
+++ b/docs/data-analysis/4.-frame/4-1.-frame-edit.md
@@ -1,17 +1,15 @@
# 4-1. Frame - Edit
+
-
-
-
-1. _**Add Column**_ adds a new column next to the last column.
-2. _**Add Row**_ adds a new row at the end.
-3. _**Delete**_ allows the deletion of one or multiple selected columns.
-4. _**Rename**_ allows the renaming of column names.
-5. _**As Type**_ enables the change of data type for the values in the column.
-6. _**To Datetime**_ converts one or multiple selected columns to the Datetime format.
-7. _**Replace**_ allows changing the values in one or multiple selected columns.
-8. _**Discretize**_ divides the continuous data in the column into intervals or categories. Works for one column only.
+1. _**Add Column**_: Add a new column.
+2. _**Add Row**_: Add a new row.
+3. _**Delete**_: Enabled when one or multiple columns are selected. It deletes the selected columns.
+4. _**Rename**_: Rename the column.
+5. _**As Type**_: Change the type of data entered.
+6. _**To Datetime**_: Enabled when one or multiple columns are selected. It changes the data in those columns to date format.
+7. _**Replace**_: Enabled when one or multiple columns are selected. Change a specific value to another.
+8. _**Discretize**_: Enabled when one column is selected. It divides the data in that column into categories or bins.
@@ -19,22 +17,15 @@
### Add Column
+
-
-
-
-1. Enter the name of the column to be added in _**New Column**_.
-2. Select the type of the column to be added.
-
- 2-1. _**Calculate**_: You can simply add specific data to the column or add calculated values between variables using _**+Variable**_.
-
- 2-2. _**Statistics**_: You can add statistical property values between one or multiple columns to the column.
-
- 2-3. _**Replace**_: You can convert values in a specific column to different values and add them to the column.
-
- 2-4. _**Condition**_: By providing specific conditions, you can transform values that meet those conditions into different values and add them to the column.
-
- 2-5. _**Apply**_: Allows for more complex conditions to be applied.
+1. _**New column**_: Name the column you want to add.
+2. _**Add type**_: Choose how you want to populate the data in the column you're adding.
+ 1. _**Calculate**_: Fill in the result of an operation between values in a specific variable or column.
+ 2. _**Statistics**_: Populate with statistics for the specific column(s).
+ 3. _**Replace**_: Replaces a specific value in the selected column with another value to fill it.
+ 4. _**Condition**_: Replaces values in the selected column that meet a specific condition with another value.
+ 5. _**Apply**_: Apply more complex conditions.
@@ -42,14 +33,13 @@
### Add Row
+
-
-
-
-1. Enter the name of the row to be added in _**New Row**_. The name will be used as the index.
-2. Select the type of the row to be added.
-
- 2-1. _**Variable**_: Enter the values to be added to the new row. You can also add calculated values between variables using _**+Variable**_.
+1. _**New row**_: Set a name for the row, which will be used as the index.
+2. Choose how you want to populate the data for the rows you want to add.
+ 1. _**Variable**_: Fill with a specific value.
+ 2. _**Column**_: Fill with values from a specific column.
+ 3. You can also use _**+Variable** to_ put the result of an operation between values or columns.
@@ -57,12 +47,12 @@
### To Datetime
+
-
-
-
-1. The name entered in _**New Column**_ becomes the new name for that column.
-2. Using _**+Add Column**_, you can add the transformed values to the last column.
+1. _**Target column**_: This lists the selected column that you want to change to time format.
+2. _**Format**_: Select the time format to be listed.
+3. If _**Day first**,_ _**True** is_ selected, the data will be interpreted as day/month/year instead of month/day/year and changed to a time format.
+4. _**Add-sub columns**_: Add a new column that represents the values in the column changed to time format in a different time type.
@@ -70,17 +60,13 @@
### Replace
+
-
-
-
-1. Choose the method of modification.
-
- 1-1. _**Replace**_ changes the values entered in _**Origin**_ to the values entered in _**Replace**_.
-
- 1-2. _**Condition**_ provides specific conditions and changes values that meet those conditions to different values.
-
- 1-3. _**Apply**_: Allows for more complex conditions to be applied.
+1. _**Column**_: The selected column is listed.
+2. _**Replace type**_: Choose how you want to modify the value.
+ 1. _**Replace**_: Replace the _**Origin value**_ with the _**Replace value**_.
+ 2. _**Condition**_: Replace values that meet a specific condition with _**Replace value**_.
+ 3. _**Apply**_: Replace value with a value that satisfies a more complex condition.
@@ -88,18 +74,13 @@
### Discretize
+
-
-
-
-1. _**New Column**_: Enter the name for the new column where the results of the operation will be displayed.
-2. _**Target Column**_: Displays the selected column for the operation.
-3. _**Bins Count**_: Set the number of intervals.
-4. _**Discretize Type**_: Choose the type of discretization. Intervals will be created based on the count set in step 3.
-
- 4-1. _**Interval based:**_ The intervals have equal spacing.
-
- 4-2. _**Quantile based**_: Intervals are set based on the quantiles of the data's distribution.
-
- 4-3. _**Direct Input**_: Allows direct input to modify labels and interval spacing.
+1. _**New column**:_ Name the new column where your work product will be listed.
+2. _**Target column**_: The name of the selected column is listed.
+3. _**Bins count**_: Set the number of bins to discretize the data into.
+4. _**Discretize type**_: Select a discretization type. The number of bins follows the value set _in **Bins count**._
+ 1. _**Interval based**_: Divide the data into equal intervals.
+ 2. _**Quantile based**_: Set and divide bins based on the quantiles of the data distribution.
+ 3. _**Direct input**_: Set the _**Left edge**_, _**Right edge**_ of each section directly.
diff --git a/docs/data-analysis/4.-frame/4-2.-frame-transform.md b/docs/data-analysis/4.-frame/4-2.-frame-transform.md
index 933226da..e21ee9f7 100644
--- a/docs/data-analysis/4.-frame/4-2.-frame-transform.md
+++ b/docs/data-analysis/4.-frame/4-2.-frame-transform.md
@@ -1,12 +1,10 @@
# 4-2. Frame - Transform
+
-
-
-
-1. _**Set Index**_ sets a chosen column as the index.
-2. _**Reset Index**_ converts the index back into a column.
-3. _**Data Shift**_ shifts the data of one or multiple selected columns by the specified amount.
+1. _**Set index**_: Set the selected column as the index.
+2. _**Reset index**_: Converts the index back to a column.
+3. _**Data shift**_: Shifts the data in the selected column up or down.
@@ -14,19 +12,11 @@
### Data Shift
+
-
-
-
-1. _**Periods**_: Enter how much to shift the data in the selected column(s).
-
- 1-1. Use a positive value to shift down and a negative value to shift up.
-
-2. _**Frequency**_: Set the unit for the value entered in Period.
-
- Ex. Period: 3, Frequency: Day -> Shift by 3 days
-
- 2-1. There should be periodic information in the index. If there is no specific periodicity, you can skip setting the frequency.
-
-3. _**Fill Value**_: Enter the value to fill the empty spaces created by the data shift.
+1. _**Periods**_: Set the amount by which you want to move the data. Use a positive value to move it down, and a negative value to move it up.
+2. Set the units for the values entered in _**Frequency**._ If it's not time data, omit this setting.
+ 1. (Ex) Periods: 3, Frequency: minute -> Move down by 3 minutes
+ 2. (Ex) Periods: 3, Frequency: X -> Move down 3 spaces
+3. _**Fill value**_: Enter a value to fill the empty space created by moving the data.
diff --git a/docs/data-analysis/4.-frame/4-3.-frame-sort.md b/docs/data-analysis/4.-frame/4-3.-frame-sort.md
index 6a571f68..30964e47 100644
--- a/docs/data-analysis/4.-frame/4-3.-frame-sort.md
+++ b/docs/data-analysis/4.-frame/4-3.-frame-sort.md
@@ -1,11 +1,9 @@
# 4-3. Frame - Sort
+
-
-
-
-1. _**Sort Index**_ sets the ascending/descending order of rows or columns.
-2. _**Sort Values**_ sorts based on the data entered in each column. You must select one or more columns.
+1. _**Sort Index**_: Sets the ascending/descending order of rows or columns.
+2. _**Sort Values**_: Sorts by the data entered in each column. At least one column must be selected to be active.
@@ -13,12 +11,10 @@
### Sort Index
+
-
-
-
-1. _**Axis**_: Select whether to change the order of rows or columns.
-2. _**Sort by level**_: When the rows or columns have multiple layers (multi-level), choose which layer to use as the basis for changing the order.
+1. _**Axis**_: Choose whether you want to reorder rows or columns.
+2. _**Sort by level**_: When a row or column has multiple tiers (multiple levels), select the level you want to use as the basis for reordering.
3. _**Ascending**_: Choose between ascending or descending order.
@@ -27,15 +23,10 @@
### Sort Values
+
-
-
-
-1. _**Sort by column**_ determines the order of columns to be the basis for sorting.
-
- 1-1. It is sorted based on the column located at the top.
-
- 1-2. When sorting based on this column, if identical values exist, it is further sorted according to the next criterion column.
-
+1. _**Sort by column**_: Decide which column you want to sort by.
+ 1. Sorted by the column listed at the top.
+ 2. When sorting by this column, if the same value exists, it will be further sorted by the next column it is based on.
2. _**Ascending**_: Choose between ascending or descending order.
diff --git a/docs/data-analysis/4.-frame/4-4.-frame-encoding.md b/docs/data-analysis/4.-frame/4-4.-frame-encoding.md
index 8d781433..27711c9f 100644
--- a/docs/data-analysis/4.-frame/4-4.-frame-encoding.md
+++ b/docs/data-analysis/4.-frame/4-4.-frame-encoding.md
@@ -1,18 +1,11 @@
# 4-4. Frame - Encoding
+
+* This feature is used for columns with categorical data entered.
-
-
-Use this feature for columns where categorical data is entered.
-
-1. _**Label Encoding**_: Assign a number for each type of variable.
-
- For example, assign 0 for spring, 1 for summer, 2 for autumn, and 3 for winter.
-
-2. _**One-Hot-Encoding**_: Assign a binary vector for each type of variable.
-
- For example, spring is assigned \[1, 0, 0, 0], summer is \[0, 1, 0, 0], autumn is \[0, 0, 1, 0],
-
- and winter is \[0, 0, 0, 1].
+1. _**Label Encoding**_: Assign a number to each type of variable.
+ 1. For example, assign 0 for spring, 1 for summer, 2 for fall, and 3 for winter.
+2. _**One-Hot-Encoding**_: Assign a binary vector to each type of variable.
+ 1. For example, spring would be assigned \[1, 0, 0, 0], summer would be \[0, 1, 0, 0], fall would be \[0, 0, 1, 0], and winter would be \[0, 0, 0, 1].
diff --git a/docs/data-analysis/4.-frame/4-5.-frame-data-cleaning.md b/docs/data-analysis/4.-frame/4-5.-frame-data-cleaning.md
index 9917922e..a1dfa6da 100644
--- a/docs/data-analysis/4.-frame/4-5.-frame-data-cleaning.md
+++ b/docs/data-analysis/4.-frame/4-5.-frame-data-cleaning.md
@@ -1,13 +1,11 @@
# 4-5. Frame - Data Cleaning
+
-
-
-
-1. _**Fill NA**_: Replace NA with another value.
-2. _**Drop NA**_: Remove rows or columns containing NA.
-3. _**Fill Outlier**_: Replace outliers in specific columns.
-4. _**Drop Outlier**_: Remove outliers in specific columns.
+1. _**Fill NA**_: Replace the value NA with another value.
+2. _**Drop NA**_: Removes rows or columns that contain NA values.
+3. _**Fill Outlier**_: Replaces outliers in a specific column.
+4. _**Drop Outlier**_: Removes outliers in a specific column.
5. _**Drop Duplicates**_: Remove duplicate values.
@@ -16,17 +14,12 @@
### Fill NA
+
-
-
-
-1. _**Method**_: Choose the filling method.
-
- 1-1. _**Value**_: Replace NA with the specified input value.
-
- 1-2. _**Forward/Back Fill**_: Replace NA with values from the front/back. If NA is consecutive, you can set the '_**Limit**_' to determine how many values to fill.
-
- 1-3. _**Statistics**_: Fill in with statistical properties.
+1. _**Method**_: Select a fill method.
+ 1. **Replace **_**Value**_: NA with the input value.
+ 2. _**Forward/Back Fill**_: Replace the NA with the value before/after it. If there are consecutive NA's, you can limit the fill to only a few NA's.
+ 3. _**Statistics**_: Replace NA with Statistics.
@@ -34,19 +27,13 @@
### Drop NA
-
-
-
+
1. _**How**_
-
- 1-1. _**Select Options**_: Keep only rows with the number of non-NA values set by the _**threshold**_, and delete the rest.
-
- 1-2. _**Any**_: Delete rows if there is at least one NA in the row.
-
- 1-3. _**All**_: Delete rows if all values in the row are NA.
-
-2. _**Ignore Index**_: Choose whether to reset the index after row deletion.
+ 1. _**Select Options**_: If the number of non-missing values in any row is less than the value set in _**Threshold**,_ delete that row.
+ 2. _**Any**_: If there is any NA in the row, delete the row.
+ 3. _**All**_: If all values in a row are NA, delete the row.
+2. _**Ignore Index**_: Choose whether to reset the index after the operation.
@@ -54,10 +41,8 @@
### Drop Duplicates
+
-
-
-
-1. _**Keep**_: Choose the value to retain among the duplicate values. Selecting _**False**_ will result in the deletion of all duplicate values.
-2. _**Ignore Index**_: Choose whether to reset the index after duplicate values deletion.
+1. _**Keep**_: Select which of the duplicate values you want to keep. If you select _**False**,_ all duplicate values will be deleted.
+2. _**Ignore Index**_: Choose whether to reset the index after the operation.
diff --git a/docs/data-analysis/4.-frame/README.md b/docs/data-analysis/4.-frame/README.md
index e3f4bf84..c2747ed2 100644
--- a/docs/data-analysis/4.-frame/README.md
+++ b/docs/data-analysis/4.-frame/README.md
@@ -6,19 +6,14 @@ description: Edit the Data Frame Easily Using Various Functions
-
+
-1. Click on the _**Frame**_ of Data Analysis
+1. Click _**Frame**_ _in **Data Analysis**_.
+
-
-
-
-2. Choose the data frame to work with from the _**DataFrame tab**_ at the top left.
-3. In the _**Allocate tab**_, you can specify a new variable name.
-
- 3-1. If you check _**Inplace**_, it will directly modify the existing data frame without creating a new one.
-
+2. _**DataFrame**_: Select the dataframe you want to work with.
+3. _**Allocate to**_: Enter a variable name to assign to the dataframe after the operation. If _**Inplace**_ is checked, the existing variable name will be used.
4. Click on the column directly to select one or multiple specific columns to work on.
diff --git a/docs/data-analysis/5.-subset.md b/docs/data-analysis/5.-subset.md
index 81b63c30..37e0057b 100644
--- a/docs/data-analysis/5.-subset.md
+++ b/docs/data-analysis/5.-subset.md
@@ -1,42 +1,31 @@
---
-description: Extract the Desired Portion from the Data Frame
+description: Extract the data that meet specific conditions from Data Frame.
---
# 5. Subset
+
-
-
-
-1. Select the _**Subset**_ in the Data Analysis category.
+1. Click the _**Subset**_ in the _**Data Analysis**_ category.
***
-
-
-
-
-1. _**Data Frame**_: Select the data frame for the operation.
-2. _**Make a copy**_: Check this option to ensure that changes do not affect the original data frame.
-3. _**Method**_: Choose the method for specifying the rows or columns on which the operation will be applied.
-4. _**Allocate to**_: Specify the variable name where the operation results will be stored.
-5. _**Row Subset**_: Enter information about rows to proceed with the operation.
-
- 5-1. _**Slicing**_: Extract rows by specifying a range.
-
- 5-2. _**Condition**_: Specify the range or rows based on conditions.
-
- 5-3. _**Indexing**_: Select desired rows directly.
-
-6. _**Column Subset**_: Enter information about columns to proceed with the operation.
-
- 6-1. _**Indexing**_: Extract desired columns directly.
-
- 6-2. _**Slicing**_: Specify the range of columns to extract.
-
-7. _**Code View**_: View the generated code.
-8. _**Data View**_: Preview the output results.
-9. _**Run**_: Execute the commands.
+
+
+2. _**Data Frame**_: Select the data frame you want to use for your work.
+3. _**Make a copy**_: Check if you don't want the operation to affect the original data frame.
+4. _**Method**_: Select a method to specify which rows or columns the action will be applied to.
+5. _**Allocate to**_: Specify the name of the variable where the results of your work will be stored.
+6. _**Row Subset**_: Select the rows you want to work with.
+ 1. _**Slicing**_: Extract rows by specifying a range.
+ 2. _**Condition**_: Specify the range of rows based on a condition.
+ 3. _**Indexing**_: Manually select the desired rows.
+7. _**Column Subset**_: Select the columns you want to work with.
+ 1. _**Indexing**_: Manually extract the desired columns.
+ 2. _**Slicing**_: Specify the range of columns to extract.
+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.
diff --git a/docs/data-analysis/6.-groupby.md b/docs/data-analysis/6.-groupby.md
index 2dcda70e..cb4d50fb 100644
--- a/docs/data-analysis/6.-groupby.md
+++ b/docs/data-analysis/6.-groupby.md
@@ -4,30 +4,23 @@ description: Group the Data in the DataFrame
# 6. Groupby
+
-
-
-
-1. Select _**Groupby**_ in the Data Analysis category.
+1. Click _**Groupby**_ in the _**Data Analysis**_ category.
***
-
-
-
-
-2. Choose the DataFrame to be grouped.
-
- **Group by column A, Apply grouping to column B, and Use method C.**
-
-3. For time-related data, check _**Grouper**_ to group data based on periods such as monthly, weekly, or yearly.
-4. Check _**Advanced**_ to apply different methods for each column.
-5. Specify the variable name to assign to the result.
-6. Reset the index to assign a new default integer index.
-7. Save and display the result in DataFrame format.
-8. Preview the code to be output.
-9. Preview the data to be output.
-10. Output the result.
+
+
+2. _**DataFrame**_: Select the dataframe you want to group by.
+3. _**Groupby**_: Group data based on the values in the selected column. For time-related data, check out _Grouper_ to group data based on time periods like monthly, weekly, or yearly.
+4. _**Columns**_: Select the columns you want to group by applying the criteria you selected in _**Group by**_.
+5. _**Method**_: Choose the information you want to see in the grouped data.
+ 1. For example, if you choose _**mean**,_ you'll see the average value for each group. If you want to see different information for each group, use _**Advanced**._
+6. _**Allocate to**_: Specify a variable name to assign to the result. Check _**Reset Index**_ to ignore the existing index and assign a new one. Check _**To DataFrame**_ to save the result in dataframe format.
+7. _**Code View**_: Preview the code that will be output.
+8. _**Data View**_: Preview the output that will be printed.
+9. _**Run**_: Print and run the code.
diff --git a/docs/data-analysis/7.-bind.md b/docs/data-analysis/7.-bind.md
index 06c91020..85473316 100644
--- a/docs/data-analysis/7.-bind.md
+++ b/docs/data-analysis/7.-bind.md
@@ -4,21 +4,15 @@ description: Concatenate or Merge DataFrames
# 7. Bind
+
+1. Click _**Bind**_ from the _**Data Analysis**_ category.
-
+
-1. Select _**Bind**_ from the Data Analysis category.
-
-
-
-
-
-2. Choose the _**Bind Type**_.
-
- 2-1. _**Concat**_ concatenates dataframes along rows or columns.
-
- 2-2. _**Merge**_ merges "two" dataframes based on a common column.
+2. _**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.
@@ -26,30 +20,22 @@ description: Concatenate or Merge DataFrames
### Concat
+
-
-
-
-1. Select the dataframes to concatenate.
-2. Choose the concatenation method
-
- 2-1. _**Outer**_: When concatenating dataframes, non-matching indices are filled with NaN.
-
- 2-2. _**Inner**_ concatenates only the data with matching indices (non-matching data is removed.)
-
-3. Choose the concatenation direction.
-
- 3-1. _**Index**_ concatenates data along the row direction (vertical).
-
- 3-2. _**Column**_ concatenates data along the column direction (horizontal).
-
-4. Choose whether to sort the index. Sorting is done in ascending order based on index numbers, which may alter the data order.
-5. Add additional options beyond those provided by Visual Python.
-6. Specify the variable name to assign to the result.
-7. Reset the index to assign a new default integer index.
-8. Preview the code that will be output in _**Code View**_.
-9. Preview the resulting dataframe in _**Data View**_.
-10. Execute the code.
+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.
@@ -57,32 +43,24 @@ description: Concatenate or Merge DataFrames
### Merge
-
-
-
-
-_**Merge**_ two dataframes based on a standard column, creating two new columns for the values from each dataframe.
-
-1. Select the two dataframes to merge.
-2. Choose the merging _**method**_.
-
- 2-1. _**Inner**_ merges based on the common values in the key column, keeping only the common values.
-
- 2-2. _**Outer**_ merges based on all rows in the key column, filling with input data.
-
- 2-3. _**Left**_ merges based on all rows in the key column from the left dataframe.
-
- 2-4. _**Left**_ merges based on all rows in the key column from the right dataframe.
-
- 2-5. _**Cross**_ outputs all combinations of data, regardless of the values in the key column.
-
-3. Select the key column that will be the reference for merging; it must be common to both dataframes.
-4. Optionally, select the key column separately for each dataframe.
-5. Append a suffix to columns with the same name other than the common key column.
-6. Add additional options beyond those provided by Visual Python.
-7. Specify the variable name to assign to the result.
-8. Reset the index to assign a new default integer index.
-9. Preview the code that will be output in _**Code View**_.
-10. Preview the resulting dataframe in _**Data View**_.
-11. Execute the code.
+
+
+* _**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.
diff --git a/docs/data-analysis/8.-reshape.md b/docs/data-analysis/8.-reshape.md
index d334e8e8..37500ee0 100644
--- a/docs/data-analysis/8.-reshape.md
+++ b/docs/data-analysis/8.-reshape.md
@@ -4,23 +4,16 @@ description: Reshape the Data into Long or Wide Format
# 8. Reshape
+
+1. Click the _**Reshape**_ under the _**Data Analysis**_ category.
-
+
-1. Choose the _**Reshape**_ under the Data Analysis category.
-
-
-
-
-
-2. Select the Reshape type.
-
- 2-1. _**Pivot**_: Reshape the data into a wide data format.
-
- 2-2. _**Pivot Table**_: Reshape the data into a wide format and apply aggregation functions if duplicate indices or columns exist.
-
- 2-3. ape the data into a long data format.
+2. **Reshape **_**type**_: Select a reshape type.
+ 1. _**Pivot**_: Reorganize the data into a wide data format.
+ 2. _**Pivot Table**_: Reorganize the data into a wide data format, and apply the _**Aggregation function**_ if there are duplicate indexes or columns.
+ 3. _**Melt**_: Reorganize the data into a long data format.
@@ -28,21 +21,20 @@ description: Reshape the Data into Long or Wide Format
### Pivot & Pivot Table
+
-
-
-
-1. Select the DataFrame to be reshaped into wide data.
-2. Choose the column to be set as the _**index**_ in the reshaped DataFrame.
-3. Select the column to be set as the _**columns**_ in the reshaped DataFrame.
-4. Choose the column to fill the _**values**_ in each column of the reshaped DataFrame.
-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.
-6. Additional code beyond the functionalities provided by Visual Python can be added.
-7. Specify the variable name to assign to the result.
-8. Reset the index to assign a new default integer index.
-9. Preview the code that will be output in _**Code View**_.
-10. Preview the resulting DataFrame in _**Data View**_.
-11. Execute the code.
+1. _**DataFrame**_: Select the dataframe that will be reconstructed _with_ wide data_._
+2. _**Index**_: Select a column to set as the index in the reorganized dataframe.
+3. _**Columns**_: Select the columns you want to set as columns in the reorganized dataframe.
+4. _**Values**_: Select the columns you want to populate with values for each column in the reorganized dataframe.
+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.
+ 1. For example, applying the _**Count**_ and _**First**_ aggregation functions to columns A, B, and C results in a total of six columns.
+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 to be printed.
+11. _**Run**_: Print and run the code.
@@ -50,17 +42,15 @@ description: Reshape the Data into Long or Wide Format
### Melt
-
-
-
-
-1. Select the DataFrame to be reshaped into long data.
-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.
-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.
-4. Optionally provide additional desired options.
-5. Specify the variable name to assign to the result.
-6. Reset the index to assign a new default integer index.
-7. Preview the code that will be output in _**Code View**_.
-8. Preview the resulting DataFrame in _**Data View**_.
-9. Execute the code.
+
+
+1. _**DataFrame**_: Select the dataframe that will be reconstructed with long data_._
+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.
+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.
+4. _**User Option**_: You can add options beyond what Visual Python provides.
+5. _**Allocate to**_: Specify a variable name to assign to the result.
+6. _**Reset Index**_: Reset the index to specify a new default integer index.
+7. _**Code View**_: Preview the code that will be output.
+8. _**Data View**_: Preview the output that will be printed.
+9. _**Run**_: Print and run the code.
diff --git a/docs/machine-learning/1.-data-sets.md b/docs/machine-learning/1.-data-sets.md
index 9f08652a..3fdd9190 100644
--- a/docs/machine-learning/1.-data-sets.md
+++ b/docs/machine-learning/1.-data-sets.md
@@ -1,14 +1,14 @@
-# 1. Data Sets
-
+---
+description: Import or Create Sample Data
+---
+# 1. Data Sets
-
+
1. Click on _**Data Sets**_ in the _**Machine Learning**_ category.
-
-
-
+
2. _**Load Type**_: You can load sample data (_**Load Data**_) or generate data (_**Create Data**_).
3. _**Allocate to**_: Specify variable names to assign to the data.
diff --git a/docs/machine-learning/10.-fit-predict.md b/docs/machine-learning/10.-fit-predict.md
index 38bffed1..797fde00 100644
--- a/docs/machine-learning/10.-fit-predict.md
+++ b/docs/machine-learning/10.-fit-predict.md
@@ -1,14 +1,10 @@
# 10. Fit/Predict
-
-
-
+
1. Click on _**Fit/Predict**_ under the _**Machine Learning**_ category.
-
-
-
+
2. _**Model**_: Select the model you want to use.
3. _**Action**_: Choose the operation to apply to the selected model.
diff --git a/docs/machine-learning/11.-model-info.md b/docs/machine-learning/11.-model-info.md
index 66b08d5c..17dc4b2f 100644
--- a/docs/machine-learning/11.-model-info.md
+++ b/docs/machine-learning/11.-model-info.md
@@ -1,24 +1,21 @@
# 11. Model Info
-
-
-
+
1. Click on _**Model Info**_ in the _**Machine Learning**_ category.
-
-
-
+
2. _**Model**_: Select the model for which you want to check information.
-3. _**Info**_: Lists of information available for each type of model are displayed:
+3. _**Info**_: Lists of information available for each type of model are displayed:
+
+**Example:**
- Example:
+* Regressor: Score, Cross Validation Score, Permutation Importance, etc.
+* Classifier: AUC, ROC Curve, Feature Importance, etc.
+* Cluster: Cluster Centers, etc.
+* Encoder: Categories, Feature Names, etc.
- * Regressor: Score, Cross Validation Score, Permutation Importance, etc.
- * Classifier: AUC, ROC Curve, Feature Importance, etc.
- * Cluster: Cluster Centers, etc.
- * Encoder: Categories, Feature Names, etc.
4. _**Options**_: Specify detailed settings to extract the information you want to check.
5. _**Code view**_: Preview the code.
6. _**Run**_: Execute the code.
diff --git a/docs/machine-learning/12.-evaluation.md b/docs/machine-learning/12.-evaluation.md
index 039f75bf..0743f838 100644
--- a/docs/machine-learning/12.-evaluation.md
+++ b/docs/machine-learning/12.-evaluation.md
@@ -1,14 +1,10 @@
# 12. Evaluation
-
-
-
+
1. Click on _**Evaluation**_ in the _**Machine Learning**_ category.
-
-
-
+
@@ -24,9 +20,7 @@
### Regression / Classification
-
-
-
+
1. _**Target Data**_: Specify the target data.
2. _**Predict Data**_: Specify the data to predict.
@@ -38,9 +32,7 @@
### Clustering
-
-
-
+
1. _**Clustered Index**_: Load the data containing index information assigned to the original data by clusters.
2. _**Feature Data**_: Load the original data. The _**Silhouette Score**_ is derived through computations with the data specified in the _**Clustered Index**_.
diff --git a/docs/machine-learning/13.-pipeline.md b/docs/machine-learning/13.-pipeline.md
index 0d170dde..7fe18030 100644
--- a/docs/machine-learning/13.-pipeline.md
+++ b/docs/machine-learning/13.-pipeline.md
@@ -1,14 +1,10 @@
# 13. Pipeline
-
-
-
+
1. Click on _**Pipeline**_ in the _**Machine Learning**_ category.
-
-
-
+
2. _**Pipeline**_: Choose the type of task:
* [Data Preparation](13.-pipeline.md#data-preparation)
diff --git a/docs/machine-learning/14.-save-load.md b/docs/machine-learning/14.-save-load.md
index 2b980efc..e93ae8e8 100644
--- a/docs/machine-learning/14.-save-load.md
+++ b/docs/machine-learning/14.-save-load.md
@@ -1,14 +1,10 @@
# 14. Save / Load
-
-
-
+
1. Click on _**Save/Load**_ in the _**Machine Learning**_ category.
-
-
-
+
2. Choose an action:
* [Save model](14.-save-load.md#save-model)
@@ -22,9 +18,7 @@
### Save model
-
-
-
+
1. _**Target**_: Select the machine learning model to save.
2. _**Save path**_: Specify the path (location) to save the model. You can easily specify it by clicking on the folder icon.
@@ -35,9 +29,7 @@
### Load model
-
-
-
+
1. _**Load path**_: Specify the path (location) where the model is saved. You can easily specify it by clicking on the folder icon.
2. _**Allocate to**_: Enter the variable name to assign to the loaded model.
diff --git a/docs/machine-learning/2.-data-split.md b/docs/machine-learning/2.-data-split.md
index e7855ceb..2c8f49ab 100644
--- a/docs/machine-learning/2.-data-split.md
+++ b/docs/machine-learning/2.-data-split.md
@@ -1,16 +1,16 @@
-# 2. Data Split
-
+---
+description: Divide Your Data into Training and Testing Data
+---
+# 2. Data Split
-
+
1. Click on _**Data Split**_ in the _**Machine Learning**_ category.
+
-
-
-
-2. _**Input Data**_: Choose whether the target data is included in the input data. If it is, select _**Feature Data**_ and _**Target Data**_ separately. You can also select specific columns from one dataset using the _**funnel icon**_.
+2. _**Input Data**_: Choose whether the target data is included in the input data. If it is, select _**Feature Data**_ and _**Target Data**_ separately. You can also select specific columns from one dataset using the funnel icon.
3. _**Test Size**_: Select the percentage of input data to use for testing purposes.
4. _**Random State**_: Generate the same random state, ensuring consistent data splits each time. (If not set, data will be randomly split differently each time.)
5. _**Shuffle**_: Shuffle the data randomly to prevent the model from relying on the order of the data, thereby reducing bias and improving generalization performance.
diff --git a/docs/machine-learning/3.-data-prep.md b/docs/machine-learning/3.-data-prep.md
index eba4bd6e..92c6f825 100644
--- a/docs/machine-learning/3.-data-prep.md
+++ b/docs/machine-learning/3.-data-prep.md
@@ -1,20 +1,79 @@
-# 3. Data Prep
-
-
+---
+description: Tools for Preprocessing(Encoding/Scaling)
+---
-
-
-1. Click on Data Prep in the Machine Learning category.
+# 3. Data Prep
+
+1. Click on **Data Prep** in the **Machine Learning** category.
-
+
2. _**Model Type**_: You can perform various preprocessing tasks:
- * Encoding
- * Scaling
- * ETC
+ * [**Encoding**](3.-data-prep.md#encoding)
+ * [**Scaling**](3.-data-prep.md#scaling)
+ * [**ETC**](3.-data-prep.md#etc-simpleimputer-smote-makecolumntransformer)
3. _**Allocate to**_: Assign variable names for the model to perform the selected preprocessing tasks.
4. _**Code View**_: Preview the code that will be output.
5. _**Run**_: Execute the code.
+
+
+***
+
+## Encoding
+
+
+
+1. _**Sparse (OneHotEncoder)**_: If _**true**,_ returns the encoding result as a sparse matrix.
+2. _**Handle unknown (OneHotEncoder, OrdinalEncoder)**_: Used when encoding, if there is a category that exists in the training data but not in the test data. If _**ignore** is_ selected, it will be set to 0, and if _**error**_ is selected, a ValueError will be raised.
+3. _**Unknown values (OrdinalEncoder)**_: Fill with a specific value, not ignore or error.
+4. _**Cols (TargetEncoder)**_: Select the columns to encode.
+5. _**Handle missing (TargetEncoder)**_: Choose how to handle missing values.
+6. _**Smoothing (TargetEncoder)**_: When the number of data in a particular category is small, it adds the entered values and calculates the average of the categories to prevent overfitting.
+
+
+
+***
+
+## Scaling
+
+
+
+1. _**With mean (StandardScaler)**_: Center the mean of the data to zero.
+2. _**With std (StandardScaler)**_: Scale the standard deviation of the data to 1.
+3. _**With centering (RobustScaler)**_: Performs centering by Q-subtracting the median from each attribute (column)_._
+4. _**With scaling (RobustScaler)**_: Scales each attribute by dividing it by its IQR.
+5. _**Feature range (MinMaxScaler)**_: Sets the minimum and maximum values for the scaled result.
+6. _**Norm (Normalizer)**:_
+ 1. _**L1**_: The sum of the absolute values of each attribute will be 1.
+ 2. _**L2**_: Scale the vectors so that their Euclidean distance is 1.
+ 3. _**Max Norm**_: Ensures that the scaling result does not exceed an existing maximum value.
+7. _**N bins (KBins Discretizer)**_: Determines how many bins to divide the variable into.
+8. _**Strategy (KBins Discretizer)**_:
+ 1. _**uniform**_: Divide the section by a uniform width.
+ 2. _**QUANTILE**_: Divide so that each bin has an even number of data.
+9. _**Encode (KBins Discretizer)**_: Specify the encoding method.
+ 1. _**ordinal**_: Encodes each interval as an integer.
+ 2. _**onehot**_: Encodes each interval as a binary vector.
+
+
+
+***
+
+## ETC(SimpleImputer / SMOTE / MakeColumnTransformer)
+
+
+
+1. _**Missing values (SimpleImputer)**_: Treats the entered values as missing.
+2. _**Fill value (SimpleImputer)**_: Replaces _the_ missing value with the input value.
+3. _**Copy (SimpleImputer)**_: Returns the original data unchanged, as new data.
+4. _**Add indicator (SimpleImputer)**_: Adds a new column with 0s and 1s, with a 1 for rows with missing values and a 0 for rows without.
+5. _**K neighbors (SMOTE)**_: Specifies the number of neighbors to group together based on center point data.
+6. _**Sampling strategy (SMOTE)**_:
+ 1. _**auto**_: Automatically adjusts the ratio of minority to majority class data to balance out class imbalances.
+ 2. _**minority**_: Makes the size of the minority class dataset equal to the size of the majority class dataset.
+ 3. _**float**_: You can specify the desired class ratio. For example, setting it to 0.5 makes the minority class dataset half the size of the majority class dataset.
+7. _**Estimator (MakeColumnTransformer)**_: You can specify different global models to apply to each column. The model selected here will be applied to the columns selected _in Columns_ below.
+
diff --git a/docs/machine-learning/4.-automl.md b/docs/machine-learning/4.-automl.md
index d47a69c7..f0a51fab 100644
--- a/docs/machine-learning/4.-automl.md
+++ b/docs/machine-learning/4.-automl.md
@@ -1,14 +1,10 @@
# 4. AutoML
-
-
-
+
1. Click on _**AutoML**_ in the _**Machine Learning**_ category.
-
-
-
+
2. _**Model Type**_: Choose the type of model to generate. You can conveniently install necessary packages through _**Install**_.
3. _**Generation(TPOT)**_: Set the number of generations to generate, evaluate, and compare. Each generation generates and evaluates candidate model pipelines. Higher values increase execution time.
diff --git a/docs/machine-learning/5.-regressor.md b/docs/machine-learning/5.-regressor.md
index 823b56a9..9498fe39 100644
--- a/docs/machine-learning/5.-regressor.md
+++ b/docs/machine-learning/5.-regressor.md
@@ -1,14 +1,10 @@
# 5. Regressor
-
-
-
+
1. Click on the _**Regressor**_ in the _**Machine Learning**_ category.
-
-
-
+
2. _**Model Type**_: Choose the regression model.
@@ -33,9 +29,7 @@
### Linear Regression
-
-
-
+
1. _**Fit Intercept**_: Choose whether to include the intercept.
@@ -45,9 +39,7 @@
### Ridge / Lasso
-
-
-
+
1. _**Alpha**_: Adjust the level of regularization.
@@ -57,9 +49,7 @@
### ElasticNet
-
-
-
+
1. _**Alpha**_: Adjust the level of regularization.
2. _**L1 ratio**_: Adjusts the balance (ratio) between _**L1 (Lasso)**_ and _**L2 (Ridge)**_ regularization.
@@ -70,18 +60,14 @@
### SVR(SupportVectorMachine Regressor)
-
-
-
+
1. _**C**_: Represents the degree of freedom for model regularization. Higher values of C make the model more complex, fitting the training data more closely.
2. _**Kernel**_: Function mapping data to a higher-dimensional space, controlling model complexity.
-
-* _**Degree(Poly)**_: Determines the degree of polynomial.
-* _**Gamma(Poly, rbf, sigmoid)**_: Adjusts the curvature of the decision boundary.
-* _**Coef0(Poly, sigmoid)**_: Additional parameter for the kernel, controlling the offset. Higher values fit the training data more closely.
-
-3. _**Random state**_: Sets the seed value for the random number generator used in model training.
+3. _**Degree(Poly)**_: Determines the degree of polynomial.
+4. _**Gamma(Poly, rbf, sigmoid)**_: Adjusts the curvature of the decision boundary.
+5. _**Coef0(Poly, sigmoid)**_: Additional parameter for the kernel, controlling the offset. Higher values fit the training data more closely.
+6. _**Random state**_: Sets the seed value for the random number generator used in model training.
@@ -89,9 +75,7 @@
### DecisionTree Regressor
-
-
-
+
1. _**Criterion**_: Specifies the measure used for node splitting.
2. _**Max depth**_: Specifies the maximum depth of the tree.
@@ -104,9 +88,7 @@
### RandomForest Regressor
-
-
-
+
1. _**N estimators**_: Specifies the number of trees in the ensemble.
2. _**Criterion**_: Specifies the measure used for node splitting.
@@ -121,9 +103,7 @@
### GradientBoosting Regressor
-
-
-
+
1. _**Loss**_: Specifies the loss function used.
2. _**Learning rate**_: Specifies the learning rate.
@@ -137,9 +117,7 @@
### XGB Regressor
-
-
-
+
1. _**N estimators**_: Specifies the number of trees in the ensemble.
2. _**Max depth**_: Specifies the maximum depth of the tree.
@@ -153,9 +131,7 @@
### LGBM Regressor
-
-
-
+
1. _**Boosting type**_: Specifies the boosting type used in the algorithm.
2. _**Max depth**_: Specifies the maximum depth of the tree.
@@ -169,9 +145,7 @@
### CatBoost Regressor
-
-
-
+
1. _**Learning rate**_: Specifies the learning rate.
2. _**Loss function**_: Specifies the loss function used.
diff --git a/docs/machine-learning/6.-classifier.md b/docs/machine-learning/6.-classifier.md
index 92cfbf75..effdeb5c 100644
--- a/docs/machine-learning/6.-classifier.md
+++ b/docs/machine-learning/6.-classifier.md
@@ -1,14 +1,10 @@
# 6. Classifier
-
-
-
+
1. Click on the _**Classifier**_ under the _**Machine Learning**_ category.
-
-
-
+
2. _**Model Type**_: Select the Model Type of the classifier you want to use:
* [Logistic Regression](6.-classifier.md#logistic-regression)
@@ -32,9 +28,7 @@
### Logistic Regression
-
-
-
+
1. _**Penalty**_: Specify the regularization method for the model. (l2 / l1 / elasticnet / none)
2. _**C**_: Adjust the regularization strength.
@@ -46,16 +40,14 @@
### SupportVectorMachine Classifier
-
-
-
+
1. _**C**_: C indicates the freedom of the model's regularization. A higher C value makes the model more complex to fit the training data.
2. _**Kernel**_: A function that maps data into higher dimensions. You can control the complexity of the model by selecting the kernel type.
- * _**Degree (Poly)**_: Degree determines the degree of the polynomial. A higher degree increases the complexity of the model.
- * _**Gamma (Poly, rbf, sigmoid)**_: Gamma adjusts the curvature of the decision boundary. A higher value makes the model fit the training data more closely.
- * _**Coef0 (Poly, sigmoid)**_: An additional parameter for the kernel, controlling the offset of the kernel. A higher value makes the model fit the training data more closely.
-3. _**Random State**_: Set the seed value for the random number generator.
+3. _**Degree (Poly)**_: Degree determines the degree of the polynomial. A higher degree increases the complexity of the model.
+4. _**Gamma (Poly, rbf, sigmoid)**_: Gamma adjusts the curvature of the decision boundary. A higher value makes the model fit the training data more closely.
+5. _**Coef0 (Poly, sigmoid)**_: An additional parameter for the kernel, controlling the offset of the kernel. A higher value makes the model fit the training data more closely.
+6. _**Random State**_: Set the seed value for the random number generator.
@@ -63,9 +55,7 @@
### DecisionTree Classifier
-
-
-
+
1. _**Criterion**_: Specify the metric used to select the node split. (squared\_error / friedman\_mse / absolute\_error / Poisson)
2. _**Max Depth**_: Specify the maximum depth of the trees.
@@ -78,9 +68,7 @@
### RandomForest Classifier
-
-
-
+
1. _**N estimators**_: Specify the number of trees to include in the ensemble.
2. _**Criterion**_: Specify the metric used to select the node split. Options include gini / entropy.
@@ -95,9 +83,7 @@
### GradientBoosting Classifier
-
-
-
+
1. _**Loss**_: Specify the loss function to be used. Options include deviance / exponential.
2. _**Learning rate**_: Adjust the contribution of each tree and the degree to which the errors of previous trees are corrected. A large value may lead to non-convergence or overfitting, while a small value may increase training time.
@@ -111,9 +97,7 @@
### XGB Classifier
-
-
-
+
1. _**N estimators**_: Specify the number of trees to include in the ensemble.
2. _**Max Depth**_: Specify the maximum depth of the trees.
@@ -127,9 +111,7 @@
### LGBM Classifier
-
-
-
+
1. _**Boosting type**_: Specify the boosting method used internally in the algorithm. (gbdt / dart / goss / rf (Random Forest))
2. _**Max Depth**_: Specify the maximum depth of the trees.
@@ -143,9 +125,7 @@
### CatBoost Classifier
-
-
-
+
1. _**Learning rate**_: Adjust the contribution of each tree and the degree to which the errors of previous trees are corrected.
2. _**Loss function**_: Specify the loss function to be used. (RMSE / absolute\_error / huber / quantile)
diff --git a/docs/machine-learning/7.-clustering.md b/docs/machine-learning/7.-clustering.md
index 5738677b..6355a54f 100644
--- a/docs/machine-learning/7.-clustering.md
+++ b/docs/machine-learning/7.-clustering.md
@@ -1,14 +1,10 @@
# 7. Clustering
-
-
-
+
1. Click on _**Clustering**_ under the _**Machine Learning**_ category.
-
-
-
+
2. _**Model type**_: Select the type of Model you want to use.
* [KMeans / AgglomerativeClustering](7.-clustering.md#kmeans-agglomerativeclustering)
@@ -24,9 +20,7 @@
### KMeans / AgglomerativeClustering
-
-
-
+
1. _**N clusters**_: Specify the number of clusters to be generated.
2. _**Random state**_: Set the seed value for the random number generator.
@@ -37,9 +31,7 @@
### GaussianMixture
-
-
-
+
1. _**N components**_: Specify the number of Gaussian distributions to be used by the model to describe the data, determining how many clusters the data will be divided into.
2. _**Random state**_: Set the seed value for the random number generator.
@@ -50,9 +42,7 @@
### DBSCAN
-
-
-
+
1. _**Eps (Epsilon)**_: Specify the maximum distance (radius) for forming clusters.
2. _**Min samples**_: Specify the minimum number of neighboring data points required for a point to be recognized as a cluster.
diff --git a/docs/machine-learning/8.-dimension.md b/docs/machine-learning/8.-dimension.md
index 369c6caf..f988e816 100644
--- a/docs/machine-learning/8.-dimension.md
+++ b/docs/machine-learning/8.-dimension.md
@@ -1,14 +1,10 @@
# 8. Dimension
-
-
-
+
1. Click on _**Dimension**_ under the _**Machine Learning**_ category.
-
-
-
+
2. _**Model type**_: Select the type of model.
3. _**N components**_: Specify the desired number of dimensions to reduce the data to.
diff --git a/docs/machine-learning/9.-gridsearch.md b/docs/machine-learning/9.-gridsearch.md
index 25dcd64d..d41822bf 100644
--- a/docs/machine-learning/9.-gridsearch.md
+++ b/docs/machine-learning/9.-gridsearch.md
@@ -1,14 +1,10 @@
# 9. GridSearch
-
-
-
+
1. Click on _**GridSearch**_ under the _**Machine Learning**_ category.
-
-
-
+
2. _**Model type**_: Select the type of machine learning model you want to use.
3. _**Scoring**_: Choose the metric for evaluating the model.
diff --git a/docs/statistics/1.-prob.-distribution.md b/docs/statistics/1.-prob.-distribution.md
index 61e0361e..8e11618f 100644
--- a/docs/statistics/1.-prob.-distribution.md
+++ b/docs/statistics/1.-prob.-distribution.md
@@ -1,18 +1,18 @@
-# 1. Prob. Distribution
-
-
-
-
+---
+description: Generate a Specific Distribution or View Information
+---
-1. Click on the _**Prob. Distribution**_ icon in the _**Statistics**_ category.
+# 1. Prob. Distribution
+
+1. Click the _**Prob. Distribution**_ in the _**Statistics**_ category.
-
+
-2. _**Code View**_: Preview the code that will be generated.
-3. _**Data View**_: View the data used in the operation.
-4. _**Run**_: Execute the code.
+2. _**Code View**_: Preview the code that will be output.
+3. _**Data View**_: Preview the data that will be output.
+4. _**Run**_: Print and run the code.
@@ -48,39 +48,33 @@
#### Generate Random Numbers
-
-
-1. _**Size**_: Set the number of samples extracted from the generated distribution.
-2. _**Random State**_: Set the seed.
-3. _**Allocate to**_: Specify the variable (call name) toate.
-4. _**Show Sampled Distribution**_: Visualize the distribution drawn from the samples.
-
+
+1. _**Size**_: Set the number of samples you want to draw from the distribution you want to generate.
+2. _**Random State**_: Set your seed.
+3. _**Allocate to**_: Specify the variable (call name).
+4. _**Show Sampled Distribution**_: If checked, visualize the distribution of the extracted samples.
#### Show Distribution Plot
-
-
-1. _**Probability Density Function**_: Output the probability density function.
-2. _**Cumulative Distribution Function**_: Output the cumulative distribution function.
-
+
+1. _**Probability Density Function**_: Outputs the probability density function.
+2. _**Cumulative Distribution Function**_: Outputs the cumulative distribution function.
#### Statistics to P-Value
-
-
-1. _**Statistic**_: You will obtain the probability of getting a value equal to or greater than the absolute value entered here in this distribution.
-2. _**Alternative**_: Two-sided; for a two-tailed test, One-sided; for a one-tailed test.
-
+
+1. _**Statistics**_: You will get the probability of getting a value from this distribution that is greater than or equal to the absolute value entered here.
+2. _**Alternative**_: _**Two-sided** for a_ two-tailed test, _**One-sided**_ for _a_ one-tailed test.
#### P-Value to Statistics
-
+
-1. _**Proportional Value**_: Enter the p-value; the range on the distribution where values equal to or greater than the entered p-value will be calculated and displayed.
-2. _**Alternative**_: Choose between two-sided or one-sided test.
+1. _**Proportional value**_: Enter a p-value; the range of the distribution with values greater than or equal to the entered p-value is calculated and displayed.
+2. _**Alternative**_: Choose between a two-tailed or one-tailed test.
@@ -90,19 +84,17 @@
### Bernoulli
-
-
-1. _**P**_: Enter the probability of getting 1 in the binary outcome of 0 and 1.
-2. _**User Option**_: Optionally provide additional desired options.
-
+
+1. _**P**_: Enter the probability of getting 1 out of a binary outcome of 0 and 1.
+2. _**User option**_: You can add options other than those provided in Visual Python.
### Binomial and Multinomial
-
+
1. _**N**_: Enter the number of trials.
-2. _**P**_: Enter the probability of success in a trial. Use the _**Funnel Icon**_ to fetch values entered in a specific dataframe column. (The form of the input values can be referred to in the generated examples.)
+2. _**P**_: Enter the probability of success for each trial. Use the funnel icon to get the values entered in a specific column of the dataframe. (Note that the values entered are arbitrary in this example).
@@ -112,71 +104,59 @@
### Normal
-
+
1. _**Loc**_: Set the mean of the normal distribution.
2. _**Scale**_: Set the standard deviation of the normal distribution.
-3. _**User Option**_: Optionally provide additional desired options.
-
-
+3. _**User option**_: You can add options other than those provided in Visual Python.
### Beta
-
-
-1. _**A**_: Set the shape parameter a of the beta distribution.
-2. _**B**_: Set the shape parameter b of the beta distribution.
-3. _**User Option**_: Optionally provide additional desired options.
-
+
+1. _**A**_: Set the shape parameter **A** for the beta distribution.
+2. _**B**_: Set the shape parameter **B** for the beta distribution.
+3. _**User option**_: You can add options other than those provided in Visual Python.
### Gamma
-
+
1. _**A**_: Set the shape parameter of the gamma distribution.
-2. _**User Option**_: Optionally provide additional desired options.
-
-
+2. _**User Option**_: You can add options other than those provided in Visual Python.
### Student's t and Chi2
-
+
1. _**Df**_: Set the degrees of freedom for the t-distribution or chi-squared distribution.
-2. _**User Option**_: Optionally provide additional desired options.
-
-
+2. _**User Option**_: You can add options other than those provided in Visual Python.
### F
-
+
1. _**Dfn**_: Set the numerator degrees of freedom.
2. _**Dfd**_: Set the denominator degrees of freedom.
* The F-distribution represents the ratio of two chi-squared distributions. _**Dfn**_ and _**Dfd**_ are the degrees of freedom for the two chi-squared distributions.
-3. _**User Option**_: Optionally provide additional desired options.
-
-
+3. _**User Option**_: You can add options other than those provided in Visual Python.
### Dirichlet
-
+
1. _**Alpha**_: Enter the importance for three categories (or dimensions) in the format _**(a, b, c)**_.
-2. _**Seed**_: If provided, the generated random numbers will be fixed.
-3. _**User Option**_: Optionally provide additional desired options.
-
-
+2. _**Seed**_: If entered, the random number generated will be fixed.
+3. _**User Option**_: You can add options other than those provided in Visual Python.
### Multivariate Normal
-
+
1. _**Mean**_: Set the mean of the distribution. For _**\[a, b]**_, the mean of the first distribution is _**a**_, and the mean of the second distribution is _**b**_.
2. _**Cov**_: Set the covariance of the distribution. For _**\[a, b]**_, the covariance of the first distribution is _**a**_, and the covariance of the second distribution is _**b**_.
3. _**Allow Singular**_: If True, allows generating the distribution even when the covariance matrix is singular.
-4. _**User Option**_: Optionally provide additional desired options.
+4. _**User Option**_: You can add options other than those provided in Visual Python.
diff --git a/docs/statistics/10.-factor-analysis.md b/docs/statistics/10.-factor-analysis.md
index 5b3e273d..182ac459 100644
--- a/docs/statistics/10.-factor-analysis.md
+++ b/docs/statistics/10.-factor-analysis.md
@@ -1,14 +1,10 @@
# 10. Factor Analysis
-
-
-
+
1. Click on _**Factor Analysis**_ in the _**Statistics**_ category
-
-
-
+
2. _**Install Package**_: You can automatically _**Import**_ the necessary packages for factor analysis.
3. _**Data**_: Select the data for factor analysis. You can also choose specific conditions using the [_**Subset**_](../data-analysis/5.-subset.md) option.
@@ -16,8 +12,8 @@
5. _**Rotation**_: Select a rotation method to analyze which factors best explain the data.
6. _**Method**_: Choose a factor analysis method:
* _**Principal**_: Principal Factor Analysis
- * _ML_: Maximum Likelihood Factor Analysis
- * _Minres_: Minimum Residual Factor Analysis
+ * _**ML**_: Maximum Likelihood Factor Analysis
+ * _**Minres**_: Minimum Residual Factor Analysis
7. _**Impute**_: Select a method for handling missing values:
* **Drop**: Remove rows with missing values.
* **Mean / Median**: Replace missing values with the mean or median.
diff --git a/docs/statistics/11.-regression.md b/docs/statistics/11.-regression.md
index 44ac6cba..cb8e687f 100644
--- a/docs/statistics/11.-regression.md
+++ b/docs/statistics/11.-regression.md
@@ -1,10 +1,10 @@
# 11. Regression
-1. Click on _**Regression**_ in the _**Statistics**_ category.
-
+
+1. Click on _**Regression**_ in the _**Statistics**_ category.
-
+
2. Choose the type of Regression you want to use.
3. _**Data**_: Select the data to be analyzed. You can also choose specific conditions for the data using [_**Subset**_](../data-analysis/5.-subset.md).
diff --git a/docs/statistics/12.-logistic-regression.md b/docs/statistics/12.-logistic-regression.md
index c055d4c6..9c761e60 100644
--- a/docs/statistics/12.-logistic-regression.md
+++ b/docs/statistics/12.-logistic-regression.md
@@ -1,19 +1,15 @@
# 12. Logistic Regression
-
-
-
+
1. Click on _**Logistic Regression**_ in the _**Statistics**_ category.
-
-
-
+
2. _**Data**_: Select the data for analysis. You can choose only the data that meets specific conditions using [_**Subset**_](../data-analysis/5.-subset.md).
3. _**Dependent Variable**_: Choose the dependent variable. You can transform continuous variables into categorical variables by checking _**Encoding Labels**_.
4. _**Independent Variable**_: Select the independent variables.
-5. _**Show Odds**_: Display the odds.
+5. _**Show Odds**_: Show _**Odds**_ (_**Odds**_: the ratio between the probability of a certain event happening and the probability of it not happening)
6. _**Multicollinearity Statistics**_: This provides statistics to check for the presence of multicollinearity.
7. _**Code View**_: Preview the code that will be output.
8. _**Data View**_: Preview the data that will be output.
diff --git a/docs/statistics/2.-descriptive-statistics.md b/docs/statistics/2.-descriptive-statistics.md
index 0a27a77d..514fc038 100644
--- a/docs/statistics/2.-descriptive-statistics.md
+++ b/docs/statistics/2.-descriptive-statistics.md
@@ -1,50 +1,40 @@
+---
+description: View the Statistics of Your Data
+---
+
# 2. Descriptive Statistics
-
+
1. Select _**Descriptive Statistics**_ in the _**Statistics category**_.
+
-
-
-
-2. _**Data**:_ Choose a DataFrame. You can apply conditions or slice through the _**Subset**_.
+2. _**Data**:_ Choose a DataFrame. You can apply conditions or slice through the [_**Subset**_](../data-analysis/5.-subset.md).
3. _**Variable**_: Select the column from the chosen DataFrame to be designated as the _**Variable**_.
4. _**Code View**_: Preview the code that will be displayed.
5. _**Data View**_: Preview the displayed results.
6. _**Run**_: Execute the code.
-
-
### Descriptive Statistics
+
-
-
-
-1. _**Central Tendency**_ outputs the selected variable's _**Mean, Median, Mode,**_ and _**Sum**_.
-2. _**Dispersion**_ outputs the _**Minimum, Maximum, Range, Standard Deviation, Variance,**_ and _**Standard Error of the Mean**_ of the selected variable.
-3. _**Distribution**_ outputs the _**Skewness**_ and _**Kurtosis**_ of the selected variable.
-4. _**Percentile Values**_ outputs _**Quantiles**_ and _**Percentiles**_ of the selected variable.
-
-
+1. _**Central tendency**_: Outputs the mean, median, minimum, maximum, and sum of the selected variables.
+2. _**Dispersion**_: Outputs the minimum, maximum, range, standard deviation, variance, and standard error of the mean of the selected variable.
+3. _**Distribution**_: Outputs the skewness and kurtosis of the selected variable.
+4. _**Percentile values**_: Outputs the percentile and percentage of the selected variable.
### Frequency Table
+
-
-
-
-1. It outputs _**Frequency, Percent, Valid Percent,**_ and _**Cumulative Percent**_ of the selected variable.
-2. _**Number of Unique Values**_ divides the data of the selected variable into units of entered values.
-
-
+1. It can output _**Frequency, Percent, Valid Percent,**_ and _**Cumulative Percent**_ of the selected variable.
+2. _**Number of unique values**_: Splits the data in the selected variable into units of the entered value.
### Display
-
-
-
+
1. Select the plots to be displayed.
diff --git a/docs/statistics/3.-normality-test.md b/docs/statistics/3.-normality-test.md
index ccf70d75..eb00c54f 100644
--- a/docs/statistics/3.-normality-test.md
+++ b/docs/statistics/3.-normality-test.md
@@ -1,21 +1,16 @@
# 3. Normality Test
-
-
-
+
1. Choose the _**Normality test**_ in the _**Statistics**_ category.
+
-
-
-
-2. Select the test method to apply.
-3. Choose the data to be tested. You can select values that satisfy certain conditions through [_**Subsets**_](../data-analysis/5.-subset.md).
-4. Choose the column to be tested among the columns with the selected values from _**Step 3**_.
-5. _**Alternative Hypothesis**_ _**(Kolmogorov-Smirnov test)**_: Choose whether to perform a two-sided or one-sided test.
-6. _**Display**_: Select the Plot to be output along with the validation results.
-7. _**Code View**_: Preview the code that will be output.
-8. _**Data View**_: Preview the data that will be output.
-9. _**Run**_: Execute the code.
+2. On the top tab, select the test method you want to apply.
+3. _**Data**_: Select the data you want to test. You can use [_**Subset**_](../data-analysis/5.-subset.md) _to_ select values that meet certain conditions. Next, select the columns you want to test from the selected values.
+4. _**Alternative hypothesis (Kolmogorov-Smirnov test)**_: Choose whether to perform a two-tailed or one-tailed test.
+5. _**Display**_: Choose a plot to output with your results.
+6. _**Code View**_: Preview the code that will be output.
+7. _**Data View**_: Preview the data that will be output.
+8. _**Run**_: Print and run the code.
diff --git a/docs/statistics/4.-equal-var.-test.md b/docs/statistics/4.-equal-var.-test.md
index 237db740..78a650f5 100644
--- a/docs/statistics/4.-equal-var.-test.md
+++ b/docs/statistics/4.-equal-var.-test.md
@@ -1,14 +1,10 @@
# 4. Equal Var. Test
-
-
-
+
1. Select the _**Equal Var. Test**_ in the _**Statistics**_ category.
-
-
-
+
2. Choose the type of test to apply.
@@ -27,15 +23,13 @@
### Long Data
-
-
-
+
1. _**Input Type**_: Select the format of the data to be tested.
2. _**Data**_: Choose the data to be tested. You can use [_**Subset**_](../data-analysis/5.-subset.md) to select only the data that satisfies specific conditions.
3. _**Factor**_: Choose the criterion for dividing the groups for testing equal variance among the data selected in 2.
4. _**Variable**_: Select the values that constitute the variance among the data chosen in 2.
-5. _**Center(Levene test, Fligner test)**_: Choose the criterion for testing equal variance of the columns.
+5. _**Center(Levene test, Fligner test)**_: Specifies the centering method. Center the data around the selected value. Check how spread out the data is around the selected value.
@@ -43,12 +37,9 @@
### Wide Data
-
-
-
+
1. _**Input Type**_: Select the format of the data to be tested.
-2. _**Data**_: Choose the data to be tested. You can use [_**Subset**_](../data-analysis/5.-subset.md) to select only the data that satisfies specific conditions.
-3. Select the columns for the data chosen above to test for equal variance.
-4. _**Center(Levene test, Fligner test)**_: Choose the criterion for testing the equal variance of the columns.
+2. _**Data**_: Choose the data to be tested. You can use [_**Subset**_](../data-analysis/5.-subset.md) to select only the data that satisfies specific conditions. Select the columns for the data chosen above to test for equal variance.
+3. _**Center(Levene test, Fligner test)**_: Specifies the centering method. Center the data around the selected value. Check how spread out the data is around the selected value.
diff --git a/docs/statistics/5.-correlation-analysis.md b/docs/statistics/5.-correlation-analysis.md
index 5d39c2c8..de9dcf88 100644
--- a/docs/statistics/5.-correlation-analysis.md
+++ b/docs/statistics/5.-correlation-analysis.md
@@ -1,14 +1,10 @@
# 5. Correlation Analysis
-
-
-
+
1. Click on _**Correlation Analysis**_ in the _**Statistics**_ category.
-
-
-
+
2. _**Data**_: Select the data for correlation analysis. You can use [_**Subset**_](../data-analysis/5.-subset.md) to choose only the data that satisfies specific conditions.
3. _**Variable:**_ Choose the variables from the selected data for which you want to analyze the correlation.
diff --git a/docs/statistics/6.-reliability-analysis.md b/docs/statistics/6.-reliability-analysis.md
index a6440919..e07e45bb 100644
--- a/docs/statistics/6.-reliability-analysis.md
+++ b/docs/statistics/6.-reliability-analysis.md
@@ -1,14 +1,10 @@
# 6. Reliability Analysis
-
-
-
+
1. Click on _**Reliability Analysis**_ in the _**Statistics**_ category.
-
-
-
+
2. _**Data**_: Select the data for Reliability Analysis. You can specify only the data that meets specific conditions from the chosen DataFrame through [_**Subset**_](../data-analysis/5.-subset.md).
3. _**Variable**_: Choose the columns for Reliability Analysis from the data selected above.
@@ -16,15 +12,11 @@
5. _**Data View**_: Preview the data that will be output.
6. _**Run**_: Execute the code.
+### Example Output
-
-### Output Example
-
-
-
-
+
* _**Cronbach Alpha**_ used in Reliability Analysis is a statistical method measuring _**Consistency**_, and this consistency is an indicator of Reliability.
-* The Cronbach Alpha value is output between 0 and 1, where closer to 1 indicates higher consistency. The _**N**_ value represents the number of columns selected.
-* _**The Item-Total Statistics t**_able displays the mean, variance, and Cronbach Alpha values for each column's presence or absence. Additionally, _**Corrected Item-Total Correlation**_ indicates how consistent each column is with the total.
+* **The Cronbach Alpha value** is output between 0 and 1, where closer to 1 indicates higher consistency. The _**N**_ value represents the number of columns selected.
+* _**The Item-Total Statistics t**_**able** displays the mean, variance, and Cronbach Alpha values for each column's presence or absence. Additionally, _**Corrected Item-Total Correlation**_ indicates how consistent each column is with the total.
diff --git a/docs/statistics/7.-chi-square-test.md b/docs/statistics/7.-chi-square-test.md
index ab6144ec..c735dba8 100644
--- a/docs/statistics/7.-chi-square-test.md
+++ b/docs/statistics/7.-chi-square-test.md
@@ -1,14 +1,10 @@
# 7. Chi-square Test
-
-
-
+
1. Click on the _**Chi-square test**_ in the _**Statistics**_ category.
-
-
-
+
2. _**Data**_: Select the data for the _**Chi-square test**_. You can choose specific data that meets certain conditions through [_**Subset**_](../data-analysis/5.-subset.md).
3. _**Row**_: In Cross tabulation for Chi-square test, choose the column to be used as a _**row**_.
diff --git a/docs/statistics/8.-students-t-test.md b/docs/statistics/8.-students-t-test.md
index c58bf273..3b6bd5bb 100644
--- a/docs/statistics/8.-students-t-test.md
+++ b/docs/statistics/8.-students-t-test.md
@@ -1,14 +1,10 @@
# 8. Student's T-test
-
-
-
+
1. Click on the _**Student's t-test**_ in the _**Statistics**_ category.
-
-
-
+
2. Choose the type of t-test you want to perform:
@@ -26,9 +22,7 @@
### One-sample t-test
-
-
-
+
1. _**Data**_: Select the data for the t-test. You can choose specific data satisfying certain conditions using [_**Subset**_](../data-analysis/5.-subset.md).
2. _**Test Variable**_: Choose the group for the t-test.
@@ -42,9 +36,7 @@
### Independent two-sample t-test
-
-
-
+
1. _**Input Type**_: Choose the type of data for the t-test.
2. _**Data**_: Select the data for the t-test. You can choose specific data satisfying certain conditions using [_**Subset**_](../data-analysis/5.-subset.md).
@@ -57,9 +49,7 @@
### Paired samples t-test
-
-
-
+
1. _**Data**_: Select the data for the t-test. You can choose specific data satisfying certain conditions using [_**Subset**_](../data-analysis/5.-subset.md).
2. _**Paired Variable**_: Choose the paired groups for the t-test.
diff --git a/docs/statistics/9.-anova.md b/docs/statistics/9.-anova.md
index 800b2004..533b9722 100644
--- a/docs/statistics/9.-anova.md
+++ b/docs/statistics/9.-anova.md
@@ -1,14 +1,10 @@
# 9. ANOVA
-
-
-
+
1. Click on _**ANOVA**_ in the _**Statistics**_ category.
-
-
-
+
2. Choose the analysis method.
@@ -26,15 +22,13 @@
### One-way ANOVA / Two-way ANOVA
-
-
-
+
1. _**Data**_: Select the data for analysis. You can choose data that satisfies specific conditions through [_**Subset**_](../data-analysis/5.-subset.md).
2. _**Dependent Variable**_: Choose the dependent variable.
3. _**Factor**_: Select the factors. For _**Two-way ANOVA**_, choose factors A and B.
4. _**Significance Level**_: Enter the significance level. (default: 0.05)
-5. _**Post hoc analysis**_: Choose the post hoc analysis method. If there is a statistically significant difference in mean among groups, further verify which groups differ.
+5. _**Post hoc analysis**_: If there is a statistically significant difference in the mean between groups, go deeper to see which groups are different.
@@ -42,9 +36,7 @@
### ANCOVA
-
-
-
+
1. _**Install Package**_: Automatically install the required packages for analysis.
2. _**Data**_: Select the data for analysis. You can choose data that satisfies specific conditions through [_**Subset**_](../data-analysis/5.-subset.md).
diff --git a/docs/visualization/1.-chart-style.md b/docs/visualization/1.-chart-style.md
index 26819f76..2ada5dfb 100644
--- a/docs/visualization/1.-chart-style.md
+++ b/docs/visualization/1.-chart-style.md
@@ -1,25 +1,21 @@
---
-description: Configuring the Basic Style of the Chart
+description: Configuring the Basic Style of the Chart from Font to Graph Size
---
# 1. Chart Style
+
+1. In the _**Visualization**_ category, click _**Chart Style**._
-
+
-1. Click on the _**Chart Style**_ in the visualization category.
-
-
-
-
-
-2. Use _**Import Library**_ in the top right corner to import _**Matplotlib**_ and _**Seaborn**_ without specific chart style settings.
-3. Set the _**figure size**_ for the chart to be displayed.
-4. Choose the _**style of the chart**_ to be displayed.
-5. Set the _**font**_ for the text entered in the chart to be displayed.
-6. Select the _**font size**_.
-7. Default settings can be applied for the configuration.
-8. Preview the code that will be generated.
-9. Output the code.
+2. _**Import Library**_: Import _**Matplotlib**_ and _**Seaborn**._
+3. _**Figure size**_: Set the size of the figure that will be output.
+4. _**Style sheet**_: Choose the style of the picture that will be output.
+5. _**Style font**_: Set the font for the text that will be entered into the graph.
+6. _**Font size**_: Choose a font size.
+7. _**Set Default**_: Specify the setting as the default value.
+8. _**Code view**_: Preview the code that will be output.
+9. _**Run**_: Print and run the code.
diff --git a/docs/visualization/2.-pandas-plot.md b/docs/visualization/2.-pandas-plot.md
index 97d8c429..4bf5db93 100644
--- a/docs/visualization/2.-pandas-plot.md
+++ b/docs/visualization/2.-pandas-plot.md
@@ -1,43 +1,34 @@
# 2. Pandas Plot
-
-
-
-
-1. Click on _**Pandas Plot**_ in the _**Visualization**_ category.
-
-
-
-
-
-2. Select the DataFrame to be represented as a plot.
-
- 2-1. Specific columns of the DataFrame can be selected using the funnel icon.
-
-
-
-**\*The following items will be set to default values if not entered:**
-
-3. Choose the _**Chart Type**_.
-4. Enter the _**Chart Title**_.
-5. Input the _**size of the output plot**_.
-6. Select the _**Font Size**_ of the text on the plot.
-7. Select the _**color theme**_ for the displayed plot.
-8. Decide on the visibility of the _**Grid**_.
-9. Decide on the visibility of the _**Legend**_.
-10. _**Rotate on the axis names**_ if needed, especially useful for long names.
-11. Specify the _**axis names**_.
-
-
-
-
-
-12. Set the _**range of values**_ to be displayed on the axes.
-13. Specify the _**interval**_ between axis tick marks.
-14. For line graphs, specify the _**style of the lines**_.
-15. _**Choose the columns**_ from the DataFrame to be represented in the plot.
-16. Simultaneously, create _**multiple plots**_ within a single figure.
-17. Specify the _**Layout**_ when creating multiple plots.
-18. Choose _**True**_ if you want to use the DataFrame's index as the X-axis.
-19. If _**True**_, represent data from multiple columns of the DataFrame in a single plot.
+
+
+1. In the _**Visualization**_ category, click _**Pandas Plot**._
+
+
+
+2. _**DataFrame**_: Select the dataframe you want to graph. You can select specific columns via the funnel icon.
+3. _**Code view**_: Preview the code that will be output.
+4. **Run**: Print and run the code.
+
+* **The items below are set to default values if not entered:**
+
+
+
+5. _**Chart Type**_: Select the type of graph you want to output.
+6. _**Chart Title**_: Enter a title for the graph you want to output.
+7. _**Figure Size**_: Enter the size of the graph to output.
+8. _**Font Size**_: Choose a font size for the text in the graph.
+9. _**Color Map**_: Choose a color theme for the graph you want to output.
+10. _**Show Grid**_: Decide whether the grid is visible or not.
+11. _**Show Legend**_: Determine the visibility of the legend.
+12. _**X Label Rotation**_: Rotate the axis name if needed, useful if the name is long.
+13. _**X Label, Y Label**_: Name the axes.
+14. _**X Limit, Y Limit**_: Set the range of values to be displayed on the axis.
+15. **X **_**Ticks, Y Ticks**_: Specify the interval between axis tick marks.
+16. _**Style**_: For line graphs, specify the style of the lines.
+17. _**X Column, Y Column**_: Select the columns in the dataframe that you want to show in the graph.
+18. _**Subplots Per Column**_: Outputs multiple graphs in a single plot.
+19. _**Subplot Layout**_: Specify the layout when outputting multiple graphs into a single plot.
+20. _**Use Index on X Ticks**_: Select True to use the index of the dataframe as the x-axis.
+21. _**Stacked**_: If true, display the values in each column stacked.
diff --git a/docs/visualization/3.-matplotlib.md b/docs/visualization/3.-matplotlib.md
index ce042640..b52e37ea 100644
--- a/docs/visualization/3.-matplotlib.md
+++ b/docs/visualization/3.-matplotlib.md
@@ -1,31 +1,23 @@
# 3. Matplotlib
+
+1. Click _**Matplotlib**_ in the _**Visualization**_ category.
-
+
-1. Select _**Matplotlib**_ from the _**Visualization**_ category.
+2. _**Import Options**_: Configure the default settings for the figure. Detailed settings are available under _**Chart Style** in the **Visualization**_ category.
+3. _**Chart Type**_: Select a graph type.
+**\* **_**Additional Options**_**: The options below are common to all graph styles.**
-
-
-
-2. Configure the default settings of the Figure through _**Import Options**_. Detailed settings can be done in the _**Chart Style**_ of the _**Visualization**_ category.
-3. Choose the _**Chart Type**_.
-
-
-
-\*_**Additional Options**_: The following options exist universally for all chart styles.
-
-
-
-4. _**Title**_: Enter the title of the FIgure.
-5. _**Label**_: Set the name of the axes.
-6. _**Limit**_: Set the range of values to be displayed on the axes.
-7. _**Legend Title**_: Enter the title of the legend.
-8. _**Legend Labels**_: Add labels to the legend to explain the meaning of each plotted data.
-9. _**Legend Position**_: Choose the position of the legend.
-10. _**Save Figure**_: Save the figure. Set the path using the file-shaped icon.
+1. _**Title**_: Enter a title for your illustration.
+2. _**X Label, Y Label**_: Set the names of the axes.
+3. _**X Limit, Y Limit**_: Set the range of values to be displayed on the axis.
+4. _**Legend Title**_: Enter a title for the legend.
+5. _**Legend Labels**_: Add labels to the legend to explain the meaning of the plotted data.
+6. _**Legend Position**_: Choose a position for the legend.
+7. _**Save Figure**_: Save the figure. Use the file-shaped icon to set the path.
@@ -61,16 +53,14 @@
### Line Plot
+
-
-
-
-1. _**Value**_: Select the data to plot. You can choose a specific column from a DataFrame using the funnel icon.
-2. _**Chart Label**_: Add labels to explain the meaning of each plotted data.
-3. _**Color**_: Check _**Use Color**_ and choose the color of the graph.
-4. _**Marker**_: Choose the style of the marker.
-5. _**Line Style**_: Choose the style of the line.
-6. _**User Option**_: Additional code beyond the functionalities provided by Visual Python can be added.
+1. _**X Value, Y Value**_: Select the data you want to graph. Use the funnel icon to select a specific column.
+2. _**Chart Label**_: Add a label to describe the meaning of the plotted graph.
+3. _**Color**_: Check '_**use color**'_ and choose a color for the line.
+4. _**Marker**_: Choose a style for your marker.
+5. _**Line Style**_: Choose a style for the line.
+6. _**User Option**_: You can add additional code beyond the functionality provided by Visual Python.
@@ -78,15 +68,13 @@
### Bar Plot 1
+
-
-
-
-1. _**X Value, Height**_: Select data to plot. You can choose a specific column for a DataFrame using the funnel icon.
-2. _**Chart Label**_: Add labels to explain the meaning of each plotted data.
-3. _**Color:**_ Check _**Use Color**_ and choose the color of the graph.
-4. _**Line Style**_: Choose the style of the line.
-5. _**User Option**_: Additional code beyond the functionalities provided by Visual Python can be added.
+1. _**X Value, Height**_: Select the data you want to graph. Use the funnel icon to select a specific column.
+2. _**Chart Label**_: Add a label to describe the meaning of the output graph.
+3. _**Color**_: Check '_**use color**'_ and choose a color for the graph.
+4. _**Line Style**_: Choose a style for the line.
+5. _**User Option**_: You can add additional code beyond the functionality provided by Visual Python.
@@ -94,15 +82,13 @@
### Bar Plot 2
+
-
-
-
-1. _**Y Value, Width**_: Select data to plot. You can choose a specific column from a DataFrame using the funnel icon.
-2. _**Chart Label**_: Add labels to explain the meaning of each plotted data.
-3. _**Color**_: Check _**Use Color**_ and choose the color of the graph.
-4. _**Line Style**_: Choose the style of the line.
-5. _**User Option**_: Additional code beyond the functionalities provided by Visual Python can be added.
+1. _**Y Value, Width**_: Select the data you want to graph. Use the funnel icon to select a specific column.
+2. _**Chart Label**_: Add a label to describe the meaning of the output graph.
+3. _**Color**_: Check '_**use color**'_ and choose a color for the graph.
+4. _**Line Style**_: Choose a style for the line.
+5. _**User Option**_: You can add additional code beyond the functionality provided by Visual Python.
@@ -110,16 +96,14 @@
### Histogram
+
-
-
-
-1. _**Value**_: Select data to plot. You can choose a specific column from a DataFrame using the funnel icon.
-2. _**Bins**_: Set the intervals (bin size) to divide the data.
-3. _**Chart Label**_: Add labels to explain the meaning of each plotted data.
-4. _**Color**_: Check _**Use Color**_ and choose the color of the graph.
-5. _**Line Style**_: Choose the style of the line.
-6. _**User Option**_: Additional code beyond the functionalities provided by Visual Python can be added.
+1. _**Value**_: Select the data you want to graph. Use the funnel icon to select a specific column.
+2. _**Bins**_: Set the interval (bin size) at which you want to divide your data.
+3. _**Chart Label**_: Add a label to describe the meaning of the output graph.
+4. _**Color**_: Check '_**use color**'_ and choose a color for the graph.
+5. _**Line Style**_: Choose a style for the line.
+6. _**User Option**_: You can add additional code beyond the functionality provided by Visual Python.
@@ -127,12 +111,10 @@
### Box Plot
+
-
-
-
-1. _**Value**_: Select data to plot. You can choose a specific column from a DataFrame using the funnel icon.
-2. _**User Option**_: Additional code beyond the functionalities provided by Visual Python can be added.
+1. _**Value**_: Select the data you want to graph. Use the funnel icon to select a specific column.
+2. _**User Option**_: You can add additional code beyond what Visual Python provides.
@@ -140,14 +122,12 @@
### Stack Plot
+
-
-
-
-1. _**Value**_: Select data to plot. You can choose a specific column from a DataFrame using the funnel icon.
-2. _**Color**_: Check _**Use Color**_ and choose the color of the graph.
-3. _**Line Style**_: Choose the style of the line.
-4. _**User Option**_: Additional code beyond the functionalities provided by Visual Python can be added.
+1. _**Value**_: Select the data you want to graph. Use the funnel icon to select a specific column.
+2. _**Color**_: Check '_**use color**'_ and choose a color for the graph.
+3. _**Line Style**_: Choose a style for the line.
+4. _**User Option**_: You can add additional code beyond the functionality provided by Visual Python.
@@ -155,12 +135,10 @@
### Pie Plot
+
-
-
-
-1. _**Value**_: Select data to plot. You can choose a specific column from a DataFrame using the funnel icon.
-2. _**User Option**_: Additional code beyond the functionalities provided by Visual Python can be added.
+1. _**Value**_: Select the data you want to graph. Use the funnel icon to select a specific column.
+2. _**User Option**_: You can add additional code beyond the functionality provided by Visual Python.
@@ -168,14 +146,12 @@
### Scatter Plot
+
-
-
-
-1. _**Value**_: Select data to plot. You can choose a specific column from a DataFrame using the funnel icon.
-2. _**Color Map**_: Choose the color theme to map to the data.
-3. _**Marker**_: Choose the style of the marker.
-4. _**User Option**_: Additional code beyond the functionalities provided by Visual Python can be added.
+1. _**Value**_: Select the data you want to graph. Use the funnel icon to select a specific column.
+2. _**Color Map**_: Choose a color theme for mapping your data.
+3. _**Marker**_: Choose a style for your marker.
+4. _**User Option**_: You can add additional code beyond the functionality provided by Visual Python.
@@ -183,14 +159,12 @@
### Hexbin
+
-
-
-
-1. _**Value**_: Select data to plot. You can choose a specific column from a DataFrame using the funnel icon.
-2. _**Chart Label**_: Add labels to explain the meaning of each plotted data.
-3. _**Color**_: Check _**Use Color**_ and choose the color of the graph.
-4. _**User Option**_: Additional code beyond the functionalities provided by Visual Python can be added.
+1. _**Value**_: Select the data you want to graph. Use the funnel icon to select a specific column.
+2. _**Chart Label**_: Add a label to describe the meaning of the output graph.
+3. _**Color**_: Check '_**use color**'_ and choose a color for the graph.
+4. _**User Option**_: You can add additional code beyond the functionality provided by Visual Python.
@@ -198,14 +172,12 @@
### Contour Plot
+
-
-
-
-1. _**Value**_: Select data to plot. You can choose a specific column from a DataFrame using the funnel icon.
-2. _**Color Map**_: Choose the color theme to map to the data.
-3. _**Chart Label**_: Add labels to explain the meaning of each plotted data.
-4. _**User Option**_: Additional code beyond the functionalities provided by Visual Python can be added.
+1. _**Value**_: Select the data you want to graph. Use the funnel icon to select a specific column.
+2. _**Color Map**_: Choose a color theme for mapping your data.
+3. _**Chart Label**_: Add a label to describe the meaning of the output graph.
+4. _**User Option**_: You can add additional code beyond the functionality provided by Visual Python.
@@ -213,13 +185,13 @@
### Image Plot
-
+
-1. _**Value**_: Select data to plot. You can choose a specific column from a DataFrame using the funnel icon.
-2. _**Extent**_: Specify the range on the X and Y axes where the image will be displayed.
-3. _**Origin**_: Choose the position of the image's origin. _**Lower**_ places it at the bottom of the coordinate plane, and _**Upper**_ places it at the top.
-4. _**Color Map**_: Choose the color theme to map to the data.
-5. _**User Option**_: Additional code beyond the functionalities provided by Visual Python can be added.
+1. _**Value**_: Select the data you want to graph. Use the funnel icon to select a specific column.
+2. _**Extent**_: Set the image display area.
+3. _**Origin**_: Select the origin location of the image. If set to "_**lower**",_ it will be at the bottom of the coordinate plane, and if set to "_**upper**",_ it will be at the top.
+4. _**Color Map**_: Choose a color theme for mapping your data.
+5. _**User Option**_: You can add additional code beyond the functionality provided by Visual Python.
@@ -227,8 +199,8 @@
### Error Bar Plot
-
+
-1. _**Value**_: Select data to plot. You can choose a specific column from a DataFrame using the funnel icon.
-2. _**User Option**_: Additional code beyond the functionalities provided by Visual Python can be added.
+1. _**Value**_: Select the data you want to graph. Use the funnel icon to select a specific column.
+2. _**User Option**_: You can add additional code beyond the functionality provided by Visual Python.
diff --git a/docs/visualization/4.-seaborn.md b/docs/visualization/4.-seaborn.md
index 06a592ec..009eb188 100644
--- a/docs/visualization/4.-seaborn.md
+++ b/docs/visualization/4.-seaborn.md
@@ -1,17 +1,13 @@
# 4. Seaborn
-
-
-
+
1. Click on _**Seaborn**_ in the _**Visualization**_ category.
+
-
-
-
-2. You can use **Settings** _to_ set the basic style of the plots displayed. For detailed configuration, use the _**Chart Style**_ in the _**Visualization**_ category.
-3. You can preview the plot to be displayed. In the top right corner, you can set the number of samples used for preview.
+2. You can use _**Settings**_ to set the default style of the graph. For detailed configuration, use _**Chart Style**_ in the _**Visualization**_ category.
+3. _**Chart Preview**_: You can preview the plot that will be displayed. In _**Sampling** on the_ top right, you can set the number of samples to use for the preview.
@@ -33,24 +29,20 @@
### Data
+
+1. _**Chart Type**_: Select the type of chart.
+2. _**Data**_: Select the data you want to graph. To select a specific column, use the funnel icon. To set X and Y separately, check _**Set X and Y individually**._
+3. _User Option_: You can add additional code beyond the functionality provided by Visual Python.
-
-
-1. Choose the style of the chart.
-2. _**Data**_: Select the data to be represented in the plot. You can use the _**Funnel Icon**_ to choose specific columns from the DataFrame. Check _**Set X and Y individually**_ to select the data separately for each axis.
-3. _**User Option**_: Optionally provide additional desired options.
-
+**\*The manuals provided below may or may not exist depending on the type of chart.**
-
-\*The manual provided below may or may not exist depending on the type of chart.
-
-4. _**Hue**_: Further segment the data already selected for display based on another column or data. The data will be displayed in different colors based on the new criteria.
-5. _**Bins**_: If the data you want to represent in the graph(_**Histogram**_) is continuous, you can express it by dividing it into a certain number of intervals. It will be represented by bars divided into a specified number of intervals.
-6. _**KDE**_(Kernel Density Estimation): To smooth the shape of the graph(_**Histogram**_), use a density plot format.
-7. _**Stat**_: Choose the meaning of each bar in the graph(_**Histogram**_). For example, if you choose the '_**Count**_', the height of each bar represents the number of data points in that interval.
-8. _**Orient**_: Choose the direction of the graph(_**Bar Plot**_).
-9. _**Annot**_: Choose whether to display data values in each cell of the graph(_**Heatmap**_).
+4. _**Hue**_: Further segment the data based on other columns or data based on the data already selected. Based on the new criteria, the data is displayed in different colors.
+5. _**Bins**_: If the data you want to represent in a histogram is continuous, you can divide it into a certain number of _bins._ It will be displayed as bars divided by the specified intervals.
+6. _**KDE**_: Use the Density Plot format to smooth the appearance of the Histogram_._
+7. _**Stat**_: Select the meaning of each bar in the Histogram, for example, if you select '_**Count**',_ the height of each bar will represent the number of data points in that interval.
+8. _**Orient**_: Choose the orientation of the graph (Bar Plot).
+9. _**Annot**_: Choose whether you want to show data values in each cell of the graph (Heatmap).
@@ -58,22 +50,13 @@
### Axes
+
-
-
-
-1. _**Limit**_: Defines the range of data to be represented on the X and Y axes.
-2. _**Ticks:**_ Sets the ticks on the X and Y axes.
-
- (a) Specifies the positions where ticks will be displayed in the following format:
-
- \[0, 1, 2, 3] -> Four ticks will be drawn.
-
- (b) Sets the labels (Characters or Numbers) to be displayed as ticks in the following format:
-
- \['a', 'b', 'c', 'd'] -> The specified values will be displayed in order as ticks.
-
-3. _**Rotate**_: Rotates the ticks by the specified angle. Useful when text overlaps.
+1. _**X Limit, Y Limit**_: Define the range of data to be displayed on the X and Y axis.
+2. _**X ticks, Y ticks**_: Set the scale for the X and Y axes.
+ 1. Specify where the tick marks should appear in the following format \[0, 1, 2, 3] -> the scale is drawn at the four locations you specify.
+ 2. Set the label (letter or number) to be displayed as a scale in the following format \['a', 'b', 'c', 'd'] -> The specified values will be displayed in order on each scale.
+3. _**Rotation**_: Rotates the scale by the specified angle. This is useful if you have overlapping text.
@@ -81,13 +64,11 @@
### Info
+
-
-
-
-1. _**Title**_: Sets the title of the plot. The name is placed at the top center of the plot.
-2. _**Labels**_: Sets the names of the X and Y axes.
-3. _**Legend**_: Sets the position to display the legend.
+1. _**Title**_: Set a title for the graph. The name is placed at the top center of the graph.
+2. _**Labels**_: Set the names for the X-axis and Y-axis.
+3. _**Legend**_: Set where to display the legend.
@@ -95,13 +76,11 @@
### Style
+
-
-
-
-1. _**Color**_: Enable the use of color and allow you to choose the color of the graph.
-2. _**Grid**_: Select whether to use a grid. Color can also be assigned.
-3. _**Marker**_: Choose the style of the marker.
+1. _**Color**_: You can check _**Use Color**_ to enable the use of color and select a color for the graph.
+2. _**Grid**_: Select whether to use a grid. You can also assign a color.
+3. _**Marker**_: Select a style for the marker.
@@ -109,9 +88,7 @@
### Code
+
-
-
-
-1. Additional code beyond the functionalities provided by Visual Python can be added.
+1. You can add additional code beyond what Visual Python provides.
diff --git a/docs/visualization/5.-plotly.md b/docs/visualization/5.-plotly.md
index e42fc24d..50155dc7 100644
--- a/docs/visualization/5.-plotly.md
+++ b/docs/visualization/5.-plotly.md
@@ -1,20 +1,16 @@
# 5. Plotly
-
-
-
+
1. Select _**Plotly**_ in the _**Visualization**_ category.
+
-
-
-
-2. _**Import Package**_: Install the necessary packages(Plotly) for code execution.
-3. _**Import Library**_: Import the libraries required for code execution.
-4. _**Preview**_: Preview the chart that will be generated.
-5. _**Code**_ _**View**_: Preview the code that will be generated.
-6. _**Run**_: Execute the code.
+2. _**Import Package**_: Install the package you need to run your code (Plotly).
+3. _**Import Library**_: Import the libraries needed to run your code.
+4. _**Preview**_: You can preview the graph that will be generated.
+5. _**Code View**_: You can preview the code that will be generated.
+6. _**Run**_: Print and run the code.
@@ -32,25 +28,17 @@
### Data
+
-
-
-
-
-
-1. _**Chart Type**_: Select the type of chart.
-2. _**Data**_: Choose the data to be represented in the chart. You can also select a specific column of a DataFrame through the _**Funnel Icon**_. _**Set X and Y Individually**_ allows selecting data from different DataFrames.
-3. _**Axis Value**_: Select the data to be used on each axis.
-
- _**Values**_ (Pie Chart): Choose the data to represent the size of each section in a Pie Chart.
-
- _**Names**_ (Pie Chart): Choose the data to represent the names of each section in a Pie Chart.
-
- _**Parents**_ (Sunburst): The Sunburst chart represents hierarchical data in a circular form. Select the data indicating which parent section each segment belongs to.
-
-4. _**Color**_: Enable the use of color and choose the color of the graph.
-5. _**Sort**_: Specify the order of categories on the X-axis. Descending arranges columns from the highest to the lowest amount of data.
-6. _**User Option**_: Optionally provide additional desired options.
+1. _**Chart Type**_: Select the type of graph.
+2. _**Data**_: Select the data you want to graph. To select a specific column, use the funnel icon. If you want to set X and Y separately, check _**Set X and Y individually**._
+3. _**Axis Value**_: Select the data you want to use for each axis.
+ 1. _**Values (Pie Chart)**_: Select the data to represent the size of each section.
+ 2. _**Names (Pie Chart)**_: Select the data to represent the name of each section.
+ 3. _**Parents (Sunburst)**_: _Sunburst_ charts represent hierarchical data in a circle; select the data that represents the parent section to which each segment belongs.
+4. _**Color**_: You can check _**Use Color** to_ enable the use of color and select a color for the graph.
+5. _**Sort**_: Specify the order of the categories on the X-axis. Descending order sorts from the column with the most data to the column with the least.
+6. _**User Option**_: You can add additional code beyond the functionality provided by Visual Python.
@@ -58,12 +46,10 @@
### Info
+
-
-
-
-1. _**Title**_: Specify the title of the chart.
-2. _**Label**_: Specify the names of each axis.
+1. _**Title**_: Set a title for the figure.
+2. _**Label**_: Set a name for each axis.
@@ -71,9 +57,7 @@
### Code
+
-
-
-
-1. Additional code beyond the functionalities provided by Visual Python can be added.
+1. You can add additional code beyond what Visual Python provides.
diff --git a/docs/visualization/6.-wordcloud.md b/docs/visualization/6.-wordcloud.md
index ed6de600..caaf0e02 100644
--- a/docs/visualization/6.-wordcloud.md
+++ b/docs/visualization/6.-wordcloud.md
@@ -1,19 +1,15 @@
# 6. WordCloud
-
-
-
+
1. Click on the _**WordCloud**_ in the _**Visualization**_ category.
+
-
-
-
-2. Click on _**Install Package**_ and _**Import Library**_ to install or load the necessary packages for code execution.
-3. _**Preview**_: the WordCloud that will be generated.
-4. _**Code View**_: Preview the _**Code**_ that will be executed.
-5. _**Run**_: Execute the code.
+2. Click _**Import Package**_, _**Import Library**_ to install or load the packages you need to run your code.
+3. _**Preview**_: Allows you to preview the WordCloud that will be created.
+4. _**Code View**_: Allows you to preview the code that will be executed.
+5. _**Run**_: Run the code.
@@ -31,27 +27,20 @@
### Data
+
-
-
-
-1. _**Data:**_ Select the Dataframe to be used for WordCloud. Alternatively, use the Funnel icon to choose specific columns.
-2. Check _**Use File**_ to load a file than the one currently in memory for WordCloud.
-3. Set the _**Number of Words**_ to be used in the WordCloud.
-
-
+1. _**Data**_: Select the data you want to graph. Use the funnel icon to select a specific column. You can check _**Use file**_ to load a file other than the one stored in memory.
+2. _**Number of words**_: Set the number of words you want to use for your WordCloud.
***
### WordCloud
-
-
-
+
1. _**Stop Words**_: Enter the words to be excluded from the WordCloud.
2. _**Font Path**_: Choose a font.
-3. _**User Option**_: Optionally provide additional desired options.
+3. _**User Option**_: You can add additional code beyond the functionality provided by Visual Python.
@@ -59,9 +48,7 @@
### Plot
+
-
-
-
-1. _**Figure Size**_: Set the size of the WordCloud Image to be generated.
+1. _**Figure Size**_: Set the size of the WordCloud image that will be generated.