May 7, 2024 · Feb 14, 2024 · Feb 14, 2024 · Feb 14, 2024 · Feb 14, 2024 · Feb 19, 2024
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 ---
 description: This manual introduces the basic usages and tips for using Visual Python.
 ---

 # Visual Python Manual

 Getting Started

 * [Welcome to Visual Python](getting-started/welcome-to-visual-python.md)
 * [How to install](getting-started/how-to-install.md)
 * [Installing FAQ](getting-started/installing-faq.md)

 Data Analysis

 1. [Import](data-analysis/1.-import.md)
 2. [File](data-analysis/2.-file.md)
 3. [Data Info](data-analysis/3.-data-info.md)
 4. [Frame](data-analysis/4.-frame/)
 5. [Subset](data-analysis/5.-subset.md)
 6. [Groupby](data-analysis/6.-groupby.md)
 7. [Bind](data-analysis/7.-bind.md)
 8. [Reshape](data-analysis/8.-reshape.md)

 Visualization

 1. [Chart Style](visualization/1.-chart-style.md)
 2. [Pandas Plot](visualization/2.-pandas-plot.md)
 3. [Matplotlib](visualization/3.-matplotlib.md)
 4. [Seaborn](visualization/4.-seaborn.md)
 5. [Plotly](visualization/5.-plotly.md)
 6. [WordCloud](visualization/6.-wordcloud.md)

 Statistics

 1. [Prob.Distribution](statistics/1.-prob.-distribution.md)
 2. [Descriptive Statistics](statistics/2.-descriptive-statistics.md)
 3. [Normality Test](statistics/3.-normality-test.md)
 4. [Equal Var. Test](statistics/4.-equal-var.-test.md)
 5. [Correlation Analysis](statistics/5.-correlation-analysis.md)
 6. [Reliability Analysis](statistics/6.-reliability-analysis.md)
 7. [Chi-square Test](statistics/7.-chi-square-test.md)
 8. [Student's T-Test](statistics/8.-students-t-test.md)
 9. [ANOVA](statistics/9.-anova.md)
 10. [Factor Analysis](statistics/10.-factor-analysis.md)
 11. [Regression](statistics/11.-regression.md)
 12. [Logistic Regression](statistics/12.-logistic-regression.md)

 Machine Learning

 1. [Data Sets](machine-learning/1.-data-sets.md)
 2. [Data Split](machine-learning/2.-data-split.md)
 3. [Data Prep](machine-learning/3.-data-prep.md)
 4. [AutoML](machine-learning/4.-automl.md)
 5. [Regressor](machine-learning/5.-regressor.md)
 6. [Classifier](machine-learning/6.-classifier.md)
 7. [Clustering](machine-learning/7.-clustering.md)
 8. [Dimension](machine-learning/8.-dimension.md)
 9. [GridSearch](machine-learning/9.-gridsearch.md)
 10. [Fit/Predict](machine-learning/10.-fit-predict.md)
 11. [Model Info](machine-learning/11.-model-info.md)
 12. [Evaluation](machine-learning/12.-evaluation.md)
 13. [Pipeline](machine-learning/13.-pipeline.md)
diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md
 # Table of contents

 * [Visual Python Manual](README.md)

 ## GETTING STARTED

 * [Welcome to Visual Python](getting-started/welcome-to-visual-python.md)
 * [How to install](getting-started/how-to-install.md)
 * [Installing FAQ](getting-started/installing-faq.md)
 * [Official homepage](https://visualpython.ai/)
 * [Link to Github](https://github.com/visualpython/visualpython)

 ## Data Analysis

 * [1. Import](data-analysis/1.-import.md)
 * [2. File](data-analysis/2.-file.md)
 * [3. Data Info](data-analysis/3.-data-info.md)
 * [4. Frame](data-analysis/4.-frame/README.md)
  * [4-1. Frame - Edit](data-analysis/4.-frame/4-1.-frame-edit.md)
  * [4-2. Frame - Transform](data-analysis/4.-frame/4-2.-frame-transform.md)
  * [4-3. Frame - Sort](data-analysis/4.-frame/4-3.-frame-sort.md)
  * [4-4. Frame - Encoding](data-analysis/4.-frame/4-4.-frame-encoding.md)
  * [4-5. Frame - Data Cleaning](data-analysis/4.-frame/4-5.-frame-data-cleaning.md)
 * [5. Subset](data-analysis/5.-subset.md)
 * [6. Groupby](data-analysis/6.-groupby.md)
 * [7. Bind](data-analysis/7.-bind.md)
 * [8. Reshape](data-analysis/8.-reshape.md)

 ## Visualization

 * [1. Chart Style](visualization/1.-chart-style.md)
 * [2. Pandas Plot](visualization/2.-pandas-plot.md)
 * [3. Matplotlib](visualization/3.-matplotlib.md)
 * [4. Seaborn](visualization/4.-seaborn.md)
 * [5. Plotly](visualization/5.-plotly.md)
 * [6. WordCloud](visualization/6.-wordcloud.md)

 ## Statistics

 * [1. Prob. Distribution](statistics/1.-prob.-distribution.md)
 * [2. Descriptive Statistics](statistics/2.-descriptive-statistics.md)
 * [3. Normality Test](statistics/3.-normality-test.md)
 * [4. Equal Var. Test](statistics/4.-equal-var.-test.md)
 * [5. Correlation Analysis](statistics/5.-correlation-analysis.md)
 * [6. Reliability Analysis](statistics/6.-reliability-analysis.md)
 * [7. Chi-square Test](statistics/7.-chi-square-test.md)
 * [8. Student's T-test](statistics/8.-students-t-test.md)
 * [9. ANOVA](statistics/9.-anova.md)
 * [10. Factor Analysis](statistics/10.-factor-analysis.md)
 * [11. Regression](statistics/11.-regression.md)
 * [12. Logistic Regression](statistics/12.-logistic-regression.md)

 ## Machine Learning

 * [1. Data Sets](machine-learning/1.-data-sets.md)
 * [2. Data Split](machine-learning/2.-data-split.md)
 * [3. Data Prep](machine-learning/3.-data-prep.md)
 * [4. AutoML](machine-learning/4.-automl.md)
 * [5. Regressor](machine-learning/5.-regressor.md)
 * [6. Classifier](machine-learning/6.-classifier.md)
 * [7. Clustering](machine-learning/7.-clustering.md)
 * [8. Dimension](machine-learning/8.-dimension.md)
 * [9. GridSearch](machine-learning/9.-gridsearch.md)
 * [10. Fit/Predict](machine-learning/10.-fit-predict.md)
 * [11. Model Info](machine-learning/11.-model-info.md)
 * [12. Evaluation](machine-learning/12.-evaluation.md)
 * [13. Pipeline](machine-learning/13.-pipeline.md)
 * [14. Save / Load](machine-learning/14.-save-load.md)
diff --git a/docs/data-analysis/1.-import.md b/docs/data-analysis/1.-import.md
 ---
 description: Import Packages and Modules
 ---

 # 1. Import



 <figure><img src="../.gitbook/assets/image (31).png" alt="" width="229"><figcaption></figcaption></figure>

 1. Click on _**Import**_ in the Data Analysis category.



 <figure><img src="../.gitbook/assets/image (30).png" alt="" width="563"><figcaption></figcaption></figure>

 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.)

 &#x20;       4-1. Use _**+Module**_ to add packages or modules.

 &#x20;       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.

diff --git a/docs/data-analysis/2.-file.md b/docs/data-analysis/2.-file.md
 ---
 description:Read or Write a File
 ---

 #2. File



 <figure><imgsrc="../.gitbook/assets/image (32).png"alt=""width="310"><figcaption></figcaption></figure>

 1. Click on the_**File**_ in the Data Analysis category.



 <figure><imgsrc="../.gitbook/assets/image (34).png"alt=""width="563"><figcaption></figcaption></figure>

 2. Choose whether to read or save a file from the top bar

 &#x20;       2-1. You can use sample data prepared by Visual Python through_**Sample Data**_.



 ###Read File



 <figure><imgsrc="../.gitbook/assets/image (35).png"alt=""width="563"><figcaption></figcaption></figure>

 1. Select the type of file to load.
 2. Choose the file path.
 3. Enter the variable name (Callable name, Identifier) for the file.



 <figure><imgsrc="../.gitbook/assets/image (36).png"alt=""width="563"><figcaption></figcaption></figure>

 4._**Additional Options**_ allow various settings.

 &#x20;       4-1. If not set separately, the default values set by Visual Python will be applied.

 &#x20;       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.

 &#x20;       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.



 ###Write File



 <figure><imgsrc="../.gitbook/assets/image (37).png"alt=""width="563"><figcaption></figcaption></figure>

 1. Select the type to save.
 2. Choose the DataFrame to save.
 3. Select the location to save.
 4._**Additional Options**_ allow various settings.

 &#x20;       4-1. If not set separately, the default values set by Visual Python will be applied.

 &#x20;       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.

 &#x20;       6-1. By default, column names are saved as the first row.

 7._**Index**_: Choose_**False**_ to exclude the index when saving.

 &#x20;       7-1. By default, the index is saved as the first column.

 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.

diff --git a/docs/data-analysis/3.-data-info.md b/docs/data-analysis/3.-data-info.md
 ---
 description: Check the Basic Information of the Data
 ---

 # 3. Data Info



 <figure><img src="../.gitbook/assets/image (8) (1).png" alt="" width="312"><figcaption></figcaption></figure>

 1. Click on _**Data Info**_ in the Data Analysis category.



 <figure><img src="../.gitbook/assets/image (9) (1).png" alt=""><figcaption></figcaption></figure>

 2. Click on the _**Data tab**_ at the top left to select the DataFrame for which you want to view information.

 &#x20;       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.&#x20;
 5. Use _**Code View**_ at the bottom left to check the generated code.



 ### General



 <figure><img src="../.gitbook/assets/image (10) (1).png" alt=""><figcaption></figcaption></figure>

 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



 <figure><img src="../.gitbook/assets/image (11) (1).png" alt=""><figcaption></figcaption></figure>

 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



 <figure><img src="../.gitbook/assets/image (12) (1).png" alt=""><figcaption></figcaption></figure>

 1. Check and confirm desired statistical values.

 &#x20;       1-1. Multiple selections are possible for square values.

 &#x20;       1-2. Multiple selections are not possible for circular values.&#x20;



 ### Correlation



 <figure><img src="../.gitbook/assets/image (13) (1).png" alt=""><figcaption></figcaption></figure>

 1. _**Correlation Table**_ shows a table indicating the correlation between each column.
 2. _**Correlation Matrix**_ represents the correlation table as a Heat Map.



 ### Distribution



 <figure><img src="../.gitbook/assets/image (14).png" alt=""><figcaption></figcaption></figure>

 1. Represents data in various forms.

 &#x20;       1-1. Histogram

 &#x20;       1-2. Scatter Matrix

 &#x20;       1-3. Box Plot

 &#x20;       1-4. Counter Plot
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,62 @@
		---
		description: This manual introduces the basic usages and tips for using Visual Python.
		---

		# Visual Python Manual

		Getting Started

		* [Welcome to Visual Python](getting-started/welcome-to-visual-python.md)
		* [How to install](getting-started/how-to-install.md)
		* [Installing FAQ](getting-started/installing-faq.md)

		Data Analysis

		1. [Import](data-analysis/1.-import.md)
		2. [File](data-analysis/2.-file.md)
		3. [Data Info](data-analysis/3.-data-info.md)
		4. [Frame](data-analysis/4.-frame/)
		5. [Subset](data-analysis/5.-subset.md)
		6. [Groupby](data-analysis/6.-groupby.md)
		7. [Bind](data-analysis/7.-bind.md)
		8. [Reshape](data-analysis/8.-reshape.md)

		Visualization

		1. [Chart Style](visualization/1.-chart-style.md)
		2. [Pandas Plot](visualization/2.-pandas-plot.md)
		3. [Matplotlib](visualization/3.-matplotlib.md)
		4. [Seaborn](visualization/4.-seaborn.md)
		5. [Plotly](visualization/5.-plotly.md)
		6. [WordCloud](visualization/6.-wordcloud.md)

		Statistics

		1. [Prob.Distribution](statistics/1.-prob.-distribution.md)
		2. [Descriptive Statistics](statistics/2.-descriptive-statistics.md)
		3. [Normality Test](statistics/3.-normality-test.md)
		4. [Equal Var. Test](statistics/4.-equal-var.-test.md)
		5. [Correlation Analysis](statistics/5.-correlation-analysis.md)
		6. [Reliability Analysis](statistics/6.-reliability-analysis.md)
		7. [Chi-square Test](statistics/7.-chi-square-test.md)
		8. [Student's T-Test](statistics/8.-students-t-test.md)
		9. [ANOVA](statistics/9.-anova.md)
		10. [Factor Analysis](statistics/10.-factor-analysis.md)
		11. [Regression](statistics/11.-regression.md)
		12. [Logistic Regression](statistics/12.-logistic-regression.md)

		Machine Learning

		1. [Data Sets](machine-learning/1.-data-sets.md)
		2. [Data Split](machine-learning/2.-data-split.md)
		3. [Data Prep](machine-learning/3.-data-prep.md)
		4. [AutoML](machine-learning/4.-automl.md)
		5. [Regressor](machine-learning/5.-regressor.md)
		6. [Classifier](machine-learning/6.-classifier.md)
		7. [Clustering](machine-learning/7.-clustering.md)
		8. [Dimension](machine-learning/8.-dimension.md)
		9. [GridSearch](machine-learning/9.-gridsearch.md)
		10. [Fit/Predict](machine-learning/10.-fit-predict.md)
		11. [Model Info](machine-learning/11.-model-info.md)
		12. [Evaluation](machine-learning/12.-evaluation.md)
		13. [Pipeline](machine-learning/13.-pipeline.md)
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,68 @@
		# Table of contents

		* [Visual Python Manual](README.md)

		## GETTING STARTED

		* [Welcome to Visual Python](getting-started/welcome-to-visual-python.md)
		* [How to install](getting-started/how-to-install.md)
		* [Installing FAQ](getting-started/installing-faq.md)
		* [Official homepage](https://visualpython.ai/)
		* [Link to Github](https://github.com/visualpython/visualpython)

		## Data Analysis

		* [1. Import](data-analysis/1.-import.md)
		* [2. File](data-analysis/2.-file.md)
		* [3. Data Info](data-analysis/3.-data-info.md)
		* [4. Frame](data-analysis/4.-frame/README.md)
		* [4-1. Frame - Edit](data-analysis/4.-frame/4-1.-frame-edit.md)
		* [4-2. Frame - Transform](data-analysis/4.-frame/4-2.-frame-transform.md)
		* [4-3. Frame - Sort](data-analysis/4.-frame/4-3.-frame-sort.md)
		* [4-4. Frame - Encoding](data-analysis/4.-frame/4-4.-frame-encoding.md)
		* [4-5. Frame - Data Cleaning](data-analysis/4.-frame/4-5.-frame-data-cleaning.md)
		* [5. Subset](data-analysis/5.-subset.md)
		* [6. Groupby](data-analysis/6.-groupby.md)
		* [7. Bind](data-analysis/7.-bind.md)
		* [8. Reshape](data-analysis/8.-reshape.md)

		## Visualization

		* [1. Chart Style](visualization/1.-chart-style.md)
		* [2. Pandas Plot](visualization/2.-pandas-plot.md)
		* [3. Matplotlib](visualization/3.-matplotlib.md)
		* [4. Seaborn](visualization/4.-seaborn.md)
		* [5. Plotly](visualization/5.-plotly.md)
		* [6. WordCloud](visualization/6.-wordcloud.md)

		## Statistics

		* [1. Prob. Distribution](statistics/1.-prob.-distribution.md)
		* [2. Descriptive Statistics](statistics/2.-descriptive-statistics.md)
		* [3. Normality Test](statistics/3.-normality-test.md)
		* [4. Equal Var. Test](statistics/4.-equal-var.-test.md)
		* [5. Correlation Analysis](statistics/5.-correlation-analysis.md)
		* [6. Reliability Analysis](statistics/6.-reliability-analysis.md)
		* [7. Chi-square Test](statistics/7.-chi-square-test.md)
		* [8. Student's T-test](statistics/8.-students-t-test.md)
		* [9. ANOVA](statistics/9.-anova.md)
		* [10. Factor Analysis](statistics/10.-factor-analysis.md)
		* [11. Regression](statistics/11.-regression.md)
		* [12. Logistic Regression](statistics/12.-logistic-regression.md)

		## Machine Learning

		* [1. Data Sets](machine-learning/1.-data-sets.md)
		* [2. Data Split](machine-learning/2.-data-split.md)
		* [3. Data Prep](machine-learning/3.-data-prep.md)
		* [4. AutoML](machine-learning/4.-automl.md)
		* [5. Regressor](machine-learning/5.-regressor.md)
		* [6. Classifier](machine-learning/6.-classifier.md)
		* [7. Clustering](machine-learning/7.-clustering.md)
		* [8. Dimension](machine-learning/8.-dimension.md)
		* [9. GridSearch](machine-learning/9.-gridsearch.md)
		* [10. Fit/Predict](machine-learning/10.-fit-predict.md)
		* [11. Model Info](machine-learning/11.-model-info.md)
		* [12. Evaluation](machine-learning/12.-evaluation.md)
		* [13. Pipeline](machine-learning/13.-pipeline.md)
		* [14. Save / Load](machine-learning/14.-save-load.md)
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,27 @@
		---
		description: Import Packages and Modules
		---

		# 1. Import



		<figure><img src="../.gitbook/assets/image (31).png" alt="" width="229"><figcaption></figcaption></figure>

		1. Click on _Import_ in the Data Analysis category.



		<figure><img src="../.gitbook/assets/image (30).png" alt="" width="563"><figcaption></figcaption></figure>

		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.
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,86 @@
		---
		description:Read or Write a File
		---

		#2. File



		<figure><imgsrc="../.gitbook/assets/image (32).png"alt=""width="310"><figcaption></figcaption></figure>

		1. Click on the_File_ in the Data Analysis category.



		<figure><imgsrc="../.gitbook/assets/image (34).png"alt=""width="563"><figcaption></figcaption></figure>

		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_.



		###Read File



		<figure><imgsrc="../.gitbook/assets/image (35).png"alt=""width="563"><figcaption></figcaption></figure>

		1. Select the type of file to load.
		2. Choose the file path.
		3. Enter the variable name (Callable name, Identifier) for the file.



		<figure><imgsrc="../.gitbook/assets/image (36).png"alt=""width="563"><figcaption></figcaption></figure>

		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.



		###Write File



		<figure><imgsrc="../.gitbook/assets/image (37).png"alt=""width="563"><figcaption></figcaption></figure>

		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.

		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.
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,93 @@
		---
		description: Check the Basic Information of the Data
		---

		# 3. Data Info



		<figure><img src="../.gitbook/assets/image (8) (1).png" alt="" width="312"><figcaption></figcaption></figure>

		1. Click on _Data Info_ in the Data Analysis category.



		<figure><img src="../.gitbook/assets/image (9) (1).png" alt=""><figcaption></figcaption></figure>

		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.



		### General



		<figure><img src="../.gitbook/assets/image (10) (1).png" alt=""><figcaption></figcaption></figure>

		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



		<figure><img src="../.gitbook/assets/image (11) (1).png" alt=""><figcaption></figcaption></figure>

		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



		<figure><img src="../.gitbook/assets/image (12) (1).png" alt=""><figcaption></figcaption></figure>

		1. Check and confirm desired statistical values.

		1-1. Multiple selections are possible for square values.

		1-2. Multiple selections are not possible for circular values.



		### Correlation



		<figure><img src="../.gitbook/assets/image (13) (1).png" alt=""><figcaption></figcaption></figure>

		1. _Correlation Table_ shows a table indicating the correlation between each column.
		2. _Correlation Matrix_ represents the correlation table as a Heat Map.



		### Distribution



		<figure><img src="../.gitbook/assets/image (14).png" alt=""><figcaption></figcaption></figure>

		1. Represents data in various forms.

		1-1. Histogram

		1-2. Scatter Matrix

		1-3. Box Plot

		1-4. Counter Plot