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Commit21976d1

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GITBOOK-40: Data Info
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‎docs/data-analysis/1.-import.md‎

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description:Importpackages for data analysis
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description:ImportPackages and Modules
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#1. Import

‎docs/data-analysis/3.-data-info.md‎

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#3. Data Info
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<figure><imgsrc="../.gitbook/assets/image (196).png"alt=""width="317"><figcaption></figcaption></figure>
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1. Click on_**Data Info**_ in the_**Data Analysis**_ category.
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<figure><imgsrc="../.gitbook/assets/image (8) (1).png"alt=""width="312"><figcaption></figcaption></figure>
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1. Click on_**Data Info**_ in the Data Analysis category.
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<figure><imgsrc="../.gitbook/assets/image (9) (1).png"alt=""><figcaption></figcaption></figure>
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2. Click on the_**Data tab**_ at the top left to select the DataFrame for which you want to view information.
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&#x20; 2-1. Click on the_**funnel icon**_ on the tab's right side allows you to extract specific columns.
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3. In the_**Info Preview**_ on the right, information is displayed briefly.
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4. Clicking_**Run**_ will execute the code without closing the window.&#x20;
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5. Use_**Code View**_ at the bottom left to check the generated code.
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<figure><imgsrc="../.gitbook/assets/image (197).png"alt=""width="563"><figcaption></figcaption></figure>
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2._**Data**_: Select the data you want to see information about. You can select only specific columns of data via the funnel icon.
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3._**Info Preview**_: The information is output as a preview.
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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.
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5._**Code View**_: Preview the code that will be output.
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* Tip: You can select one or multiple columns.
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###General
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<figure><imgsrc="../.gitbook/assets/image (10) (1).png"alt=""><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (198).png"alt=""width="563"><figcaption></figcaption></figure>
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1._**Info**_ provides basic information such as Column, Non-Null Count, Dtype, etc.
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2._**Describe**_ displays basic statistics for each column.
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3._**Head**_ shows the top five rows of the DataFrame.
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4._**Tail**_ displays the bottom five rows of the DataFrame.
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###Status
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<figure><imgsrc="../.gitbook/assets/image (11) (1).png"alt=""><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (199).png"alt=""width="532"><figcaption></figcaption></figure>
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1._**Null Count**_ shows the count of Null and Non-Null values for each column.
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2._**Duplicated**_ reveals the count of duplicated values.
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3._**Unique**_ works on a single column. Shows the unique values in a column.
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4._**Value Counts**_ displays the count of each value in each column. For continuous variables, it shows the count within arbitrarily defined intervals.
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###Statistics
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<figure><imgsrc="../.gitbook/assets/image (12) (1).png"alt=""><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (200).png"alt=""width="497"><figcaption></figcaption></figure>
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1. Check and confirm desired statistical values.
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###Correlation
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<figure><imgsrc="../.gitbook/assets/image (13) (1).png"alt=""><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (201).png"alt=""width="549"><figcaption></figcaption></figure>
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1._**Correlation Table**_ shows a table indicating the correlation between each column.
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2._**Correlation Matrix**_ represents the correlation table as a Heat Map.
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###Distribution
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<figure><imgsrc="../.gitbook/assets/image (14).png"alt=""><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (202).png"alt=""width="521"><figcaption></figcaption></figure>
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1. Represents data in various forms.
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