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# Explore the data

Once you’ve imported a dataset, you’ll want to start exploring it through the Explore and Charts tabs!

The Explore tab of a dataset provides a tabular view of your data where you can start to examine it, while the Charts tab has a drag-and-drop interface for data visualizations.

## Objectives

In this section, you will:

- Learn about the sampling method for a dataset.

- Compute the row count of a dataset.

- Analyze a column.

- Visualize your data using charts and a pivot table.

## Dataset sampling

As an environment capable of handling large datasets, Dataiku shows only a sample of a dataset when you are working interactively.

You can see the sampling method in the top left of the Explore tab. By default, the sample in this tab includes the first 10,000 records of the dataset.

1. To view the total row count of your dataset, select Compute row count (the cyclic arrows icon).

2. To change the sample settings of a dataset, select the Sample badge, which opens a panel on the left.

See also

For more information, see the Sampling article in the reference documentation.

## Storage type and meaning of dataset columns

In the dataset, beneath each column name, Dataiku indicates:

- The storage type (in gray)

- The meaning (in blue)

Here, Dataiku detects a meaning of text for the id column, based upon the fact that most values in the sample for customer_id are strings.

The data quality bar shows green for all columns, which means that all rows are valid.

Note

In this dataset, we do not have any NOK (Not OK) or missing values. But if we did:

- NOK values would be represented in red in both the data quality bar and column cells.

- Missing values would be in gray in both the data quality bar and column cells.

See also

For more information, see the Schemas, storage types and meanings article in the reference documentation.

## Analyze window

You can analyze the content of each column using the Analyze window. Let’s analyze the content of the cardholder_fico_range column. To do so:

1. Click on the column name cardholder_fico_range, and select Analyze from the dropdown menu.

   The Analyze window opens, showing the analysis of the data sample.

2. To extend the analysis to the whole data, select Whole data instead of sample in the dropdown next to the column name, then click Save and Compute.

3. Click the arrows next to the column name to switch from one column to another.

   Important

   As we are analyzing the whole dataset, we have to click Compute again at the top of the window to display the analysis for the whole data of the new column. It would be automatically computed if we were just looking at the sample.

4. Close the window when you’re done with the analysis.

## Charts

You can use charts to explore a dataset. For example, we might want to know which reward program is the most frequent for specific age ranges.

Here, let’s use two types of charts and a pivot table to show you different approaches.

### Add a vertical bars chart

1. Click on the Charts tab (or use the keyboard shortcut `g` + `v`).

2. From the Data panel, drag and drop:

   - Count of records as the Y variable.

   - reward_program as the X variable.

   - cardholder_age to the color droplet field.

   The chart reveals that the cash_back reward program is the most popular, whatever the age of card holders.

   Important

   The Sample badge at the top of the chart reminds you that the chart is sampled like the dataset. Clicking on the badge allows you to change the sampling method if needed.

3. Open the dropdown menu of the cardholder_age variable and check the Adjust bin size for nicer bounds option to have five nicer bins for the age of card holders.

4. On the chart, hover over any of the orange bars to filter on card holders aged between 40 and 60. Once the menu is displayed, click on the bar to keep the menu on and select the drill down icon (the down arrow) at the right of the cardholder_age variable.

   This action adds a filter to the Filters section of the Setup tab of the left panel.

5. To go back to the default display, extend the age range from `18` to `100` in the Filters section of the Setup tab.

6. In the left panel, go to the Format tab to customize the chart:

   - Under X Axis, open the Title dropdown, and enter `Reward program` in the Axis title field.

   - Under Color, select the built-in Pastel 2 palette.

### Add a pie chart

Let’s add a second chart.

1. At the bottom of the screen, click + Chart.

2. In the chart type dropdown, select Pie.

3. From the panel on the left, drag and drop:

   - Count of records in the Show field.

   - reward_program in the By field.

The chart confirms that the cash_back reward program is the most popular.

### Add a pivot chart

Lastly, let’s add a pivot table to represent in a different way the distribution of card holders per age across the different reward programs.

1. At the bottom of the screen, click + Chart.

2. In the chart type dropdown, select Pivot table.

3. From the panel on the left, drag and drop:

   - cardholder_age in the Rows field.

   - reward_program in the Columns field.

   - Count of records in the Value field.

4. In the dropdown menu of the cardholder_age variable:

   - Enter `5` as the Number of bins.

   - Check the Adjust bin size for nicer bounds option to have five nicer bins for the age of card holders.

See also

For more information, see the Tutorial | Charts and pivot tables article.

## What’s next?

Congratulations! You’ve created your first project, imported your first dataset, and built your first charts.

We recommend you to check the Tutorial | Join recipe, where we join the three datasets from this project to enrich each unique transaction with data about the information on credit card holders and merchants for that transaction.