First Time In Row64!

So you’ve downloaded Row64, now what?

First Steps

When you first open Row64, you’ll land on a blank Dataframe:

Part of the strength of Row64, is that it’s not just a spreadsheet. In fact, the major feature of Row64 is it’s Dataframe. We’ve made the Dataframe act as close as possible to a spreadsheet, while maintaining many Python top features. In addition, the Dataframe is build on top of our proprietary data structure that lets you scroll & view millions of rows very quickly.

Click on the Recipes button to get pre-built examples of what you can do in Row64.

You’ll see a pop-up with different data analysis categories. Click on the Charts category to find example of different charts you can make in Row64.

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If you click the Run button for HeatMap, you’ll see it generates a heatmap!

In the top left, you can see that the Row64 recipe generates Excel-like commands that import the dataset and generates the heatmap.

If you’re feeling adventurous or are a Python pro, you can flip into a split view mode where you can write your Python code. Do this by toggling the language on the top right.

We designed Row64 to be simple for users who prefer to work in an Excel-like environment AND for technical users who prefer to work in a coding environment.

The Recipes are curated Python scripts that can map into Excel-like commands. For example, LOADCSV is a command that lets you import a csv file. Just replace the argument text with your own CSV path to import your CSV.

You can mix and match recipes to do a number of data operations, including data manipulation and cleaning.

If you would rather not use the Excel-like syntax, you can write your own custom python code in the editor and run it. Row64 also operates as an advanced Python editor with capabilities to display generated dataframes.

Importing a CSV Manually

If you’re trying to view and inspect a large CSV file (i.e., >5 million records), we recommend you use the Row64 manual ingest tool. This will allow you to import files much faster and larger than Python can handle.

Navigate the Import tab and select Import Data:

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You’ll see a pop-up that asks whether you want to do an Automatic Import or Detailed Import. If you select Automatic Import, Row64 will import your file, making its best guess on file delimiters and column data types before converting your file into a Row64 Dataframe.

For a more custom import, select the Detailed Import option:

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Then select the file you want to open. Here, we’re going to import a CSV containing world cell towers data. This CSV has about 44 million records.

Once you click OK, you’ll see a preview of the CSV in what we call the ImportSheet. This sheet type is not interactive, but lets you scroll though the data and view it before you commit to converting the dataset into Row64’s fast Dataframe format.

You can recognize sheet types by looking at the tabs on the bottom. Notice how the ImportSheet has a little arrow icon next to the sheet name. This indicates that the sheet we’re on is an ImportSheet type.

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Next, click on the Run Diagnostic button at the top:

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This will cause Row64 to crawl the dataset and give you a summary of what it finds. Here we can see that Row64 has detected 43,902,162 rows in the dataset. It also gives you a summary of the differnt types of entries it finds in each column. For example, in Column E, it found 4,3897,078 records had small integer numbers, while 5,082 records had large integer numbers, therefore, Row64 recommends specifying that column to be of BIG INT type.

Here, we can see that this dataset has geographic information. Columns G and H are longitudes and latitudes. Row64 has a special type for longitudes and latitudes that let you take advantage of its geo analysis features. Click on the Selected Type combo box to change the type for that column.

Click OK when you are done.

Now you’ll see that the top bar has changed. We can now click Complete Import to finish and convert our CSV into the Row64 dataframe.

Notice how the tab at the bottom has a little columns icon next to the sheet name. This lets you know you’re on a Dataframe sheet.

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Now you are in the Row64 dataframe. This sheet type, lets you sort, filter, and search through columns quickly. Click on the letter in each column to open the filter menu. This lets you drill down in to the data.

For numeric columns, you can use simple operators to drill down in the dataset. Here, we are going to filter for mcc values that are greater than or equal to 310 and less than or equal to 316.

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You can clear your filter by right clicking and selecting Reset All.
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An there you have it! You’re ready to go exploring in Row64.

Check out posts on other how-tos and features in Row64 under the Learning Row64 and Features categories in the forum!