How to Explore Datasets in Go
To analyze a dataset, you first need to understand the data. Sometimes, you might have no forehand knowledge of a dataset, preventing you from getting the most out of it. As a data analyst, you can use Exploratory data analysis (EDA) to gain knowledge of your dataset before in-depth analysis.
Exploratory data analysis (EDA) investigates a dataset to gain meaningful insights. The process of performing EDA involves querying information about the structure and contents of a dataset.
Installing the Gota Package
The Gota package is the most popular fordata analysisin Go; it’s like thePython Pandas packagebut for Go. The Gota package contains many methods for analyzing datasets and reading JSON, CSV, and HTML formats.
Run this command on your terminal in the directory where you have initialized a Go module file:
The command will install Gota in the local directory, ready for you to import the package to use it.
Just like Pandas, Gota supports series and dataframes operations. There are two sub-packages in the Gota package: the series, and the dataframe package. you may import either one or both, depending on your needs.
Reading a Dataset Using the Gota Package
You can use any CSV file you like, but the following examples show results froma Kaggle dataset, containing laptop price data.
Gota lets you read CSV, JSON, and HTML file formats to create dataframes using theReadCSV,ReadJSON, andReadHTMLmethods. Here’s how you load a CSV file into a dataframe object:
You can use theOpenmethod of theospackage to open a CSV file. The ReadCSV method reads the file object and returns a dataframe object.
When you print this object, the output is in a tabular format. You can further manipulate the dataframe object using the various methods Gota provides.
The object will only print some of the columns if a dataset has more than a set value.
Fetching the Dimension of the Dataset
The dimensions of a dataframe are the number of rows and columns it contains. you’re able to fetch these dimensions using theDimsmethod of the dataframe object.
Replace one of the variables with an underscore to fetch the other dimension only. You can also query the number of rows and columns individually, using theNrowandNcolmethods.
Fetching the Data Types of Columns
You’ll need to know the composite data types in a dataset’s columns to analyze it. You can fetch these using theTypesmethod of your dataframe object:
The Types method returns a slice containing the column’s data types:
Fetching the Column Names
You’ll need the column names to select specific columns for operations. You can use theNamesmethod to fetch them.
The Names method returns a slice of the column names.
Checking for Missing Values
You might have a dataset that contains null or non-numeric values. You can check for such values using theHasNaNandIsNaNmethods of a series object:
HasNan checks if a column contains null elements. IsNaN returns a slice of booleans representing whether each value in the column is a number.
Performing Descriptive Statistical Analysis
Descriptive statistical analysishelps you understand the distribution of numerical columns. Using theDescribemethod, you can generate a descriptive statistical analysis of your dataset:
The Describe method returns metrics like the mean, standard deviation, and maximum values of columns in a dataset. It summarizes these in a tabular format.
You can also be specific and focus on columns and metrics by selecting a particular column, then querying for the metric you want. You should first fetch the series representing a specific column, then use its methods like so:
These methods mirror the results from the descriptive statistical analysis that Describe performs.
Fetching the Elements in a Column
One of the final tasks you’ll want to perform is to check the values in a column for a general overview. You can use theRecordsmethod to view the values of a column.
This method returns a slice of strings containing the values in your selected column:
Exporting a Gota Dataframe to a File
If you choose to go further and use the Gota package for full data analysis, you’ll need to save data in files. you may use theWriteCSVandWriteJSONmethods of dataframe to export files. The methods take in a file that you’ll create using theospackage’sCreatemethod.
Here’s how you can export a dataframe using the Gota package.
ThedataFramevariable is a representation of the dataframe. When you use theCreatemethod of theospackage, it creates a new, empty file with the specified name and returns the file. The WriteCSV method takes in the file instance and returns an error ornilif there’s no error.
Exploratory Data Analysis Is Important
An understanding of data and datasets is essential for data analysts and machine learning specialists. It is a critical operation in their work cycle, and exploratory data analysis is one of the techniques they use to achieve that.
There’s more to the Gota package. You can use it for various data wrangling functions in the same way that you’d use the Python Pandas library for data analysis. However, Gota doesn’t support quite as much functionality as Pandas.
Read and write data to Excel sheets, modify DataFrames in one line of code, remove all rows containing null values… you can do it all with pandas.
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