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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

DuckDB: An Introduction

C
Ciência de Dados Last Updated : 12 Oct, 2024
6 min read

This article was published as a part of the Data Science Blogathon.

Introduction

DuckDB is designed to support analytical query workloads, also known as Online analytical processing (OLAP).”  The duck was chosen as the mascot for this database management system (DBMS) because it is a very versatile animal that can fly, walk and swim. In short, it is designed to be your DBMS for local analysis. So let’s see what features it has to offer.

Structured Query Language,SQL or “sequel” for the more intimate, is a fundamental language for anyone who wants to deal with databases.

Some time ago, I was researching how to use SQL in python, so I found this wonderful solution called DuckDB. In my opinion, the best parts of this tool are the ability to read CSV and Parquet files directly in the query and its excellent performance.

It is also worth remembering that DuckDB focuses on analysis activities rather than transactions like SQLite.

DuckDB Features

  • Simple installation
  • Embedded: no server management
  • Processing and storing tabular datasets, e.g. from CSV or Parquet files
  • Single file storage format
  • Fast analytical processing
  • Fast transfer between R/Python and RDBMS
  • Interactive data analysis, e.g. Joining & aggregate multiple large tables
  • Concurrent large changes to multiple large tables, e.g. appending rows, adding/removing/updating columns

So, let’s see what you will learn in this article!

font: https://duckdb.org/why_duckdb

What will you Learn?

In this article, you will learn how to install, import the DuckDB, read and write CSV and Parquet files, the Meta Queries and DBeaver SQL IDE; in short: 

  1. Getting started
  2. Import and export CSV and parquet files
  3. Meta queries
  4. DBeaver SQL IDE

Getting Started with DuckDB

So enough talk, and let’s get our hands dirty with the code 😉

Installing DuckDB in your system:

Import and connect to a database:

 
import duckdb
import pandas as pd
con = duckdb.connect() 
df = pd.DataFrame({'Column 1' : [100]})
con.execute("SELECT * FROM df").df() # query the Pandas DataFrame "df"
In the next section, you will learn how to work with CSV and read and write in a very convenient and fast format! The brilliant Parquet.

Import and Export Files

So let’s start with the data scientist’s great friend, the CSV file.

Load a CSV file into a table using the read_csv_auto function:

con.execute("SELECT * FROM read_csv_auto('my_local_file.csv')").df()

Create a new table:

con.execute("CREATE TABLE tbl AS SELECT * FROM read_csv_auto('my_local_file.csv')")

Export the data from a table to a CSV file:

con.execute("COPY tbl TO 'my_export_file.csv' (HEADER, DELIMITER ',')")

If you have not been introduced to him yet, it is time to meet him. The Parquet file is essential when working with a large amount of data; we won’t go into detail in this article regarding its advantages, but see at this point that working with this file format with DuckDB is quite simple and easy.

Load a Parquet file into a table using the read_parquet function:

con.execute("SELECT * FROM read_parquet('my_local_file.parquet')").df()

Create a new table:

con.execute("CREATE TABLE tbl5 AS SELECT * FROM read_parquet('my_local_file.parquet')")

Export the data from a table to a Parquet file:

con.execute("COPY tbl TO 'my_export_file.parquet' (FORMAT PARQUET)")

Ok! But how about the huge table? Let’s talk about it in the next section!

Meta Queries

Well, it can be very convenient to list them all after creating several tables, can’t it? We can do this with SHOW TABLES.

con.execute("SHOW TABLES").df()

To view the schema of a table, use DESCRIBE followed by the table name.

df = pd.DataFrame({'Col1' : [100,90,30],'Col2' : [1,5,8]})
con.execute("CREATE TABLE tbl_df AS SELECT * FROM df")
con.execute(“DESCRIBE tbl_df”).df()

In my opinion, the most useful command is SUMMARIZE; it returns the column name, column type, minimum value, maximum value, the number of unique values, the mean, the standard deviation, the quartiles, the number of values, and the percentage of null values. A lot of information with a single command!

con.execute("SUMMARIZE tbl").df()

In reality there is so much information that sometimes we don’t want it, or it can be difficult to see. We can use the SUMMARIZE command in conjunction with SELECT to get a summary of only the desired columns, for example.

con.execute("SUMMARIZE SELECT Col1 FROM tbl").df()

Well, If you like SQL and Python, then you must give DuckDB a try!

DBeaver SQL IDE

“DBeaver is a powerful and popular desktop sql editor and integrated development environment (IDE). It has both an open source and enterprise version. It is useful for visually inspecting the available tables in DuckDB and for quickly building complex queries. DuckDB’s JDBC connector allows DBeaver to query DuckDB files, and by extension, any other files that DuckDB can access (like parquet files).”

If you have liked DubkDB, you will love this IDE; it is straightforward and lightweight. In this section, we are going to install, connect, and create a database in memory, local, and you know what? Let’s create, read and save a parquet file!

So let’s go! In this tutorial, I will show you how to install the Windows version: the download page.

At the beginning of the installation, you can choose your preferred language:

DBeaver Community:

Now, in the Database menu, click on New Data Base Connection:

Guess which one we will choose now? Sure! DuckDB, isn’t it?

On the “Path” to start playing, we can fill in with “:memory:”:

Click on the button “Test Connection…”:

Click “Download” to download DuckDB’s JDBC driver from Maven. Once the download is complete, click “OK”, then click “Finish”:

If you got this message, the connection was successful. Congratulations!

The big moment of our “Hello World!” in DBeaver SQL IDE:

 
 

And now the big final moment! Create and write a Parquet file:

Conclusion

In this article, you have had your first contact with DuckDB, learned how to read and write CSVs and Parquet files, summarize your table, and install and use DBeaver Community SQL IDE.

Key takeaways:

  • On the initial point, you learned how to install and import DuckDB;
  • To help you practice daily, you learn how to read and write CSV and Parquet files;
  • Now you also know how to display all tables and summarize them;
  • We know how an Integrated Development Environment can help you organize all your work, so we introduce you to the DBeaver SQL IDE.

Finally, I hope you learned from this article; feel free to leave a friendly comment, and see you next time!

If you need additional information, see the documentation at https://duckdb.org/.

<script src=”https://gist.github.com/rafaelaugusto1983/c2fcadcd9c75526e2642df8f38e1d2c5.js“>

Thanks for reading!

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Ciência de Dados

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