#dataframes

Coronavirus variants predicts virus evolving to escape current vaccines, and allergic reactions +

Face masks are a ticking plastic bomb https://www.eurekalert.org/pub_releases/2021-03/uosd-fma031021.php The impact of lockdown drives us to make poorer choices https://www.eurekalert.org/pub_releases/2021-03/uodc-tio030821.php#.YEZn0TK-DXc.wordpress Real world data reveal risks of allergic reactions after receiving COVID-19 mRNA vaccines…

Welcome to this lecture in the Data Cleaning and Preprocessing module of Pandas! In this lesson, we recap the importance of … source

Data processing and machine learning operations have been difficult to separate in big data analytics. Data engineers used Apache Spark for large-scale data processing in BigQuery, while data scientists used pandas and scikit-learn for machine learning. This disconnected approach caused inefficiencies, data duplication, and data insight delays.

At the same time, AI success depends on massive data. Thus, any firm must generate and handle synthetic data, which replicates real-world data. Algorithmically modelling production datasets or training ML algorithms like generative AI generate synthetic data. This synthetic data can simulate operational or production data for ML model training or mathematical model evaluation.

BigQuery DataFrames Solutions

BigQuery DataFrames unites data processing with machine learning on a scalable, cost-effective platform. This helps organizations expedite data-driven initiatives, boost teamwork, and maximize data potential. BigQuery DataFrames is an open-source Python package with pandas-like DataFrames and scikit-learn-like ML libraries for huge data.

It runs on BigQuery and Google Cloud storage and compute. Integrating with Google Cloud Functions allows compute extensibility, while Vertex AI delivers generative AI capabilities, including state-of-the-art models. BigQuey DataFrames can be utilized to build scalable AI applications due to their versatility.

BigQuery DataFrames lets you generate artificial data at scale and avoids concerns with transporting data beyond your ecosystem or using third-party solutions. When handling sensitive personal data, synthetic data protects privacy. It permits dataset sharing and collaboration without disclosing personal details.

Google Cloud can also apply analytical models in production. Testing and validation are safe with synthetic data. Simulate edge cases, outliers, and uncommon events that may not be in your dataset. Synthetic data also lets you model data warehouse schema or ETL process modifications before making them, eliminating costly errors and downtime.

Synthetic data generation with BigQuery DataFrames

Many applications require synthetic data generation:

Real data generation is costly and slow.

Unlike synthetic data, original data is governed by strict laws, restrictions, and oversight.

Simulations require larger data.

What is a data schema

Data schema

Let’s use BigQuery DataFrames and LLMs to produce synthetic data in BigQuery. Two primary stages and several substages comprise this process:

Code creation

Set the Schema and instruct LLM.

The user knows the expected data schema.

They understand data-generating programmes at a high degree.

They intend to build small-scale data generation code in a natural language (NL) prompt.

Add hints to the prompt to help LLM generate correct code.

Send LLM prompt and get code.

Executing code

Run the code as a remote function at the specified scale.

Post-process Data to desired form.

Library setup and initialization.

Start by installing, importing, and initializing BigQuery DataFrames.

Start with user-specified schema to generate synthetic data.

Provide high-level schema.

Consider generating demographic data with name, age, and gender using gender-inclusive Latin American names. The prompt states our aim. They also provide other information to help the LLM generate the proper code:

Use Faker, a popular Python fake data module, as a foundation.

Pandas DataFrame holds lesser data.

Generate code with LLM.

Note that they will produce code to construct 100 rows of the intended data before scaling it.

Run code

They gave LLMs all the guidance they needed and described the dataset structure in the preceding stage. The code is verified and executed here. This process is crucial since it involves humans and validates output.

Local code verification with a tiny sample

The prior stage’s code appears fine.

They would return to the prompt and update it and repeat the procedures if the created code hadn’t ran or Google wanted to fine-tune the data distribution.

The LLM prompt might include the created code and the issue to repair.

Deploy code as remote function

The data matches what they wanted, so Google may deploy the app as a remote function. Remote functions offer scalar transformation, thus Google can utilize an indicator (in this case integer) input and make a string output, which is the code’s serialized dataframe in json. Google Cloud must additionally mention external package dependencies, such as faker and pandas.

Scale data generation

Create one million synthetic data rows. An indicator dataframe with 1M/100 = 10K indicator rows can be initialized since our created code generates 100 rows every run. They can use the remote function to generate 100 synthetic data rows each indication row.

Flatten JSON

Each item in df[“json_data”] is a 100-record json serialized array. Use direct SQL to flatten that into one record per row.

The result_df DataFrame contains one million synthetic data rows suitable for usage or saving in a BigQuery database (using the to_gbq method). BigQuery, Vertex AI, Cloud Functions, Cloud Run, Cloud Build, and Artefact Registry fees are involved. BigQuery DataFrames pricing details. BigQuery jobs utilized ~276K slot milliseconds and processed ~62MB bytes.

Creating synthetic data from a table structure

A schema can generate synthetic data, as seen in the preceding step. Synthetic data for an existing table is possible. You may be copying the production dataset for development. The goal is to ensure data distribution and schema similarity. This requires creating the LLM prompt from the table’s column names, types, and descriptions. The prompt could also include data profiling metrics derived from the table’s data, such as:

Any numeric column distribution. DataFrame.describe returns column statistics.

Any suggestions for string or date/time column data format. Use DataFrame.sample or Series.sample.

Any tips on unique categorical column values. You can use Series.unique.

Existing dimension table fact table generation

They could create a synthetic fact table for a dimension table and join it back. If your usersTable has schema (userId, userName, age, gender), you can construct a transactionsTable with schema (userId, transactionDate, transactionAmount) where userId is the key relationship. To accomplish this, take these steps:

Create LLM prompt to produce schema data (transactionDate, transactionAmount).

(Optional) In the prompt, tell the algorithm to generate a random number of rows between 0 and 100 instead of 100 to give fact data a more natural distribution. You need adjust batch_size to 50 (assuming symmetrical distribution). Due to unpredictability, the final data may differ from the desired_num_rows.

Replace the schema range with userId from the usersTable to initialise the indicator dataframe.

As with the given schema, run the LLM-generated code remote function on the indicator dataframe.

Select userId and (transactionDate, transactionAmount) in final result.

Conclusions and resources

This example used BigQuery DataFrames to generate synthetic data, essential in today’s AI world. Synthetic data is a good alternative for training machine learning models and testing systems due to data privacy concerns and the necessity for big datasets. BigQuery DataFrames integrates easily with your data warehouse, Vertex AI, and the advanced Gemini model. This lets you generate data in your data warehouse without third-party solutions or data transfer.

Google Cloud demonstrated BigQuery DataFrames and LLMs synthetic data generation step-by-step. This involves:

Set the data format and use natural language prompts to tell the LLM to generate code.

Code execution: Scaling the code as a remote function to generate massive amounts of synthetic data.

Get the full Colab Enterprise notebook source code here.

Google also offered three ways to use their technique to demonstrate its versatility:

From user-specified schema, generate data: Ideal for pricey data production or rigorous governance.

Generate data from a table schema: Useful for production-like development datasets.

Create a dimension table fact table: Allows entity-linked synthetic transactional data creation.

BigQuery DataFrames and LLMs may easily generate synthetic data, alleviating data privacy concerns and boosting AI development.

Hoy continuaremos nuestra exploración en la investigación sobre la propensión al alcoholismo y su correlación con los trastornos de ansiedad. Como siguiente paso, nos sumergiremos en un análisis exploratorio de los datos obtenidos del estudio NESARC.

Dada la magnitud del conjunto de datos, nos enfrentamos a un desafío inicial debido a su considerable tamaño. Al cargar el archivo como un dataframe, notamos un consumo significativo de memoria, lo que podría potencialmente ralentizar la ejecución del código.

Para optimizar nuestra investigación, decidimos conservar únicamente las variables relevantes para nuestro estudio, las cuales se detallan en el libro de códigos.

LIBRO DE CÓDIGO.pdf

Durante este proceso, identificamos que algunas columnas no se importaron con el tipo de dato correcto. Por ejemplo, variables como 'S2AQ11', 'S3AQ2A1', y 'S3AQ3A1R', que representan el número de tragos tolerados sin sentirse intoxicado, la edad de inicio del consumo de tabaco, y la duración en horas desde la última vez que se consumió tabaco, respectivamente, deberían ser de tipo numérico, sin embargo, se detectaron como tipo 'objeto'. Del mismo modo, una lista de aproximadamente 32 variables que deberían ser categóricas también se identificó erróneamente como tipo 'objeto'.

A continuación se presenta la salida en consola del método pandas.info() después de filtrar el dataframe original.

Durante la corrección del tipo de dato, además, reemplazamos las entradas que contenían espacios en blanco con valores NaN de la biblioteca NumPy, lo que permite a Python reconocer adecuadamente los valores faltantes.

A continuación se presenta la salida en consola del método pandas.info() después de corregir el tipo de dato para cada columna del dataframe:

Es importante destacar que, al realizar estos ajustes, logramos reducir significativamente el uso de memoria del dataframe. Cuando filtramos las columnas, conseguimos reducir el uso de memoria de 989.6MB a solo 26.6MB. Además, al dejar el dataframe con los tipos de columnas correctos, también conseguimos reducir el uso de memoria, quedando solamente 5.6MB. Esta reducción en el uso de memoria es crucial, ya que permite un procesamiento más eficiente de los datos y mejora el rendimiento del análisis exploratorio.

Con el dataframe corregido y cada columna ahora con su tipo de dato correcto, procedimos con el análisis exploratorio de los datos. Dado que contamos con un total de 81 variables, para una mejor comprensión de la estructura del conjunto de datos, decidimos presentar de manera ilustrativa la frecuencia de las variables de Edad, Sexo y Estado Civil.

Observamos que en el estudio hay más mujeres que hombres y el tipo de estado civil más común es el de Casado.

Posteriormente, analizamos el número de valores faltantes y el número de valores únicos por columna.

Por último, presentamos un resumen estadístico de las variables cuantitativas, lo que nos permite obtener una comprensión más profunda del comportamiento de los datos en nuestra muestra.

Con los datos limpios y un análisis exploratorio detallado en marcha, estamos en un punto crucial de nuestra investigación sobre la relación entre el alcoholismo y los trastornos de ansiedad. Esta optimización en el uso de memoria y la corrección de los tipos de datos nos permiten ahora adentrarnos aún más en el análisis de estos datos fundamentales.

Próximamente, estaremos explorando patrones y tendencias en los datos que podrían arrojar luz sobre la compleja interacción entre el alcoholismo y los trastornos de ansiedad. Manténgase atento para descubrir cómo estos hallazgos podrían informar no solo nuestra comprensión científica, sino también las posibles intervenciones y políticas para abordar estos desafíos de salud mental.

También puedes consultar el código utilizado aquí:

analisisexploratorio.py

Y descargar el dataset con el que estamos trabajando aquí:

A full 2 hour Python course to get you started with Financial Analysis. You will learn how to make automated financial analysis … source

Python stands out as a powerful programming language. One of its key strengths lies in its ability to handle structured data efficiently. At the heart of this capability lies the concept of a DataFrame. If you're stepping into the world of data science or analytics, understanding what a DataFrame is and how to wield its power is crucial.

What is a DataFrame?

In simple terms, a DataFrame is a two-dimensional, tabular data structure commonly used in Python for handling and analyzing data. It's analogous to a spreadsheet or SQL table, where data is arranged in rows and columns. The real magic happens when we introduce the pandas library, a popular data manipulation library in Python, which provides the DataFrame structure.

Pandas: The Wizard Behind the Curtain

Pandas is to DataFrames what a wizard is to spells – an indispensable tool that brings magic to the world of data manipulation. With Pandas, you can easily create, manipulate, and analyze datasets, making it an essential component of any data scientist's toolkit.

Creating a DataFrame:

Creating a DataFrame is a breeze with Pandas. You can construct one from various data structures like lists, dictionaries, or even other DataFrames. For example, using the following code snippet:

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import pandas as pd data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'City': ['New York', 'San Francisco', 'Los Angeles']} df = pd.DataFrame(data)

In this example, we've created a DataFrame with columns for 'Name', 'Age', and 'City' using a Python dictionary. Each key-value pair represents a column name and its corresponding data.

Manipulating Data:

DataFrames shine brightest when it comes to data manipulation. Pandas provides an extensive array of functions for filtering, sorting, grouping, and transforming data. Whether you want to calculate statistics, merge datasets, or clean messy data, Pandas offers a plethora of tools at your disposal.

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# Filtering data youngsters = df[df['Age'] < 30] # Sorting data sorted_df = df.sort_values(by='Age') # Grouping data city_stats = df.groupby('City').mean()

These operations demonstrate the flexibility and ease with which you can mold your data to suit your analytical needs.

Handling Missing Data:

Real-world datasets are rarely perfect. They often come with missing or incomplete information. DataFrames in Pandas make it simple to handle such scenarios, allowing you to fill missing values, drop rows or columns, or interpolate data.

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# Handling missing values df_filled = df.fillna(0) # Dropping rows with missing values df_cleaned = df.dropna()

Conclusion:

In the realm of data analysis with Python, understanding and mastering DataFrames is akin to wielding a powerful instrument. Pandas provides the wand, and you are the wizard who can extract insights, reveal patterns, and uncover hidden truths within the vast sea of data.