(testing signal)

Tag: Pandas

Dask DataFrame is not Pandas

This article is the second article of an ongoing series on using Dask in practice. Each article in this series will be simple enough for beginners, but provide useful tips for real work. The next article in the series is about parallelizing for loops, and other embarrassingly parallel operations with dask.delayed.

Parallel Computing Tool in Python for Big Data

When you open a large Dataset with Python’s Pandas and try to get a few metrics, the entire thing just stops badly. If you work with Big Data on a regular basis, you’re probably aware that if you’re using Pandas, a simple loading of a series for a couple of million rows can take up to a minute! In the industry, the term/technique parallel computing is used for this. In relation to parallel computing, we will cover parallel computing and the Dask library, which is preferred for such tasks in this article. We will also go through different machine learning features as well available…

A PySpark Example for Dealing with Larger than Memory Datasets

A step-by-step tutorial on how to use Spark to perform exploratory data analysis on larger than memory datasets.Analyzing datasets that are larger than the available RAM memory using Jupyter notebooks and Pandas Data Frames is a challenging issue. This problem has already been addressed (for instance here or here) but my objective here is a little different. I will be presenting a method for performing exploratory analysis on a large data set with the purpose of identifying and filtering out…

3 Ways to Render Pandas DataFrames

A practical guide to instant documentationPhoto by Thought Catalog on UnsplashSystems read files but Humans read documents. The more stories we document, the more collaborative analysis would be.Dataframes are the key structures of analysis. They hold the critical data supporting key decision makings, at several stages, in the process of solving a problem. Often, decision-making involves multiple stakeholders who need to look at the data held in these data frames.As a good data scientist and…

KDnuggets™ News 21:n38, Oct 6: Build a Strong Data Science Portfolio; Surpassing Trillion Parameters with Switch Transformers — a path to AGI?

How to Build Strong Data Science Portfolio as a Beginner; Surpassing Trillion Parameters and GPT-3 with Switch Transformers — a path to AGI?; How Deep Is That Data Lake?; Data Science Process Lifecycle; Use These Unique Data Sets to Sharpen Your Data Science Skills; How to Auto-Detect the Date/Datetime Columns and Set Their Datatype When Reading a CSV File in Pandas

Deep learning model to predict mRNA degradation

We will be using TensorFlow as our main library to build and train our model and JSON/Pandas to ingest the data. For visualization, we are going to use Plotly and for data manipulation Numpy.# Dataframeimport jsonimport pandas as pdimport numpy as np# Visualizationimport plotly.express as px# Deeplearningimport tensorflow.keras.layers as Limport tensorflow as tf# Sklearnfrom sklearn.model_selection import train_test_split#Setting seedstf.random.set_seed(2021)np.random.seed(2021)Target…

Continue reading: https://pub.towardsai.net/deep-learning-model-to-predict-mrna-degradation-1533a7f32ad4?source=rss—-98111c9905da—4

Source: pub.towardsai.net

What’s in a “Random Forest”? Predicting Diabetes

Python Implementation

Now that we’ve gone through some conceptual context behind what a random forest and a decision tree is, and how it makes its decisions, let’s actually implement the algorithm in Python!

For this implementation, I’ll be using real-life recent data from patients at the Sylhet Diabetes Hospital in Sylhet, Bangladesh. The data was collected and published just last year in June 2020, in this research paper by Dr. MM Faniqul Islam and others (cited below), and is freely available on the UC Irvine Machine Learning Repository at this link.

First, you’ll need to import the CSV file once it’s downloaded from the Repository. The dataset, once formatted in the Pandas Data-Frame module, should look like this:

Image credit: Raveena Jayadev, author

Once we’ve downloaded the data, it’s good standard practice to convert the “Yes” and “No” answers into 1’s and 0’s, respectively in a numerical format.… Read more...

Teaching AI to Classify Time-series Patterns with Synthetic Data – KDnuggets

What do we want to achieve?

We want to train an AI agent or model that can do something like this,

Image source: Prepared by the author using this Pixabay image (Free to use)

Variances, anomalies, shifts

Little more specifically, we want to train an AI agent (or model) to identify/classify time-series data for,

low/medium/high variance
anomaly frequencies (little or high fraction of anomalies)
anomaly scales (are the anomalies too far from the normal or close)
a positive or negative shift in the time-series data (in the presence of some anomalies)

But, we don’t want to complicate things

However, we don’t want to do a ton of feature engineering or learn complicated time-series algorithms (e.g. ARIMA) and properties (e.g.… Read more...

How to Auto-Detect the Date/Datetime Columns and Set Their Datatype When Reading a CSV File in Pandas – KDnuggets

By David B Rosen (PhD), Lead Data Scientist for Automated Credit Approval at IBM Global Financing

Say I have a CSV data file that I want to read into a Pandas dataframe, and some of its columns are dates or datetimes, but I don’t want to bother identifying/specifying the names of these columns in advance. Instead I would like to automatically obtain the datatypes shown in the df.info() output pictured above, where the appropriate columns have been automatically given a datetime datatype (green outline boxes). Here’s how to accomplish that:

from dt_auto import read_csv

Note that I did not invoke pd.read_csv (the Pandas version of read_csv) above directly. My dt_auto.read_csv… Read more...

The Ultimate Beginner Guide to Web Scraping | by Michel Kana, Ph.D | Sep, 2021


Building a Structured Financial Newsfeed Using Python, SpaCy and Streamlit – KDnuggets

By Harshit Tyagi, Data Science Instructor | Mentor | YouTuber

One of the very interesting and widely used applications of NLP is Named Entity Recognition(NER).

Getting insights from raw and unstructured data is of vital importance. Uploading a document and getting the important bits of information from it is called information retrieval.

Information retrieval has been a major task/challenge in NLP. And NER(or NEL — Named Entity Linking) is used in several domains(finance, drugs, e-commerce, etc.) for information retrieval purposes.

In this tutorial post, I’ll show you how you can leverage NEL to develop a custom stock market news feed that lists down the buzzing stocks on the internet.


There are no such pre-requisites as such.


Enchanced Tabular Data Visualization (Pandas)

Simple but efficient techniques to improve pandas dataframe representation

From Pixabay

In this article, we’ll discuss some useful options and functions to efficiently visualize dataframes as a set of tabular data in pandas. Let’s start with creating a dataframe for our further experiments:

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(20, 40))# Renaming columns
df.columns = [x for x in 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMN']
# Adding some missing values
df.iloc[3,4] = np.nan
df.iloc[2,0] = np.nan
df.iloc[4,5] = np.nan
df.iloc[0,6] = np.nan
Image by Author

Attention: the code from this article was run in pandas version 1.3.2. Some of the functions are quite new and will throw an error in the older versions.


Tips For Data Mapping And Replacing With Pandas And Numpy

In order to summarize main characteristics, spot anomalies, and visualize information, you should know how to rearrange and transform datasets. In other words, transforming data helps you play with your dataset, make sense of it, and gather as many insights as you can. In this article, I will show you some of my commonly used methods to play with data, and hope this would be helpful.

I will create a simple score dataset, which includes information about different classes’ grades.

info = {'Class':['A1', 'A2', 'A3', 'A4','A5'],
'AverageScore':[3.2, 3.3, 2.1, 2.9, 'three']}
data = pd.DataFrame(info)


Fig 1: DataFrame

As the Average Score of Class A5 in our data is a string object, I want to replace it with a corresponding number for easier data manipulation.


Data Analysis Using Scala – KDnuggets

By Roman Zykov, Founder/Data Scientist @ TopDataLab

It is very important to choose the right tool for data analysis. On the Kaggle.com forums, where international Data Science competitions are held, people often ask which tool is better. R and Python are at the top of the list. In this article we will tell you about an alternative stack of data analysis technologies, based on Scala programming language and Spark distributed computing platform.

How did we come up with it? At Retail Rocket we do a lot of machine learning on very large data sets. We used to use a bunch of IPython + Pyhs2 (hive driver for Python) + Pandas + Sklearn to develop prototypes. At the end of summer 2014 we made a fundamental decision to switch to Spark, as experiments have shown that we will get 3-4 times the performance improvement on the same park of servers.


Complete Guide to Spark and PySpark Setup for Data Science

Complete A-Z on how to set-up Spark for Data Science including using Spark with Scala and with Python via PySpark as well as integration with Jupyter notebooks

Photo by Rakicevic Nenad from Pexels


Apache Spark is a unified analytics engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing. It is fast becoming the de-facto tool for data scientists to investigate big data.

In similar fashion to most data scientists Python has always been my go-to programming language for anything from data collection via web scraping tools such as Scrapy and Selenium to data wrangling with pandas and machine leaning/deep learning with all the fantastic libraries available in Python such as Pytorch and Tensorflow.