Dataframe | Geeky Codes- Code Like A Geek But In Style

12 Mar

What is the difference between the append() and insert() list methods in Python?

geekycodesco0 CommentsPythonCoding, Dataframe, DataScience, Datasets, Machine learning, Programming, Python

Difference between append() and insert () Append(): This function is used to modify an already existing list. Adds a new specific element at the end of the list. Syntax: List_Name.append(item) Insert(): This function also modifies an already existing list. The only difference between append() and insert() is that the insert function allows us to add a specific element at a specified index of the list unlike append() where we can add the element only at end of the list. Syntax: List_Name.insert(index, item) Refer below example for better understanding Important Notice for college students If you’re a college student and have…

11 Mar

How is the axis indexed in NumPy’s array?

geekycodesco0 CommentsPandas, PythonDataframe, Indexing, Machine learning, Numpy, Numpy Indexing, Programming, Python

By definition, the axis number of the dimension is the index of that dimension within the array's shape. It is also the position used to access that dimension during indexing. So we'll be learning how is the axis indexed For example, if a 2D array a has shape (5,6), then you can access a[0,0] up to a[4,5]. Axis 0 is thus the first dimension (the "rows"), and axis 1 is the second dimension (the "columns"). In higher dimensions, where "row" and "column" stop really making sense, try to think of the axes in terms of the shapes and indices involved. If you do .sum(axis=n), for example,…

09 Mar

Python Dictionary

geekycodesco0 CommentsPythonCoding, Dataframe, DataScience, delete elements from dictionary, Dictionary, key, Programming, Python, value

Dictionary (Dict) in Python is an unordered collection of data values, used to store data values like a map, which, unlike other Data Types that hold only a single value as an element, Dictionary holds the key: value pair. Key-value is provided in the dictionary to make it more optimized. Note – Keys in a dictionary don’t allow Polymorphism. Disclamer: It is important to note that Dictionaries have been modified to maintain insertion order with the release of Python 3.7, so they are now ordered collection of data values. Creating a Dictionary In Python, a Dictionary can be created by placing a sequence of…

01 Mar

How to create a voice recorder in Python

geekycodesco0 CommentsPython, Real Life ExamplesCoding, Dataframe, Machine learning, Programming, Python, Voice recorder

Python can be used to perform a variety of tasks. One of them is creating a voice recorder. We can use python’s sounddevice module to record and play audio. This module along with the wavio or the scipy module provides the way to save recorded audio. Installation sounddevice: This module provides functions to play and record NumPy arrays containing audio signals. Let’s install it by running the following command: $ pip3 install sounddevice We can use either wavio and scipy to save the recorded audio in file format. We will see both of them here.To install wavio: $ pip3 install wavio To install scipy: $ pip3 install scipy Now, we are done with installing the…

28 Feb

Stationarity Analysis in Time Series Data

Avinash0 CommentsMachine Learning, Pandas, Python, Time SeriesADF Test, Coding, Dataframe, KPSS Test, Machine learning, Programming, Python, Rolling Statistics Test, Zivot-Andrews Test

Hey Geeks !!! in this blog, we'll dive into the concept of stationarity using time series data. We'll first understand what is time-series data, what is stationarity, why and when data should be stationary etc...We'll use the dataset I created specifically for this blog to analyze whether the data is stationary or not. We'll also see how to convert the non-stationary data to stationary. Index IntroductionImport Libraries and DependenciesDefine TimeSeriesData ClassImport DatasetAccumulating Number of Sales by monthCreate objectStationarity TestsGraphical TestRolling-Statistics TestAugmented Dickey-Fuller Test (ADF)Kwiatkowski-Phillips-Schmidt-Shin Test (KPSS)Zivot-Andrews TestConclusionConvert data to StationaryDerivativesTransformation using Logarithmic FunctionADF TestKPSS TestZivot-Andrews TestRolling-Statistics TestConclusion 1. Introduction 1.1…

20 Feb

How to Predict Movie will be Flop or Hit and it’s Revenue?

geekycodesco0 CommentsData Science, Machine Learning, Pandas, Python, Random Forest, Real Life ExamplesClassification, Coding, Dataframe, DataScience, Datasets, Linear regression, Machine learning, Programming, Python, Regression, XG Boost, XGB

The Birth of the motion picture camera in the late 18th century gave birth to the most powerful form of entertainment available: Cinema. Movies have been able to entertain audiences from the emergence of a single second of horse racing in the 1890s to the introduction of sound in the 1920s to the birth of color in the 1930s to create 3D Movies in early 2010. Cinema had a humble background in terms of design, direction and acting (especially due to its very short time in its early days) but since then, the film industry around the world has been…

17 Feb

How to collect Covid19 Data using API in Python

geekycodesco0 CommentsData Engineering, Data Pipelines, Pandas, Python, Real Life ExamplesData Collection, Data Engineering, data pipelines, Dataframe, DataScience, Machine learning, Programming, Python

In this tutorial, we will be collecting covid19 Data using API in Python. What is API? API (Application Programming Interface) is a computing interface that interacts between multiple software. What is JSON? JSON (JavaScript Object Notation) is a lightweight format for storing and transporting data. It is used to send data from server to web. Required modules: matplotlibrequestspandasjson Commands to install modules: pip install matplotlib pip install requests pip install pandas Ignore this section if you've already installed these modules. Steps: Importing all required modules.Calling API and getting JSON data.Getting the Data for a particular stateVisualization of data. The below URL redirects…

14 Feb

How to start programming? Step By Step Guide

geekycodesco0 CommentsBlog, Interview, Real Life Examples, TutorialsC Programming, CSV files, Dataframe, DataScience, Machine learning, Programming, Python

Programming is the key to getting success in a tech carrier, when I talk about programming I mean efficient programming. It means the coding which is time and space-efficient. How can we make programs which is time and space-efficient by learning data structure and Algorithms? So that’s why data structure and algorithms are so much required for preparing for interviews. Now there is a huge list of websites that are there for preparing these algorithms and these algorithms and data structures are mostly used during interviews as when we are working in Industry we mostly focus on API development and using the…

14 Feb

What are the good websites to learn data structures and algorithms?

geekycodesco0 CommentsAlgorithms, Blog, Interview, Real Life ExamplesC Programming, Coding, Dataframe, DataScience, Datasets, Machine learning, Programming, Python, SAP

Well! Programming is fun once you get it and the great part is a decent developer gets an enormous check from Top Tech Giants Like (Google, Amazon, Walmart, Microsoft, Facebook, and Apple). Data Structure and algorithms are needed for breaking interviews in these first-rate organizations. Regardless of whether you are a fledgling or middle in Algorithm abilities for the most part learning complete data structure, required 2-3 months. Likewise, getting ready code without help from anyone else is the fundamental model for the arrangement cycle. The following are a few decent assets for learning Data structure and Algorithms: 1. Geeksforgeeks: Geeksforgeeks has an expanse of issues.…

10 Jan

Interpreting ACF and PACF | Time Series

geekycodesco0 CommentsData Science, Machine Learning, Pandas, Python, Random ForestCoding, Dataframe, DataScience, Datasets, Machine learning, Programming, Python

Introduction Autocorrelation analysis is an important step in the Exploratory Data Analysis (EDA) of time series. The autocorrelation analysis helps in detecting hidden patterns and seasonality and in checking for randomness. It is especially important when you intend to use an ARIMA model for forecasting because the autocorrelation analysis helps to identify the AR and MA parameters for the ARIMA model. Overview FundamentalsAuto-Regressive and Moving Average ModelsStationarityAutocorrelation Function and Partial Autocorrelation FunctionOrder of AR, MA, and ARMA ModelExamplesAR(1) ProcessAR(2) ProcessMA(1) ProcessMA(2) ProcessPeriodicalTrendWhite NoiseRandom-WalkConstant🚀 Cheat SheetCase StudyBitcoinEthereumDiscussion on Random-Walk import numpy as np # linear algebra from numpy.random import seed import math import…