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Category: Python programming

A Practical Guide to Data Normalization using Z-Tests in Python

A Practical Guide to Data Normalization using Z-Tests in Python

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Today, I’ll introduce one method for data normalization, utilizing the biomass with N and P uptake data available on my GitHub. I also aim to create regression graphs illustrating the relationship between biomass and either nitrogen or phosphorus. First, I’ll generate a regression graph for biomass with either nitrogen or phosphorus to observe the data patterns. I notice a clear pattern between biomass and nitrogen. However, when combining nitrogen and phosphorus in the same panel due to their different data…

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How to import Kaggle datasets directly into Google Colab?

How to import Kaggle datasets directly into Google Colab?

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Kaggle is a popular online platform for data science and machine learning competitions, datasets, and tutorials. You can find high-quality data on Kaggle to practice data analysis. I have uploaded some of my data on Kaggle to share it with others. Recently, I’ve begun learning machine learning, and one of the most fundamental datasets for this purpose is the Titanic dataset. By visiting the website below, you can download the Titanic survivor data and practice machine learning with this foundational…

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How to Upload Data from GitHub Using R and Python?

How to Upload Data from GitHub Using R and Python?

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I have soybean yield data that I want to upload to Github and access from R. First, let’s upload the data to Github. The data should be in .csv format. Click Add file, choose Upload files, and, after uploading, select the Raw button to view the data in .csv format as text. and you can find the address for this data, starting with https://raw.githubusercontent.com/… Let’s copy this address. Next, I’ll bring this data into R from Github. Before that, let’s…

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Python Data Preprocessing: Practice

Python Data Preprocessing: Practice

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Before diving into in-depth data analysis, a crucial step is data preprocessing. This essential process not only ensures better data quality but also significantly improves the efficiency of your analysis. In this guide, I will introduce a range of powerful Python methods for data preprocessing, equipping you with the tools to optimize your data for more accurate and insightful analysis. I use Goolge Colab when using Python because it’s more user friendly. Please refer how to setup Google Colab in…

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How to Specify a Folder Path in Google Colab: A Tutorial

How to Specify a Folder Path in Google Colab: A Tutorial

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□ How to use Google Colab for Python (power tool to analyze data)? Last time, I introduced how to mount your Google Drive in Google Colab. This time, I will explain how to set a folder path in Google Colab. First, let’s establish the connection between Google Colab and your Google Drive. The code is as follows: Then, the folders from your Google Drive will appear in the left sidebar of Google Colab. I created a folder called “coding_practice” in…

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How to draw a normal distribution graph using Python?

How to draw a normal distribution graph using Python?

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In this session, using Python, I will draw a normal distribution graph using the actual data I collected. I measured individual grain areas for two wheat genotypes. For Cultivar_A, I measured the area for 1,225 grains and for Cultivar_B, I measured the area for 1,841 grains. Therefore, the total number of grain area data is 3,066. This is natural data collected by me in the actual cultivation field, and it would be interesting to see if it also follows a…

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What is the F-ratio in statistics?

What is the F-ratio in statistics?

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Today, I will explain the meaning of the F-value in testing for significance through statistical processing. Let me give you an example. Suppose we want to determine whether there are differences in the yield according to the varieties (A, B, C). The total experimental unit is 12 (3 varieties x 4 replicates). What would happen if there is a significant difference in yield among varieties A and C? If there is a large difference in yield between these varieties, the…

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How to use Google Colab for Python (power tool to analyze data)?

How to use Google Colab for Python (power tool to analyze data)?

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Google Colaboratory (aka. Colab) is a cloud-based platform that provides a Jupyter notebook environment and therefore users can write and run Python code. You don’t have to install Anaconda to use Jupyter notebook. If you have a google account, simply you can analyze data. Google Colab is a powerful tool for collaborative coding and data analysis, providing users with an easy-to-use platform with a wide range of features and resource. I introduce how to set up Google Colab. Step 1)…

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