Blog Archives

A journey into the unknown with Tech Leaders

In my previous post I mentioned that I got into Tech Leaders program. Now, after a few months I would like to relate my experiences of doing something unbelievable, which I’ve never thought I will be able to do. Tech

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Posted in learning

Statistics in Python: Measures of Dispersion

In this article I would like to concentrate on 4 main measures of statistical dispersion: range (and the biggest and the smallest number as well), average deviation, variance and standard deviation. In Python, we can easily compute them with a

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Posted in data analysis, python

Statistics in Python: Central Tendency

In the central tendency there are 3 most common measures: mean (arithmetic average), median and mode. Their manual calculation in Python is presented below. Mean Arithmetic mean is a sum of a collection of numbers divided by the total number

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Posted in data analysis, python

Statistics in Python: Frequency Distribution

Previous articles concentrated on managing and visualizing data with numpy and mathplotlib.pyplot libraries. Now, it is time to count more statistics, but manually, without built-in functions. For the beginning, let’s write some code to show a frequency distribution of the

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Posted in data analysis, python

Mars Craters Research Project #4: Visualizing Data

This study shows graphically the distribution of crater morphologies, crater location plot and relationship between diameter and depth of a crater. The Python program below should call the graphs, but unfortunately it is impossible due to traceback error (the matplot.pyplot

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Posted in data analysis, python

Python Exercises: List Methods and Dictionaries

Below presented exercises come from An Introduction to Interactive Programming in Python (Part 2) course available on Coursera. All methods have one goal: to subordinate names of week days to numbers, starting from 0, ending on 6. Exercise 1 Write a function 𝚍𝚊𝚢_𝚝𝚘_𝚗𝚞𝚖𝚋𝚎𝚛(𝚍𝚊𝚢)

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Posted in python

Mars Craters Research Project #3: Managing Data

There were 3 variables worked out in this Python program: diameter of a crater (DIAM_CIRCLE_IMAGE), depth (DEPTH_RIMFLOOR_TOPOG) and ejecta morphologies (MORPHOLOGY_EJECTA_1, MORPHOLOGY_EJECTA_2). Below you can find the output of this program: counts and percentages for chosen variables. To have better organized

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Posted in data analysis, python