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chore(i18n,learn): processed translations (#48105)

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17
curriculum/challenges/ukrainian/08-data-analysis-with-python/data-analysis-with-python-projects/demographic-data-analyzer.md
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@@ -1,6 +1,6 @@
---
id: 5e46f7e5ac417301a38fb929
title: Demographic Data Analyzer
title: Аналізатор демографічних даних
challengeType: 10
forumTopicId: 462367
dashedName: demographic-data-analyzer
@@ -8,12 +8,13 @@ dashedName: demographic-data-analyzer
# --description--
You will be [working on this project with our Replit starter code](https://replit.com/github/freeCodeCamp/boilerplate-demographic-data-analyzer).
Ви будете <a href="https://replit.com/github/freeCodeCamp/boilerplate-demographic-data-analyzer" target="_blank" rel="noopener noreferrer nofollow">працювати над цим проєктом з нашим стартовим кодом Replit</a>.
We are still developing the interactive instructional part of the Python curriculum. For now, here are some videos on the freeCodeCamp.org YouTube channel that will teach you everything you need to know to complete this project:
- [Python for Everybody Video Course](https://www.freecodecamp.org/news/python-for-everybody/) (14 hours)
- [Learn Python Video Course](https://www.freecodecamp.org/news/learn-python-video-course/) (10 hours)
- <a href="https://www.freecodecamp.org/news/python-for-everybody/" target="_blank" rel="noopener noreferrer nofollow">Python for Everybody Video Course</a> (14 hours)
- <a href="https://www.freecodecamp.org/news/how-to-analyze-data-with-python-pandas/" target="_blank" rel="noopener noreferrer nofollow">How to Analyze Data with Python Pandas</a> (10 hours)
# --instructions--
@@ -47,11 +48,11 @@ Unit tests are written for you under `test_module.py`.
## Development
For development, you can use `main.py` to test your functions. Click the "run" button and `main.py` will run.
For development, you can use `main.py` to test your functions. Натисніть кнопку «запустити» і `main.py` запуститься.
## Testing
We imported the tests from `test_module.py` to `main.py` for your convenience. The tests will run automatically whenever you hit the "run" button.
We imported the tests from `test_module.py` to `main.py` for your convenience. Тести запустяться автоматично, коли ви натиснете на кнопку «запустити».
## Submitting
@@ -59,11 +60,11 @@ Copy your project's URL and submit it to freeCodeCamp.
## Dataset Source
Dua, D. and Graff, C. (2019). [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml). Irvine, CA: University of California, School of Information and Computer Science.
Dua, D. and Graff, C. (2019). <a href="http://archive.ics.uci.edu/ml" target="_blank" rel="noopener noreferrer nofollow">UCI Machine Learning Repository</a>. Irvine, CA: University of California, School of Information and Computer Science.
# --hints--
It should pass all Python tests.
Він повинен пройти усі тести Python.
```js

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curriculum/challenges/ukrainian/08-data-analysis-with-python/data-analysis-with-python-projects/mean-variance-standard-deviation-calculator.md
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@@ -1,6 +1,6 @@
---
id: 5e46f7e5ac417301a38fb928
title: Mean-Variance-Standard Deviation Calculator
title: Калькулятор середньоквадратичного відхилення
challengeType: 10
forumTopicId: 462366
dashedName: mean-variance-standard-deviation-calculator
@@ -8,18 +8,19 @@ dashedName: mean-variance-standard-deviation-calculator
# --description--
You will be [working on this project with our Replit starter code](https://replit.com/github/freeCodeCamp/boilerplate-mean-variance-standard-deviation-calculator).
Ви будете <a href="https://replit.com/github/freeCodeCamp/boilerplate-mean-variance-standard-deviation-calculator" target="_blank" rel="noopener noreferrer nofollow">працювати над цим проєктом з нашим стартовим кодом Replit</a>.
We are still developing the interactive instructional part of the Python curriculum. For now, here are some videos on the freeCodeCamp.org YouTube channel that will teach you everything you need to know to complete this project:
Ми все ще розробляємо інтерактивну частину навчального курсу Python. Наразі ось кілька відео на YouTube-каналі freeCodeCamp.org, які навчать вас усього необхідного, щоб виконати цей проєкт:
- [Python for Everybody Video Course](https://www.freecodecamp.org/news/python-for-everybody/) (14 hours)
- [Learn Python Video Course](https://www.freecodecamp.org/news/learn-python-video-course/) (10 hours)
- <a href="https://www.freecodecamp.org/news/python-for-everybody/" target="_blank" rel="noopener noreferrer nofollow">Python for Everybody Video Course</a>(14 hours)
- <a href="https://www.freecodecamp.org/news/how-to-analyze-data-with-python-pandas/" target="_blank" rel="noopener noreferrer nofollow">How to Analyze Data with Python Pandas</a> (10 hours)
# --instructions--
Create a function named `calculate()` in `mean_var_std.py` that uses Numpy to output the mean, variance, standard deviation, max, min, and sum of the rows, columns, and elements in a 3 x 3 matrix.
Створіть функцію `calculate()` в `mean_var_std.py`, яку використовує Numpy для виведення середнього значення, дисперсії, стандартного відхилення, максимуму, мінімуму та суми рядків, стовпців, і елементи в матриці 3 x 3.
The input of the function should be a list containing 9 digits. The function should convert the list into a 3 x 3 Numpy array, and then return a dictionary containing the mean, variance, standard deviation, max, min, and sum along both axes and for the flattened matrix.
Вхідним значенням функції має бути список, що містить 9 цифр. Функція повинна перетворити список у масив Numpy 3 x 3, а потім повернути словник, що містить середнє значення, дисперсію, стандартне відхилення, максимум, мінімум і суму вздовж обох осей і для сплощеної матриці.
The returned dictionary should follow this format:
@@ -34,7 +35,7 @@ The returned dictionary should follow this format:
}
```
If a list containing less than 9 elements is passed into the function, it should raise a `ValueError` exception with the message: "List must contain nine numbers." The values in the returned dictionary should be lists and not Numpy arrays.
Якщо у функцію передається список, що містить менше 9 елементів, вона має викликати виняток `ValueError` з повідомленням: «Список має містити дев’ять чисел.» Значеннями у повернутому словнику мають бути списки, а не числові масиви Numpy.
For example, `calculate([0,1,2,3,4,5,6,7,8])` should return:
@@ -53,11 +54,11 @@ The unit tests for this project are in `test_module.py`.
## Development
For development, you can use `main.py` to test your `calculate()` function. Click the "run" button and `main.py` will run.
For development, you can use `main.py` to test your `calculate()` function. Натисніть кнопку «запустити» і `main.py` запуститься.
## Testing
We imported the tests from `test_module.py` to `main.py` for your convenience. The tests will run automatically whenever you hit the "run" button.
We imported the tests from `test_module.py` to `main.py` for your convenience. Тести запустяться автоматично, коли ви натиснете на кнопку «запустити».
## Submitting
@@ -65,7 +66,7 @@ Copy your project's URL and submit it to freeCodeCamp.
# --hints--
It should pass all Python tests.
Потрібно виконати всі тести Python.
```js

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curriculum/challenges/ukrainian/08-data-analysis-with-python/data-analysis-with-python-projects/medical-data-visualizer.md
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@@ -1,6 +1,6 @@
---
id: 5e46f7f8ac417301a38fb92a
title: Medical Data Visualizer
title: Візуалізатор медичних даних
challengeType: 10
forumTopicId: 462368
dashedName: medical-data-visualizer
@@ -8,12 +8,13 @@ dashedName: medical-data-visualizer
# --description--
You will be [working on this project with our Replit starter code](https://replit.com/github/freeCodeCamp/boilerplate-medical-data-visualizer).
Ви будете <a href="https://replit.com/github/freeCodeCamp/boilerplate-medical-data-visualizer" target="_blank" rel="noopener noreferrer nofollow">працювати над цим проєктом з нашим стартовим кодом Replit</a>.
We are still developing the interactive instructional part of the Python curriculum. For now, here are some videos on the freeCodeCamp.org YouTube channel that will teach you everything you need to know to complete this project:
- [Python for Everybody Video Course](https://www.freecodecamp.org/news/python-for-everybody/) (14 hours)
- [Learn Python Video Course](https://www.freecodecamp.org/news/learn-python-video-course/) (10 hours)
- <a href="https://www.freecodecamp.org/news/python-for-everybody/" target="_blank" rel="noopener noreferrer nofollow">Python for Everybody Video Course</a>(14 hours)
- <a href="https://www.freecodecamp.org/news/how-to-analyze-data-with-python-pandas/" target="_blank" rel="noopener noreferrer nofollow">How to Analyze Data with Python Pandas</a> (10 hours)
# --instructions--
@@ -25,20 +26,20 @@ The rows in the dataset represent patients and the columns represent information
File name: medical_examination.csv
| Feature | Variable Type | Variable | Value Type |
|:-------:|:------------:|:-------------:|:----------:|
| Age | Objective Feature | age | int (days) |
| Height | Objective Feature | height | int (cm) |
| Weight | Objective Feature | weight | float (kg) |
| Gender | Objective Feature | gender | categorical code |
| Systolic blood pressure | Examination Feature | ap_hi | int |
| Diastolic blood pressure | Examination Feature | ap_lo | int |
| Cholesterol | Examination Feature | cholesterol | 1: normal, 2: above normal, 3: well above normal |
| Glucose | Examination Feature | gluc | 1: normal, 2: above normal, 3: well above normal |
| Smoking | Subjective Feature | smoke | binary |
| Alcohol intake | Subjective Feature | alco | binary |
| Physical activity | Subjective Feature | active | binary |
| Presence or absence of cardiovascular disease | Target Variable | cardio | binary |
| Feature | Variable Type | Variable | Value Type |
|:---------------------------------------------:|:-------------------:|:-------------:|:------------------------------------------------:|
| Age | Objective Feature | `age` | int (days) |
| Height | Objective Feature | `height` | int (cm) |
| Weight | Objective Feature | `weight` | float (kg) |
| Gender | Objective Feature | `gender` | categorical code |
| Systolic blood pressure | Examination Feature | `ap_hi` | int |
| Diastolic blood pressure | Examination Feature | `ap_lo` | int |
| Cholesterol | Examination Feature | `cholesterol` | 1: normal, 2: above normal, 3: well above normal |
| Glucose | Examination Feature | `gluc` | 1: normal, 2: above normal, 3: well above normal |
| Smoking | Subjective Feature | `smoke` | binary |
| Alcohol intake | Subjective Feature | `alco` | binary |
| Physical activity | Subjective Feature | `active` | binary |
| Presence or absence of cardiovascular disease | Target Variable | `cardio` | binary |
## Tasks
@@ -63,11 +64,11 @@ Unit tests are written for you under `test_module.py`.
## Development
For development, you can use `main.py` to test your functions. Click the "run" button and `main.py` will run.
For development, you can use `main.py` to test your functions. Натисніть кнопку «запустити» і `main.py` запуститься.
## Testing
We imported the tests from `test_module.py` to `main.py` for your convenience. The tests will run automatically whenever you hit the "run" button.
We imported the tests from `test_module.py` to `main.py` for your convenience. Тести запустяться автоматично, коли ви натиснете на кнопку «запустити».
## Submitting
@@ -75,7 +76,7 @@ Copy your project's URL and submit it to freeCodeCamp.
# --hints--
It should pass all Python tests.
Він повинен пройти усі тести Python.
```js

23
curriculum/challenges/ukrainian/08-data-analysis-with-python/data-analysis-with-python-projects/page-view-time-series-visualizer.md
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@@ -1,6 +1,6 @@
---
id: 5e46f802ac417301a38fb92b
title: Page View Time Series Visualizer
title: Візуалізатор часового ряду перегляду сторінки
challengeType: 10
forumTopicId: 462369
dashedName: page-view-time-series-visualizer
@@ -8,12 +8,13 @@ dashedName: page-view-time-series-visualizer
# --description--
You will be [working on this project with our Replit starter code](https://replit.com/github/freeCodeCamp/boilerplate-page-view-time-series-visualizer).
Ви будете <a href="https://replit.com/github/freeCodeCamp/boilerplate-page-view-time-series-visualizer" target="_blank" rel="noopener noreferrer nofollow">працювати над цим проєктом з нашим стартовим кодом Replit</a>.
We are still developing the interactive instructional part of the Python curriculum. For now, here are some videos on the freeCodeCamp.org YouTube channel that will teach you everything you need to know to complete this project:
- [Python for Everybody Video Course](https://www.freecodecamp.org/news/python-for-everybody/) (14 hours)
- [Learn Python Video Course](https://www.freecodecamp.org/news/learn-python-video-course/) (10 hours)
- <a href="https://www.freecodecamp.org/news/python-for-everybody/" target="_blank" rel="noopener noreferrer nofollow">Python for Everybody Video Course</a>(14 hours)
- <a href="https://www.freecodecamp.org/news/how-to-analyze-data-with-python-pandas/" target="_blank" rel="noopener noreferrer nofollow">How to Analyze Data with Python Pandas</a> (10 hours)
# --instructions--
@@ -21,11 +22,11 @@ For this project you will visualize time series data using a line chart, bar cha
Use the data to complete the following tasks:
- Use Pandas to import the data from "fcc-forum-pageviews.csv". Set the index to the "date" column.
- Use Pandas to import the data from "fcc-forum-pageviews.csv". Set the index to the `date` column.
- Clean the data by filtering out days when the page views were in the top 2.5% of the dataset or bottom 2.5% of the dataset.
- Create a `draw_line_plot` function that uses Matplotlib to draw a line chart similar to "examples/Figure_1.png". The title should be "Daily freeCodeCamp Forum Page Views 5/2016-12/2019". The label on the x axis should be "Date" and the label on the y axis should be "Page Views".
- Create a `draw_bar_plot` function that draws a bar chart similar to "examples/Figure_2.png". It should show average daily page views for each month grouped by year. The legend should show month labels and have a title of "Months". On the chart, the label on the x axis should be "Years" and the label on the y axis should be "Average Page Views".
- Create a `draw_box_plot` function that uses Searborn to draw two adjacent box plots similar to "examples/Figure_3.png". These box plots should show how the values are distributed within a given year or month and how it compares over time. The title of the first chart should be "Year-wise Box Plot (Trend)" and the title of the second chart should be "Month-wise Box Plot (Seasonality)". Make sure the month labels on bottom start at "Jan" and the x and x axis are labeled correctly. The boilerplate includes commands to prepare the data.
- Create a `draw_line_plot` function that uses Matplotlib to draw a line chart similar to "examples/Figure_1.png". The title should be `Daily freeCodeCamp Forum Page Views 5/2016-12/2019`. The label on the x axis should be `Date` and the label on the y axis should be `Page Views`.
- Create a `draw_bar_plot` function that draws a bar chart similar to "examples/Figure_2.png". It should show average daily page views for each month grouped by year. The legend should show month labels and have a title of `Months`. On the chart, the label on the x axis should be `Years` and the label on the y axis should be `Average Page Views`.
- Create a `draw_box_plot` function that uses Seaborn to draw two adjacent box plots similar to "examples/Figure_3.png". These box plots should show how the values are distributed within a given year or month and how it compares over time. The title of the first chart should be `Year-wise Box Plot (Trend)` and the title of the second chart should be `Month-wise Box Plot (Seasonality)`. Make sure the month labels on bottom start at `Jan` and the x and y axis are labeled correctly. The boilerplate includes commands to prepare the data.
For each chart, make sure to use a copy of the data frame. Unit tests are written for you under `test_module.py`.
@@ -33,11 +34,11 @@ The boilerplate also includes commands to save and return the image.
## Development
For development, you can use `main.py` to test your functions. Click the "run" button and `main.py` will run.
For development, you can use `main.py` to test your functions. Натисніть кнопку «запустити» і `main.py` запуститься.
## Testing
We imported the tests from `test_module.py` to `main.py` for your convenience. The tests will run automatically whenever you hit the "run" button.
We imported the tests from `test_module.py` to `main.py` for your convenience. Тести запустяться автоматично, коли ви натиснете на кнопку «запустити».
## Submitting
@@ -45,7 +46,7 @@ Copy your project's URL and submit it to freeCodeCamp.
# --hints--
It should pass all Python tests.
Він повинен пройти усі тести Python.
```js

22
curriculum/challenges/ukrainian/08-data-analysis-with-python/data-analysis-with-python-projects/sea-level-predictor.md
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@@ -1,6 +1,6 @@
---
id: 5e4f5c4b570f7e3a4949899f
title: Sea Level Predictor
title: Прогнозування змін рівня моря
challengeType: 10
forumTopicId: 462370
dashedName: sea-level-predictor
@@ -8,12 +8,13 @@ dashedName: sea-level-predictor
# --description--
You will be [working on this project with our Replit starter code](https://replit.com/github/freeCodeCamp/boilerplate-sea-level-predictor).
Ви будете <a href="https://replit.com/github/freeCodeCamp/boilerplate-sea-level-predictor" target="_blank" rel="noopener noreferrer nofollow">працювати над цим проєктом з нашим стартовим кодом Replit</a>.
We are still developing the interactive instructional part of the Python curriculum. For now, here are some videos on the freeCodeCamp.org YouTube channel that will teach you everything you need to know to complete this project:
- [Python for Everybody Video Course](https://www.freecodecamp.org/news/python-for-everybody/) (14 hours)
- [Learn Python Video Course](https://www.freecodecamp.org/news/learn-python-video-course/) (10 hours)
- <a href="https://www.freecodecamp.org/news/python-for-everybody/" target="_blank" rel="noopener noreferrer nofollow">Python for Everybody Video Course</a>(14 hours)
- <a href="https://www.freecodecamp.org/news/how-to-analyze-data-with-python-pandas/" target="_blank" rel="noopener noreferrer nofollow">How to Analyze Data with Python Pandas</a> (10 hours)
# --instructions--
@@ -22,10 +23,10 @@ You will analyze a dataset of the global average sea level change since 1880. Yo
Use the data to complete the following tasks:
- Use Pandas to import the data from `epa-sea-level.csv`.
- Use matplotlib to create a scatter plot using the "Year" column as the x-axis and the "CSIRO Adjusted Sea Level" column as the y-axix.
- Use matplotlib to create a scatter plot using the `Year` column as the x-axis and the `CSIRO Adjusted Sea Level` column as the y-axix.
- Use the `linregress` function from `scipy.stats` to get the slope and y-intercept of the line of best fit. Plot the line of best fit over the top of the scatter plot. Make the line go through the year 2050 to predict the sea level rise in 2050.
- Plot a new line of best fit just using the data from year 2000 through the most recent year in the dataset. Make the line also go through the year 2050 to predict the sea level rise in 2050 if the rate of rise continues as it has since the year 2000.
- The x label should be "Year", the y label should be "Sea Level (inches)", and the title should be "Rise in Sea Level".
- The x label should be `Year`, the y label should be `Sea Level (inches)`, and the title should be `Rise in Sea Level`.
Unit tests are written for you under `test_module.py`.
@@ -33,23 +34,24 @@ The boilerplate also includes commands to save and return the image.
## Development
For development, you can use `main.py` to test your functions. Click the "run" button and `main.py` will run.
For development, you can use `main.py` to test your functions. Натисніть кнопку «запустити» і `main.py` запуститься.
## Testing
We imported the tests from `test_module.py` to `main.py` for your convenience. The tests will run automatically whenever you hit the "run" button.
We imported the tests from `test_module.py` to `main.py` for your convenience. Тести запустяться автоматично, коли ви натиснете на кнопку «запустити».
## Submitting
Copy your project's URL and submit it to freeCodeCamp.
## Data Source
[Global Average Absolute Sea Level Change](https://datahub.io/core/sea-level-rise), 1880-2014 from the US Environmental Protection Agency using data from CSIRO, 2015; NOAA, 2015.
<a href="https://datahub.io/core/sea-level-rise" target="_blank" rel="noopener noreferrer nofollow">Global Average Absolute Sea Level Change</a>, 1880-2014 from the US Environmental Protection Agency using data from CSIRO, 2015; NOAA, 2015.
# --hints--
It should pass all Python tests.
Він повинен пройти усі тести Python.
```js