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fix(curriculum, i18n): solve issues in crowdin comments (#46597)

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Ilenia
2022-06-21 16:23:13 +02:00
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14 changed files with 26 additions and 24 deletions
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8
curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/page-view-time-series-visualizer.md
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@@ -21,11 +21,11 @@ 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 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 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`.

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curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/sea-level-predictor.md
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@@ -22,10 +22,10 @@ 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`.