diff --git a/curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/demographic-data-analyzer.md b/curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/demographic-data-analyzer.md
index 052ba4bde55..bea12e1b9f2 100644
--- a/curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/demographic-data-analyzer.md
+++ b/curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/demographic-data-analyzer.md
@@ -8,12 +8,7 @@ dashedName: demographic-data-analyzer
# --description--
-You will be working on this project with our Replit starter code.
-
-- Start by importing the project on Replit.
-- Next, you will see a `.replit` window.
-- Select `Use run command` and click the `Done` button.
-
+You will be working on this project with our Gitpod starter code.
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:
@@ -47,17 +42,15 @@ You must use Pandas to answer the following questions:
- What country has the highest percentage of people that earn >50K and what is that percentage?
- Identify the most popular occupation for those who earn >50K in India.
-Use the starter code in the file `demographic_data_analyzer`. Update the code so all variables set to "None" are set to the appropriate calculation or code. Round all decimals to the nearest tenth.
-
-Unit tests are written for you under `test_module.py`.
+Use the starter code in the file `demographic_data_analyzer.py`. Update the code so all variables set to `None` are set to the appropriate calculation or code. Round all decimals to the nearest tenth.
## Development
-For development, you can use `main.py` to test your functions. Click the "run" button and `main.py` will run.
+Write your code in `demographic_data_analyzer.py`. For development, you can use `main.py` to test your code.
## 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.
+The unit tests for this project are in `test_module.py`. We imported the tests from `test_module.py` to `main.py` for your convenience.
## Submitting
diff --git a/curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/mean-variance-standard-deviation-calculator.md b/curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/mean-variance-standard-deviation-calculator.md
index 0dc2fd0472b..be067a6c898 100644
--- a/curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/mean-variance-standard-deviation-calculator.md
+++ b/curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/mean-variance-standard-deviation-calculator.md
@@ -8,12 +8,7 @@ dashedName: mean-variance-standard-deviation-calculator
# --description--
-You will be working on this project with our Replit starter code.
-
-- Start by importing the project on Replit.
-- Next, you will see a `.replit` window.
-- Select `Use run command` and click the `Done` button.
-
+You will be working on this project with our Gitpod starter code.
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:
@@ -55,15 +50,13 @@ For example, `calculate([0,1,2,3,4,5,6,7,8])` should return:
}
```
-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.
+Write your code in `mean_var_std.py`. For development, you can use `main.py` to test your code.
## 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.
+The unit tests for this project are in `test_module.py`. We imported the tests from `test_module.py` to `main.py` for your convenience.
## Submitting
diff --git a/curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/medical-data-visualizer.md b/curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/medical-data-visualizer.md
index b0afa419906..cca8412c90e 100644
--- a/curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/medical-data-visualizer.md
+++ b/curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/medical-data-visualizer.md
@@ -8,12 +8,7 @@ dashedName: medical-data-visualizer
# --description--
-You will be working on this project with our Replit starter code.
-
-- Start by importing the project on Replit.
-- Next, you will see a `.replit` window.
-- Select `Use run command` and click the `Done` button.
-
+You will be working on this project with our Gitpod starter code.
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:
@@ -65,15 +60,13 @@ Use the data to complete the following tasks in `medical_data_visualizer.py`:
Any time a variable is set to `None`, make sure to set it to the correct code.
-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.
+Write your code in `medical_data_visualizer.py`. For development, you can use `main.py` to test your code.
## 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.
+The unit tests for this project are in `test_module.py`. We imported the tests from `test_module.py` to `main.py` for your convenience.
## Submitting
diff --git a/curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/page-view-time-series-visualizer.md b/curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/page-view-time-series-visualizer.md
index 26a070fab0c..1e4a8cb12d6 100644
--- a/curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/page-view-time-series-visualizer.md
+++ b/curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/page-view-time-series-visualizer.md
@@ -8,12 +8,7 @@ dashedName: page-view-time-series-visualizer
# --description--
-You will be working on this project with our Replit starter code.
-
-- Start by importing the project on Replit.
-- Next, you will see a `.replit` window.
-- Select `Use run command` and click the `Done` button.
-
+You will be working on this project with our Gitpod starter code.
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:
@@ -33,17 +28,17 @@ Use the data to complete the following tasks:
- 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`.
+For each chart, make sure to use a copy of the data frame.
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.
+Write your code in `time_series_visualizer.py`. For development, you can use `main.py` to test your code.
## 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.
+The unit tests for this project are in `test_module.py`. We imported the tests from `test_module.py` to `main.py` for your convenience.
## Submitting
diff --git a/curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/sea-level-predictor.md b/curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/sea-level-predictor.md
index b7780a9ec12..381ab0dbf86 100644
--- a/curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/sea-level-predictor.md
+++ b/curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-projects/sea-level-predictor.md
@@ -8,12 +8,7 @@ dashedName: sea-level-predictor
# --description--
-You will be working on this project with our Replit starter code.
-
-- Start by importing the project on Replit.
-- Next, you will see a `.replit` window.
-- Select `Use run command` and click the `Done` button.
-
+You will be working on this project with our Gitpod starter code.
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:
@@ -33,17 +28,15 @@ Use the data to complete the following tasks:
- 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`.
-Unit tests are written for you under `test_module.py`.
-
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.
+Write your code in `sea_level_predictor.py`. For development, you can use `main.py` to test your code.
## 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.
+The unit tests for this project are in `test_module.py`. We imported the tests from `test_module.py` to `main.py` for your convenience.
## Submitting