These projects are part of freecodecamp's Data Analysis with Python certification
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.
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.
In this challenge you must analyze demographic data using Pandas. You are given a dataset of demographic data that was extracted from the 1994 Census database. You must use Pandas to answer various questions, like 'What percentage of people without advanced education make more than 50K?' and 'What is the minimum number of hours a person works per week?'
Use the data to complete the following tasks in medical_data_visualizer.py:
- Add an overweight column to the data. To determine if a person is overweight, first calculate their BMI by dividing their weight in kilograms by the square of their height in meters. If that value is > 25 then the person is overweight. Use the value 0 for NOT overweight and the value 1 for overweight.
- Normalize the data by making 0 always good and 1 always bad. If the value of cholesterol or gluc is 1, make the value 0. If the value is more than 1, make the value 1.
- Convert the data into long format and create a chart that shows the value counts of the categorical features using seaborn's catplot(). The dataset should be split by 'Cardio' so there is one chart for each cardio value.
- Clean the data. Filter out the following patient segments that represent incorrect data: diastolic pressure is higher than systolic height is less than the 2.5th percentile height is more than the 97.5th percentile weight is less than the 2.5th percentile weight is more than the 97.5th percentile
- Create a correlation matrix using the dataset. Plot the correlation matrix using seaborn's heatmap(). Mask the upper triangle.
vem ai
You will analyze a dataset of the global average sea level change since 1880. You will use the data to predict the sea level change through year 2050.
Use the data to complete the following tasks:
- 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.