Download the "iris.csv" file from here. Save it in a file iris_data.csv
Load the data from the file into a DataFrame called iris_df.
Print the entire DataFrame.
Print only the "species" column of the DataFrame.
Print the first 5 rows in the DataFrame.
Print only the rows of the DataFrame where the "petal_length" is greater than 5.
Create a new DataFrame, which contains the mean and standard deviation of sepal length for each species
Save the DataFrame to a new CSV file called "new_iris.csv".
Provide statistics for the species setosa. You should create a new DataFrame called iris_df_setosa and use the describe function.
Create a scatter plot of the sepal_length column on the x-axis and the sepal_width column on the y-axis. Each species should have a different color.
Add a title to the plot and label the x and y axes. Then, Save the plot to a pdf file using the savefig() function.
First, let's load GBIF data.
What are the columns of this dataframe? How many rows does it have?
Group occurrences by canton (stateProvince). Print all cantons where the birds where observed.
Which canton has the most number of occurence?
Create a bar chart with Plots.jl, that shows the total number of observations for each canton
Drop the rows where the decimalLongitude or decimalLatitude is missing
Normalize the decimalLongitude and decimalLatitude, so that it scales between 0 and 1.
For this, construct a function normalize, that takes in a vector of floating points and a new normalized vector. This function should use the . operator.