Exercises
Contents
Exercises¶
Probability¶
A newspaper article quotes a statistic about the relationship between the El Nino index and high precipitation in Southern California. A high precipitation winter is defined as being in the top 10% of all winters. According to the article, 75% of high precipitation winters in Southern California occur during El Nino years. El Nino events occur approximately every five years.
Based on this information, what is the probability of high precipitation occuring during an El Nino year?
Boolean logic¶
Predict the output from each of the following three lines of code.
print(True == (0 == 3))
(False == (1 == True)) != False
False = False
Pandas data frames¶
Create a list of unique station ID’s (“STNNBR”) found in the survey data. Call it stns. How many unique stations are there in the data?
Exploratory analysis¶
What scientific questions can be addressed with this data set?
What relationships might occur between different variables?
What differences might occur within the same variables, but at different locations or times?
Create exploratory plots (one PDF, one scatter plot)
Dataframe indexing¶
What do you expect to happen when you execute:
df[0:1]
df[:4]
df[:-1]
What do you expect to happen when you call:
df.iloc[0:4, 1:4]
df.loc[0:4, 1:4]
How are the two commands different?
Dataframe subsets 1¶
Select a subset of rows in the df DataFrame that contains data from a pressure range between 500 and 1000 dbar. How many rows did you end up with? What did your neighbor get?
Dataframe subsets 2¶
You can use the isin command in Python to query a DataFrame based upon a list of values as follows:
df[df['STNNBR'].isin([listGoesHere])]
Use the isin function to find all samples from station numbers 11 and 12.
Dataframe subsets 3¶
pH values are contained in the df['PH_TOT'] DataArray of the WCOA data set.
Quality control flags for pH are contained in the df['PH_TOT_FLAG_W'] DataArray.
World Ocean Circulation Experiment (WOCE) quality control flags are used: 2 = good value, 3 = questionable value, 4 = bad value , 5 = value not reported, 6 = mean of replicate measurements, 9 = sample not drawn.
a. Create a new DataFrame called dfsub1 that excludes all bad, questionable and missing pH values. Plot the probability density function for pH.
b. Create a new DataFrame called dfsub2 that excludes all bad, questionable and missing pH and CTD oxygen values. Plot oxygen vs. pH.
Linear regression¶
Create a linear model of pH as a function of CTD oxygen. Plot the linear model with the data.