introduction to data science in python assignment 1 answers

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Coursera Course - Introduction of Data Science in Python Assignment 1

I'm taking this course on Coursera, and I'm running some issues while doing the first assignment. The task is to basically use regular expression to get certain values from the given file. Then, the function should output a dictionary containing these values:

This is just a screenshot of the file. Due to some reasons, the link doesn't work if it's not open directly from Coursera. I apologize in advance for the bad formatting. One thing I must point out is that for some cases, as you can see in the first example, there's no username. Instead '-' is used.

This is what I currently have right now. However, the output is None. I guess there's something wrong in my pattern.

2 Answers 2

You can use the following expression:

See the regex demo . See the Python demo :

• Thank you so much!!! It worked!!! However, may I just ask a question regarding your solution? It probably sounds stupid, but don't you need to include everything in the parenthesis? For example, ("?P<request>[^"]*"). Or are they the same? Also, may you please explain the meaning of "?:" in your regular expression –  BryantHsiung Commented Oct 19, 2020 at 13:20
• @BryantHsiung You can't use ("?P<request>[^"]*") , it is an invalid regex construct. See more about non-capturing groups here . –  Wiktor Stribiżew Commented Oct 19, 2020 at 13:42
• 1 Just did! Thanks again! –  BryantHsiung Commented Oct 20, 2020 at 12:42
• 1 I am working on the same question but I don't know why my for loop doesn't give me an output! I check my regex pattern on regex101 and they are all seem to be working the way they should. –  Anoushiravan R Commented Jan 9, 2022 at 20:22
• @AnoushiravanR Without seeing your code, I can't help. –  Wiktor Stribiżew Commented Jan 9, 2022 at 20:29

Check using following code:

For more information regarding regex, read the following documentation, it would be very useful for beginners: https://docs.python.org/3/library/re.html#module-re

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Data Science: A First Introduction (Python Edition)

Python and Pandas

1. python and pandas #, 1.1. overview #.

This chapter provides an introduction to data science and the Python programming language. The goal here is to get your hands dirty right from the start! We will walk through an entire data analysis, and along the way introduce different types of data analysis question, some fundamental programming concepts in Python, and the basics of loading, cleaning, and visualizing data. In the following chapters, we will dig into each of these steps in much more detail; but for now, let’s jump in to see how much we can do with data science!

1.2. Chapter learning objectives #

By the end of the chapter, readers will be able to do the following:

Identify the different types of data analysis question and categorize a question into the correct type.

Load the pandas package into Python.

Read tabular data with read_csv .

Create new variables and objects in Python using the assignment symbol.

Create and organize subsets of tabular data using [] , loc[] , sort_values , and head .

Add and modify columns in tabular data using column assignment.

Chain multiple operations in sequence.

Visualize data with an altair bar plot.

Use help() and ? to access help and documentation tools in Python.

1.3. Canadian languages data set #

In this chapter, we will walk through a full analysis of a data set relating to languages spoken at home by Canadian residents ( Fig. 1.1 ). Many Indigenous peoples exist in Canada with their own cultures and languages; these languages are often unique to Canada and not spoken anywhere else in the world [ Statistics Canada, 2018 ] . Sadly, colonization has led to the loss of many of these languages. For instance, generations of children were not allowed to speak their mother tongue (the first language an individual learns in childhood) in Canadian residential schools. Colonizers also renamed places they had “discovered” [ Wilson, 2018 ] . Acts such as these have significantly harmed the continuity of Indigenous languages in Canada, and some languages are considered “endangered” as few people report speaking them. To learn more, please see Canadian Geographic ’s article, “Mapping Indigenous Languages in Canada” [ Walker, 2017 ] , They Came for the Children: Canada, Aboriginal peoples, and Residential Schools [ Truth and Reconciliation Commission of Canada, 2012 ] and the Truth and Reconciliation Commission of Canada’s Calls to Action [ Truth and Reconciliation Commission of Canada, 2015 ] .

Fig. 1.1 Map of Canada #

The data set we will study in this chapter is taken from the canlang R data package [ Timbers, 2020 ] , which has population language data collected during the 2016 Canadian census [ Statistics Canada, 2016 ] . In this data, there are 214 languages recorded, each having six different properties:

category : Higher-level language category, describing whether the language is an Official Canadian language, an Aboriginal (i.e., Indigenous) language, or a Non-Official and Non-Aboriginal language.

language : The name of the language.

mother_tongue : Number of Canadian residents who reported the language as their mother tongue. Mother tongue is generally defined as the language someone was exposed to since birth.

most_at_home : Number of Canadian residents who reported the language as being spoken most often at home.

most_at_work : Number of Canadian residents who reported the language as being used most often at work.

lang_known : Number of Canadian residents who reported knowledge of the language.

According to the census, more than 60 Aboriginal languages were reported as being spoken in Canada. Suppose we want to know which are the most common; then we might ask the following question, which we wish to answer using our data:

Which ten Aboriginal languages were most often reported in 2016 as mother tongues in Canada, and how many people speak each of them?

Data science cannot be done without a deep understanding of the data and problem domain. In this book, we have simplified the data sets used in our examples to concentrate on methods and fundamental concepts. But in real life, you cannot and should not do data science without a domain expert. Alternatively, it is common to practice data science in your own domain of expertise! Remember that when you work with data, it is essential to think about how the data were collected, which affects the conclusions you can draw. If your data are biased, then your results will be biased!

1.4. Asking a question #

Every good data analysis begins with a question —like the above—that you aim to answer using data. As it turns out, there are actually a number of different types of question regarding data: descriptive, exploratory, predictive, inferential, causal, and mechanistic, all of which are defined in Table 1.1 . [ Leek and Peng, 2015 , Peng and Matsui, 2015 ] Carefully formulating a question as early as possible in your analysis—and correctly identifying which type of question it is—will guide your overall approach to the analysis as well as the selection of appropriate tools.

In this book, you will learn techniques to answer the first four types of question: descriptive, exploratory, predictive, and inferential; causal and mechanistic questions are beyond the scope of this book. In particular, you will learn how to apply the following analysis tools:

Summarization: computing and reporting aggregated values pertaining to a data set. Summarization is most often used to answer descriptive questions, and can occasionally help with answering exploratory questions. For example, you might use summarization to answer the following question: What is the average race time for runners in this data set? Tools for summarization are covered in detail in Chapters 2 and 3 , but appear regularly throughout the text.

Visualization: plotting data graphically. Visualization is typically used to answer descriptive and exploratory questions, but plays a critical supporting role in answering all of the types of question in Table 1.1 . For example, you might use visualization to answer the following question: Is there any relationship between race time and age for runners in this data set? This is covered in detail in Chapter 4 , but again appears regularly throughout the book.

Classification: predicting a class or category for a new observation. Classification is used to answer predictive questions. For example, you might use classification to answer the following question: Given measurements of a tumor’s average cell area and perimeter, is the tumor benign or malignant? Classification is covered in Chapters 5 and 6 .

Regression: predicting a quantitative value for a new observation. Regression is also used to answer predictive questions. For example, you might use regression to answer the following question: What will be the race time for a 20-year-old runner who weighs 50kg? Regression is covered in Chapters 7 and 8 .

Clustering: finding previously unknown/unlabeled subgroups in a data set. Clustering is often used to answer exploratory questions. For example, you might use clustering to answer the following question: What products are commonly bought together on Amazon? Clustering is covered in Chapter 9 .

Estimation: taking measurements for a small number of items from a large group and making a good guess for the average or proportion for the large group. Estimation is used to answer inferential questions. For example, you might use estimation to answer the following question: Given a survey of cellphone ownership of 100 Canadians, what proportion of the entire Canadian population own Android phones? Estimation is covered in Chapter 10 .

Referring to Table 1.1 , our question about Aboriginal languages is an example of a descriptive question : we are summarizing the characteristics of a data set without further interpretation. And referring to the list above, it looks like we should use visualization and perhaps some summarization to answer the question. So in the remainder of this chapter, we will work towards making a visualization that shows us the ten most common Aboriginal languages in Canada and their associated counts, according to the 2016 census.

1.5. Loading a tabular data set #

A data set is, at its core essence, a structured collection of numbers and characters. Aside from that, there are really no strict rules; data sets can come in many different forms! Perhaps the most common form of data set that you will find in the wild, however, is tabular data . Think spreadsheets in Microsoft Excel: tabular data are rectangular-shaped and spreadsheet-like, as shown in Fig. 1.2 . In this book, we will focus primarily on tabular data.

Since we are using Python for data analysis in this book, the first step for us is to load the data into Python. When we load tabular data into Python, it is represented as a data frame object. Fig. 1.2 shows that a Python data frame is very similar to a spreadsheet. We refer to the rows as observations ; these are the individual objects for which we collect data. In Fig. 1.2 , the observations are languages. We refer to the columns as variables ; these are the characteristics of each observation. In Fig. 1.2 , the variables are the the language’s category, its name, the number of mother tongue speakers, etc.

Fig. 1.2 A spreadsheet versus a data frame in Python #

The first kind of data file that we will learn how to load into Python as a data frame is the comma-separated values format ( .csv for short). These files have names ending in .csv , and can be opened and saved using common spreadsheet programs like Microsoft Excel and Google Sheets. For example, the .csv file named can_lang.csv is included with the code for this book . If we were to open this data in a plain text editor (a program like Notepad that just shows text with no formatting), we would see each row on its own line, and each entry in the table separated by a comma:

To load this data into Python so that we can do things with it (e.g., perform analyses or create data visualizations), we will need to use a function. A function is a special word in Python that takes instructions (we call these arguments ) and does something. The function we will use to load a .csv file into Python is called read_csv . In its most basic use-case, read_csv expects that the data file:

has column names (or headers ),

uses a comma ( , ) to separate the columns, and

does not have row names.

Below you’ll see the code used to load the data into Python using the read_csv function. Note that the read_csv function is not included in the base installation of Python, meaning that it is not one of the primary functions ready to use when you install Python. Therefore, you need to load it from somewhere else before you can use it. The place from which we will load it is called a Python package . A Python package is a collection of functions that can be used in addition to the built-in Python package functions once loaded. The read_csv function, in particular, can be made accessible by loading the pandas Python package [ The Pandas Development Team, 2020 , Wes McKinney, 2010 ] using the import command. The pandas package contains many functions that we will use throughout this book to load, clean, wrangle, and visualize data.

This command has two parts. The first is import pandas , which loads the pandas package. The second is as pd , which give the pandas package the much shorter alias (another name) pd . We can now use the read_csv function by writing pd.read_csv , i.e., the package name, then a dot, then the function name. You can see why we gave pandas a shorter alias; if we had to type pandas. before every function we wanted to use, our code would become much longer and harder to read!

Now that the pandas package is loaded, we can use the read_csv function by passing it a single argument: the name of the file, "can_lang.csv" . We have to put quotes around file names and other letters and words that we use in our code to distinguish it from the special words (like functions!) that make up the Python programming language. The file’s name is the only argument we need to provide because our file satisfies everything else that the read_csv function expects in the default use-case. Fig. 1.3 describes how we use the read_csv to read data into Python.

Fig. 1.3 Syntax for the read_csv function #

214 rows × 6 columns

1.6. Naming things in Python #

When we loaded the 2016 Canadian census language data using read_csv , we did not give this data frame a name. Therefore the data was just printed on the screen, and we cannot do anything else with it. That isn’t very useful. What would be more useful would be to give a name to the data frame that read_csv outputs, so that we can refer to it later for analysis and visualization.

The way to assign a name to a value in Python is via the assignment symbol = . On the left side of the assignment symbol you put the name that you want to use, and on the right side of the assignment symbol you put the value that you want the name to refer to. Names can be used to refer to almost anything in Python, such as numbers, words (also known as strings of characters), and data frames! Below, we set my_number to 3 (the result of 1+2 ) and we set name to the string "Alice" .

Note that when we name something in Python using the assignment symbol, = , we do not need to surround the name we are creating with quotes. This is because we are formally telling Python that this special word denotes the value of whatever is on the right-hand side. Only characters and words that act as values on the right-hand side of the assignment symbol—e.g., the file name "data/can_lang.csv" that we specified before, or "Alice" above—need to be surrounded by quotes.

After making the assignment, we can use the special name words we have created in place of their values. For example, if we want to do something with the value 3 later on, we can just use my_number instead. Let’s try adding 2 to my_number ; you will see that Python just interprets this as adding 2 and 3:

Object names can consist of letters, numbers, and underscores ( _ ). Other symbols won’t work since they have their own meanings in Python. For example, - is the subtraction symbol; if we try to assign a name with the - symbol, Python will complain and we will get an error!

There are certain conventions for naming objects in Python. When naming an object we suggest using only lowercase letters, numbers and underscores _ to separate the words in a name. Python is case sensitive, which means that Letter and letter would be two different objects in Python. You should also try to give your objects meaningful names. For instance, you can name a data frame x . However, using more meaningful terms, such as language_data , will help you remember what each name in your code represents. We recommend following the PEP 8 naming conventions outlined in the PEP 8 [ Guido van Rossum, 2001 ] . Let’s now use the assignment symbol to give the name can_lang to the 2016 Canadian census language data frame that we get from read_csv .

Wait a minute, nothing happened this time! Where’s our data? Actually, something did happen: the data was loaded in and now has the name can_lang associated with it. And we can use that name to access the data frame and do things with it. For example, we can type the name of the data frame to print both the first few rows and the last few rows. The three dots ( ... ) indicate that there are additional rows that are not printed. You will also see that the number of observations (i.e., rows) and variables (i.e., columns) are printed just underneath the data frame (214 rows and 6 columns in this case). Printing a few rows from data frame like this is a handy way to get a quick sense for what is contained in it.

1.7. Creating subsets of data frames with [] & loc[] #

Now that we’ve loaded our data into Python, we can start wrangling the data to find the ten Aboriginal languages that were most often reported in 2016 as mother tongues in Canada. In particular, we want to construct a table with the ten Aboriginal languages that have the largest counts in the mother_tongue column. The first step is to extract from our can_lang data only those rows that correspond to Aboriginal languages, and then the second step is to keep only the language and mother_tongue columns. The [] and loc[] operations on the pandas data frame will help us here. The [] allows you to obtain a subset of (i.e., filter ) the rows of a data frame, or to obtain a subset of (i.e., select ) the columns of a data frame. The loc[] operation allows you to both filter rows and select columns at the same time. We will first investigate filtering rows and selecting columns with the [] operation, and then use loc[] to do both in our analysis of the Aboriginal languages data.

The [] and loc[] operations, and related operations, in pandas are much more powerful than we describe in this chapter. You will learn more sophisticated ways to index data frames later on in Chapter 3 .

1.7.1. Using [] to filter rows #

Looking at the can_lang data above, we see the column category contains different high-level categories of languages, which include “Aboriginal languages”, “Non-Official & Non-Aboriginal languages” and “Official languages”. To answer our question we want to filter our data set so we restrict our attention to only those languages in the “Aboriginal languages” category.

We can use the [] operation to obtain the subset of rows with desired values from a data frame. Fig. 1.4 shows the syntax we need to use to filter rows with the [] operation. First, we type the name of the data frame—here, can_lang —followed by square brackets. Inside the square brackets, we write a logical statement to use when filtering the rows. A logical statement evaluates to either True or False for each row in the data frame; the [] operation keeps only those rows for which the logical statement evaluates to True . For example, in our analysis, we are interested in keeping only languages in the "Aboriginal languages" higher-level category. We can use the equivalency operator == to compare the values of the category column—denoted by can_lang["category"] —with the value "Aboriginal languages" . You will learn about many other kinds of logical statement in Chapter 3 . Similar to when we loaded the data file and put quotes around the file name, here we need to put quotes around both "Aboriginal languages" and "category" . Using quotes tells Python that this is a string value (e.g., a column name, or word data) and not one of the special words that make up the Python programming language, or one of the names we have given to objects in the code we have already written.

In Python, single quotes ( ' ) and double quotes ( " ) are generally treated the same. So we could have written 'Aboriginal languages' instead of "Aboriginal languages" above, or 'category' instead of "category" . Try both out for yourself!

Fig. 1.4 Syntax for using the [] operation to filter rows. #

This operation returns a data frame that has all the columns of the input data frame, but only those rows corresponding to Aboriginal languages that we asked for in the logical statement.

67 rows × 6 columns

1.7.2. Using [] to select columns #

We can also use the [] operation to select columns from a data frame. Fig. 1.5 displays the syntax needed to select columns. We again first type the name of the data frame—here, can_lang —followed by square brackets. Inside the square brackets, we provide a list of column names. In Python, we denote a list using square brackets, where each item is separated by a comma ( , ). So if we are interested in selecting only the language and mother_tongue columns from our original can_lang data frame, we put the list ["language", "mother_tongue"] containing those two column names inside the square brackets of the [] operation.

Fig. 1.5 Syntax for using the [] operation to select columns. #

This operation returns a data frame that has all the rows of the input data frame, but only those columns that we named in the selection list.

214 rows × 2 columns

1.7.3. Using loc[] to filter rows and select columns #

The [] operation is only used when you want to filter rows or select columns; it cannot be used to do both operations at the same time. But in order to answer our original data analysis question in this chapter, we need to both filter the rows for Aboriginal languages, and select the language and mother_tongue columns. Fortunately, pandas provides the loc[] operation, which lets us do just that. The syntax is very similar to the [] operation we have already covered: we will essentially combine both our row filtering and column selection steps from before. In particular, we first write the name of the data frame— can_lang again—then follow that with the .loc[] operation. Inside the square brackets, we write our row filtering logical statement, then a comma, then our list of columns to select.

Fig. 1.6 Syntax for using the loc[] operation to filter rows and select columns. #

There is one very important thing to notice in this code example. The first is that we used the loc[] operation on the can_lang data frame by writing can_lang.loc[] —first the data frame name, then a dot, then loc[] . There’s that dot again! If you recall, earlier in this chapter we used the read_csv function from pandas (aliased as pd ), and wrote pd.read_csv . The dot means that the thing on the left ( pd , i.e., the pandas package) provides the thing on the right (the read_csv function). In the case of can_lang.loc[] , the thing on the left (the can_lang data frame) provides the thing on the right (the loc[] operation). In Python, both packages (like pandas ) and objects (like our can_lang data frame) can provide functions and other objects that we access using the dot syntax.

A note on terminology: when an object obj provides a function f with the dot syntax (as in obj.f() ), sometimes we call that function f a method of obj or an operation on obj . Similarly, when an object obj provides another object x with the dot syntax (as in obj.x ), sometimes we call the object x an attribute of obj . We will use all of these terms throughout the book, as you will see them used commonly in the community. And just because we programmers like to be confusing for no apparent reason: we don’t use the “method”, “operation”, or “attribute” terminology when referring to functions and objects from packages, like pandas . So for example, pd.read_csv would typically just be referred to as a function, but not as a method or operation, even though it uses the dot syntax.

At this point, if we have done everything correctly, aboriginal_lang should be a data frame containing only rows where the category is "Aboriginal languages" , and containing only the language and mother_tongue columns. Any time you take a step in a data analysis, it’s good practice to check the output by printing the result.

67 rows × 2 columns

We can see the original can_lang data set contained 214 rows with multiple kinds of category . The data frame aboriginal_lang contains only 67 rows, and looks like it only contains Aboriginal languages. So it looks like the loc[] operation gave us the result we wanted!

1.8. Using sort_values and head to select rows by ordered values #

We have used the [] and loc[] operations on a data frame to obtain a table with only the Aboriginal languages in the data set and their associated counts. However, we want to know the ten languages that are spoken most often. As a next step, we will order the mother_tongue column from largest to smallest value and then extract only the top ten rows. This is where the sort_values and head functions come to the rescue!

The sort_values function allows us to order the rows of a data frame by the values of a particular column. We need to specify the column name by which we want to sort the data frame by passing it to the argument by . Since we want to choose the ten Aboriginal languages most often reported as a mother tongue language, we will use the sort_values function to order the rows in our selected_lang data frame by the mother_tongue column. We want to arrange the rows in descending order (from largest to smallest), so we specify the argument ascending as False .

Fig. 1.7 Syntax for using sort_values to arrange rows in decending order. #

Next, we will obtain the ten most common Aboriginal languages by selecting only the first ten rows of the arranged_lang data frame. We do this using the head function, and specifying the argument 10 .

1.9. Adding and modifying columns #

Recall that our data analysis question referred to the count of Canadians that speak each of the top ten most commonly reported Aboriginal languages as their mother tongue, and the ten_lang data frame indeed contains those counts… But perhaps, seeing these numbers, we became curious about the percentage of the population of Canada associated with each count. It is common to come up with new data analysis questions in the process of answering a first one—so fear not and explore! To answer this small question along the way, we need to divide each count in the mother_tongue column by the total Canadian population according to the 2016 census—i.e., 35,151,728—and multiply it by 100. We can perform this computation using the code 100 * ten_lang["mother_tongue"] / canadian_population . Then to store the result in a new column (or overwrite an existing column), we specify the name of the new column to create (or old column to modify), then the assignment symbol = , and then the computation to store in that column. In this case, we will opt to create a new column called mother_tongue_percent .

You will see below that we write the Canadian population in Python as 35_151_728 . The underscores ( _ ) are just there for readability, and do not affect how Python interprets the number. In other words, 35151728 and 35_151_728 are treated identically in Python, although the latter is much clearer!

The ten_lang_percent data frame shows that the ten Aboriginal languages in the ten_lang data frame were spoken as a mother tongue by between 0.008% and 0.18% of the Canadian population.

1.10. Combining steps with chaining and multiline expressions #

It took us 3 steps to find the ten Aboriginal languages most often reported in 2016 as mother tongues in Canada. Starting from the can_lang data frame, we:

used loc to filter the rows so that only the Aboriginal languages category remained, and selected the language and mother_tongue columns,

used sort_values to sort the rows by mother_tongue in descending order, and

obtained only the top 10 values using head .

One way of performing these steps is to just write multiple lines of code, storing temporary, intermediate objects as you go.

You might find that code hard to read. You’re not wrong; it is! There are two main issues with readability here. First, each line of code is quite long. It is hard to keep track of what methods are being called, and what arguments were used. Second, each line introduces a new temporary object. In this case, both aboriginal_lang and arranged_lang_sorted are just temporary results on the way to producing the ten_lang data frame. This makes the code hard to read, as one has to trace where each temporary object goes, and hard to understand, since introducing many named objects also suggests that they are of some importance, when really they are just intermediates. The need to call multiple methods in a sequence to process a data frame is quite common, so this is an important issue to address!

To solve the first problem, we can actually split the long expressions above across multiple lines. Although in most cases, a single expression in Python must be contained in a single line of code, there are a small number of situations where lets us do this. Let’s rewrite this code in a more readable format using multiline expressions.

This code is the same as the code we showed earlier; you can see the same sequence of methods and arguments is used. But long expressions are split across multiple lines when they would otherwise get long and unwieldy, improving the readability of the code. How does Python know when to keep reading on the next line for a single expression? For the line starting with aboriginal_lang = ... , Python sees that the line ends with a left bracket symbol [ , and knows that our expression cannot end until we close it with an appropriate corresponding right bracket symbol ] . We put the same two arguments as we did before, and then the corresponding right bracket appears after ["language", "mother_tongue"] ). For the line starting with arranged_lang_sorted = ... , Python sees that the line ends with a left parenthesis symbol ( , and knows the expression cannot end until we close it with the corresponding right parenthesis symbol ) . Again we use the same two arguments as before, and then the corresponding right parenthesis appears right after ascending=False . In both cases, Python keeps reading the next line to figure out what the rest of the expression is. We could, of course, put all of the code on one line of code, but splitting it across multiple lines helps a lot with code readability.

We still have to handle the issue that each line of code—i.e., each step in the analysis—introduces a new temporary object. To address this issue, we can chain multiple operations together without assigning intermediate objects. The key idea of chaining is that the output of each step in the analysis is a data frame, which means that you can just directly keep calling methods that operate on the output of each step in a sequence! This simplifies the code and makes it easier to read. The code below demonstrates the use of both multiline expressions and chaining together. The code is now much cleaner, and the ten_lang data frame that we get is equivalent to the one from the messy code above!

Let’s parse this new block of code piece by piece. The code above starts with a left parenthesis, ( , and so Python knows to keep reading to subsequent lines until it finds the corresponding right parenthesis symbol ) . The loc method performs the filtering and selecting steps as before. The line after this starts with a period ( . ) that “chains” the output of the loc step with the next operation, sort_values . Since the output of loc is a data frame, we can use the sort_values method on it without first giving it a name! That is what the .sort_values does on the next line. Finally, we once again “chain” together the output of sort_values with head to ask for the 10 most common languages. Finally, the right parenthesis ) corresponding to the very first left parenthesis appears on the second last line, completing the multiline expression. Instead of creating intermediate objects, with chaining, we take the output of one operation and use that to perform the next operation. In doing so, we remove the need to create and store intermediates. This can help with readability by simplifying the code.

Now that we’ve shown you chaining as an alternative to storing temporary objects and composing code, does this mean you should never store temporary objects or compose code? Not necessarily! There are times when temporary objects are handy to keep around. For example, you might store a temporary object before feeding it into a plot function so you can iteratively change the plot without having to redo all of your data transformations. Chaining many functions can be overwhelming and difficult to debug; you may want to store a temporary object midway through to inspect your result before moving on with further steps.

1.11. Exploring data with visualizations #

The ten_lang table answers our initial data analysis question. Are we done? Well, not quite; tables are almost never the best way to present the result of your analysis to your audience. Even the ten_lang table with only two columns presents some difficulty: for example, you have to scrutinize the table quite closely to get a sense for the relative numbers of speakers of each language. When you move on to more complicated analyses, this issue only gets worse. In contrast, a visualization would convey this information in a much more easily understood format. Visualizations are a great tool for summarizing information to help you effectively communicate with your audience, and creating effective data visualizations is an essential component of any data analysis. In this section we will develop a visualization of the ten Aboriginal languages that were most often reported in 2016 as mother tongues in Canada, as well as the number of people that speak each of them.

1.11.1. Using altair to create a bar plot #

In our data set, we can see that language and mother_tongue are in separate columns (or variables). In addition, there is a single row (or observation) for each language. The data are, therefore, in what we call a tidy data format. Tidy data is a fundamental concept and will be a significant focus in the remainder of this book: many of the functions from pandas require tidy data, as does the altair package that we will use shortly for our visualization. We will formally introduce tidy data in Chapter 3 .

We will make a bar plot to visualize our data. A bar plot is a chart where the lengths of the bars represent certain values, like counts or proportions. We will make a bar plot using the mother_tongue and language columns from our ten_lang data frame. To create a bar plot of these two variables using the altair package, we must specify the data frame, which variables to put on the x and y axes, and what kind of plot to create. First, we need to import the altair package.

The fundamental object in altair is the Chart , which takes a data frame as an argument: alt.Chart(ten_lang) . With a chart object in hand, we can now specify how we would like the data to be visualized. We first indicate what kind of graphical mark we want to use to represent the data. Here we set the mark attribute of the chart object using the Chart.mark_bar function, because we want to create a bar chart. Next, we need to encode the variables of the data frame using the x and y channels (which represent the x-axis and y-axis position of the points). We use the encode() function to handle this: we specify that the language column should correspond to the x-axis, and that the mother_tongue column should correspond to the y-axis.

Fig. 1.8 Syntax for using altair to make a bar chart. #

Fig. 1.9 Bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue #

1.11.2. Formatting altair charts #

It is exciting that we can already visualize our data to help answer our question, but we are not done yet! We can (and should) do more to improve the interpretability of the data visualization that we created. For example, by default, Python uses the column names as the axis labels. Usually these column names do not have enough information about the variable in the column. We really should replace this default with a more informative label. For the example above, Python uses the column name mother_tongue as the label for the y axis, but most people will not know what that is. And even if they did, they will not know how we measured this variable, or the group of people on which the measurements were taken. An axis label that reads “Mother Tongue (Number of Canadian Residents)” would be much more informative. To make the code easier to read, we’re spreading it out over multiple lines just as we did in the previous section with pandas.

Adding additional labels to our visualizations that we create in altair is one common and easy way to improve and refine our data visualizations. We can add titles for the axes in the altair objects using alt.X and alt.Y with the title method to make the axes titles more informative (you will learn more about alt.X and alt.Y in Chapter 4 ). Again, since we are specifying words (e.g. "Mother Tongue (Number of Canadian Residents)" ) as arguments to the title method, we surround them with quotation marks. We can do many other modifications to format the plot further, and we will explore these in Chapter 4 .

Fig. 1.10 Bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue with x and y labels. Note that this visualization is not done yet; there are still improvements to be made. #

The result is shown in Fig. 1.10 . This is already quite an improvement! Let’s tackle the next major issue with the visualization in Fig. 1.10 : the vertical x axis labels, which are currently making it difficult to read the different language names. One solution is to rotate the plot such that the bars are horizontal rather than vertical. To accomplish this, we will swap the x and y coordinate axes:

Fig. 1.11 Horizontal bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue. There are no more serious issues with this visualization, but it could be refined further. #

Another big step forward, as shown in Fig. 1.11 ! There are no more serious issues with the visualization. Now comes time to refine the visualization to make it even more well-suited to answering the question we asked earlier in this chapter. For example, the visualization could be made more transparent by organizing the bars according to the number of Canadian residents reporting each language, rather than in alphabetical order. We can reorder the bars using the sort method, which orders a variable (here language ) based on the values of the variable( mother_tongue ) on the x-axis .

Fig. 1.12 Bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue with bars reordered. #

Fig. 1.12 provides a very clear and well-organized answer to our original question; we can see what the ten most often reported Aboriginal languages were, according to the 2016 Canadian census, and how many people speak each of them. For instance, we can see that the Aboriginal language most often reported was Cree n.o.s. with over 60,000 Canadian residents reporting it as their mother tongue.

“n.o.s.” means “not otherwise specified”, so Cree n.o.s. refers to individuals who reported Cree as their mother tongue. In this data set, the Cree languages include the following categories: Cree n.o.s., Swampy Cree, Plains Cree, Woods Cree, and a ‘Cree not included elsewhere’ category (which includes Moose Cree, Northern East Cree and Southern East Cree) [ Statistics Canada, 2016 ] .

1.11.3. Putting it all together #

In the block of code below, we put everything from this chapter together, with a few modifications. In particular, we have combined all of our steps into one expression split across multiple lines using the left and right parenthesis symbols ( and ) . We have also provided comments next to many of the lines of code below using the hash symbol # . When Python sees a # sign, it will ignore all of the text that comes after the symbol on that line. So you can use comments to explain lines of code for others, and perhaps more importantly, your future self! It’s good practice to get in the habit of commenting your code to improve its readability.

This exercise demonstrates the power of Python. In relatively few lines of code, we performed an entire data science workflow with a highly effective data visualization! We asked a question, loaded the data into Python, wrangled the data (using [] , loc[] , sort_values , and head ) and created a data visualization to help answer our question. In this chapter, you got a quick taste of the data science workflow; continue on with the next few chapters to learn each of these steps in much more detail!

Fig. 1.13 Bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue #

1.12. Accessing documentation #

There are many Python functions in the pandas package (and beyond!), and nobody can be expected to remember what every one of them does or all of the arguments we have to give them. Fortunately, Python provides the help function, which provides an easy way to pull up the documentation for most functions quickly. To use the help function to access the documentation, you just put the name of the function you are curious about as an argument inside the help function. For example, if you had forgotten what the pd.read_csv function did or exactly what arguments to pass in, you could run the following code:

Fig. 1.14 shows the documentation that will pop up, including a high-level description of the function, its arguments, a description of each, and more. Note that you may find some of the text in the documentation a bit too technical right now. Fear not: as you work through this book, many of these terms will be introduced to you, and slowly but surely you will become more adept at understanding and navigating documentation like that shown in Fig. 1.14 . But do keep in mind that the documentation is not written to teach you about a function; it is just there as a reference to remind you about the different arguments and usage of functions that you have already learned about elsewhere.

Fig. 1.14 The documentation for the read_csv function including a high-level description, a list of arguments and their meanings, and more. #

If you are working in a Jupyter Lab environment, there are some conveniences that will help you lookup function names and access the documentation. First, rather than help , you can use the more concise ? character. So for example, to read the documentation for the pd.read_csv function, you can run the following code:

You can also type the first characters of the function you want to use, and then press Tab to bring up small menu that shows you all the available functions that starts with those characters. This is helpful both for remembering function names and to prevent typos.

Fig. 1.15 The suggestions that are shown after typing pd.read and pressing Tab . #

To get more info on the function you want to use, you can type out the full name and then hold Shift while pressing Tab to bring up a help dialogue including the same information as when using help() .

Fig. 1.16 The help dialog that is shown after typing pd.read_csv and then pressing Shift + Tab . #

Finally, it can be helpful to have this help dialog open at all times, especially when you start out learning about programming and data science. You can achieve this by clicking on the Help text in the menu bar at the top and then selecting Show Contextual Help .

1.13. Exercises #

Practice exercises for the material covered in this chapter can be found in the accompanying worksheets repository in the “Python and Pandas” row. You can launch an interactive version of the worksheet in your browser by clicking the “launch binder” button. You can also preview a non-interactive version of the worksheet by clicking “view worksheet.” If you instead decide to download the worksheet and run it on your own machine, make sure to follow the instructions for computer setup found in Chapter 13 . This will ensure that the automated feedback and guidance that the worksheets provide will function as intended.

1.14. References #

Nick Coghlan Guido van Rossum, Barry Warsaw. PEP 8 – Style Guide for Python Code . 2001. URL: https://peps.python.org/pep-0008/ .

Jeffrey Leek and Roger Peng. What is the question? Science , 347(6228):1314–1315, 2015.

Roger D Peng and Elizabeth Matsui. The Art of Data Science: A Guide for Anyone Who Works with Data . Skybrude Consulting, LLC, 2015. URL: https://bookdown.org/rdpeng/artofdatascience/ .

Tiffany Timbers. canlang: Canadian Census language data . 2020. R package version 0.0.9. URL: https://ttimbers.github.io/canlang/ .

Nick Walker. Mapping indigenous languages in Canada. Canadian Geographic , 2017. URL: https://www.canadiangeographic.ca/article/mapping-indigenous-languages-canada (visited on 2021-05-27).

Kory Wilson. Pulling Together: Foundations Guide . BCcampus, 2018. URL: https://opentextbc.ca/indigenizationfoundations/ (visited on 2021-05-27).

Statistics Canada. Population census. 2016. URL: https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/index-eng.cfm .

Statistics Canada. The Aboriginal languages of First Nations people, Métis and Inuit. 2016. URL: https://www12.statcan.gc.ca/census-recensement/2016/as-sa/98-200-x/2016022/98-200-x2016022-eng.cfm .

Statistics Canada. The evolution of language populations in Canada, by mother tongue, from 1901 to 2016. 2018. URL: https://www150.statcan.gc.ca/n1/pub/11-630-x/11-630-x2018001-eng.htm (visited on 2021-05-27).

The Pandas Development Team. pandas-dev/pandas: Pandas . February 2020. URL: https://doi.org/10.5281/zenodo.3509134 , doi:10.5281/zenodo.3509134 .

Truth and Reconciliation Commission of Canada. They Came for the Children: Canada, Aboriginal Peoples, and the Residential Schools . Public Works & Government Services Canada, 2012.

Truth and Reconciliation Commission of Canada. Calls to Action. 2015. URL: https://www2.gov.bc.ca/assets/gov/british-columbians-our-governments/indigenous-people/aboriginal-peoples-documents/calls_to_action_english2.pdf .

Wes McKinney. Data Structures for Statistical Computing in Python. In Stéfan van der Walt and Jarrod Millman, editors, Proceedings of the 9th Python in Science Conference , 56 – 61. 2010. doi:10.25080/Majora-92bf1922-00a .

Introduction to Data Science with Python

Arvind Krishna, Lizhen Shi, Emre Besler, and Arend Kuyper

September 20, 2022

This book is developed for the course STAT303-1 (Data Science with Python-1). The first two chapters of the book are a review of python, and will be covered very quickly. Students are expected to know the contents of these chapters beforehand, or be willing to learn it quickly. Students may use the STAT201 book (https://nustat.github.io/Intro_to_programming_for_data_sci/) to review the python basics required for the STAT303 sequence. The core part of the course begins from the third chapter - Reading data .

Please feel free to let the instructors know in case of any typos/mistakes/general feedback in this book.

Programming for Data Science

Teaching data scientists the tools they need to use computers to do data science

Assignments

Programming with python assignments.

• Assignment 1

Advanced Python Assignments

• Assignment 2
• Assignment 3
• Assignment 4
• Assignment 5
• Assignment 6
• Assignment 7
• Assignment 8
• Assignment 9
• Assignment 10
• Assignment 11
• Assignment 12
• Assignment 13

Data Science in Practice

Python introduction, python introduction ¶.

This is a demo assignment that is openly available for the Data Science in Practice Course.

How to complete assignments ¶

Whenever you see:

You need to replace (meaning: delete) these lines of code with some code that answers the questions and meets the specified criteria. Make sure you remove the ‘raise’ line when you do this (or your notebook will raise an error, regardless of any other code, and thus fail the grading tests).

You should write the answer to the questions in those cells (the ones with # YOUR CODE HERE ), but you can also add extra cells to explore / investigate things if you need / want to.

Any cell with assert statements in it is a test cell. You should not try to change or delete these cells. Note that there might be more than one assert that tests a particular question.

If a test does fail, reading the error that is printed out should let you know which test failed, which may be useful for fixing it.

Note that some cells, including the test cells, may be read only, which means they won’t let you edit them. If you cannot edit a cell - that is normal, and you shouldn’t need to edit that cell.

All outside packages/modules that will be used will be specified. You may not use other libraries in the assignments.

Finally, note that questions have points as specified in the detailed instructions.

Introduction ¶

The purpose of this assignment is to make sure you have the tools you’ll need to use for COGS108. Notably, we’ll be using Python, Jupyter notebooks, and git/GitHub. Since we’re using datahub , you won’t need a local version of Jupyter or Python on your computer. So, for this assignment, we’ll focus on getting you set up on GitHub.

Most assignments will be completed completely within the Jupyter Notebook and submitted on datahub; however, for this first assignment, we want you to be comfortable in GitHub. Tasks to complete Part 1 below will require work outside of this notebook.

Python (5 points) ¶

This part of the assignment is focused on some practice with Python, and with practicing working with the format of the assignments.

This class assumes some prior knowledge of Python. In the following questions, you will need to work with basic (standard library) data types (floats, lists, dictionaries, etc.) and control flow (conditionals, loops, functions, etc). If the questions in this section are totally unfamiliar to you, you may need to revisit some practice materials to catch up with some of the programming.

Through these questions, we will also prompt you to use a couple slightly more advanced standard library functions (for example, enumerate and zip ), that may be new to you.

Each question should be answerable with a relatively small number of lines of code, up to about 5-7 lines at most.

If you are having any trouble, remember to visit the course tutorials ( https://github.com/COGS108/Tutorials ). Assignment questions often follow the structure of examples provided in the tutorials, and a large number of relevant links and external materials are also indexed in the tutorials.

Q1: Defining Variables (0.25 points)

Create the following variables:

a variable called my_int that stores the integer 29

a variable called my_float that stores the float 7.29

a variable called my_string that stores the string ‘COGS108’

a variable called my_bool that stores the boolean True

Q2: Defining Variables: Lists & Tuples (0.25 points)

Define a list called var_a , that contains individual letters a-j (inclusively).

Define a tuple called var_b , that contains the numbers 1-10 (inclusively).

Q3: Defining Variables: Dictionaries (0.5 points)

Create a Python dict called dictionary where the keys are the elements in var_a and the values are the corresponding elements in var_b . (Note: Use var_a and var_b you created above.)

The zip function may be useful.

You might also make use of a Python dictionary comprehension.

Q4: Value Comparisons (0.5 points)

Store values in the variables comp_val_1 , comp_val_2 , comp_val_3 , and comp_val_4 such that the assert cell will pass silently (meaning the tests will pass and not produce an error).

Q5: Control Flow (0.5 points)

Loop through the provided list my_list . For each element, check if it is an even number. If the element is an even number, append the INDEX of that element to the list inds .

Note that you are adding the index to inds , not the element itself . (Reminder: Python uses zero-based indexing, so the index of the first element in the list is 0.)

To check if a number is even, you can use the modulo % operator.

To loop through an iterable, keeping track of the index, you can use the enumerate function.

Q6: Indexing (0.5 points)

Using the four lists provided in the cell below, complete the following indexing:

Use forward indexing to store the second value in list_1 to index_1

Use negative indexing to store the last value in list_2 to index_2

Store the first three values of list_3 to index_3

Store the last two values of list_4 to index_4

Q7: Looping through Dictionaries (0.75 points)

Using the students dictionary provided below, write a for loop that loops across the dictionary and collects all subject numbers (ex. ‘S2’) where the dictionary value is False .

Imagine, for example, the dictionary indicates whether a student has completed an assignment, and we wanted to get a list of the students who had not yet completed the assignment .

To answer this question, use a for loop across the students dictionary. You then need to get the associated value in each iteration, and check if it is True . If it is True , you can use continue to skip ahead to the next iteration. Otherwise, append the subject number (i.e. ‘S2’) to a list called incomplete .

Q8: Functions I (0.5 points)

Write a function return_odd that will take a list as its input and return a list of all the odd values as its output.

Note that this differs from what you did above in two ways: (1) it returns the values, not the index, and (2) it’s looking for odd values, not even.

For example:

Q9: Functions II (0.5 points)

Write a function squared_diff that takes two number inputs and returns the squared difference of the two numbers i.e., \((a - b)^2\) . For example:

Q10: Putting it all together (0.75 points)

Here, we’ll update the values in dictionary , storing the output in a dictionary called other_dictionary . This new dictionary will have the same keys, but some values will be updated.

The values in other_dictionary should be updated, such that if the value in the original dictionary is…

odd: update the the value stored in the dictionary to store the squared difference of the original value and ‘10’. Remember, you created a function to do this above.)

even: store the original value (from dictionary ).

to loop through key-value pairs in a dictionary, check out the .items method

You created a squared_diff function above.

This is the end of the first assignment!

The goal here was to check your basic Python understanding. From here, assignments will be more data science centric, using lots of pandas and working with data!

Have a look back over your answers, and also make sure to Restart & Run All from the kernel menu to double check that everything is working properly. You can also use the ‘Validate’ button above, which runs your notebook from top to bottom and checks to ensure all assert statements pass silently. When you are ready, submit on datahub!

Appendix: Version Control

Introduction to Data Science with Python

Learn python for data analysis.

Join Harvard University Instructor Pavlos Protopapas in this online course to learn how to use Python to harness and analyze data.

What You'll Learn

Every single minute, computers across the world collect millions of gigabytes of data. What can you do to make sense of this mountain of data? How do data scientists use this data for the applications that power our modern world?

Data science is an ever-evolving field, using algorithms and scientific methods to parse complex data sets. Data scientists use a range of programming languages, such as Python and R, to harness and analyze data. This course focuses on using Python in data science. By the end of the course, you’ll have a fundamental understanding of machine learning models and basic concepts around Machine Learning (ML) and Artificial Intelligence (AI). 

Using Python, learners will study regression models (Linear, Multilinear, and Polynomial) and classification models (kNN, Logistic), utilizing popular libraries such as sklearn, Pandas, matplotlib, and numPy. The course will cover key concepts of machine learning such as: picking the right complexity, preventing overfitting, regularization, assessing uncertainty, weighing trade-offs, and model evaluation. Participation in this course will build your confidence in using Python, preparing you for more advanced study in Machine Learning (ML) and Artificial Intelligence (AI), and advancement in your career.   Learners must have a minimum baseline of programming knowledge (preferably in Python) and statistics in order to be successful in this course. Python prerequisites can be met with an introductory Python course offered through CS50’s Introduction to Programming with Python , and statistics prerequisites can be met via Fat Chance or with Stat110 offered through HarvardX.

The course will be delivered via edX and connect learners around the world. By the end of the course, participants will learn:

• Gain hands-on experience and practice using Python to solve real data science challenges
• Practice Python coding for modeling, statistics, and storytelling
• Utilize popular libraries such as Pandas, numPy, matplotlib, and SKLearn
• Run basic machine learning models using Python, evaluate how those models are performing, and apply those models to real-world problems
• Build a foundation for the use of Python in machine learning and artificial intelligence, preparing you for future Python study

Your Instructor

Pavlos Protopapas is the Scientific Program Director of the Institute for Applied Computational Science(IACS) at the Harvard John A. Paulson School of Engineering and Applied Sciences. He has had a long and distinguished career as a scientist and data science educator, and currently teaches the CS109 course series for basic and advanced data science at Harvard University, as well as the capstone course (industry-sponsored data science projects) for the IACS master’s program at Harvard. Pavlos has a Ph.D in theoretical physics from the University of Pennsylvania and has focused recently on the use of machine learning and AI in astronomy, and computer science. He was Deputy Director of the National Expandable Clusters Program (NSCP) at the University of Pennsylvania, and was instrumental in creating the Initiative in Innovative Computing (IIC) at Harvard. Pavlos has taught multiple courses on machine learning and computational science at Harvard, and at summer schools, and at programs internationally.

Course Overview

• Linear Regression
• Multiple and Polynomial Regression
• Model Selection and Cross-Validation
• Bias, Variance, and Hyperparameters
• Classification and Logistic Regression
• Multi-logstic Regression and Missingness
• Bootstrap, Confidence Intervals, and Hypothesis Testing
• Capstone Project

Ways to take this course

When you enroll in this course, you will have the option of pursuing a Verified Certificate or Auditing the Course.

A Verified Certificate costs $299 and provides unlimited access to full course materials, activities, tests, and forums. At the end of the course, learners who earn a passing grade can receive a certificate. 

Alternatively, learners can Audit the course for free and have access to select course material, activities, tests, and forums.  Please note that this track does not offer a certificate for learners who earn a passing grade.

Related Courses

Data science professional certificate.

The HarvardX Data Science program prepares you with the necessary knowledge base and useful skills to tackle real-world data analysis challenges.

Machine Learning and AI with Python

Join Harvard University Instructor Pavlos Protopapas to learn how to use decision trees, the foundational algorithm for your understanding of machine learning and artificial intelligence.

Data Science for Business

Designed for managers, this course provides a hands-on approach for demystifying the data science ecosystem and making you a more conscientious consumer of information.

Key Word(s): pandas

CS109a Introduction to Data Science

Lecture 2, exercise 1: pandas intro ¶.

Harvard University Fall 2021 Instructors : Pavlos Protopapas and Natesh Pillai

Exercise 1: PANDAS Intro

As discussed in class, PANDAS is Python library that contains highly useful data structures, including DataFrames, which makes Exploratory Data Analysis (EDA) easy. Here, we get practice with some of the elementary functions.

For this exercise, we will be working with StudentsPerformance dataset made available through Kaggle . It contains information about the exame score of ( fictional ) high school students.

PANDAS Basics 🐼 ¶

Let's get started with basic functionality of PANDAS!

Exercise In the cell below fill in the blank to display general dataframe info rmation.

_Tip: The Pandas documention will be your best friend. But in many cases, a simple tab autocomplete can find what your looking for._

Examine the output carefully. There's a lot in there. Can you interpret each column? What about the details in header footer?

Exercise In the cell below, fill in the blank so that the variable cols stores the df 's column names. NOTE: Please keep the type of the data structure as a <class 'pandas.core.indexes.base.Index'> . Do not have to convert this to a list.\

Tip: Reviewing the DataFrame object itself might help

Exercise In the cell below, fill in the blank so that:

• num_cols stores the number of columns in df \ ( HINT )

Exercise In the cell below, fill in the blank so that first_seven is equal to the first 7 rows. ( HINT )

Exercise In the cell below, fill in the blank so that last_four is equal to the last 4 rows. ( HINT )

Exercise In the cell below, fill in the blank so that the unique_parental_education_levels variable stores a list of the 6 distinct values found within the parental level of education column of df .\

Tip: Again, try searching the documentation

Exercise In the cell below, fill in the blank so that the scored_100_at_math variable stores the DataFrame row(s) that correspond to everyone who scored 100 at math.\

Hint: Think 'indexing.' Specifically, boolean indexing

Some observations about conditions

Exercise In the cell below, fill in the blank to display scores' descriptive statistics ( HINT ).

Exercise In the cell below, fill in the blanks so that the uncompleted_with_good_writing_score variable stores the DataFrame rows that correspond to everyone who hasn't completed the preparation course and there writing score is above the median.

251 rows × 8 columns

Obvervation: the '&' operator differs from the 'and' operator

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assignment1.py

Latest commit, file metadata and controls.

Introduction to Data Science with Python

Join Harvard University instructor Pavlos Protopapas in this online course to learn how to use Python to harness and analyze data.

Associated Schools

Harvard School of Engineering and Applied Sciences

What you'll learn.

Gain hands-on experience and practice using Python to solve real data science challenges

Practice Python coding for modeling, statistics, and storytelling

Utilize popular libraries such as Pandas, numPy, matplotlib, and SKLearn

Run basic machine learning models using Python, evaluate how those models are performing, and apply those models to real-world problems

Build a foundation for the use of Python in machine learning and artificial intelligence, preparing you for future Python study

Course description

Every single minute, computers across the world collect millions of gigabytes of data. What can you do to make sense of this mountain of data? How do data scientists use this data for the applications that power our modern world?

Data science is an ever-evolving field, using algorithms and scientific methods to parse complex data sets. Data scientists use a range of programming languages, such as Python and R, to harness and analyze data. This course focuses on using Python in data science. By the end of the course, you’ll have a fundamental understanding of machine learning models and basic concepts around Machine Learning (ML) and Artificial Intelligence (AI). 

Using Python, learners will study regression models (Linear, Multilinear, and Polynomial) and classification models (kNN, Logistic), utilizing popular libraries such as sklearn, Pandas, matplotlib, and numPy. The course will cover key concepts of machine learning such as: picking the right complexity, preventing overfitting, regularization, assessing uncertainty, weighing trade-offs, and model evaluation. Participation in this course will build your confidence in using Python, preparing you for more advanced study in Machine Learning (ML) and Artificial Intelligence (AI), and advancement in your career.

Learners must have a minimum baseline of programming knowledge (preferably in Python) and statistics in order to be successful in this course. Python prerequisites can be met with an introductory Python course offered through CS50’s Introduction to Programming with Python, and statistics prerequisites can be met via Fat Chance or with Stat110 offered through HarvardX.

Instructors

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What does -> mean in Python function definitions?

Python, known for its simplicity and readability, sometimes introduces symbols or syntax that may seem unfamiliar to beginners. One such symbol is “->”, often seen in function definitions. In this article, we’ll delve into what “->” signifies in Python function definitions and how it contributes to the language’s expressiveness.

What is -> mean in Python?

In Python , “->” denotes the return type of a function. While Python is dynamically typed, meaning variable types are inferred at runtime, specifying return types can improve code clarity and enable better static analysis tools to catch errors early. This notation was introduced in Python 3.5 as part of function annotations, allowing developers to annotate parameters and return values with type hints.

Now let us see a few examples better to understand the use of “->” in Python.

In this example, the -> str part indicates that the function greet is expected to return a string.

In this example, the ‘add()’ function is supposed to return an integer datatype. The function takes two parameters and adds them together and returns the final result.

Understanding “->” in Python function definitions adds clarity and readability to your code by specifying return types. While optional, return type annotations improve code documentation and enable better tooling support for static type checking. By incorporating “->” into your Python functions, you enhance code maintainability and reduce the likelihood of runtime errors. So, embrace this Pythonic notation and elevate your programming prowess!

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