Your job is to group by ‘Event_gender’ and ‘Gender’ and count the rows. The duplicates can be dropped using the .drop_duplicates() method, leaving behind the unique observations. In this exercise, you will use .pivot_table() first to aggregate the total medals by type. The result is returned as a Series of counts indexed by unique entries from the original Series with values (counts) ranked in descending order. Suppose you have loaded the data into a DataFrame medals.

Extracting and transforming data

How the negative number is coded is also very important. Using parentheses or adding a minus sign are common solutions. Some people who have low vision or are colorblind may incorrectly conclude that a number is positive if red text is the only indicator that it is negative.

Your job is to use the .apply() method to perform this conversion on the ‘Mean TemperatureF’ and ‘Mean Dew PointF’ columns of the weather DataFrame. In this exercise you’ll take daily weather data in Pittsburgh in 2013 obtained from Weather Underground. You will focus specifically on the ‘age’ and ‘cabin’ columns in this exercise.

You will pivot, unstack, group, slice, and reshape your data as you explore this dataset and uncover some truly fascinating insights. This is a rich dataset that will allow you to fully apply the data manipulation techniques you have learned. Here, you will bring together everything you have learned in this course while working with data recorded from the Summer Olympic games that goes as far back as 1896! The mean of the ‘survived’ column is the fraction of passengers who lived. The ‘survived’ column has the value 1 if the passenger survived and 0 otherwise.

In this course, you’ll learn how to leverage pandas’ extremely powerful data manipulation engine to get the most out of your data. Translate each of the following symbolic statements into words. Sometimes, a compound statement within parentheses may need to be negated as a single unit. Sometimes a compound statement within parentheses may need to be negated as a group.

• In this exercise, you will practice using the ‘count’ and len aggregation functions – which produce the same result – on the users DataFrame.
• For example, in the sample ticket being used, the driver was charged with careless driving.
• To get started, print the sales DataFrame in the IPython Shell and verify that the index has no name, only its data (the month names).
• This skill will become more useful as you work with compound statements in later.For example, we can represent some mammals that live in the ocean by letter \(p\).
• The .apply() method can be used on a pandas DataFrame to apply an arbitrary Python function to every element.

Sorting rows

The Pennsylvania US election results data set that you have been using so far is ordered by county name. You’ll then assign a new DataFrame by selecting the list of columns ‘winner’, ‘total’, ‘voters’. There are circumstances in which it’s useful to modify the order of your DataFrame columns. In this exercise, you will use the Pennsylvania election results again. In this exercise, the DataFrame election is provided for you.

The joining of two logical statements with the word “or” forms a compound statement called a disjunction. In logic, for a conjunction to be true, all the independent logical statements that make it up must be true. The joining of two logical statements with the word “and” or “but” forms a compound statement called a conjunction. The second independent clause in this compound statement is, “My friend did not pass the road test.” The word “but” is the connective used to join these two statements together, forming a compound statement. The statement, “My friend passed the written test,” is an independent clause because it is a complete thought or idea that can stand on its own. The negation of logical statements that use the quantifiers all, some, or none is a little more complicated than just adding or removing the word not.

What You’ll Learn

In this exercise your job is to investigate survival rates of passengers on the Titanic by ‘age’ and ‘pclass’. You can also mix and match column grouping with Series grouping. Sometimes, you may instead want to group by a function/transformation of a column. First, you’ll identify how many units each company bought for verification. The .apply() method will handle the iteration over individual groups and then re-combine them back into a Series or DataFrame. By using .apply(), you can write functions that filter rows within groups.

In this section, the symbols that are listed are used as some sorts of punctuation marks in mathematical reasoning, or as abbreviations of natural language phrases. A clear advantage of blackboard bold is that these symbols cannot be confused with anything else. For symbols that are used only in mathematical logic, or are rarely used, see List of logic symbols. These letters alone are not sufficient for the needs of mathematicians, and many other symbols are used.

Your job in this exercise is to unstack and then stack the ‘city’ level, as you did previously for ‘weekday’. You are now going to continue working with the users DataFrame. You will then use .stack() on the unstacked DataFrame to see if you get back the original layout of users.

Adding names for readability

In this section, we will begin with the parts that make up all logical arguments. If so, you and your friends likely started by gathering some tools and supplies to work with, such as hammers, saws, screwdrivers, wood, nails, and screws. Have you ever built a club house, tree house, or fort with your friends?

While a false statement is still considered a logical statement, a strong logical argument starts with true statements. How can we present numbers to clients in both written and numerical form without using the parentheses, which may indicate a negative number? For example, in financial statements, a number like (500) could signify a loss of 500 units of currency. Using parentheses to indicate negative numbers is a widely accepted format in financial reporting and is often considered easier to read than using a minus sign.

When the driver appears in court, the ticket number is used to reference the specific charge and traffic stop. On the top of the ticket are a series of numbers, including the court ID, a prefix and the ticket number. There is a lot of information on an NJ ticket, and understanding what is on the ticket is key to avoiding the most egregious consequences. Those who pay a ticket in NJ can be hit with fines, points, surcharges and more. We use Python and Pandas to analyze the data, focusing on the total number of posts over time, the number of posts since the peak … The material is from the course I completed the exercises If you find the conten…

• Consider the previous example of your friend trying to get their driver’s license.
• To get a NJ ticket dismissed, one must challenge the facts of the offense or the legality of the traffic stop itself.
• Across virtually every landscape—legal, financial, political, ethical—the impact has been profound.
• Let \(p\) represent the statement, “My friend passed the written test,” let \(q\) represent the statement, “My friend passed the road test,” and let \(r\) represent the statement, “My friend received a driver’s license.”
• To do this, you’ll group by the ‘sex’ and ‘pclass’ columns and transform each group with a custom function to call .fillna() and impute the median value.
• Groupby operations can also be performed on transformations of the index values.

You can explicitly specify the columns that should remain in the reshaped DataFrame with id_vars, and list which columns to convert into values with value_vars. Notice how in the pivoted DataFrame, the index is labeled ‘weekday’, the columns are labeled ‘city’, and the values are populated how to prepare accounts receivable aging reports by the number of visitors. For example, in the video, Dhavide used ‘treatment’ to index the rows, ‘gender’ to index the columns, and ‘response’ to populate the cells.

The .nunique() method is the principal aggregation here. That is a data error as you can confirm with a web search. You will see that, according to the data, Joyce Chepchumba was a man that won a medal in a women’s event.

Changing index of a DataFrame

A logical statement has the form of a complete sentence, and it must make a claim that can be identified as being true or false. The building block of any logical argument is a logical statement, or simply a statement. Some were used in classical logic for indicating the logical dependence between sentences written in plain language. However, these may not be used by all screen reader users, especially if it is not obvious that the number they are reading might be negative.

Because var_name or value_name parameters weren’t specified, the melted DataFrame has the default variable and value column names. Also note how specifying the value_name parameter has renamed the ‘value’ column to ‘visitors’. As Dhavide demonstrated, if you don’t pass a name to the values in d.melt(), you will lose the name of your variable. The goal here is to convert bycity back to something that looks like users. To begin, print both users and bycity in the IPython Shell. Continuing from the previous exercise, you will now use .swaplevel(0, 1) to flip the index levels.

Understanding the following logical connectives, along with their properties, symbols, and names, will be key to applying the topics presented in this chapter. After passing the written test, your friend must accounting source documents also pass a road test to demonstrate that they can perform the physical task of driving safely within the rules of the law. Write each of the following symbolic statements in words. For example, consider the logical statement, “All oranges are citrus fruits.” This statement expresses as a subset relationship.

Any statement in parentheses should be treated as a single component of the expression. Write the symbolic form of each of the following compound statements. When translating English statements into symbolic form, do not assume that every connective “and” means you are dealing with a compound statement. My friend passed the written test, but they did not pass the road test. Your friend passed the written test, but they did not pass the road test.