Cosmopolitan USA April Playboy South Africa March Leave a Reply Cancel reply Your email address will not be published. Video Tutorials. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing.
The third case study is a financial portfolio analysis application that engages you with time series analysis — pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. Your email address will not be published. Save my name, email, and website in this browser for the next time I comment. How to Visualize Data with D3 [Video]. Listing Moving your data from where you have it stored into your analytical tools and back out again can be a difficult task if you don't know what you are doing.
Python and its libraries try to make it as easy as possible. See Listing to learn how to load data from a CSV file. Henley and Dave Wolf 5 A. Or, rather, there are headers, but they weren't loaded as headers; they were loaded as row one of your data. To load data that includes headers, you can use the code shown in Listing To add them, we can use one of the options shown in Listing Let's find out.
Go to the following website, which contains U. Now, try to import that data into Python. Maybe you are just using Python to massage some data for later analysis in another tool. Or maybe you have some other reason to export your dataframe to a CSV file.
The code shown in Listing is an example of how to do this. Line 7 is the code to export the dataframe df to a CSV file called studentgrades.
The only parameters we use are index and header. Setting these parameters to false will prevent the index and header names from being exported. Change the values of these parameters to get a better understanding of their use.
Let's see how to load data from an Excel file Listing ExcelWriter 'dataframe. Sometimes we will need to combine the data from several Excel files into the same dataframe. We can do this either the long way or the short way. First, let's see the long way Listing Why do we call this the long way? Because if we were loading a hundred files instead of three, it would take hundreds of lines of code to do it this way.
In the words of my friends in the startup community, it doesn't scale well. The short way, however, does scale. Now, let's see the short way Listing DataFrame for f in glob. Since we only have three datafiles, the difference in code isn't that noticeable. However, if we were loading a hundred files, the difference in the amount of code would be huge.
This code will load all the Excel files whose names begin with data that are in the datasets directory no matter how many there are. Your task is to try to load all of that data into one dataframe. Let's learn how to load our data from a sqlite database file Listing If you don't know the names of the tables in a sqlite database, you can find out by changing the SQL statement to that shown in Listing As always, if you need more information about the command, you can run the code shown in Listing However, sometimes you will need to create random values.
Say we wanted to make a random list of baby names. We could get started as shown in Listing In the last line, we create a list of the names we will randomly select from. Next, we add the code shown in Listing What we will try to do is this: 1. We will do all of this in the code shown in Listing Getting data ready for analytical tools can be a difficult task. With just a few lines of code, you will be able to get your data ready for analysis. This means it should be consistent, relevant, and standardized.
Henley and Dave Wolf 19 A. What if you went to high school with Bill Gates? Finding the outliers allows you to remove the values that are so high or so low that they skew the overall view of the data.
We cover two main ways of detecting outliers: 1. Standard Deviations: If the data is normally distributed, then 95 percent of the data is within 1. So we can drop the values either above or below that range. Let's see what these look like Listings and Can you remove the outliers?
Try it with both methods. It can make it impossible or unpredictable to compute most aggregate statistics or to generate pivot tables. If you look for missing data points in a row dataset it is fairly easy. However, if you try to find a missing data point in a ,row dataset it can be much tougher. Python's pandas library has functions to help you find, delete, or change missing data Listing We can use the resulting dataframe to practice dealing with missing data. To drop all the rows with missing NaN data, use the code shown in Listing To fill in missing grades with each gender's mean value of grade, see Listing Your mission, if you choose to accept it, is to delete rows with missing grades and to replace the missing values in hours of exercise by the mean value for that gender.
Heck, sometimes you need to worry about that even if you did collect it yourself! Python's pandas library has the ability to filter out the bad values see Listing Did the same data get reported twice, or recorded twice, or just copied and pasted? It can be difficult to check the veracity of each and every data point, but it is quite easy to check if the data is duplicated.
Python's pandas library has a function for finding not only duplicated rows, but also the unique rows Listing In this case, where we know that a duplicate name means a duplicate entry, we can use the code seen in Listing We figure people with the same address are duplicates.
Can you drop the duplicated rows while keeping the first? Let's load some data to see how to address that Listing Even though they all refer to January 3, , analysis tools may not recognize them all as dates if you are switching back and forth between the different formats in the same column Listing Maybe they use hyphens in those numbers, and maybe they don't.
This section quickly covers how to standardize how these types of data are stored see Listing It's a rare dataset in which every question you need answered is directly addressed by a variable. Think: converting numeric grades to letter grades. In this lesson, we will learn about binning Listing This is because there needs to be a top and bottom limit for each bin. And if we want to count the number of observations for each category, we can do that too Listing This is for a master's program that requires a grade of 80 or above for a student to pass.
The number two reason is the ease of applying functions to my data. To see this, first we need to load up some data Listing Applying a function to a group can be seen in Listing In other words, create an object that represents that particular grouping.
In this case, we group grades by the gender. Then, compute the mean hours of exercise of the female students with a 'status' of passing. This is when you need ranking Listing Listing shows the code to create a new column that is the rank of the value of grade in ascending order. In this section, we will learn how to do this Listing So, using the following data Listing , let's see how we would do this.
However, we can also use it with column selectors to create a new column using our fname and lname columns Listing Many analytical tools won't work on text, but if you convert those values to numbers it makes things much simpler Listing First, so our analysis tools work with them correctly. Failing to format a missing value code or a dummy variable correctly will have major consequences for your data analysis.
Second, it's much faster to run the analysis and interpret results if you don't have to keep looking up which variable Q is. Either something is left out that should have been included or something was left in that should have been removed. So, let's start with the dataset in Listing We can add a column filled with zeros by setting the new column name to be equal to a 0 Listing We create a series and set the column equal to the series Listing
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