Data Wrangling With Python

But on their website, there are plenty of raw data from different users. Here the concept of Data Munging or Data Wrangling is used. As we know Data is not Wrangled by System. This process is done by Data Scientists. So, the data Scientist will wrangle data in such a way that they will sort that motivational books that are sold more or have high ratings or user buy this book with these package of Books, etc. On the basis of that, the new user will make choice. This will explain the importance of Data wrangling.

Data Wrangling with Python

Data wrangling involves processing the data in various formats like - merging, grouping, concatenating etc. for the purpose of analysing or getting them ready to be used with another set of data.Python has built-in features to apply these wrangling methods to various data sets to achieve the analytical goal. In this chapter we will look at few examples describing these methods.

Python is popular because of its simplicity and flexibility, but also because of the huge number of libraries and frameworks that data scientists can use. Here are five of the most useful and popular for data wrangling:

This is a great question for showcasing data wrangling techniques because all the hard work lies in molding your dataset into the proper format. Once you have the appropriate analytical base table (ABT), answering the question becomes simple.

See how we now have delta_7 and delta_14 in the same row? This is the start of our analytical base table. All we need to do now is merge all of our melted dataframes together with a base dataframe of other features we might want.

R, just like Python, has powerful libraries such as tidyr and dplyr that help us greatly with munging data in very few lines of code. The ability to effectively perform data wrangling, along with the availability of many statistical models, have made R a very popular choice for data munging, as well as data science.

Now our data frame is a lot easier to read, as we removed the irrelevant color column. More importantly, we extracted the inaccurate mpg entries in our table. There are many ways to deal with missing/incorrect values in a data frame, but for an introduction into data wrangling, removing them would be sufficient.

Data wrangling is the process of transforming raw data into a more structured format. The process includes collecting, processing, analyzing, and tidying the raw data so that it can be easily read and analyzed. We can use the common library in python, that is "pandas".

After displaying a data set, what if you want to display data from rows 5 to 20 of a dataset? To anticipate this, pandas can also display data within a certain range, both ranges for rows only, only columns, and ranges for rows and columns. This is the function

The median is used when the data presented has a high outlier. The median was chosen because it is the middle value, which means it is not the result of calculations involving outlier data. In some cases, outlier data is considered disturbing and often considered noisy because it can affect class distribution and interfere with clustering analysis.

This video is geared towards learners who have prior experience with python and are eager to learn more about data cleansing and data analysis. The examples shown in the video are executed in Jupyter Notebooks. If viewers do not have Jupyter Notebooks, they should download Anaconda Navigator.

I wanted to write a quick post today about a task that most of us do routinely but often think very little about - loading CSV (comma-separated value) data into Python. This simple action has a variety of obstacles that need to be overcome due to the nature of serialization and data transfer. In fact, I'm routinely surprised how often I have to jump through hoops to deal with this type of data, when it feels like it should be as easy as JSON or other serialization formats.

The basic problem is this: CSVs have inherent schemas. In fact, most of the CSVs that I work with are dumps from a database. While the database can maintain schema information alongside the data, the scheme is lost when serializing to disk. Worse, if the dump is denormalized (a join of two tables), then the relationships are also lost, making it harder to extract entities. Although a header row can give us the names of the fields in the file, it won't give us the type, and there is nothing structural about the serialization format (like there is with JSON) that we can infer the type from.

That said, I love CSVs. CSVs are a compact data format - one row, one record. CSVs can be grown to massive sizes without cause for concern. I don't flinch when reading 4 GB CSV files with Python because they can be split into multiple files, read one row at a time for memory efficiency, and multiprocessed with seeks to speed up the job. This is in stark contrast to JSON or XML, which have to be read completely from end to end in order to get the full data (JSON has to be completely loaded into memory and with XML you have to use a streaming parser like SAX).

CSVs are the file format of choice for big data appliances like Hadoop for good reason. If you can get past encoding issues, extra dependencies, schema inference, and typing; CSVs are a great serialization format. In this post, I will provide you with a series of pro tips that I have discovered for using and wrangling CSV data.

Python has a built in csv module that handles all the ins and outs of processing CSV files, from dealing with dialects (Excel, anyone?) to quoting fields that may contain the delimiter to handling a variety of delimiters. Very simply, this is how you would read all the data from the funding CSV file:

This is powerful because it means that even for much larger data sets you will have efficient, portable code. Moreover, as we start looking at wrangling or munging the data from the CSV file, you'll have well encapsulated code that can handle a variety of situations. In fact, in code that has to read and parse files from a variety of sources, it is common to wrap the csv module in a class so that you can persist statistics about the data and provide multiple reads to the CSV file. In the above example you have to ensure that you call the function to get the data every time you want to read or do any computation, whereas a class can save some state of the data between reads. Here is an example of persisted data for our funding class:

The utilities for analysis that Pandas gives you, especially DataFrames, are extremely useful, and there is obviously a 1:1 relationship between DataFrames and CSV files. I routinely use Pandas for data analyses, quick insights, and even data wrangling of smaller files. The problem is that Pandas is not meant for production-level ingestion or wrangling systems. It is meant for data analysis that can happen completely in memory. As such, when you run this line of code, the entirety of the CSV file is loaded into memory. Likewise, Numpy arrays are also immutable data types that are completely loaded into memory. You've just lost your memory efficiency, especially for larger data sets.

If you're lucky enough to be able to use Python 3, you can skip this section. But as mentioned previously, production ingestion and wrangling systems are usually run on micro or small servers in the cloud, usually on the system Python. So if you're like me and many other data scientists, you're using Python 2.6+ which is currently what ships with Linux servers.

Now that we can easily extract data from a CSV file with a memory efficient and encoding-tolerant method, we can begin to look at actually wrangling our data. CSVs don't provide a lot in terms of types or indications about how to deal with the data that is coming in as strings. As noted previously, if there is a header line, we may get information about the various field names, but that's about it. We have already parsed our rows into Python dictionaries using the built in csv.DictReader, but this definitely depends on the header row being there. If the header row is not there, then you can pass a list of field names to the csv.DictReader and you can get dictionaries back.

Dictionaries are great because you can access your data by name rather than by position, and these names may help you parse the data that's coming in. However, dictionaries aren't schemas. They are key-value pairs that are completely mutable. When using a single CSV file, you may expect to have dictionaries with all the same keys, but these keys aren't recorded anywhere in your code, making your code dependent on the structure of the CSV file in an implicit way.

Moreover, dictionaries have a bit more overhead associated with them in order to make them mutable. They use more memory when instantiated and in order to provide fast lookups, they are hashed (unordered). They are great for when you don't know what data is coming in, but when you do, you can use something much better: namedtuples.

These challenges with CSV as a default serialization format may prompt us to find a different solution, especially as we propagate data throughout our data pipelines. My suggestion is to use a binary serialization format like Avro, which stores the schema alongside compressed data. Because the file is stored with a binary seralization, it is as compact as csv and can be read in a memory efficient manner. Because the data is stored with its schema, it can be read from any code and immediately extracted and automatically parsed.

So let's return to our original example with the funding.csv data set. In order to convert our parsed FundingRecord rows, we need to first define a schema. Schemas in Avro are simply JSON documents that define the field names along with their types. The schema for a FundingRecord is as follows:

Once saved to disk, the savings become apparent. The original file was approximately 92K. After serializing with Avro, we have compressed our data to 64K! Not only do we have a compressed file format, but we also have the schema associated with the data. Reading the data is pretty easy now, and requires no parsing as before. 041b061a72