Curtis Miller [Curtis Miller] - Hands-On Data Analysis with NumPy and pandas
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Get to grips with the most popular Python packages that make data analysis possible
About This Book- Explore the tools you need to become a data analyst
- Discover practical examples to help you grasp data processing concepts
- Walk through hierarchical indexing and grouping for data analysis
Hands-On Data Analysis with NumPy and Pandas is for you if you are a Python developer and want to take your first steps into the world of data analysis. No previous experience of data analysis is required to enjoy this book.
What You Will Learn- Understand how to install and manage Anaconda
- Read, sort, and map data using NumPy and pandas
- Find out how to create and slice data arrays using NumPy
- Discover how to subset your DataFrames using pandas
- Handle missing data in a pandas DataFrame
- Explore hierarchical indexing and plotting with pandas
Python, a multi-paradigm programming language, has become the language of choice for data scientists for visualization, data analysis, and machine learning.
Hands-On Data Analysis with NumPy and Pandas starts by guiding you in setting up the right environment for data analysis with Python, along with helping you install the correct Python distribution. In addition to this, you will work with the Jupyter notebook and set up a database. Once you have covered Jupyter, you will dig deep into Pythons NumPy package, a powerful extension with advanced mathematical functions. You will then move on to creating NumPy arrays and employing different array methods and functions. You will explore Pythons pandas extension which will help you get to grips with data mining and learn to subset your data. Last but not the least you will grasp how to manage your datasets by sorting and ranking them.
By the end of this book, you will have learned to index and group your data for sophisticated data analysis and manipulation.
Style and approachA step-by-step approach, taking you through the different concepts and features of Data Analysis using Python libraries and tools.
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pandas provides methods that allow us to clearly state how we want to index. We can also distinguish between indexing based on values of the index of the series, and indexing based on the position of objects in the series, as would be the case if we were working with a list. The two methods we'll focus on are loc and iloc. loc focuses on selecting based on the index of the series, and if we try to select key elements that don't exist, we will get an error. iloc indexes as if we were working with a Python list; that is, it indexes based on integer position. So, if we were to try to index with a non-integer in iloc, or try to select an element outside of the range of valid integers, an error will be produced. There is a hybrid method, ix, that acts like loc, but if passed input that cannot be interpreted with respect to the index, it will act like iloc. Because of the ambiguity about how ix will behave, I recommend sticking with loc or iloc most of the time.
Let's return to our example. It turns out that square brackets, in this case, index like iloc; that is, they index based on integer position as if srs2 were a list. If we wanted to index based on the index of srs2, we could use loc to do so, getting the other possible result. Again, notice that in this case, both endpoints were included. This is unlike the behavior we normally associate with the colon operator:
If you are a Python developer and want to take your first steps into the world of data analysis, then this is the book you have been waiting for!
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When talking about sorting, we need to think about what exactly we are sorting. There are rows, columns, their indices, and the data they contain. Let's first look at index sorting. We can use the sort_index method to rearrange the rows of a DataFrame so that the row indices are in order. We can also sort the columns by setting the access parameter of sort_index to 1. By default, sorting is done in ascending order; later rows have larger values than earlier rows, but we can change this behavior by setting the ascending value of the sort_index value to false. This sorts in descending order. By default, this is not done in place; you need to set the in place argument of sort_index to true for that.
While I have emphasized sorting for DataFrames, sorting a series is effectively the same. Let's see an example. After loading in NumPy and pandas, we create a DataFrame with values to sort, shown in the following screenshot:
Let's sort the index; notice that this is not done in place:
Let's sort the columns this time , and we will do them in reverse order by setting ascending=False ; so the first column is now CCC and the last is AAA, shown as follows:
, Setting Up a Python Data Analysis Environment , discusses installing Anaconda and managing it. Anaconda is a software package we will use in the following chapters of this book.
, Diving into NumPY , discusses NumPy data types controlled by dtype objects, which are the way NumPy stores and manages data.
, Operations on NumPy Arrays , will cover what every NumPy user should know about array slicing, arithmetic, linear algebra with arrays, and employing array methods and functions.
, pandas are Fun! What is pandas? , introduces pandas and looks at what it does. We explore pandas series, DataFrames, and creating them.
, Arithmetic, Function Application, and Mapping with pandas , revisits some topics discussed previously, regarding applying functions in arithmetic to a multivariate object and handling missing data in pandas.
, Managing, Indexing, and Plotting , looks at sorting and ranking. We'll see how to achieve this in pandas, looking at hierarchical indexing and plotting with pandas.
If we wish to sort the rows of a DataFrame or the elements of a series, we need to use the sort_values method. For a series, you'd call sort_values and call it a day. For a DataFrame though, you would need to set the by parameter; you can set by to a string, indicating the column you want to sort by, or to a list of strings, indicating column names. Sorting will first proceed according to the first column in this list; then, when ties appear, sorting will be according to the next column, and so on.
So, let's demonstrate some of these sorting techniques. We sort the values of the DataFrame according to the column AAA, shown in the following screenshot:
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