Big Data Analysis with Python

The IPython shell (https://ipython.org/) is an interactive Python command interpreter that can handle several languages. It allows us to test ideas quickly rather than going through creating files and running them. Most Python installations have a bundled command interpreter, usually called the shell, where you can execute commands iteratively. Although it's handy, this standard Python shell is a bit cumbersome to use. IPython has more features:

Getting started with the Python shell is simple. Let's follow these steps to interact with the IPython shell:

Notice the number on the left, indicating the instruction number. This number always increases. We attributed the value to a variable and executed an operation on that variable, getting the result interactively. We will use IPython in the following sections.

The Jupyter notebook (https://jupyter.org/) started as part of IPython but was separated in version 4 and extended, and lives now as a separate project. The notebook concept is based on the extension of the interactive shell model, creating documents that can run code, show documentation, and present results such as graphs and images.

Jupyter is a web application, so it runs in your web browser directly, without having to install separate software, and enabling it to be used across the internet. Jupyter can use IPython as a kernel for running Python, but it has support for more than 40 kernels that are contributed by the developer community.

It has become a de facto platform for performing operations related to data science from beginners to power users, and from small to large enterprises, and even academia. Its popularity has increased tremendously in the last few years. A Jupyter notebook contains both the input and the output of the code you run on it. It allows text, images, mathematical formulas, and more, and is an excellent platform for developing code and communicating results. Because of its web format, notebooks can be shared over the internet. It also supports the Markdown markup language and renders Markdown text as rich text, with formatting and other features supported.

As we've seen before, each notebook has a kernel. This kernel is the interpreter that will execute the code in the cells. The basic unit of a notebook is called a cell. A cell is a container for either code or text. We have two main types of cells:

A code cell accepts code to be executed in the kernel, displaying the output just below it. A Markdown cell accepts Markdown and will parse the text in Markdown to formatted text when the cell is executed.

Let's run the following exercise to get hands-on experience in the Jupyter notebook.

The fundamental component of a notebook is a cell, which can accept code or text depending on the mode that is selected.

Let's start a notebook to demonstrate how to work with cells, which have two states:

When in edit mode, the contents of the cell can be edited, while in run mode, the cell is ready to be executed, either by the kernel or by being parsed to formatted text.

You can add a new cell by using the Insert menu option or using a keyboard shortcut, Ctrl + B. Cells can be converted between Markdown mode and code mode again using the menu or the Y shortcut key for a code cell and M for a Markdown cell.

To execute a cell, click on the Run option or use the Ctrl + Enter shortcut.

Let's execute the following steps to demonstrate how to start to execute simple programs in a Jupyter notebook.

Working with a Jupyter notebook for the first time can be a little confusing, but let's try to explore its interface and functionality. The reference notebook for this exercise is provided on GitHub.

Now, start a Jupyter notebook server and work on it by following these steps:

As we will see in the rest of the book, the notebook is the cornerstone of our analysis process. The steps that we just followed illustrate the use of different kinds of cells and the different ways we can document our analysis.

Both IPython and Jupyter have a place in the analysis workflow. Usually, the IPython shell is used for quick interaction and more data-heavy work, such as debugging scripts or running asynchronous tasks. Jupyter notebooks, on the other hand, are great for presenting results and generating visual narratives with code, text, and figures. Most of the examples that we will show can be executed in both, except the graphical parts.

IPython is capable of showing graphs, but usually, the inclusion of graphs is more natural in a notebook. We will usually use Jupyter notebooks in this book, but the instructions should also be applicable to IPython notebooks.

Let's demonstrate common Python development in IPython and Jupyter. We will import NumPy, define a function, and iterate the results:

We now know how to handle functions and change function definitions on the fly in the notebook. This is very helpful when we are exploring and discovering the right approach for some code or an analysis. The iterative approach allowed by the notebook can be very productive in prototyping and faster than writing code to a script and executing that script, checking the results, and changing the script again.

NumPy (http://www.numpy.org) is a package that came from the Python scientific computing community. NumPy is great for manipulating multidimensional arrays and applying linear algebra functions to those arrays. It also has tools to integrate C, C++, and Fortran code, increasing its performance capabilities even more. There are a large number of Python packages that use NumPy as their numerical engine, including pandas and scikit-learn. These packages are part of SciPy, an ecosystem for packages used in mathematics, science, and engineering.

To import the package, open the Jupyter notebook used in the previous activity and type the following command:

import numpy as np

The basic NumPy object is ndarray, a homogeneous multidimensional array, usually composed of numbers, but it can hold generic data. NumPy also includes several functions for array manipulation, linear algebra, matrix operations, statistics, and other areas. One of the ways that NumPy shines is in scientific computing, where matrix and linear algebra operations are common. Another strength of NumPy is its tools that integrate with C++ and FORTRAN code. NumPy is also heavily used by other Python libraries, such as pandas.

SciPy (https://www.scipy.org) is an ecosystem of libraries for mathematics, science, and engineering. NumPy, SciPy, scikit-learn, and others are part of this ecosystem. It is also the name of a library that includes the core functionality for lots of scientific areas.

Matplotlib (https://matplotlib.org) is a plotting library for Python for 2D graphs. It's capable of generating figures in a variety of hard-copy formats for interactive use. It can use native Python data types, NumPy arrays, and pandas DataFrames as data sources. Matplotlib supports several backend—the part that supports the output generation in interactive or file format. This allows Matplotlib to be multiplatform. This flexibility also allows Matplotlib to be extended with toolkits that generate other kinds of plots, such as geographical plots and 3D plots.

The interactive interface for Matplotlib was inspired by the MATLAB plotting interface. It can be accessed via the matplotlib.pyplot module. The file output can write files directly to disk. Matplotlib can be used in scripts, in IPython or Jupyter environments, in web servers, and in other platforms. Matplotlib is sometimes considered low level because several lines of code are needed to generate a plot with more details. One of the tools that we will look at in this book that plots graphs, which are common in analysis, is the Seaborn library, one of the extensions that we mentioned before.

To import the interactive interface, use the following command in the Jupyter notebook:

import matplotlib.pyplot as plt

To have access to the plotting capabilities. We will show how to use Matplotlib in more detail in the next chapter.

Pandas (https://pandas.pydata.org) is a data manipulation and analysis library that's widely used in the data science community. Pandas is designed to work with tabular or labeled data, similar to SQL tables and Excel files.

We will explore the operations that are possible with pandas in more detail. For now, it's important to learn about the two basic pandas data structures: the series, a unidimensional data structure; and the data science workhorse, the bi-dimensional DataFrame, a two-dimensional data structure that supports indexes.

Data in DataFrames and series can be ordered or unordered, homogeneous, or heterogeneous. Other great pandas features are the ability to easily add or remove rows and columns, and operations that SQL users are more familiar with, such as GroupBy, joins, subsetting, and indexing columns. Pandas is also great at handling time series data, with easy and flexible datetime indexing and selection.

Let's import pandas into the Jupyter notebook from the previous activity with the following command:

import pandas as pd

Pandas accepts several data formats and ways to ingest data. Let's start with the more common way, reading a CSV file. Pandas has a function called read_csv, which can be used to read a CSV file, either locally or from a URL. Let's read some data from the Socrata Open Data initiative, a RadNet Laboratory Analysis from the U.S. Environmental Protection Agency (EPA), which lists the radioactive content collected by the EPA.

How can an analyst start data analysis without data? We need to learn how to get data from an internet source into our notebook so that we can start our analysis. Let's demonstrate how pandas can read CSV data from an internet source so we can analyze it:

Pandas can read more formats:

Try to read other formats from pandas, such as Excel sheets.

By data manipulation we mean any selection, transformation, or aggregation that is applied over the data. Data manipulation can be done for several reasons:

Pandas was designed to let the analyst do these transformations in an efficient way.

Pandas DataFrames can be sliced similarly to Python lists. For example, to select a subset of the first 10 rows of the DataFrame, we can use the [0:10] notation. We can see in the following screenshot that the selection of the interval [1:3] that in the NumPy representation selects the rows 1 and 2.

Figure 1.6: Selection in a pandas DataFrame

In the following section, we'll explore the selection and filtering operation in depth.

When performing data analysis, we usually want to see how data behaves differently under certain conditions, such as comparing a few columns, selecting only a few columns to help read the data, or even plotting. We may want to check specific values, such as the behavior of the rest of the data when one column has a specific value.

After selecting with slicing, we can use other methods, such as the head method, to select only a few rows from the beginning of the DataFrame. But how can we select some columns in a DataFrame?

To select a column, just use the name of the column. We will use the notebook. Let's select the cylinders column in our DataFrame using the following command:

df['State']

The output is as follows:

Figure 1.7: DataFrame showing the state

Another form of selection that can be done is filtering by a specific value in a column. For example, let's say that we want to select all rows that have the State column with the MN value. How can we do that? Try to use the Python equality operator and the DataFrame selection operation:

df[df.State == "MN"]

Figure 1.8: DataFrame showing the MN states

More than one filter can be applied at the same time. The OR, NOT, and AND logic operations can be used when combining more than one filter. For example, to select all rows that have State equal to AK and a Location of Nome, use the & operator:

df[(df.State == "AK") & (df.Location == "Nome")]

Figure 1.9: DataFrame showing State AK and Location Nome

Another powerful method is .loc. This method has two arguments, the row selection and the column selection, enabling fine-grained selection. An important caveat at this point is that, depending on the applied operation, the return type can be either a DataFrame or a series. The .loc method returns a series, as selecting only a column. This is expected, because each DataFrame column is a series. This is also important when more than one column should be selected. To do that, use two brackets instead of one, and use as many columns as you want to select.

As we saw before, selecting data, separating variables, and viewing columns and rows of interest is fundamental to the analysis process. Let's say we want to analyze the radiation from I-131 in the state of Minnesota:

In this exercise, we learned how to filter and select values, either on columns or rows, using the NumPy slice notation or the .loc method. This can help when analyzing data, as we can check and manipulate only a subset of the data instead having to handle the entire dataset at the same time.

Data is never clean. There are always cleaning tasks that have to be done before a dataset can be analyzed. One of the most common tasks in data cleaning is applying a function to a column, changing a value to a more adequate one. In our example dataset, when no concentration was measured, the non-detect value was inserted. As this column is a numerical one, analyzing it could become complicated. We can apply a transformation over a column, changing from non-detect to numpy.NaN, which makes manipulating numerical values more easy, filling with other values such as the mean, and so on.

To apply a function to more than one column, use the applymap method, with the same logic as the apply method. For example, another common operation is removing spaces from strings. Again, we can use the apply and applymap functions to fix the data. We can also apply a function to rows instead of to columns, using the axis parameter (0 for rows, 1 for columns).

Before starting an analysis, we need to check for data problems, and when we find them (which is very common!), we have to correct the issues by transforming the DataFrame. One way to do that, for instance, is by applying a function to a column, or to the entire DataFrame. It's common for some numbers in a DataFrame, when it's read, to not be converted correctly to floating-point numbers. Let's fix this issue by applying functions:

The spaces in column names can be extraneous and can make selection and filtering more complicated. Fixing the numeric types helps when statistics must be computed using the data. If there is a value that is not valid for a numeric column, such as a string in a numeric column, the statistical operations will not work. This scenario happens, for example, when there is an error in the data input process, where the operator types the information by hand and makes mistakes, or the storage file was converted from one format to another, leaving incorrect values in the columns.

Transform the data types in our example DataFrame to the correct types with the pandas astype function. Let's use the sample dataset from https://opendata.socrata.com/:

Now our dataset looks fine, with all values correctly cast to the right types. But correcting the data is only part of the story. We want, as analysts, to understand the data from different perspectives. For example, we may want to know which state has the most contamination, or the radionuclide that is the least prevalent across cities. We may ask about the number of valid measurements present in the dataset. All these questions have in common transformations that involve grouping data together and aggregating several values. With pandas, this is accomplished with GroupBy. Let's see how we can use it by key and aggregate the data.

Remember that we have to answer the question of which state has, on average, the highest radionuclide concentration. As there are several cities per state, we have to combine the values of all cities in one state and calculate the average. This is one of the applications of GroupBy: calculating the average values of one variable as per a grouping. We can answer the question using GroupBy:

NumPy functions can be applied to DataFrames directly or through the apply and applymap methods. Other NumPy functions, such as np.where, also work with DataFrames.

After finishing our analysis, we may want to save our transformed dataset with all the corrections, so if we want to share this dataset or redo our analysis, we don't have to transform the dataset again. We can also include our analysis as part of a larger data pipeline or even use the prepared data in the analysis as input to a machine learning algorithm. We can accomplish data exporting our DataFrame to a file with the right format:

To finish up our activity, let's redo all the previous steps and plot graphs with the results, as we would do in a preliminary analysis:

We are getting a bit ahead of ourselves here with the plotting, so we don't need to worry about the details of the plot or how we attribute titles, labels, and so on. The important takeaway here is understanding that we can plot directly from the DataFrame for quick analysis and visualization.