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Bioinformatics with Python Cookbook
Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook: Use modern Python libraries and applications to solve real-world computational biology problems , Third Edition

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Bioinformatics with Python Cookbook

Getting to Know NumPy, pandas, Arrow, and Matplotlib

One of Python’s biggest strengths is its profusion of high-quality science and data processing libraries. At the core of all of them is NumPy, which provides efficient array and matrix support. On top of NumPy, we can find almost all of the scientific libraries. For example, in our field, there’s Biopython. But other generic data analysis libraries can also be used in our field. For example, pandas is the de facto standard for processing tabled data. More recently, Apache Arrow provides efficient implementations of some of pandas’ functionality, along with language interoperability. Finally, Matplotlib is the most common plotting library in the Python space and is appropriate for scientific computing. While these are general libraries with wide applicability, they are fundamental for bioinformatics processing, so we will study them in this chapter.

We will start by looking at pandas as it provides a high-level library with very broad practical applicability. Then, we’ll introduce Arrow, which we will use only in the scope of supporting pandas. After that, we’ll discuss NumPy, the workhorse behind almost everything we do. Finally, we’ll introduce Matplotlib.

Our recipes are very introductory – each of these libraries could easily occupy a full book, but the recipes should be enough to help you through this book. If you are using Docker, and because all these libraries are fundamental for data analysis, they can be found in the tiagoantao/bioinformatics_base Docker image from Chapter 1.

In this chapter, we will cover the following recipes:

  • Using pandas to process vaccine-adverse events
  • Dealing with the pitfalls of joining pandas DataFrames
  • Reducing the memory usage of pandas DataFrames
  • Accelerating pandas processing with Apache Arrow
  • Understanding NumPy as the engine behind Python data science and bioinformatics
  • Introducing Matplotlib for chart generation

Using pandas to process vaccine-adverse events

We will be introducing pandas with a concrete bioinformatics data analysis example: we will be studying data from the Vaccine Adverse Event Reporting System (VAERS, https://vaers.hhs.gov/). VAERS, which is maintained by the US Department of Health and Human Services, includes a database of vaccine-adverse events going back to 1990.

VAERS makes data available in comma-separated values (CSV) format. The CSV format is quite simple and can even be opened with a simple text editor (be careful with very large file sizes as they may crash your editor) or a spreadsheet such as Excel. pandas can work very easily with this format.

Getting ready

First, we need to download the data. It is available at https://vaers.hhs.gov/data/datasets.html. Please download the ZIP file: we will be using the 2021 file; do not download a single CSV file only. After downloading the file, unzip it, and then recompress all the files individually with gzip –9 *csv to save disk space.

Feel free to have a look at the files with a text editor, or preferably with a tool such as less (zless for compressed files). You can find documentation for the content of the files at https://vaers.hhs.gov/docs/VAERSDataUseGuide_en_September2021.pdf.

If you are using the Notebooks, code is provided at the beginning of them so that you can take care of the necessary processing. If you are using Docker, the base image is enough.

The code can be found in Chapter02/Pandas_Basic.py.

How to do it...

Follow these steps:

  1. Let’s start by loading the main data file and gathering the basic statistics:
    vdata = pd.read_csv(
        "2021VAERSDATA.csv.gz", encoding="iso-8859-1")
    vdata.columns
    vdata.dtypes
    vdata.shape

We start by loading the data. In most cases, there is no need to worry about the text encoding as the default, UTF-8, will work, but in this case, the text encoding is legacy iso-8859-1. Then, we print the column names, which start with VAERS_ID, RECVDATE, STATE, AGE_YRS, and so on. They include 35 entries corresponding to each of the columns. Then, we print the types of each column. Here are the first few entries:

VAERS_ID          int64
RECVDATE         object
STATE            object
AGE_YRS         float64
CAGE_YR         float64
CAGE_MO         float64
SEX              object

By doing this, we get the shape of the data: (654986, 35). This means 654,986 rows and 35 columns. You can use any of the preceding strategies to get the information you need regarding the metadata of the table.

  1. Now, let’s explore the data:
    vdata.iloc[0]
    vdata = vdata.set_index("VAERS_ID")
    vdata.loc[916600]
    vdata.head(3)
    vdata.iloc[:3]
    vdata.iloc[:5, 2:4]

There are many ways we can look at the data. We will start by inspecting the first row, based on location. Here is an abridged version:

VAERS_ID                                       916600
RECVDATE                                       01/01/2021
STATE                                          TX
AGE_YRS                                        33.0
CAGE_YR                                        33.0
CAGE_MO                                        NaN
SEX                                            F

TODAYS_DATE                                          01/01/2021
BIRTH_DEFECT                                  NaN
OFC_VISIT                                     Y
ER_ED_VISIT                                       NaN
ALLERGIES                                       Pcn and bee venom

After we index by VAERS_ID, we can use one ID to get a row. We can use 916600 (which is the ID from the preceding record) and get the same result.

Then, we retrieve the first three rows. Notice the two different ways we can do so:

  • Using the head method
  • Using the more general array specification; that is, iloc[:3]

Finally, we retrieve the first five rows, but only the second and third columns –iloc[:5, 2:4]. Here is the output:

          AGE_YRS  CAGE_YR
VAERS_ID                  
916600       33.0     33.0
916601       73.0     73.0
916602       23.0     23.0
916603       58.0     58.0
916604       47.0     47.0
  1. Let’s do some basic computations now, namely computing the maximum age in the dataset:
    vdata["AGE_YRS"].max()
    vdata.AGE_YRS.max()

The maximum value is 119 years. More importantly than the result, notice the two dialects for accessing AGE_YRS (as a dictionary key and as an object field) for the access columns.

  1. Now, let’s plot the ages involved:
    vdata["AGE_YRS"].sort_values().plot(use_index=False)
    vdata["AGE_YRS"].plot.hist(bins=20) 

This generates two plots (a condensed version is shown in the following step). We use pandas plotting machinery here, which uses Matplotib underneath.

  1. While we have a full recipe for charting with Matplotlib (Introducing Matplotlib for chart generation), let’s have a sneak peek here by using it directly:
    import matplotlib.pylot as plt
    fig, ax = plt.subplots(1, 2, sharey=True)
    fig.suptitle("Age of adverse events")
    vdata["AGE_YRS"].sort_values().plot(
        use_index=False, ax=ax[0],
        xlabel="Obervation", ylabel="Age")
    vdata["AGE_YRS"].plot.hist(bins=20, orientation="horizontal")

This includes both figures from the previous steps. Here is the output:

Figure 2.1 – Left – the age for each observation of adverse effect; 
right – a histogram showing the distribution of ages

Figure 2.1 – Left – the age for each observation of adverse effect; right – a histogram showing the distribution of ages

  1. We can also take a non-graphical, more analytical approach, such as counting the events per year:
    vdata["AGE_YRS"].dropna().apply(lambda x: int(x)).value_counts()

The output will be as follows:

50     11006
65     10948
60     10616
51     10513
58     10362
      ...
  1. Now, let’s see how many people died:
    vdata.DIED.value_counts(dropna=False)
    vdata["is_dead"] = (vdata.DIED == "Y")

The output of the count is as follows:

NaN    646450
Y        8536
Name: DIED, dtype: int64

Note that the type of DIED is not a Boolean. It’s more declarative to have a Boolean representation of a Boolean characteristic, so we create is_dead for it.

Tip

Here, we are assuming that NaN is to be interpreted as False. In general, we must be careful with the interpretation of NaN. It may mean False or it may simply mean – as in most cases – a lack of data. If that were the case, it should not be converted into False.

  1. Now, let’s associate the individual data about deaths with the type of vaccine involved:
    dead = vdata[vdata.is_dead]
    vax = pd.read_csv("2021VAERSVAX.csv.gz", encoding="iso-8859-1").set_index("VAERS_ID")
    vax.groupby("VAX_TYPE").size().sort_values()
    vax19 = vax[vax.VAX_TYPE == "COVID19"]
    vax19_dead = dead.join(vax19)

After we get a DataFrame containing just deaths, we must read the data that contains vaccine information. First, we must do some exploratory analysis of the types of vaccines and their adverse events. Here is the abridged output:

           …
HPV9         1506
FLU4         3342
UNK          7941
VARZOS      11034
COVID19    648723

After that, we must choose just the COVID-related vaccines and join them with individual data.

  1. Finally, let’s see the top 10 COVID vaccine lots that are overrepresented in terms of deaths and how many US states were affected by each lot:
    baddies = vax19_dead.groupby("VAX_LOT").size().sort_values(ascending=False)
    for I, (lot, cnt) in enumerate(baddies.items()):
        print(lot, cnt, len(vax19_dead[vax19_dead.VAX_LOT == lot].groupby""STAT"")))
        if i == 10:
            break

The output is as follows:

Unknown 254 34
EN6201 120 30
EN5318 102 26
EN6200 101 22
EN6198 90 23
039K20A 89 13
EL3248 87 17
EL9261 86 21
EM9810 84 21
EL9269 76 18
EN6202 75 18

That concludes this recipe!

There’s more...

The preceding data about vaccines and lots is not completely correct; we will cover some data analysis pitfalls in the next recipe.

In the Introducing Matplotlib for chart generation recipe, we will introduce Matplotlib, a chart library that provides the backend for pandas plotting. It is a fundamental component of Python’s data analysis ecosystem.

See also

The following is some extra information that may be useful:

Dealing with the pitfalls of joining pandas DataFrames

The previous recipe was a whirlwind tour that introduced pandas and exposed most of the features that we will use in this book. While an exhaustive discussion about pandas would require a complete book, in this recipe – and in the next one – we are going to discuss topics that impact data analysis and are seldom discussed in the literature but are very important.

In this recipe, we are going to discuss some pitfalls that deal with relating DataFrames through joins: it turns out that many data analysis errors are introduced by carelessly joining data. We will introduce techniques to reduce such problems here.

Getting ready

We will be using the same data as in the previous recipe, but we will jumble it a bit so that we can discuss typical data analysis pitfalls. Once again, we will be joining the main adverse events table with the vaccination table, but we will randomly sample 90% of the data from each. This mimics, for example, the scenario where you only have incomplete information. This is one of the many examples where joins between tables do not have intuitively obvious results.

Use the following code to prepare our files by randomly sampling 90% of the data:

vdata = pd.read_csv("2021VAERSDATA.csv.gz", encoding="iso-8859-1")
vdata.sample(frac=0.9).to_csv("vdata_sample.csv.gz", index=False)
vax = pd.read_csv("2021VAERSVAX.csv.gz", encoding="iso-8859-1")
vax.sample(frac=0.9).to_csv("vax_sample.csv.gz", index=False)

Because this code involves random sampling, the results that you will get will be different from the ones reported here. If you want to get the same results, I have provided the files that I used in the Chapter02 directory. The code for this recipe can be found in Chapter02/Pandas_Join.py.

How to do it...

Follow these steps:

  1. Let’s start by doing an inner join of the individual and vaccine tables:
    vdata = pd.read_csv("vdata_sample.csv.gz")
    vax = pd.read_csv("vax_sample.csv.gz")
    vdata_with_vax = vdata.join(
        vax.set_index("VAERS_ID"),
        on="VAERS_ID",
        how="inner")
    len(vdata), len(vax), len(vdata_with_vax)

The len output for this code is 589,487 for the individual data, 620,361 for the vaccination data, and 558,220 for the join. This suggests that some individual and vaccine data was not captured.

  1. Let’s find the data that was not captured with the following join:
    lost_vdata = vdata.loc[~vdata.index.isin(vdata_with_vax.index)]
    lost_vdata
    lost_vax = vax[~vax["VAERS_ID"].isin(vdata.index)]
    lost_vax

You will see that 56,524 rows of individual data aren’t joined and that there are 62,141 rows of vaccinated data.

  1. There are other ways to join data. The default way is by performing a left outer join:
    vdata_with_vax_left = vdata.join(
        vax.set_index("VAERS_ID"),
        on="VAERS_ID")
    vdata_with_vax_left.groupby("VAERS_ID").size().sort_values()

A left outer join assures that all the rows on the left table are always represented. If there are no rows on the right, then all the right columns will be filled with None values.

Warning

There is a caveat that you should be careful with. Remember that the left table – vdata – had one entry per VAERS_ID. When you left join, you may end up with a case where the left-hand side is repeated several times. For example, the groupby operation that we did previously shows that VAERS_ID of 962303 has 11 entries. This is correct, but it’s not uncommon to have the incorrect expectation that you will still have a single row on the output per row on the left-hand side. This is because the left join returns 1 or more left entries, whereas the inner join above returns 0 or 1 entries, where sometimes, we would like to have precisely 1 entry. Be sure to always test the output for what you want in terms of the number of entries.

  1. There is a right join as well. Let’s right join COVID vaccines – the left table – with death events – the right table:
    dead = vdata[vdata.DIED == "Y"]
    vax19 = vax[vax.VAX_TYPE == "COVID19"]
    vax19_dead = vax19.join(dead.set_index("VAERS_ID"), on="VAERS_ID", how="right")
    len(vax19), len(dead), len(vax19_dead)
    len(vax19_dead[vax19_dead.VAERS_ID.duplicated()])
    len(vax19_dead) - len(dead)

As you may expect, a right join will ensure that all the rows on the right table are represented. So, we end up with 583,817 COVID entries, 7,670 dead entries, and a right join of 8,624 entries.

We also check the number of duplicated entries on the joined table and we get 954. If we subtract the length of the dead table from the joined table, we also get, as expected, 954. Make sure you have checks like this when you’re making joins.

  1. Finally, we are going to revisit the problematic COVID lot calculations since we now understand that we might be overcounting lots:
    vax19_dead["STATE"] = vax19_dead["STATE"].str.upper()
    dead_lot = vax19_dead[["VAERS_ID", "VAX_LOT", "STATE"]].set_index(["VAERS_ID", "VAX_LOT"])
    dead_lot_clean = dead_lot[~dead_lot.index.duplicated()]
    dead_lot_clean = dead_lot_clean.reset_index()
    dead_lot_clean[dead_lot_clean.VAERS_ID.isna()]
    baddies = dead_lot_clean.groupby("VAX_LOT").size().sort_values(ascending=False)
    for i, (lot, cnt) in enumerate(baddies.items()):
        print(lot, cnt, len(dead_lot_clean[dead_lot_clean.VAX_LOT == lot].groupby("STATE")))
        if i == 10:
            break

Note that the strategies that we’ve used here ensure that we don’t get repeats: first, we limit the number of columns to the ones we will be using, then we remove repeated indexes and empty VAERS_ID. This ensures no repetition of the VAERS_ID, VAX_LOT pair, and that no lots are associated with no IDs.

There’s more...

There are other types of joins other than left, inner, and right. Most notably, there is the outer join, which assures all entries from both tables have representation.

Make sure you have tests and assertions for your joins: a very common bug is having the wrong expectations for how joins behave. You should also make sure that there are no empty values on the columns where you are joining, as they can produce a lot of excess tuples.

Reducing the memory usage of pandas DataFrames

When you are dealing with lots of information – for example, when analyzing whole genome sequencing data – memory usage may become a limitation for your analysis. It turns out that naïve pandas is not very efficient from a memory perspective, and we can substantially reduce its consumption.

In this recipe, we are going to revisit our VAERS data and look at several ways to reduce pandas memory usage. The impact of these changes can be massive: in many cases, reducing memory consumption may mean the difference between being able to use pandas or requiring a more alternative and complex approach, such as Dask or Spark.

Getting ready

We will be using the data from the first recipe. If you have run it, you are all set; if not, please follow the steps discussed there. You can find this code in Chapter02/Pandas_Memory.py.

How to do it…

Follow these steps:

  1. First, let’s load the data and inspect the size of the DataFrame:
    import numpy as np
    import pandas as pd
    vdata = pd.read_csv("2021VAERSDATA.csv.gz", encoding="iso-8859-1")
    vdata.info(memory_usage="deep")

Here is an abridged version of the output:

RangeIndex: 654986 entries, 0 to 654985
Data columns (total 35 columns):
#   Column        Non-Null Count   Dtype  
---  ------        --------------   -----  
0   VAERS_ID      654986 non-null  int64  
2   STATE         572236 non-null  object 
3   AGE_YRS       583424 non-null  float64
6   SEX           654986 non-null  object 
8   SYMPTOM_TEXT  654828 non-null  object 
9   DIED          8536 non-null    object 
31  BIRTH_DEFECT  383 non-null     object 
34  ALLERGIES     330630 non-null  object 
dtypes: float64(5), int64(2), object(28)
memory usage: 1.3 GB

Here, we have information about the number of rows and the type and non-null values of each row. Finally, we can see that the DataFrame requires a whopping 1.3 GB.

  1. We can also inspect the size of each column:
    for name in vdata.columns:
        col_bytes = vdata[name].memory_usage(index=False, deep=True)
        col_type = vdata[name].dtype
        print(
            name,
            col_type, col_bytes // (1024 ** 2))

Here is an abridged version of the output:

VAERS_ID int64 4
STATE object 34
AGE_YRS float64 4
SEX object 36
RPT_DATE object 20
SYMPTOM_TEXT object 442
DIED object 20
ALLERGIES object 34

SYMPTOM_TEXT occupies 442 MB, so 1/3 of our entire table.

  1. Now, let’s look at the DIED column. Can we find a more efficient representation?
    vdata.DIED.memory_usage(index=False, deep=True)
    vdata.DIED.fillna(False).astype(bool).memory_usage(index=False, deep=True)

The original column takes 21,181,488 bytes, whereas our compact representation takes 656,986 bytes. That’s 32 times less!

  1. What about the STATE column? Can we do better?
    vdata["STATE"] = vdata.STATE.str.upper()
    states = list(vdata["STATE"].unique())
    vdata["encoded_state"] = vdata.STATE.apply(lambda state: states.index(state))
    vdata["encoded_state"] = vdata["encoded_state"].astype(np.uint8)
    vdata["STATE"].memory_usage(index=False, deep=True)
    vdata["encoded_state"].memory_usage(index=False, deep=True)

Here, we convert the STATE column, which is text, into encoded_state, which is a number. This number is the position of the state’s name in the list state. We use this number to look up the list of states. The original column takes around 36 MB, whereas the encoded column takes 0.6 MB.

As an alternative to this approach, you can look at categorical variables in pandas. I prefer to use them as they have wider applications.

  1. We can apply most of these optimizations when we load the data, so let’s prepare for that. But now, we have a chicken-and-egg problem: to be able to know the content of the state table, we have to do a first pass to get the list of states, like so:
    states = list(pd.read_csv(
        "vdata_sample.csv.gz",
        converters={
           "STATE": lambda state: state.upper()
        },
        usecols=["STATE"]
    )["STATE"].unique())

We have a converter that simply returns the uppercase version of the state. We only return the STATE column to save memory and processing time. Finally, we get the STATE column from the DataFrame (which has only a single column).

  1. The ultimate optimization is not to load the data. Imagine that we don’t need SYMPTOM_TEXT – that is around 1/3 of the data. In that case, we can just skip it. Here is the final version:
    vdata = pd.read_csv(
        "vdata_sample.csv.gz",
        index_col="VAERS_ID",
        converters={
           "DIED": lambda died: died == "Y",
           "STATE": lambda state: states.index(state.upper())
        },
        usecols=lambda name: name != "SYMPTOM_TEXT"
    )
    vdata["STATE"] = vdata["STATE"].astype(np.uint8)
    vdata.info(memory_usage="deep") 

We are now at 714 MB, which is a bit over half of the original. This could be still substantially reduced by applying the methods we used for STATE and DIED to all other columns.

See also

The following is some extra information that may be useful:

  • If you are willing to use a support library to help with Python processing, check the next recipe on Apache Arrow, which will allow you to have extra memory savings for more memory efficiency.
  • If you end up with DataFrames that take more memory than you have available on a single machine, then you must step up your game and use chunking - which we will not cover in the Pandas context - or something that can deal with large data automatically. Dask, which we’ll cover in Chapter 11, Parallel Processing with Dask and Zarr, allows you to work with larger-than-memory datasets with, among others, a pandas-like interface.

Accelerating pandas processing with 
Apache Arrow

When dealing with large amounts of data, such as in whole genome sequencing, pandas is both slow and memory-consuming. Apache Arrow provides faster and more memory-efficient implementations of several pandas operations and can interoperate with it.

Apache Arrow is a project co-founded by Wes McKinney, the founder of pandas, and it has several objectives, including working with tabular data in a language-agnostic way, which allows for language interoperability while providing a memory- and computation-efficient implementation. Here, we will only be concerned with the second part: getting more efficiency for large-data processing. We will do this in an integrated way with pandas.

Here, we will once again use VAERS data and show how Apache Arrow can be used to accelerate pandas data loading and reduce memory consumption.

Getting ready

Again, we will be using data from the first recipe. Be sure you download and prepare it, as explained in the Getting ready section of the Using pandas to process vaccine-adverse events recipe. The code is available in Chapter02/Arrow.py.

How to do it...

Follow these steps:

  1. Let’s start by loading the data using both pandas and Arrow:
    import gzip
    import pandas as pd
    from pyarrow import csv
    import pyarrow.compute as pc 
    vdata_pd = pd.read_csv("2021VAERSDATA.csv.gz", encoding="iso-8859-1")
    columns = list(vdata_pd.columns)
    vdata_pd.info(memory_usage="deep") 
    vdata_arrow = csv.read_csv("2021VAERSDATA.csv.gz")
    tot_bytes = sum([
        vdata_arrow[name].nbytes
        for name in vdata_arrow.column_names])
    print(f"Total {tot_bytes // (1024 ** 2)} MB")

pandas requires 1.3 GB, whereas Arrow requires 614 MB: less than half the memory. For large files like this, this may mean the difference between being able to process data in memory or needing to find another solution, such as Dask. While some functions in Arrow have similar names to pandas (for example, read_csv), that is not the most common occurrence. For example, note the way we compute the total size of the DataFrame: by getting the size of each column and performing a sum, which is a different approach from pandas.

  1. Let’s do a side-by-side comparison of the inferred types:
    for name in vdata_arrow.column_names:
        arr_bytes = vdata_arrow[name].nbytes
        arr_type = vdata_arrow[name].type
        pd_bytes = vdata_pd[name].memory_usage(index=False, deep=True)
        pd_type = vdata_pd[name].dtype
        print(
            name,
            arr_type, arr_bytes // (1024 ** 2),
            pd_type, pd_bytes // (1024 ** 2),)

Here is an abridged version of the output:

VAERS_ID int64 4 int64 4
RECVDATE string 8 object 41
STATE string 3 object 34
CAGE_YR int64 5 float64 4
SEX string 3 object 36
RPT_DATE string 2 object 20
DIED string 2 object 20
L_THREAT string 2 object 20
ER_VISIT string 2 object 19
HOSPITAL string 2 object 20
HOSPDAYS int64 5 float64 4

As you can see, Arrow is generally more specific with type inference and is one of the main reasons why memory usage is substantially lower.

  1. Now, let’s do a time performance comparison:
    %timeit pd.read_csv("2021VAERSDATA.csv.gz", encoding="iso-8859-1")
    %timeit csv.read_csv("2021VAERSDATA.csv.gz")

On my computer, the results are as follows:

7.36 s ± 201 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
2.28 s ± 70.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Arrow’s implementation is three times faster. The results on your computer will vary as this is dependent on the hardware.

  1. Let’s repeat the memory occupation comparison while not loading the SYMPTOM_TEXT column. This is a fairer comparison as most numerical datasets do not tend to have a very large text column:
    vdata_pd = pd.read_csv("2021VAERSDATA.csv.gz", encoding="iso-8859-1", usecols=lambda x: x != "SYMPTOM_TEXT")
    vdata_pd.info(memory_usage="deep")
    columns.remove("SYMPTOM_TEXT")
    vdata_arrow = csv.read_csv(
        "2021VAERSDATA.csv.gz",
         convert_options=csv.ConvertOptions(include_columns=columns))
    vdata_arrow.nbytes

pandas requires 847 MB, whereas Arrow requires 205 MB: four times less.

  1. Our objective is to use Arrow to load data into pandas. For that, we need to convert the data structure:
    vdata = vdata_arrow.to_pandas()
    vdata.info(memory_usage="deep")

There are two very important points to be made here: the pandas representation created by Arrow uses only 1 GB, whereas the pandas representation, from its native read_csv, is 1.3 GB. This means that even if you use pandas to process data, Arrow can create a more compact representation to start with.

The preceding code has one problem regarding memory consumption: when the converter is running, it will require memory to hold both the pandas and the Arrow representations, hence defeating the purpose of using less memory. Arrow can self-destruct its representation while creating the pandas version, hence resolving the problem. The line for this is vdata = vdata_arrow.to_pandas(self_destruct=True).

There’s more...

If you have a very large DataFrame that cannot be processed by pandas, even after it’s been loaded by Arrow, then maybe Arrow can do all the processing as it has a computing engine as well. That being said, Arrow’s engine is, at the time of writing, substantially less complete in terms of functionality than pandas. Remember that Arrow has many other features, such as language interoperability, but we will not be making use of those in this book.

Understanding NumPy as the engine behind Python data science and bioinformatics

Most of your analysis will make use of NumPy, even if you don’t use it explicitly. NumPy is an array manipulation library that is behind libraries such as pandas, Matplotlib, Biopython, and scikit-learn, among many others. While much of your bioinformatics work may not require explicit direct use of NumPy, you should be aware of its existence as it underpins almost everything you do, even if only indirectly via the other libraries.

In this recipe, we will use VAERS data to demonstrate how NumPy is behind many of the core libraries that we use. This is a very light introduction to the library so that you are aware that it exists and that it is behind almost everything. Our example will extract the number of cases from the five US states with more adverse effects, splitting them into age bins: 0 to 19 years, 20 to 39, up to 100 to 119.

Getting ready

Once again, we will be using the data from the first recipe, so make sure it’s available. The code for it can be found in Chapter02/NumPy.py.

How to do it…

Follow these steps:

  1. Let’s start by loading the data with pandas and reducing the data so that it’s related to the top five US states only:
    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    vdata = pd.read_csv(
        "2021VAERSDATA.csv.gz", encoding="iso-8859-1")
    vdata["STATE"] = vdata["STATE"].str.upper()
    top_states = pd.DataFrame({
        "size": vdata.groupby("STATE").size().sort_values(ascending=False).head(5)}).reset_index()
    top_states["rank"] = top_states.index
    top_states = top_states.set_index("STATE")
    top_vdata = vdata[vdata["STATE"].isin(top_states.index)]
    top_vdata["state_code"] = top_vdata["STATE"].apply(
        lambda state: top_states["rank"].at[state]
    ).astype(np.uint8)
    top_vdata = top_vdata[top_vdata["AGE_YRS"].notna()]
    top_vdata.loc[:,"AGE_YRS"] = top_vdata["AGE_YRS"].astype(int)
    top_states

The top states are as follows. This rank will be used later to construct a NumPy matrix:

Figure 2.2 – US states with largest numbers of adverse effects

Figure 2.2 – US states with largest numbers of adverse effects

  1. Now, let’s extract the two NumPy arrays that contain age and state data:
    age_state = top_vdata[["state_code", "AGE_YRS"]]
    age_state["state_code"]
    state_code_arr = age_state["state_code"].values
    type(state_code_arr), state_code_arr.shape, state_code_arr.dtype
    age_arr = age_state["AGE_YRS"].values
    type(age_arr), age_arr.shape, age_arr.dtype

Note that the data that underlies pandas is NumPy data (the values call for both Series returns NumPy types). Also, you may recall that pandas has properties such as .shape or .dtype: these were inspired by NumPy and behave the same.

  1. Now, let’s create a NumPy matrix from scratch (a 2D array), where each row is a state and each column represents an age group:
    age_state_mat = np.zeros((5,6), dtype=np.uint64)
    for row in age_state.itertuples():
        age_state_mat[row.state_code, row.AGE_YRS//20] += 1
    age_state_mat

The array has five rows – one for each state – and six columns – one for each age group. All the cells in the array must have the same type.

We initialize the array with zeros. There are many ways to initialize arrays, but if you have a very large array, initializing it may take a lot of time. Sometimes, depending on your task, it might be OK that the array is empty at the beginning (meaning it was initialized with random trash). In that case, using np.empty will be much faster. We use pandas iteration here: this is not the best way to do things from a pandas perspective, but we want to make the NumPy part very explicit.

  1. We can extract a single row – in our case, the data for a state – very easily. The same applies to a column. Let’s take California data and then the 0-19 age group:
    cal = age_state_mat[0,:]
    kids = age_state_mat[:,0]

Note the syntax to extract a row or a column. It should be familiar to you, given that pandas copied the syntax from NumPy and we encountered it in previous recipes.

  1. Now, let’s compute a new matrix where we have the fraction of cases per age group:
    def compute_frac(arr_1d):
        return arr_1d / arr_1d.sum()
    frac_age_stat_mat = np.apply_along_axis(compute_frac, 1, age_state_mat)

The last line applies the compute_frac function to all rows. compute_frac takes a single row and returns a new row where all the elements are divided by the total sum.

  1. Now, let’s create a new matrix that acts as a percentage instead of a fraction – simply because it reads better:
    perc_age_stat_mat = frac_age_stat_mat * 100
    perc_age_stat_mat = perc_age_stat_mat.astype(np.uint8)
    perc_age_stat_mat

The first line simply multiplies all the elements of the 2D array by 100. Matplotlib is smart enough to traverse different array structures. That line will work if it’s presented with an array with any dimensions and would do exactly what is expected.

Here is the result:

Figure 2.3 – A matrix representing the distribution of vaccine-adverse effects 
in the five US states with the most cases

Figure 2.3 – A matrix representing the distribution of vaccine-adverse effects in the five US states with the most cases

  1. Finally, let’s create a graphical representation of the matrix using Matplotlib:
    fig = plt.figure()
    ax = fig.add_subplot()
    ax.matshow(perc_age_stat_mat, cmap=plt.get_cmap("Greys"))
    ax.set_yticks(range(5))
    ax.set_yticklabels(top_states.index)
    ax.set_xticks(range(6))
    ax.set_xticklabels(["0-19", "20-39", "40-59", "60-79", "80-99", "100-119"])
    fig.savefig("matrix.png")

Do not dwell too much on the Matplotlib code – we are going to discuss it in the next recipe. The fundamental point here is that you can pass NumPy data structures to Matplotlib. Matplotlib, like pandas, is based on NumPy.

See also

The following is some extra information that may be useful:

  • NumPy has many more features than the ones we’ve discussed here. There are plenty of books and tutorials on them. The official documentation is a good place to start: https://numpy.org/doc/stable/.
  • There are many important issues to discover with NumPy, but probably one of the most important is broadcasting: NumPy’s ability to take arrays of different structures and get the operations right. For details, go to https://numpy.org/doc/stable/user/theory.broadcasting.html.

Introducing Matplotlib for chart generation

Matplotlib is the most common Python library for generating charts. There are more modern alternatives, such as Bokeh, which is web-centered, but the advantage of Matplotlib is not only that it is the most widely available and widely documented chart library but also, in the computational biology world, we want a chart library that is both web- and paper-centric. This is because many of our charts will be submitted to scientific journals, which are equally concerned with both formats. Matplotlib can handle this for us.

Many of the examples in this recipe could also be done directly with pandas (hence indirectly with Matplotlib), but the point here is to exercise Matplotlib.

Once again, we are going to use VAERS data to plot some information about the DataFrame’s metadata and summarize the epidemiological data.

Getting ready

Again, we will be using the data from the first recipe. The code can be found in Chapter02/Matplotlib.py.

How to do it...

Follow these steps:

  1. The first thing that we will do is plot the fraction of nulls per column:
    import numpy as np
    import pandas as pd
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    vdata = pd.read_csv(
        "2021VAERSDATA.csv.gz", encoding="iso-8859-1",
        usecols=lambda name: name != "SYMPTOM_TEXT")
    num_rows = len(vdata)
    perc_nan = {}
    for col_name in vdata.columns:
        num_nans = len(vdata[col_name][vdata[col_name].isna()])
        perc_nan[col_name] = 100 * num_nans / num_rows
    labels = perc_nan.keys()
    bar_values = list(perc_nan.values())
    x_positions = np.arange(len(labels))

labels is the column names that we are analyzing, bar_values is the fraction of null values, and x_positions is the location of the bars on the bar chart that we are going to plot next.

  1. Here is the code for the first version of the bar plot:
    fig = plt.figure()
    fig.suptitle("Fraction of empty values per column")
    ax = fig.add_subplot()
    ax.bar(x_positions, bar_values)
    ax.set_ylabel("Percent of empty values")
    ax.set_ylabel("Column")
    ax.set_xticks(x_positions)
    ax.set_xticklabels(labels)
    ax.legend()
    fig.savefig("naive_chart.png")

We start by creating a figure object with a title. The figure will have a subplot that will contain the bar chart. We also set several labels and only used defaults. Here is the sad result:

Figure 2.4 – Our first chart attempt, just using the defaults

Figure 2.4 – Our first chart attempt, just using the defaults

  1. Surely, we can do better. Let’s format the chart substantially more:
    fig = plt.figure(figsize=(16, 9), tight_layout=True, dpi=600)
    fig.suptitle("Fraction of empty values per column", fontsize="48")
    ax = fig.add_subplot()
    b1 = ax.bar(x_positions, bar_values)
    ax.set_ylabel("Percent of empty values", fontsize="xx-large")
    ax.set_xticks(x_positions)
    ax.set_xticklabels(labels, rotation=45, ha="right")
    ax.set_ylim(0, 100)
    ax.set_xlim(-0.5, len(labels))
    for i, x in enumerate(x_positions):
        ax.text(
            x, 2, "%.1f" % bar_values[i], rotation=90,
            va="bottom", ha="center",
            backgroundcolor="white")
    fig.text(0.2, 0.01, "Column", fontsize="xx-large")
    fig.savefig("cleaner_chart.png")

The first thing that we do is set up a bigger figure for Matplotlib to provide a tighter layout. We rotate the x-axis tick labels 45 degrees so that they fit better. We also put the values on the bars. Finally, we do not have a standard x-axis label as it would be on top of the tick labels. Instead, we write the text explicitly. Note that the coordinate system of the figure can be completely different from the coordinate system of the subplot – for example, compare the coordinates of ax.text and fig.text. Here is the result:

Figure 2.5 – Our second chart attempt, while taking care of the layout

Figure 2.5 – Our second chart attempt, while taking care of the layout

  1. Now, we are going to do some summary analysis of our data based on four plots on a single figure. We will chart the vaccines involved in deaths, the days between administration and death, the deaths over time, and the sex of people who have died for the top 10 states in terms of their quantity:
    dead = vdata[vdata.DIED == "Y"]
    vax = pd.read_csv("2021VAERSVAX.csv.gz", encoding="iso-8859-1").set_index("VAERS_ID")
    vax_dead = dead.join(vax, on="VAERS_ID", how="inner")
    dead_counts = vax_dead["VAX_TYPE"].value_counts()
    large_values = dead_counts[dead_counts >= 10]
    other_sum = dead_counts[dead_counts < 10].sum()
    large_values = large_values.append(pd.Series({"OTHER": other_sum}))
    distance_df = vax_dead[vax_dead.DATEDIED.notna() & vax_dead.VAX_DATE.notna()]
    distance_df["DATEDIED"] = pd.to_datetime(distance_df["DATEDIED"])
    distance_df["VAX_DATE"] = pd.to_datetime(distance_df["VAX_DATE"])
    distance_df = distance_df[distance_df.DATEDIED >= "2021"]
    distance_df = distance_df[distance_df.VAX_DATE >= "2021"]
    distance_df = distance_df[distance_df.DATEDIED >= distance_df.VAX_DATE]
    time_distances = distance_df["DATEDIED"] - distance_df["VAX_DATE"]
    time_distances_d = time_distances.astype(int) / (10**9 * 60 * 60 * 24)
    date_died = pd.to_datetime(vax_dead[vax_dead.DATEDIED.notna()]["DATEDIED"])
    date_died = date_died[date_died >= "2021"]
    date_died_counts = date_died.value_counts().sort_index()
    cum_deaths = date_died_counts.cumsum()
    state_dead = vax_dead[vax_dead["STATE"].notna()][["STATE", "SEX"]]
    top_states = sorted(state_dead["STATE"].value_counts().head(10).index)
    top_state_dead = state_dead[state_dead["STATE"].isin(top_states)].groupby(["STATE", "SEX"]).size()#.reset_index()
    top_state_dead.loc["MN", "U"] = 0  # XXXX
    top_state_dead = top_state_dead.sort_index().reset_index()
    top_state_females = top_state_dead[top_state_dead.SEX == "F"][0]
    top_state_males = top_state_dead[top_state_dead.SEX == "M"][0]
    top_state_unk = top_state_dead[top_state_dead.SEX == "U"][0]

The preceding code is strictly pandas-based and was made in preparation for the plotting activity.

  1. The following code plots all the information simultaneously. We are going to have four subplots organized in 2 by 2 format:
    fig, ((vax_cnt, time_dist), (death_time, state_reps)) = plt.subplots(
        2, 2,
        figsize=(16, 9), tight_layout=True)
    vax_cnt.set_title("Vaccines involved in deaths")
    wedges, texts = vax_cnt.pie(large_values)
    vax_cnt.legend(wedges, large_values.index, loc="lower left")
    time_dist.hist(time_distances_d, bins=50)
    time_dist.set_title("Days between vaccine administration and death")
    time_dist.set_xlabel("Days")
    time_dist.set_ylabel("Observations")
    death_time.plot(date_died_counts.index, date_died_counts, ".")
    death_time.set_title("Deaths over time")
    death_time.set_ylabel("Daily deaths")
    death_time.set_xlabel("Date")
    tw = death_time.twinx()
    tw.plot(cum_deaths.index, cum_deaths)
    tw.set_ylabel("Cummulative deaths")
    state_reps.set_title("Deaths per state stratified by sex") state_reps.bar(top_states, top_state_females, label="Females")
    state_reps.bar(top_states, top_state_males, label="Males", bottom=top_state_females)
    state_reps.bar(top_states, top_state_unk, label="Unknown",
                   bottom=top_state_females.values + top_state_males.values)
    state_reps.legend()
    state_reps.set_xlabel("State")
    state_reps.set_ylabel("Deaths")
    fig.savefig("summary.png")

We start by creating a figure with 2x2 subplots. The subplots function returns, along with the figure object, four axes objects that we can use to create our charts. Note that the legend is positioned in the pie chart, we have used a twin axis on the time distance plot, and we have a way to compute stacked bars on the death per state chart. Here is the result:

Figure 2.6 – Four combined charts summarizing the vaccine data

Figure 2.6 – Four combined charts summarizing the vaccine data

There’s more...

Matplotlib has two interfaces you can use – an older interface, designed to be similar to MATLAB, and a more powerful object-oriented (OO) interface. Try as much as possible to avoid mixing the two. Using the OO interface is probably more future-proof. The MATLAB-like interface is below the matplotlib.pyplot module. To make things confusing, the entry points for the OO interface are in that module – that is, matplotlib.pyplot.figure and matplotlib.pyplot.subplots.

See also

The following is some extra information that may be useful:

  • The documentation for Matplolib is really, really good. For example, there’s a gallery of visual samples with links to the code for generating each sample. This can be found at https://matplotlib.org/stable/gallery/index.html. The API documentation is generally very complete.
  • Another way to improve the looks of Matplotlib charts is to use the Seaborn library. Seaborn’s main purpose is to add statistical visualization artifacts, but as a side effect, when imported, it changes the defaults of Matplotlib to something more palatable. We will be using Seaborn throughout this book; check out the plots provided in the next chapter.
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Key benefits

  • Perform complex bioinformatics analysis using the most essential Python libraries and applications
  • Implement next-generation sequencing, metagenomics, automating analysis, population genetics, and much more
  • Explore various statistical and machine learning techniques for bioinformatics data analysis

Description

Bioinformatics is an active research field that uses a range of simple-to-advanced computations to extract valuable information from biological data, and this book will show you how to manage these tasks using Python. This updated third edition of the Bioinformatics with Python Cookbook begins with a quick overview of the various tools and libraries in the Python ecosystem that will help you convert, analyze, and visualize biological datasets. Next, you'll cover key techniques for next-generation sequencing, single-cell analysis, genomics, metagenomics, population genetics, phylogenetics, and proteomics with the help of real-world examples. You'll learn how to work with important pipeline systems, such as Galaxy servers and Snakemake, and understand the various modules in Python for functional and asynchronous programming. This book will also help you explore topics such as SNP discovery using statistical approaches under high-performance computing frameworks, including Dask and Spark. In addition to this, you’ll explore the application of machine learning algorithms in bioinformatics. By the end of this bioinformatics Python book, you'll be equipped with the knowledge you need to implement the latest programming techniques and frameworks, empowering you to deal with bioinformatics data on every scale.

Who is this book for?

This book is for bioinformatics analysts, data scientists, computational biologists, researchers, and Python developers who want to address intermediate-to-advanced biological and bioinformatics problems. Working knowledge of the Python programming language is expected. Basic knowledge of biology will also be helpful.

What you will learn

  • Become well-versed with data processing libraries such as NumPy, pandas, arrow, and zarr in the context of bioinformatic analysis
  • Interact with genomic databases
  • Solve real-world problems in the fields of population genetics, phylogenetics, and proteomics
  • Build bioinformatics pipelines using a Galaxy server and Snakemake
  • Work with functools and itertools for functional programming
  • Perform parallel processing with Dask on biological data
  • Explore principal component analysis (PCA) techniques with scikit-learn
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Table of Contents

14 Chapters
Chapter 1: Python and the Surrounding Software Ecology Chevron down icon Chevron up icon
Chapter 2: Getting to Know NumPy, pandas, Arrow, and Matplotlib Chevron down icon Chevron up icon
Chapter 3: Next-Generation Sequencing Chevron down icon Chevron up icon
Chapter 4: Advanced NGS Data Processing Chevron down icon Chevron up icon
Chapter 5: Working with Genomes Chevron down icon Chevron up icon
Chapter 6: Population Genetics Chevron down icon Chevron up icon
Chapter 7: Phylogenetics Chevron down icon Chevron up icon
Chapter 8: Using the Protein Data Bank Chevron down icon Chevron up icon
Chapter 9: Bioinformatics Pipelines Chevron down icon Chevron up icon
Chapter 10: Machine Learning for Bioinformatics Chevron down icon Chevron up icon
Chapter 11: Parallel Processing with Dask and Zarr Chevron down icon Chevron up icon
Chapter 12: Functional Programming for Bioinformatics Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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Paul Darby Oct 16, 2022
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If you are proficient with python and need a good reference book for bioinformatics. This book covers many of the important applications one may come across in bioinformatics. From basic NCBI I/O applications to NGS this book covers many of the topics with excellent code examples. The book covers several important topics in scientific programming like Machine Learning, NUMPY, PANDAS and DOCKER which are some core tools used in the data sciences.
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Seth Oct 17, 2022
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Disclaimer: I was sent a copy of this book to review.I have been working in the bioinformatics industry for 5+ years on all manner of bioinformatics problems. It's a shame this book didn't enter my life sooner. It's both a cookbook, and more than that. Each sections recipes build upon themselves in a cohesive and useful manner. I've found myself just working through a 5-page recipe per day as a method of self study, and gaining exposure to some of the niche facets of bioinformatics that we don't tackle day-to-day.All the tools referenced are up to date, and while the methods for doing things may not always fall into "best practices" they are all good foundations that someone could start with an build upon. I specifically enjoyed the sections at the end about processing data with Dask / Zarr, and the section on population genomics, both of which I was able to make use of in my own work.Overall, I'd recommend this book both to those that have just started down the bioinformatics path and need sample code to get going on tasks (but have a least beginner Python knowledge), and to those who have already been at this a while and just want to see some new and updated ways of doing things.
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Qirui Cui Sep 28, 2022
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There are many books explaining the need for bioinformatics using Python, its methodology, and the myriad designs and implementation pathways that can be taken. The missing book is one that covers all of these from start to finish in a complete, detailed, and comprehensive fashion. From justifying the project, gathering requirements, developing the bioinformatic architectural framework, designing the proper approach for NGS data, integrating the data, generating advanced analytics, dealing with “shadow systems,” understanding and dealing with organizational relationships, managing the full project life cycle, and finally creating centers of excellence—this book covers the entire gambit of creating a sustainable bioinformatic system in Python environment.Mr. Tiago Antao’s deep understanding of technical implementations is only matched by his understanding of the underpinning rudiments behind many of the decision points in the development of the bioinformatic components. These rudiments will help you determine the best deployment options for your specific situation—so invaluable in today’s confusing and mixed messages bioinformatic world!I highly recommend this book to anyone just starting out in bioinformatics using Python particularly, who has a legacy environment that needs renovating or just wants to understand the entire implementation picture from start to finish. Mr. Tiago Antao’s mastery of all the critical implementation activities means you are receiving the best advice for creating a world-class python environment for bioinformatics that will last for long haul. Nicely done, Mr. Tiago Antao.
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Jun, D. Oct 14, 2022
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A good entry-level book, covers quite a bit of the most popular packages, such as Biopython, scikit-learn, qiime, etc, covers sequencing, phylogenetics, metagenomics, etc., A good book to get familiar with the bioinformatics, it is especially good for one want to practice both python and bioinformatics, since it provide relatively good coverage for both.
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LadyGator Nov 12, 2022
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I was very pleased to receive a review copy of Tiago Antao’s latest edition of the Bioinformatics with Python Cookbook.As an instructor and researcher who works in a bioinformatics core at a university in Boston, I can highly recommend this book as a resource for instruction as well as a practical guide to everyday problems in bioinformatics. Some highlights of the book are the excellent practical exercises which walkthrough common tasks, such as downloading data from NCBI and constructing meaningful plots using matplotlib.I was impressed with the more advanced materials, such as how to access the Galaxy platform using the API and running workflows with snakemake. The book assumes some familiarity with Python code, but even a beginner can follow the logic of the exercises and examples. Many helpful links are provided to freely available resources on the topics that are discussed. I would highly recommend this manual to put on your office bookshelf if you teach or use Python to analyze bioinformatics data.
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Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela
What is custom duty/charge? Chevron down icon Chevron up icon

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela