Caveats and Gotchas in Pandas

Introduction

Before learning about the caveats and gotchas in Pandas, let us first get a brief introduction to the Pandas module.

Pandas library is an open-source (free to use) library that is built on top of another very useful Python library i.e., NumPy library. Pandas is an open-source package (or library) that provides us with highly optimized data structures and data analysis tools. Pandas library is widely used in the field of data science, machine learning, and data analytics as it simplifies data importing and data analysis.

Pandas Python package offers us a wide variety of data structures and operations that helps in easy manipulation (add, update, delete) of numerical data as well as the time series. The prime reason for the Pandas package's popularity is its easy importing feature and easy data analyzing data feature. Pandas module is quite fast and comes in very handy because of its high performance and productivity. Before learning about the Pandas package, we must be familiar with Python programming concepts such as lists, tuples, sets, dictionaries, functions, loops, iterations, etc. We should also be familiar with the basic concepts of the NumPy module.

Now a question comes to our mind what are caveats and gotchas in Pandas? Well, The meaning of Caveats is a warning, and the meaning of Gotchas is an unseen problem. Let us see the various ways to deal with the caveats and gotchas in pandas in the next section.

Using If/Truth Statement with Pandas

As we know, that Pandas library is built using the NumPy library. So, the Pandas module inheritably follows the conversion of the NumPy module. So when we convert anything (any value) to a Boolean value, the Python interpreter raises an error.

The error happens to be in an if or when we are using the Boolean operations like and, or, or not. The result or the outcome is not clear whenever the operation takes place. Whether the result should come out to be True because it is not zerolength? Or should it come out as False because there are False values as well? The final output or result is not clear ao; instead of converting, the Pandas module raises an error (namely - ValueError) −

Output:

Now for the evaluation of the single element of Pandas object in the context of a boolean value, we use the bool() method. For example:

Output:

To deal with the errors, we can use the methods like any(), all(), or empty().

Bitwise Boolean

We can also use operators equal to == and not equal to != to return a Boolean Series, which is almost always what we want. For example -

Output:

isin Operation

We can use Python's in operator to check the membership of the index, but it cannot check the membership among the values. So, to test the membership in the values, the Pandas module uses a method called isin().

We can use the isin() method to return a Boolean Series whether each element in the Series is exactly contained in the passed sequence of values. For example -

Output:

NaN, Integer NA values and NA type promotions

Let us now learn about Nan and NA type conversion and promotion.

Choice of NA representation

NaN means Not A Number. When there is a lack of NA or missing support from the ground up in NumPy, we have to choose between either:

1. A solution of the masked array which is nothing but an array of data and boolean values that indicates the value.
2. A special sentinel value, a bit pattern, or a set of sentinel values that denotes NA across the dtypes.

We generally choose the second option as it is proven to be better after several years of usage. We also use the NaN value in place of the NA value. We have some API functions like isnull() and notnull() functions to detect the NA values across the dtypes.

Support for integer NA

When high performance is absent, NA support is built into NumPy from the ground up. The primary casualty is the ability to represent NAs in integer arrays. For example:

Output:

We mainly use this conversion for high memory and performance reasons. This also makes the resulting Series a numeric one. We can also use the dtype=object arrays instead.

NA type promotions

When presenting NAs into an existent Series or DataFrame by way of reindex or different resources, boolean and number types will progress in business to a various dtype so that it stores the NAs. Let us summarize this in a table:

Why not make NumPy like R?

We have another rule-specific mathematical setup language, i.e., R language. Now, many people have submitted that the R languages' NA support should also be used in the NumPy.

Let us now look at that part of the reason that used the NumPy type hierarchy:

The R language has a very small number of built-in data types. The R language primarily supports number data type, numeric (floating-point) data type, character data type, and boolean data type. Now, if we want to use the NA data type, then we can do so by reserving distinctive slice patterns for each type to be secondhand as the gone profit. We can do the NumPy type conversion using the hierarchy table, but it will be a more substantial trade-off(for the 8 and 16-bit data types, it would be exceptional).

Integer indexing

Label-wise indexing accompanying integer pivot labels is a problematic issue. It has happened discussed heavily on posting lists and between various communities of the controlled Python society. In Pandas, our general attitude is that labels matter as well as number regions. Therefore, with a number axis index, only label-located indexing is possible to accompany the standard finishes like .ix. This deliberate conclusion was created for fear that ambiguities and delicate bugs.

Label-based slicing conventions

Let us now learn about label-based slicing conversions.

Non-monotonic indexes require exact matches

If the index of a Series or a DataFrame is monotonically growing or diminishing, before the bounds of a label-based slice may be outside the range of the index, much like slice indexing a rational Python list. The monotonicity of an index may be proven by accompanying the is_monotonic_increasing and is_monotonic_decreasing attributes.

Example:

Output:

Endpoints are inclusive

Compared with the standard Python series slicing in which the endpoint in the slicing is not included, the other labeling method i.e. label-based slicing, which is used in Pandas, has the endpoint included. The basic reason why the endpoint is included is that it is generally not possible to determine the successor easily after a particular label in an index.

Example:

Output:

Miscellaneous indexing gotchas

Let us now learn about re-indexing and ix gotchas.

Re-indexing vs. ix Gotcha

Many people use the ix indexing as a concise means of selecting data from the Pandas object. Some people may think that the ix and reindex methods are the same. They both are the same in all cases except in the case of integer indexing. Hence we must remember that the reindex is strict label indexing only.

Re-index potentially changes underlying Series dtype

By using the reindex_like method, we can even change the dtype or data type of a Series. Let us take an example for more clarity.

Output:

The conversion happens because the reindex_like inserts the NaNs value silently, and the dtype is changed accordingly. This changing of the value causes some issues whenever we are using numpy ufuncs such as numpy.logical_and, etc.

Parsing Dates from Text Files

When parsing multiple text file columns into an alone date column, the data and then index_col specification is indexed off of the new set of columns rather than the original ones.

Differences with NumPy

Whenever we are using the Series or the DataFrame objects, the var value is normalized by N-1 value and hence it produces unbiased estimates of the sample variance, on the other hand the NumPy’s var variable gets normalized by the N value, which is used to measure the variance of the actual sample.

Thread-safety

Till the version of the Pandas 0.11, Pandas are not 100% treated as thread-safe. The currently known issues have a connection with the DataFrame.copy design method. If we are doing plenty of imitating of DataFrame objects among clothes, we approve holding the locks in the threads where the copying of the data happens.

HTML Table Parsing

The libraries that are used to parse HTML tables have a few versioning issues related to the top-level Pandas i/o function read_html().

Let us now look at some issues:

1. Issues with lxml: The lxml is quite fast and it requires the installation of Cython. But the problem is that the lxml does not guarantee the result unless we strictly give a valid markup to it.
2. Issues with BeautifulSoup4 using lxml as a background: The above-mentioned issues are still there, and BS4 or the BeautifulSoup4 is essentially just a wrapper around a parser backend.

Byte-Ordering Issues

There may be situations when we need to deal with the data that was created on some different byte order than the machine we are running the Python. The issue may be like this:

Now to deal with such an error, we need to convert the NumPy's' underlying array to the byte order of the native system even before passing the Series or DataFrame or the Panel constructors to the Series.