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pandas is a powerful data analysis library with a rich API that offers multiple ways to perform any given data manipulation task. Some of these approaches are better than others, and pandas users often learn suboptimal coding practices that become their default workflows. This post highlights four common pandas anti-patterns and outlines a complementary set of techniques that you should use instead.*

For illustrative examples of good and bad pandas patterns, I'm using this Netflix dataset from Kaggle, which characterises almost 6,000 Netflix shows and movies with respect to 15 features spanning various data types.

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Most pandas practitioners first learn data processing with pandas by sequentially mutating DataFrames as a series of distinct, line-by-line operations. There are a few reasons why excessive mutation of pandas DataFrames can cause problems:

Chaining transforms a DataFrame according to a multi-step procedure all at once. This guarantees the full and proper application of each pandas method, thus mitigating the risk of bugs. The code is more readable with each line cleanly representing a distinct operation (note: many Python code formatters will destroy this structure - wrap your pandas code blocks with '#fmt: off' and '#fmt: on' to prevent this). Chaining will feel natural for R users familiar with the magrittr %>% operator.

Occasionally, you'll need to perform complex data manipulation processes that can't be cleanly implemented using off-the-shelf chaining methods. This is where pandas' .pipe can be used to abstract away complex DataFrame transformations into separately defined functions.

The use of for loops in pandas is a code smell that should always be eliminated. This includes pandas' built-in generator methods DataFrame.iterrows() and DataFrame.itertuples(). There are two reasons to avoid looping in pandas:

Once pandas practitioners learn about .apply, they often end up applying it everywhere. This isn't always a problem, as the .apply approach produces coherent code and performs adequately with modestly-sized datasets.

Optimising the data types for each column in a pandas DataFrame will improve performance and memory usage. When working with large datasets, significant gains can be made by shrinking the default float64 and int64 data types to smaller equivalents, such as float16 and int8, for columns where this doesn't result in data loss.

However, the most egregious data type mismatch worth eliminating from your pandas code is using strings instead of categoricals. Converting a low cardinality column of categorical data from its default object type to a category type often achieves memory usage improvements of 100x and computation speed ups of 10x. The code sample below demonstrates how this conversion can be performed within a chained workflow.

In this article, I've shown four pandas anti-patterns, and alternative approaches you should adopt instead. The code sample below illustrates how these best practices can be combined into a coherent workflow. This particular example shows how we can calculate the mean adjusted score of the shows, depending on the prevalence rank of the first production country.

Adopting these practices allows for the complex data transformations and processing to all be conducted in a single chained statement. The code is performant, readable, and simple to maintain and extend. If you're not already coding pandas in this way, I recommend giving it a try!

Starting from pandas 1.0, an experimental pd.NA value (singleton) is available to represent scalar missing values. At this moment, it is used in the nullable integer, boolean and dedicated string data types as the missing value indicator.

Note the capital 'F' to distinguish from np.float32 or np.float64, also note string which is the new pandas StringDtype (from Pandas 1.0) and not str or object.Also pd.Int64 (from pandas 0.24) nullable integer capital 'I' and not np.int64.

So honestly what exactly does pandamonium do, I know it turns all my unicorn cards into pandas but what does that mean really do my unicorn cards no longer have effects or do effects for unicorns just not touch my cards or both?

Red pandas are endangered [9] and listed as an appendix I species in the Convention on International Trade in Endangered Species of Wild Fauna and Flora [14]. Their global population has declined by 50% in the last three generations [9]. Available studies have reported habitat loss and fragmentation as the major conservation challenges [15,16,17,18,19]. However, most of these studies are based on sign surveys, and they have not attempted to examine how this threatened species responds to habitat loss, fragmentation, and disturbances. Telemetry studies can provide much needed ecological information that can help advance conservation strategies to secure the survival of this threatened species.

Studies have involved capturing and handling free-living red pandas in the past [13,20,21,22,23,24,25]. However, in those studies, the capture methods, both direct and indirect, are incompletely described [21,22,24,25]. Direct capturing methods employ climbing trees and capturing the red panda using the noose pole method [13]. Direct capturing has been employed in China using dogs to chase animals to a tree, but the method is poorly described [24,25]. Indirect methods include log traps [13,20,23], and leg-snare traps [23]. Red pandas have been trapped successfully in log traps designed for giant pandas, Ailuropoda melanoleuca, on two occasions in China [20,23]. A log trap is a type of box trap with wooden walls [26]. Of these, one red panda was caught as a by-catch in one of the traps targeted for giant pandas [20].

Red panda feces. Usually, fresh feces are greenish due to bamboo consumption in their diet. The color of the feces gradually fades with time. They use the same latrine sites for defecation where droppings of different ages can be easily seen (as in the figure). One animal can have many latrine sites within their home range. These latrine sites are believed to serve as territory markers. (Photo credit: Red Panda Network).

(a) Trapping instruments: cage trap, trapping poles, and net. (b) Fence built around a tree with a red panda. The fence was nearly 2.5 m high, built using a canvas sheet around the tree. Bamboo and wooden pegs were used to support the fence. This canvas sheet weighed nearly 25 kg. The cage trap (highlighted with a red circle) was placed downslope of the enclosure. (c) A view of the cage trap from outside the enclosure.

Before placing the cage trap, we had to ensure that the treed animal did not flee. Connected tree branches provide access for red pandas to walk over the canopy to easily escape. Initially, each team member took a position under all possible trees from where the animal could climb down and escape. Then, we had to drive the red panda onto the most isolated tree having minimal connected branches with neighboring trees. Before we built the fence around the base of the tree (Figure 2b), we pruned connected branches and chopped off smaller saplings and shrubs around the base of the tree whilst minimizing disturbances to understory vegetation. Furthermore, our study area was managed by the local people as the community forest where the use of forest resources for timber, firewood, and fodder is permitted and commonly practiced.

Initially, we prepared a fence that was 1.5 m high [31]. However, a fence lower than 2 m failed to prevent red pandas from jumping over it in high slope areas. So we added another layer of green netting on top of the fence to increase its height if the site had a high slopes. We maintained the minimum height of the fence at 2.5 m above the ground in areas with steep slope, and the distance between the tree base and fence at between 2.5 and 4 m so that the animal could not jump over the fence. After the fence was completed, we installed the cage trap on the downhill side of the enclosure (Figure 2c). Finally, we placed a pair of trail cameras facing the cage trap from inside and outside the fence to record the movement and behavior of the trapped animal.

We covered the cage trap with hessian bags as soon as the panda was trapped to reduce their stress [2]. Three or four people went inside the fence and took the animal out from the trap with the help of a 1.5 m long net with a 50 50 cm mouth (Figure 3). Handlers had to wear protective gloves and masks to prevent biting, scratching and possible transmission of zoonotic diseases. Then, we tied the mouth of the net and weighed the animal using a handheld digital scale. We transferred the trapped animal to a pre-identified flat site close by for immobilization, collaring, and to record morphometric measurements and undertake a health examination.

After confirming immobilization by examining the eyes and responses to stimulus, we fitted a Global Positioning System (GPS) collar weighing between 224 and 229 g (LiteTrack Iridium 150 TRD, Havelock North, New Zealand). These collars had circumferences ranging from 210 to 230 mm, an auto drop off function, and battery life of one year. Auto drop off occurred at 60 weeks. Collars were set to provide one GPS fix every two hours and also had a Very High Frequency (VHF) transmitter. Based on trials with two red pandas in the Rotterdam zoo for six months, we fitted collars with the diameter of the index finger of an adult man between the neck and collar strap for adults, while for sub-adults, the gap was increased by 50%, which was within the range suggested by Dickinson et al. [38]. The collar battery and transmitter were placed onto the red panda ventrally and dorsally, respectively. Then, we recorded morphometric measurements and conducted a health examination after attaching the GPS collar.

The effort taken to track and capture red pandas in this study was much less than that of Yonzon [13], who spotted red pandas on 10 occasions and collared six individuals. We established 89.7 person-hours (0.89 panda/day) as our spotting rate, which is higher than previous reports [13]. There has been a community-based red panda conservation program in the study area for more than a decade, which also supports red panda tourism [50,51]. Thus, the involvement of experienced red panda trackers likely helped to increase the sighting rate in our study. Habituation to human presence due to increasing tourism activities could also have increased our sighting rate. Likewise, our success rate of trapping is similar to Yonzon [13]. Other studies [21,23,25] have not mentioned the research effort for the sighting and successful capture of red panda. 17dc91bb1f