Common Python Data Science Programming Issues and Solutions-Ink Wash Data

Addressing Pain Points, Learning from Practical Experience

Hello, Python data science friends! Today we're going to discuss some common issues encountered during programming and their solutions. As an experienced Python practitioner, I've found that many programmers often face tricky problems during actual operations. These issues may seem small, but if not resolved promptly, they can seriously affect development efficiency. So today, we'll share some practical experiences, hoping to provide you with some inspiration.

Data Visualization

Violin Plot Parameter Settings

Data visualization is a crucial part of data science. A good visualization can make data vivid and enhance persuasiveness. Have you encountered such a problem in practice? For example, when using Seaborn to draw a violin plot, you find that the inner_kws parameter setting is ineffective, even if you adjust parameters like line style, the image still doesn't change.

This is indeed frustrating, and I was also stuck on this problem for a long time. Later, I found that the reason might be a compatibility issue with the Seaborn version. We need to ensure that the inner_kws parameter is set correctly, and that the parameters passed when calling sns.violinplot() are compatible with the current Seaborn version. If it still can't be resolved, try updating the Seaborn library to the latest version.

You see, sometimes a small version issue can lead us on a big detour. So when programming, we must always pay attention to these details to be twice as efficient with half the effort.

Handling Skewed Data

Besides visualization, handling skewed data is also a common challenge in data science. For example, when analyzing data from an online article, you find that the "number of shares" indicator is extremely right-skewed, meaning that the vast majority of articles have very low share counts, but a few articles have extremely high share counts, leading to an imbalanced data distribution.

Faced with this situation, we can consider using logarithmic transformation or Box-Cox transformation to reduce skewness. The specific operation is as follows:

import numpy as np


shares = np.array([1, 2, 3, 100, 200])
scaled_shares = np.log1p(shares)  # Using logarithmic transformation

Through this transformation, we can effectively reduce the skewness of the data, thereby improving the predictive ability of subsequent models. However, it should be noted that when performing the transformation, we need to ensure there are no zero or negative values in the data, otherwise data processing is required first.

You see, handling skewed data is not actually difficult. As long as you master the correct techniques, you can easily solve this common data analysis challenge.

Time Series Analysis

AutoTS Multi-Model Prediction

Time series analysis is widely used in many fields, such as finance and weather forecasting. In practice, you might encounter this problem: you want to use the powerful prediction package AutoTS for multivariate time series prediction, but by default, it only gives the single best model, while you want to select the prediction results of the top 3 models from the model results.

This is indeed a very practical need. The solution is that we can modify the AutoTS configuration, use the model.results() method to get the results of all models, and then select the top 3 models for prediction ourselves. When doing this, be sure to carefully read the AutoTS documentation and correctly set the model_selection parameter.

In this way, you can fully utilize the powerful features of AutoTS, obtain more comprehensive and accurate prediction results, and provide strong support for subsequent decision-making.

N-BEATS Multivariate Model

Speaking of time series models, the N-BEATS algorithm is also a popular choice. Readers have asked how to implement a multivariate N-BEATS model?

It's actually quite simple, we can use the NBEATSModel class to build a multivariate model. The key is to ensure that the input data format is correct, and to specify multiple feature variables when training the model. For specific implementation details, you can refer to the official N-BEATS documentation, which has detailed example code and explains how to correctly set input dimensions and other parameters.

By mastering these techniques, you can easily build a multivariate N-BEATS model, thereby better capturing complex patterns in time series data and improving prediction accuracy.

Data Processing and Analysis

Polars DataFrame Operations

In addition to visualization and time series analysis, data processing and analysis is also a focus in Python data science. Here we'll share a tip using the popular Polars library as an example.

Suppose you have a Polars DataFrame containing boolean data, and you want to quickly check if there are any True values in this DataFrame, how should you do it?

It's simple, just call the df.any() method. Here's a specific example:

import polars as pl

df = pl.DataFrame({
    "a": [False, False, True],
    "b": [False, False, False]
})

result = df.any()
print(result)  # Outputs True

With this method, you can quickly determine if the DataFrame contains a certain value, which is very useful. However, note that the any() method returns a Series, if you want to get a scalar value, you need to do an .item() conversion.

Handling Skewed Data

We mentioned earlier how to handle univariate skewed data, here's another practical tip.

Sometimes you might encounter a situation where a continuous target variable is extremely right-skewed, like the "number of shares" indicator. We introduced the logarithmic transformation method earlier, but this method won't work if the data contains zeros or negative values.

In this case, you can consider using Box-Cox transformation, which can better handle skewed data containing zeros and negative values. This function is built into the NumPy library and is easy to use:

from scipy import stats
import numpy as np


shares = np.array([1, 2, 0, -1, 100, 200]) 
scaled_shares, _ = stats.boxcox(shares+abs(min(shares))+1)

Through Box-Cox transformation, we can effectively compress the distribution range of skewed data, paving the way for subsequent modeling work.

You see, faced with various programming challenges, as long as you master the correct methods and are diligent in thinking and practicing, you can always find a solution. Maintaining curiosity and being brave in exploration are the inexhaustible driving forces for a programmer's growth.

Continuous Learning, Accumulating Knowledge for Future Use

Today, we shared some common issues and solution ideas in data visualization, time series analysis, and data processing from a practical perspective. In fact, the path of programming is not difficult, the key is to have a heart that is diligent in learning and good at thinking. We encounter various problems every day, as long as we maintain an open mind and are willing to share our experiences, we can continuously improve and eventually become excellent Python data scientists.

So, what interesting problems have you encountered in practice? Feel free to leave a message in the comments section, let's discuss and progress together! On the Python journey, we walk hand in hand.

xxx