Opening Remarks

Hello everyone, today let's talk about the applications of data science in Python! As an experienced Python programmer and blogger, I often have to deal with various data processing and analysis needs. But don't worry, once you master the right methods and techniques, applying data science in Python is actually quite simple!

Advanced Operations with Pandas DataFrames

Selecting Multiple Columns (Based on MultiIndex)

Let me give you an example. Say you have a DataFrame with MultiIndex columns, and you want to select specific columns from it. How do you do that? It's simple, just use the pd.DataFrame.xs() method, like this:

selected_columns = df.xs('level_name', axis=1, level='level_index')

This will return a new DataFrame containing only the columns you specified at that level. Convenient, isn't it?

Extracting Hyperlinks from Excel

Also, I recently had a requirement to extract hyperlinks from an Excel file and create a DataFrame. Is there a good way to do this?

Of course there is! We can use the openpyxl library, then use a for loop to iterate through each row, check if the cell contains a hyperlink, and extract it. Here's the code:

import pandas as pd
from openpyxl import load_workbook

wb = load_workbook('file.xlsx')
ws = wb.active

data = []
for row in ws.iter_rows(values_only=True):
    data.append([cell.hyperlink.target if cell.hyperlink else cell.value for cell in row])

df = pd.DataFrame(data)

This creates a DataFrame containing the hyperlinks. Cool, right?

Advanced Data Analysis Techniques

Multivariate N-Beats Model

Speaking of time series data processing, a friend recently asked me how to implement a multivariate N-Beats model in Python. I thought about it and came up with this solution:

First, you need to preprocess the data, standardizing each feature. Then, when building the model, modify the shape of the input layer to match the number of features, like this:

model = NBeatsModel(input_shape=(n_timesteps, n_features), ...)

This allows the model to handle multiple input features.

Handling Skewed Data

In the data analysis process, we often encounter data with very abnormal distributions, such as some dependent variables that are extremely right-skewed. In this case, you can consider applying a logarithmic transformation or Box-Cox transformation to the data to mitigate the impact of skewness.

For example, for a column called shares, representing the number of shares of an article, if its distribution is too right-skewed, you can do this:

data['log_shares'] = np.log(data['shares'] + 1)

After the logarithmic transformation, the model can better fit this type of data.

Evaluating Similarity in High-Dimensional Data

Sometimes we also need to evaluate the similarity of high-dimensional data at different time points. How do we do this? My suggestion is to first calculate the correlation matrix of the data at two time points, then visualize it as a heatmap, which will give you a clear view of the correlations.

import seaborn as sns
import matplotlib.pyplot as plt

correlation_matrix = data.corr()
sns.heatmap(correlation_matrix, annot=True)
plt.show()

This way, through visualization, you can intuitively understand the similarities and differences in the data at different time points.

Conclusion

Alright, that's all I'll introduce to you today. By mastering these data processing and analysis techniques, I believe applying data science in Python shouldn't be difficult anymore, right? Of course, this is just the tip of the iceberg, the ocean of data science is vast! If you have any questions, feel free to ask me anytime, and I'll patiently answer them. Let's go further together on this path!