如何在Pandas中对日期与ID分组为列？多索引数据可用于Scikit Learn线性回归吗？

First off, let's tackle your two main questions: using your MultiIndex DataFrame with Scikit-learn, and restructuring it into the desired row-wise format.

Can your MultiIndex structure be used for Scikit-learn Linear Regression? #

Yes, but it’s not the most straightforward or compatible option. Scikit-learn expects input data (X) to be a 2D array-like structure (e.g., a flat-column DataFrame) where each column is a single feature. While some Scikit-learn functions can handle MultiIndex columns, many utilities (like feature selection, preprocessing pipelines, or model interpretation) work better with flat, human-readable column names.

To make your MultiIndex DataFrame ML-ready, flatten the column names by joining the two levels:

# Flatten MultiIndex columns
pivot02_flat = pivot02.copy()
pivot02_flat.columns = ['_'.join(col) for col in pivot02_flat.columns]

# Example: Separate features and target (if predicting Number Of Awards)
target_cols = [col for col in pivot02_flat.columns if col.startswith('Number Of Awards')]
feature_cols = [col for col in pivot02_flat.columns if not col.startswith('Number Of Awards')]

X = pivot02_flat[feature_cols]
y = pivot02_flat[target_cols]

# Fit a linear regression model
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X, y)

Restructuring to your desired row-wise format #

You want each row to contain GeographyCode, Number of Awards, Secondary School Stage, SCQF Level, and DateCode. This is a "tidy" long data format, which is easy to achieve by stacking the DateCode level from your columns into the index, then resetting the index to turn those levels into columns:

# Stack the DateCode column level into the row index
long_format_df = pivot02.stack(level='DateCode').reset_index()

# Rename columns to match your desired structure
long_format_df.columns = ['GeographyCode', 'DateCode', 'Number Of Awards', 'SCQF Level', 'Secondary School Stage']

Let me break this down:

• stack(level='DateCode') moves the DateCode values from the column MultiIndex into the row index, creating a new MultiIndex of (GeographyCode, DateCode). The columns now become your three core metrics.
• reset_index() converts both index levels (GeographyCode and DateCode) into regular columns, giving you exactly the row structure you described.

ML with the long format #

If you prefer to use the long format for your model (e.g., predicting Number Of Awards based on time and other metrics), you’ll need to encode the DateCode (since it’s a string). For time-based data, converting it to an ordinal value (e.g., 0 for 2002/2003, 1 for 2003/2004) is often useful:

# Encode DateCode as ordinal values to capture time sequence
date_order = sorted(long_format_df['DateCode'].unique())
long_format_df['DateOrdinal'] = long_format_df['DateCode'].map({date: i for i, date in enumerate(date_order)})

# Prepare features and target
X = long_format_df[['DateOrdinal', 'SCQF Level', 'Secondary School Stage']]
y = long_format_df['Number Of Awards']

# Fit model
model = LinearRegression()
model.fit(X, y)

This approach treats each GeographyCode + DateCode combination as a separate sample, which is ideal if you want to model how metrics change over time per geography.

内容的提问来源于stack exchange，提问作者user3617574