NumPy数组追加至Pandas DataFrame及索引匹配、列删除问题

The core issue here is that your prediction DataFrame uses a default 0-based index, while your actual data has random, non-sequential indices—so pandas can’t align rows correctly when you try to concatenate or assign directly. Let’s fix this step by step:

Step 1: Get the index mapping for predictions #

You mentioned prediction index 2 corresponds to first_100_train index 480, so you need a list/array that maps each prediction in first_100 to its matching row index in first_100_train. Let’s call this actual_indices:

# Example: Replace with your actual index mapping
actual_indices = [66, 201, 480, ...]  # Length 100, matches first_100's size

If you don’t have this list yet, retrieve it from how you generated first_100. For example, if you sampled rows from first_100_train to get your predictions, use:

# If you used something like first_100_train_subset = first_100_train.sample(100)
actual_indices = first_100_train_subset.index.values

Step 2: Create a properly indexed prediction DataFrame #

Instead of using the default 0-based index, set the prediction DataFrame’s index to match actual_indices:

import pandas as pd

# Convert NumPy array to DataFrame with aligned indices
pred_df = pd.DataFrame(first_100, columns=['Prediction'], index=actual_indices)

Now pred_df shares the same indices as its corresponding rows in first_100_train, so pandas can align them perfectly.

Step 3: Merge predictions with actual data #

Choose one of these options based on your needs:

• Keep all rows in first_100_train (with NaNs for rows without predictions):

  combined_df = first_100_train.join(pred_df)
  

• Only keep rows with predictions (the 100 matching rows):

  combined_df = first_100_train.loc[actual_indices].join(pred_df)
  

Step 4: Delete the last column #

Once merged, remove the final column using either of these methods:

• Create a new DataFrame excluding the last column:

  combined_df = combined_df.iloc[:, :-1]
  

• Modify the DataFrame in place:

  combined_df.drop(columns=combined_df.columns[-1], inplace=True)
  

Example Test Case #

Let’s verify with a small sample:

# Sample actual data
first_100_train = pd.DataFrame({'Actual': [10,20,30,40,50]}, index=[66,201,480,32,15])
# Sample predictions
first_100 = np.array([11,22,49])
# Index mapping
actual_indices = [66,201,480]

# Execute steps 2-4
pred_df = pd.DataFrame(first_100, columns=['Prediction'], index=actual_indices)
combined_df = first_100_train.join(pred_df)
combined_df = combined_df.iloc[:, :-1]

The final combined_df will have correctly aligned rows (no mismatched NaNs) and the last column removed.

Content of the question comes from Stack Exchange, asked by Raph117