DataFrame中sort_values对Prd No列排序异常问题求助

Alright, let's break down why you're seeing that wonky 1, 10, 100... 2, 20... sort order and fix it for good. That's classic lexicographical (dictionary-style) sorting, which means your Prd No column is still being treated as a string—even if you tried converting it to integers. Let's walk through the steps to fix this:

1. First, Verify the Actual Data Type of Prd No #

Before anything else, confirm what type Pandas is really treating Prd No as. Sometimes astype(int) fails silently if there's dirty data, leaving the column as object (string):

# Check the column's data type
print(df['Prd No'].dtype)
# Or check individual value types to spot anomalies
print(df['Prd No'].apply(type).unique())

If you see <class 'str'> or object in the output, that's the root cause.

2. Clean and Convert Prd No to a Proper Integer Type #

Chances are there's hidden cruft (like spaces, empty strings, or non-numeric characters) preventing a successful type conversion. Let's clean the column first, then use a nullable integer type to avoid falling back to strings:

import pandas as pd

# Step 1: Strip whitespace and remove any non-numeric characters
df['Prd No'] = df['Prd No'].str.strip().str.replace(r'\D', '', regex=True)

# Step 2: Convert to numeric, coerce errors to NaN, then use nullable Int64 type
df['Prd No'] = pd.to_numeric(df['Prd No'], errors='coerce').astype('Int64')

Using Int64 (capital I) instead of regular int lets you keep integer values while supporting missing data—no more accidental float columns or forced string types.

3. Sort Correctly (Account for Your Client ID Index) #

Since Client ID is your index, make sure you're sorting against the Prd No column properly. Here are two common approaches:

# Option 1: Sort the DataFrame while keeping Client ID as the index
df_sorted = df.sort_values(by='Prd No')

# Option 2: Reset the index, sort, then re-set Client ID as index (if needed)
df_sorted = df.reset_index().sort_values(by='Prd No').set_index('Client ID')

After the type conversion, this should give you the natural numeric order you want: 1, 2, 3... 10, 11...

4. Fix the Issue at the Source (During wide_to_long) #

To avoid this problem entirely next time, tell wide_to_long that your Prd No suffixes are numeric using the suffix parameter. This ensures the resulting Prd No column is integer type right off the bat:

# Example: If your wide-format columns look like PrdWeight_1, PrdWeight_2...
df_long = pd.wide_to_long(
    df_wide,
    stubnames=['Prd Weight'],
    i='Client ID',
    j='Prd No',
    suffix='\\d+'  # This matches numeric suffixes and converts them to integers
)

内容的提问来源于stack exchange，提问作者Sam