Python Pandas中ffill()方法在object类型列无法生效的问题咨询

I see the issue here—let's break down why your ffill() isn't working and how to fix it quickly.

The Root Cause #

When you used np.matrix() to create your DataFrame, you hit a key limitation of numpy matrices: they're homogeneous (all elements must be the same type). Since your data mixes integers, strings, and np.nan, numpy converts everything to strings. That means those "nan" values in your object-type columns aren't actual Pandas/numpy missing values—they're literal string 'nan'!

Pandas' ffill() only recognizes real missing values (like pd.NA, np.nan, or None), so it ignores the string 'nan' entirely.

Solution 1: Convert String 'nan' to Real Missing Values #

First, we'll replace all instances of the string 'nan' with pd.NA (Pandas' dedicated missing value marker), then run ffill() as usual:

import pandas as pd
import numpy as np

# Your original data setup
data = np.matrix([[4,3,6,4,1,7,5,5], [1,2,3,6,4,2,4,9], ['a',np.nan, np.nan, 'b', np.nan, 'c', np.nan, 'd'],[1,np.nan, np.nan, 2, np.nan, 2, np.nan, 2]]).T
data = pd.DataFrame(data)

# Step 1: Replace string 'nan' with pd.NA
data = data.replace('nan', pd.NA)

# Step 2: Forward fill the missing values
data_filled = data.ffill()

print(data_filled)

This will output the filled DataFrame you expect:

0  1  2  3
0  4  1  a  1
1  3  2  a  1
2  6  3  a  1
3  4  6  b  2
4  1  4  b  2
5  7  2  c  2
6  5  4  c  2
7  5  9  d  2

Solution 2: Avoid np.matrix Altogether (Recommended) #

Numpy matrices are rarely necessary for Pandas workflows. Instead, create your DataFrame directly from a list of lists—this lets Pandas infer column types correctly and preserves real missing values:

import pandas as pd
import numpy as np

# Create data as a list of lists (no np.matrix!)
data = [
    [4, 1, 'a', 1],
    [3, 2, np.nan, np.nan],
    [6, 3, np.nan, np.nan],
    [4, 6, 'b', 2],
    [1, 4, np.nan, np.nan],
    [7, 2, 'c', 2],
    [5, 4, np.nan, np.nan],
    [5, 9, 'd', 2]
]
data = pd.DataFrame(data)

# Now ffill() works immediately
data_filled = data.ffill()

print(data_filled)

Quick Check to Verify Missing Values #

If you ever want to confirm what's being treated as a missing value, use data.isna(). In your original DataFrame, this would have returned False for those "nan" entries—after replacing with pd.NA, it will correctly return True.

内容的提问来源于stack exchange，提问作者Eric Kim