如何在DataFrame指定多列中提取最频繁值并新增列存储

Here's how you can add column F to your DataFrame, where F holds the most frequent character from columns A-E for each row:

First, let's recreate your sample DataFrame:

import pandas as pd

# Create the original DataFrame
data = {
    'S': [1, 2, 3, 4],
    'A': ['N', 'N', 'Y', 'Y'],
    'B': ['N', 'Y', 'N', 'N'],
    'C': ['N', 'Y', 'Y', 'Y'],
    'D': ['N', 'N', 'N', 'Y'],
    'E': ['N', 'N', 'N', 'Y']
}

df = pd.DataFrame(data)

Next, we'll use apply() with a lambda function to calculate the most frequent value across columns A-E for each row. We use value_counts().idxmax() which gives us the value with the highest count:

# Add column F with the most frequent character from A-E
df['F'] = df[['A', 'B', 'C', 'D', 'E']].apply(lambda row: row.value_counts().idxmax(), axis=1)

Alternatively, you can use pandas' mode() method, which directly returns the most frequent value(s) for each row. Since we have an odd number of columns (5), there will always be a single most frequent value, so we can take the first result:

# Alternative approach using mode()
df['F'] = df[['A', 'B', 'C', 'D', 'E']].mode(axis=1)[0]

Either way, the resulting DataFrame will match your expected output:

S  A  B  C  D  E  F
0  1  N  N  N  N  N  N
1  2  N  Y  Y  N  N  N
2  3  Y  N  Y  N  N  N
3  4  Y  N  Y  Y  Y  Y

Explanation: #

• df[['A', 'B', 'C', 'D', 'E']] selects only the columns we want to analyze.
• apply(..., axis=1) runs the function across each row instead of columns.
• row.value_counts() counts occurrences of each character in the row, sorted from most to least frequent.
• idxmax() picks the value with the highest count (the first one in the sorted counts).
• The mode() method is a more concise way to get the most frequent value, and since we have 5 columns (no ties possible), taking the first column of the result gives us the correct value.

内容的提问来源于stack exchange，提问作者shome