如何使用Pandas创建透视表，按年份获取计数最高的姓名及对应计数值？

Hey there! Let's figure out how to get the top name and its count per year—you're close, just need to adjust your approach a bit.

Why Your Previous Code Didn't Work #

Your goal is to get the name with the highest count for each year, but your existing code misses this core requirement:

• Code Example 1: The pivot table uses ['Year','Name'] as the index and max on Count. This gives you the maximum count for each name per year (if a name appears multiple times in a year), not the name with the overall highest count for the year.
• Code Example 2: Using max on both Name and Count doesn't work because max for strings uses lexicographical order (e.g., a name starting with "Z" would be considered "max" regardless of its count), which has no relation to the highest count value.

Fixes & Solutions #

First, let's fix the data type issue: your line bn['Count'].astype(int) doesn't modify the original DataFrame—you need to assign the result back:

bn['Count'] = bn['Count'].astype(int)

Now here are two straightforward ways to achieve your desired output:

Method 1: groupby + idxmax (Most Efficient)

idxmax() returns the index of the row with the highest Count value for each year. We then use loc to extract those rows:

import pandas as pd

# Load data
bn = pd.read_csv("baby_names.csv")
# Correct Count column type
bn['Count'] = bn['Count'].astype(int)

# Get indices of rows with max Count per Year
max_count_indices = bn.groupby('Year')['Count'].idxmax()
# Extract the relevant rows and columns
top_names = bn.loc[max_count_indices, ['Year', 'Name', 'Count']].reset_index(drop=True)

print(top_names)

Method 2: groupby + Custom Function (Flexible for Edge Cases)

If multiple names share the highest count in a year, this method lets you handle them (e.g., return the first one, or all of them):

import pandas as pd

bn = pd.read_csv("baby_names.csv")
bn['Count'] = bn['Count'].astype(int)

def get_top_name(group):
    # Filter rows where Count equals the max for the year
    max_rows = group[group['Count'] == group['Count'].max()]
    # Return the first result (replace with `return max_rows` to keep all ties)
    return max_rows[['Name', 'Count']].iloc[0]

top_names = bn.groupby('Year').apply(get_top_name).reset_index()

print(top_names)

Both methods will produce output in your desired format, with each row showing the year, the top name, and its count.

内容的提问来源于stack exchange，提问作者DataMonkey