检测并删除重复项
使用 duplicated() 和 drop_duplicates() 查找完全重复项和部分重复项，并决定保留哪条重复记录。
检测并删除重复项 是 CoddyKit 上的免费 Pandas & NumPy Academy 课时。 这是第 1 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 Pandas & NumPy Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Pandas & NumPy Academy 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
Why Duplicates Matter
Duplicate rows silently inflate counts, revenue totals, and averages without any error message. A sales dataset with 500 duplicate order records will overstate revenue by the exact sum of those 500 orders. Detecting and removing duplicates is one of the first cleaning steps you should perform, before any aggregation or modelling, because downstream errors compound from this single source of corruption.
import pandas as pd
df = pd.read_csv('orders.csv')
print('Shape before dedup:', df.shape)
print('Exact duplicates:', df.duplicated().sum())Finding Exact Duplicates
df.duplicated() returns a boolean Series that is True for every row that is an exact copy of a previous row. The default keep='first' marks all but the first occurrence. Passing keep=False marks every occurrence of a duplicate — useful when you want to inspect all copies of a duplicated record before deciding which to keep.
# Mark only the second+ occurrences
partial_dups = df[df.duplicated(keep='first')]
print('Rows to remove:', len(partial_dups))
# Mark ALL copies of any duplicate
all_dups = df[df.duplicated(keep=False)]
print('Rows involved in duplicates:', len(all_dups))Dropping Exact Duplicates
Remove exact duplicate rows with df.drop_duplicates(). By default it keeps the first occurrence and drops all others. Pass keep='last' to keep the most recent record if your data has a timestamp and later rows are considered more authoritative. Always check the row count before and after to confirm the expected number of rows were removed.
df_clean = df.drop_duplicates(keep='first')
print('Rows removed:', len(df) - len(df_clean))
print('Shape after dedup:', df_clean.shape)Partial Duplicates: Key-Based
Rows may be partial duplicates: they share a business key (like order_id) but differ in other columns due to an upstream bug that created two versions of the same record. Use df.duplicated(subset=['order_id']) to find rows with the same key but potentially different values in other columns. Inspect these rows before deciding how to reconcile them.
key_dups = df[df.duplicated(subset=['order_id'], keep=False)]
print('Orders with duplicate IDs:')
print(key_dups.sort_values('order_id').head(10))Choosing Which Duplicate to Keep
When partial duplicates exist, you must decide which record is authoritative. Common strategies: keep the row with the latest timestamp (most recent update), keep the row with the most non-null values, or keep the row with the higher dollar amount if one record has corrections. Sort by your tie-breaking criterion and then drop duplicates keeping the first (or last) occurrence.
# Keep the record with the latest update timestamp
df_sorted = df.sort_values('updated_at', ascending=False)
df_dedup = df_sorted.drop_duplicates(subset=['order_id'], keep='first')
print('Kept:', len(df_dedup), 'unique orders')Near-Duplicate Detection
Near-duplicates are rows that represent the same entity but differ slightly — for example, the same customer name with a typo, or the same transaction with a 1-cent rounding difference. Exact duplicate detection misses these. One approach is to group by the key column and count: if a customer name appears with slight variations, use .str.strip().str.lower() to normalise before deduplication.
df['customer_normalized'] = df['customer_name'].str.strip().str.lower()
near_dups = df.groupby('customer_normalized').size().sort_values(ascending=False)
print(near_dups[near_dups > 1].head())Deduplicating with GroupBy Aggregation
When you need to merge duplicate rows rather than simply drop them, use groupby().agg(). For example, if two rows represent the same order but one has the shipping address and the other has the billing address, you can aggregate with 'first' (non-null preferred) or a custom function that coalesces non-null values across duplicates.
def coalesce(*args):
for a in args:
if pd.notna(a):
return a
return None
df_merged = df.groupby('order_id').agg(
customer=('customer_name', 'first'),
amount=('amount', 'max'),
date=('order_date', 'min')
).reset_index()
print(df_merged.head())Checking for Row-Order Sensitivity
The result of drop_duplicates(keep='first') depends on row order. If the DataFrame was loaded from a database query without an ORDER BY clause, row order is non-deterministic, meaning the kept record may vary between runs. Always sort by a stable key (e.g. primary key or timestamp) before deduplication to make the process deterministic and reproducible.
# Sort by primary key before deduplication for deterministic results
df = df.sort_values('order_id').reset_index(drop=True)
df_clean = df.drop_duplicates(subset=['order_id'], keep='first')
print('Deterministic dedup complete.')Verifying the Deduplication Result
After deduplication, verify the result with three checks: no remaining exact duplicates (df_clean.duplicated().sum() == 0), no duplicate business keys (df_clean.duplicated(subset=['order_id']).sum() == 0), and the expected row count is plausible. Write these as assertions so they fail loudly if future data changes invalidate the cleaning logic.
assert df_clean.duplicated().sum() == 0, 'Exact duplicates remain'
assert df_clean.duplicated(subset=['order_id']).sum() == 0, 'Duplicate order IDs remain'
print('Deduplication verified. Rows:', len(df_clean))Documenting Duplicates Removed
Good data cleaning is reproducible and auditable. Log the number of rows removed, the deduplication key, and the tie-breaking rule into a cleaning report dictionary. Persist this report alongside the clean data file so anyone running the pipeline later can verify the cleaning decisions without re-reading the code. Transparency in data cleaning builds trust in analysis results.
cleaning_log = {
'original_rows': len(df),
'dedup_key': 'order_id',
'tiebreak': 'keep first by updated_at desc',
'rows_removed': len(df) - len(df_clean),
'clean_rows': len(df_clean)
}
for k, v in cleaning_log.items():
print(f'{k}: {v}')Saving the Deduplicated Dataset
Save the deduplicated DataFrame to a new file rather than overwriting the original. This preserves the raw data for auditing. Name the file clearly with a _clean or _deduped suffix and include the run date so multiple cleaning passes are identifiable. Use Parquet for fast reloading in subsequent pipeline steps.
from datetime import date
output_path = f'orders_clean_{date.today()}.parquet'
df_clean.to_parquet(output_path, index=False)
print(f'Saved {len(df_clean)} rows to {output_path}')Quick Check
Test your understanding of Data Analysis concepts from this lesson.
Lesson Recap
In this lesson you learned: detecting exact and partial duplicates with duplicated() and drop_duplicates(), choosing which duplicate to keep using sorting and aggregation strategies, and verifying results with assertions and logging the cleaning decisions. Next up we explore outlier detection and treatment techniques.
常见问题解答
「检测并删除重复项」课时是免费的吗？
是的 — 「检测并删除重复项」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Pandas & NumPy Academy 课程的其余内容，请升级到 CoddyKit PRO。 Pandas & NumPy Academy 课程共包含 4 节课。
「检测并删除重复项」这节课中我会学到什么？
使用 duplicated() 和 drop_duplicates() 查找完全重复项和部分重复项，并决定保留哪条重复记录。 你通过在浏览器中直接运行的动手代码来练习 Pandas & NumPy Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 Pandas & NumPy Academy 需要有经验吗？
无需任何先前经验。CoddyKit 上的 Pandas & NumPy Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 1 节课，共 4 节。
「检测并删除重复项」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 Pandas & NumPy Academy 课中编写并运行代码吗？
能。每节 Pandas & NumPy Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。