重複の検出と削除
duplicated()とdrop_duplicates()で完全一致および部分的な重複を見つけ、どの重複レコードを残すかを判断します。
「重複の検出と削除」はCoddyKit上の無料Pandas & NumPy Academyレッスンです。 これはレッスン1/4です。 下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。 これはPandas & NumPy Academy学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 Pandas & NumPy Academyコースには全4レッスンが含まれています。
このレッスンの一部はまだ翻訳されておらず、英語で表示されています。
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.
よくある質問
「重複の検出と削除」レッスンは無料ですか？
はい。「重複の検出と削除」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、Pandas & NumPy Academyコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 Pandas & NumPy Academyコースには全4レッスンが含まれています。
「重複の検出と削除」で何を学びますか？
duplicated()とdrop_duplicates()で完全一致および部分的な重複を見つけ、どの重複レコードを残すかを判断します。 ブラウザで直接実行するハンズオンコードでPandas & NumPy Academyを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。
Pandas & NumPy Academyを始めるのに経験は必要ですか？
事前経験は必要ありません。CoddyKitのPandas & NumPy Academyは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。 これはレッスン1/4です。
「重複の検出と削除」レッスンにはどのくらい時間がかかりますか？
ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。
このPandas & NumPy Academyレッスンでコードを書いて実行できますか？
はい。すべてのPandas & NumPy Academyレッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。