In data analysis and processing, Pandas and SQL each have their own strengths and ideal use cases.
SQL excels at efficiently querying, filtering, aggregating, and joining large datasets stored in databases,while Pandas offers flexible, Python-based tools for data cleaning, exploration, and visualization.
In real-world workflows, analysts often switch between the two — for example, using SQL to extract raw data, then Pandas to perform deeper analysis. Some people may feel more comfortable with SQL, others with Pandas, but learning to translate commands/functions between them helps bridge that gap. By comparing their syntax and logic, you can not only understand both tools more intuitively but also choose the most efficient one for each task.
Below is a list of the most common operations, showing their names in English (EN), German (DE), and Chinese (ZH), along with the corresponding SQL and Pandas commands/functions.
🧱 Basic Operations
| English | Deutsch | 中文 | SQL | Pandas |
| Read Data | Daten lesen | 读取数据 | SELECT * FROM table; | df = pd.read_csv('file.csv') |
| View first rows | Erste Zeilen anzeigen | 查看前几行 | SELECT * FROM table LIMIT 5; | df.head(5) |
| View structure | Struktur anzeigen | 查看表结构 | DESCRIBE table; | df.info() |
| Summary statistics | Zusammenfassung | 查看统计摘要 | SELECT AVG(col), MIN(col), MAX(col) FROM table; | df.describe() |
🎯 Selection and Filtering
| English | Deutsch | 中文 | SQL | Pandas |
| Select columns | Spalten auswählen | 选择列 | SELECT col1, col2 FROM table; | df[['col1', 'col2']] |
| Filter rows | Zeilen filtern | 条件过滤 | SELECT * FROM table WHERE col > 10; | df[df['col'] > 10] |
| Multiple conditions | Mehrere Bedingungen | 多条件过滤 | WHERE col1 > 10 AND col2 = 'A' | df[(df['col1'] > 10) & (df['col2'] == 'A')] |
| Sort rows | Sortieren | 排序 | ORDER BY col DESC | df.sort_values('col', ascending=False) |
| Remove duplicates | Duplikate entfernen | 去重 | SELECT DISTINCT col FROM table; | df['col'].drop_duplicates() |
🔄 Aggregation and Grouping
| English | Deutsch | 中文 | SQL | Pandas |
| Group and aggregate | Gruppieren und Aggregieren | 分组聚合 | SELECT col, AVG(val) FROM table GROUP BY col; | df.groupby('col')['val'].mean() |
| Multiple aggregations | Mehrere Aggregationen | 多重聚合 | SELECT col, COUNT(*), SUM(val) FROM table GROUP BY col; | df.groupby('col').agg({'val': ['count', 'sum']}) |
| Count rows | Zeilen zählen | 计数 | SELECT COUNT(*) FROM table; | len(df) or df.shape[0] |
🔗 Joins
| English | Deutsch | 中文 | SQL | Pandas |
| Inner join | Inner Join | 内连接 | SELECT * FROM A INNER JOIN B ON A.id = B.id; | pd.merge(A, B, on='id', how='inner') |
| Left join | Left Join | 左连接 | LEFT JOIN | pd.merge(A, B, on='id', how='left') |
| Right join | Right Join | 右连接 | RIGHT JOIN | pd.merge(A, B, on='id', how='right') |
| Full outer join | Vollständiger Join | 全连接 | FULL OUTER JOIN | pd.merge(A, B, on='id', how='outer') |
🧩 Window Functions
| English | Deutsch | 中文 | SQL | Pandas |
| Ranking | Rangfolge | 排名 | RANK() OVER (PARTITION BY col ORDER BY val) | df['rank'] = df.groupby('col')['val'].rank() |
| Rolling average | Gleitender Durchschnitt | 滚动平均 | AVG(val) OVER (ORDER BY date ROWS 3 PRECEDING) | df['rolling_avg'] = df['val'].rolling(3).mean() |
🧮 Data Modification
| English | Deutsch | 中文 | SQL | Pandas |
| Add column | Spalte hinzufügen | 新增列 | ALTER TABLE ADD col_new ... | df['col_new'] = df['col1'] + df['col2'] |
| Update values | Werte aktualisieren | 更新列 | UPDATE table SET col = ... WHERE ... | df.loc[df['col1']>0, 'col2'] = 'Yes' |
| Delete column | Spalte löschen | 删除列 | ALTER TABLE DROP COLUMN col; | df.drop('col', axis=1, inplace=True) |
| Delete rows | Zeilen löschen | 删除行 | DELETE FROM table WHERE col < 0; | df = df[df['col'] >= 0] |
📦 Merging and Stacking
| English | Deutsch | 中文 | SQL | Pandas |
| Union (distinct) | Vereinigung ohne Duplikate | UNION（去重） | SELECT * FROM A UNION SELECT * FROM B; | pd.concat([A, B]).drop_duplicates() |
| Union all | Vereinigung mit Duplikaten | UNION ALL（保留重复） | SELECT * FROM A UNION ALL SELECT * FROM B; | pd.concat([A, B], ignore_index=True) |
🧹 Miscellaneous Operations
| English | Deutsch | 中文 | SQL | Pandas |
| Rename column | Spalte umbenennen | 重命名列 | SELECT col AS new_name FROM table; | df.rename(columns={'col': 'new_name'}) |
| Subquery | Unterabfrage | 子查询 | SELECT * FROM (SELECT ...) AS sub; | Use intermediate variable: sub = df[df['x']>0] |
| Conditional column | Bedingte Spalte | 条件列 | CASE WHEN col>0 THEN 'pos' ELSE 'neg' END | np.where(df['col']>0, 'pos', 'neg') |
| Null handling | Null-Werte behandeln | 空值处理 | IS NULL / IS NOT NULL | df.isna() / df.notna() |
| Fill nulls | Null-Werte auffüllen | 填充空值 | COALESCE(col, 0) | df['col'].fillna(0) |
⚠️ Note
Most SQL and Pandas operations above correspond closely, but a few points to be aware of:
-
df.describe() returns additional statistics beyond SQL’s AVG, MIN, and MAX. For exact correspondence, use individual aggregation functions. - Pandas
rolling() requires sorting (e.g., by date) to fully match SQL window functions. -
df.groupby().rank() may use a different ranking method than SQL’s RANK(). Use method='min' or method='dense' to align results if needed.
✅ Tip:
If you often switch between SQL and Pandas, try libraries like pandasql, duckdb, or polars to query DataFrames using SQL syntax :)