> ## Documentation Index
> Fetch the complete documentation index at: https://private-7c7dfe99-parallel-read-in-order-multi-part.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# DataStore Pandas compatibility

> Complete list of pandas-compatible methods in DataStore (209 DataFrame methods)

DataStore implements **209 pandas DataFrame methods** for full API compatibility. Your existing pandas code works with minimal changes.

<h2 id="approach">
  Compatibility Approach
</h2>

```python theme={null}
# Typical migration - just change the import
- import pandas as pd
+ from chdb import datastore as pd

# Your code works unchanged
df = pd.read_csv("data.csv")
result = df[df['age'] > 25].groupby('city')['salary'].mean()
```

**Key principles:**

* All 209 pandas DataFrame methods implemented
* Lazy evaluation for SQL optimization
* Automatic type wrapping (DataFrame → DataStore, Series → ColumnExpr)
* Immutable operations (no `inplace=True`)

***

<h2 id="attributes">
  Attributes and Properties
</h2>

| Property | Description | Triggers Execution |
| - | - | - |
| `shape` | (rows, columns) tuple | Yes |
| `columns` | Column names (Index) | Yes |
| `dtypes` | Column data types | Yes |
| `values` | NumPy array | Yes |
| `index` | Row index | Yes |
| `size` | Number of elements | Yes |
| `ndim` | Number of dimensions | No |
| `empty` | Is DataFrame empty | Yes |
| `T` | Transpose | Yes |
| `axes` | List of axes | Yes |

**Examples:**

```python theme={null}
from chdb import datastore as pd

ds = pd.read_csv("data.csv")

print(ds.shape)      # (1000, 5)
print(ds.columns)    # Index(['name', 'age', 'city', 'salary', 'dept'])
print(ds.dtypes)     # name: object, age: int64, ...
print(ds.empty)      # False
```

***

<h2 id="indexing">
  Indexing and Selection
</h2>

| Method | Description | Example |
| - | - | - |
| `df['col']` | Select column | `ds['age']` |
| `df[['col1', 'col2']]` | Select columns | `ds[['name', 'age']]` |
| `df[condition]` | Boolean indexing | `ds[ds['age'] > 25]` |
| `df.loc[...]` | Label-based access | `ds.loc[0:10, 'name']` |
| `df.iloc[...]` | Integer-based access | `ds.iloc[0:10, 0:3]` |
| `df.at[...]` | Single value by label | `ds.at[0, 'name']` |
| `df.iat[...]` | Single value by position | `ds.iat[0, 0]` |
| `df.head(n)` | First n rows | `ds.head(10)` |
| `df.tail(n)` | Last n rows | `ds.tail(10)` |
| `df.sample(n)` | Random sample | `ds.sample(100)` |
| `df.select_dtypes()` | Select by Dtype | `ds.select_dtypes(include='number')` |
| `df.query()` | Query expression | `ds.query('age > 25')` |
| `df.where()` | Conditional replace | `ds.where(ds['age'] > 0, 0)` |
| `df.mask()` | Inverse where | `ds.mask(ds['age'] < 0, 0)` |
| `df.isin()` | Value membership | `ds['city'].isin(['NYC', 'LA'])` |
| `df.get()` | Safe column access | `ds.get('col', default=None)` |
| `df.xs()` | Cross-section | `ds.xs('key')` |
| `df.pop()` | Remove column | `ds.pop('col')` |

***

<h2 id="statistical">
  Statistical Methods
</h2>

| Method | Description | SQL Equivalent |
| - | - | - |
| `mean()` | Mean value | `AVG()` |
| `median()` | Median value | `MEDIAN()` |
| `mode()` | Mode value | - |
| `std()` | Standard deviation | `STDDEV()` |
| `var()` | Variance | `VAR()` |
| `min()` | Minimum | `MIN()` |
| `max()` | Maximum | `MAX()` |
| `sum()` | Sum | `SUM()` |
| `prod()` | Product | - |
| `count()` | Non-null count | `COUNT()` |
| `nunique()` | Unique count | `UNIQ()` |
| `value_counts()` | Value frequencies | `GROUP BY` |
| `quantile()` | Quantile | `QUANTILE()` |
| `describe()` | Summary statistics | - |
| `corr()` | Correlation matrix | `CORR()` |
| `cov()` | Covariance matrix | `COV()` |
| `corrwith()` | Pairwise correlation | - |
| `rank()` | Rank values | `RANK()` |
| `abs()` | Absolute values | `ABS()` |
| `round()` | Round values | `ROUND()` |
| `clip()` | Clip values | - |
| `cumsum()` | Cumulative sum | Window function |
| `cumprod()` | Cumulative product | Window function |
| `cummin()` | Cumulative min | Window function |
| `cummax()` | Cumulative max | Window function |
| `diff()` | Difference | Window function |
| `pct_change()` | Percent change | Window function |
| `skew()` | Skewness | `SKEW()` |
| `kurt()` | Kurtosis | `KURT()` |
| `sem()` | Standard error | - |
| `all()` | All true | - |
| `any()` | Any true | - |
| `idxmin()` | Index of min | - |
| `idxmax()` | Index of max | - |

**Examples:**

```python theme={null}
ds = pd.read_csv("data.csv")

# Basic statistics
print(ds['salary'].mean())
print(ds['age'].std())
print(ds.describe())

# Group statistics
print(ds.groupby('department')['salary'].mean())
print(ds.groupby('city').agg({'salary': ['mean', 'std'], 'age': 'count'}))
```

***

<h2 id="manipulation">
  Data Manipulation
</h2>

| Method | Description |
| - | - |
| `drop()` | Drop rows/columns |
| `drop_duplicates()` | Remove duplicates |
| `duplicated()` | Mark duplicates |
| `dropna()` | Remove missing values |
| `fillna()` | Fill missing values |
| `ffill()` | Forward fill |
| `bfill()` | Backward fill |
| `interpolate()` | Interpolate values |
| `replace()` | Replace values |
| `rename()` | Rename columns/index |
| `rename_axis()` | Rename axis |
| `assign()` | Add new columns |
| `astype()` | Convert types |
| `convert_dtypes()` | Infer types |
| `copy()` | Copy DataFrame |

**Examples:**

```python theme={null}
ds = pd.read_csv("data.csv")

# Drop operations
result = ds.drop(columns=['unused_col'])
result = ds.drop_duplicates(subset=['user_id'])
result = ds.dropna(subset=['email'])

# Fill operations
result = ds.fillna(0)
result = ds.fillna({'age': 0, 'name': 'Unknown'})

# Transform operations
result = ds.rename(columns={'old_name': 'new_name'})
result = ds.assign(
    full_name=lambda x: x['first_name'] + ' ' + x['last_name'],
    age_group=lambda x: pd.cut(x['age'], bins=[0, 25, 50, 100])
)
```

***

<h2 id="sorting">
  Sorting and Ranking
</h2>

| Method | Description |
| - | - |
| `sort_values()` | Sort by values |
| `sort_index()` | Sort by index |
| `nlargest()` | N largest values |
| `nsmallest()` | N smallest values |

**Examples:**

```python theme={null}
# Sort by single column
result = ds.sort_values('salary', ascending=False)

# Sort by multiple columns
result = ds.sort_values(['department', 'salary'], ascending=[True, False])

# Get top/bottom N
result = ds.nlargest(10, 'salary')
result = ds.nsmallest(5, 'age')
```

***

<h2 id="reshaping">
  Reshaping
</h2>

| Method | Description |
| - | - |
| `pivot()` | Pivot table |
| `pivot_table()` | Pivot with aggregation |
| `melt()` | Unpivot |
| `stack()` | Stack columns to index |
| `unstack()` | Unstack index to columns |
| `transpose()` / `T` | Transpose |
| `explode()` | Explode lists to rows |
| `squeeze()` | Reduce dimensions |
| `droplevel()` | Drop index level |
| `swaplevel()` | Swap index levels |
| `reorder_levels()` | Reorder levels |

**Examples:**

```python theme={null}
# Pivot table
result = ds.pivot_table(
    values='amount',
    index='region',
    columns='product',
    aggfunc='sum'
)

# Melt (unpivot)
result = ds.melt(
    id_vars=['name'],
    value_vars=['score1', 'score2', 'score3'],
    var_name='test',
    value_name='score'
)

# Explode arrays
result = ds.explode('tags')
```

***

<h2 id="combining">
  Combining / Joining
</h2>

| Method | Description |
| - | - |
| `merge()` | SQL-style merge |
| `join()` | Join on index |
| `concat()` | Concatenate |
| `append()` | Append rows |
| `combine()` | Combine with function |
| `combine_first()` | Combine with priority |
| `update()` | Update values |
| `compare()` | Show differences |

**Examples:**

```python theme={null}
# Merge (join)
result = pd.merge(df1, df2, on='id', how='left')
result = df1.join(df2, on='id')

# Concatenate
result = pd.concat([df1, df2, df3])
result = pd.concat([df1, df2], axis=1)
```

***

<h2 id="binary">
  Binary Operations
</h2>

| Method | Description |
| - | - |
| `add()` / `radd()` | Addition |
| `sub()` / `rsub()` | Subtraction |
| `mul()` / `rmul()` | Multiplication |
| `div()` / `rdiv()` | Division |
| `truediv()` / `rtruediv()` | True division |
| `floordiv()` / `rfloordiv()` | Floor division |
| `mod()` / `rmod()` | Modulo |
| `pow()` / `rpow()` | Power |
| `dot()` | Matrix multiplication |

**Examples:**

```python theme={null}
# Arithmetic operations
result = ds['col1'].add(ds['col2'])
result = ds['price'].mul(ds['quantity'])

# With fill_value for missing data
result = ds['col1'].add(ds['col2'], fill_value=0)
```

***

<h2 id="comparison">
  Comparison Operations
</h2>

| Method | Description |
| - | - |
| `eq()` | Equal |
| `ne()` | Not equal |
| `lt()` | Less than |
| `le()` | Less than or equal |
| `gt()` | Greater than |
| `ge()` | Greater than or equal |
| `equals()` | Test equality |
| `compare()` | Show differences |

***

<h2 id="application">
  Function Application
</h2>

| Method | Description |
| - | - |
| `apply()` | Apply function |
| `applymap()` | Apply element-wise |
| `map()` | Map values |
| `agg()` / `aggregate()` | Aggregate |
| `transform()` | Transform |
| `pipe()` | Pipe functions |
| `groupby()` | Group by |

**Examples:**

```python theme={null}
# Apply function
result = ds['name'].apply(lambda x: x.upper())
result = ds.apply(lambda row: row['a'] + row['b'], axis=1)

# Aggregate
result = ds.agg({'col1': 'sum', 'col2': 'mean'})
result = ds.agg(['sum', 'mean', 'std'])

# Pipe
result = (ds
    .pipe(filter_active)
    .pipe(calculate_metrics)
    .pipe(format_output)
)
```

***

<h2 id="timeseries">
  Time Series
</h2>

| Method | Description |
| - | - |
| `rolling()` | Rolling window |
| `expanding()` | Expanding window |
| `ewm()` | Exponentially weighted |
| `resample()` | Resample time series |
| `shift()` | Shift values |
| `asfreq()` | Convert frequency |
| `asof()` | Latest value as of |
| `at_time()` | Select at time |
| `between_time()` | Select time range |
| `first()` / `last()` | First/last periods |
| `to_period()` | Convert to period |
| `to_timestamp()` | Convert to timestamp |
| `tz_convert()` | Convert timezone |
| `tz_localize()` | Localize timezone |

**Examples:**

```python theme={null}
# Rolling window
result = ds['value'].rolling(window=7).mean()

# Expanding window
result = ds['value'].expanding().sum()

# Shift
result = ds['value'].shift(1)  # Lag
result = ds['value'].shift(-1)  # Lead
```

***

<h2 id="missing">
  Missing Data
</h2>

| Method | Description |
| - | - |
| `isna()` / `isnull()` | Detect missing |
| `notna()` / `notnull()` | Detect non-missing |
| `dropna()` | Drop missing |
| `fillna()` | Fill missing |
| `ffill()` | Forward fill |
| `bfill()` | Backward fill |
| `interpolate()` | Interpolate |
| `replace()` | Replace values |

***

<h2 id="io">
  I/O Methods
</h2>

| Method | Description |
| - | - |
| `to_csv()` | Export to CSV |
| `to_json()` | Export to JSON |
| `to_excel()` | Export to Excel |
| `to_parquet()` | Export to Parquet |
| `to_feather()` | Export to Feather |
| `to_sql()` | Export to SQL database |
| `to_pickle()` | Pickle |
| `to_html()` | HTML table |
| `to_latex()` | LaTeX table |
| `to_markdown()` | Markdown table |
| `to_string()` | String representation |
| `to_dict()` | Dictionary |
| `to_records()` | Records |
| `to_numpy()` | NumPy array |
| `to_clipboard()` | Clipboard |

See [I/O Operations](/chdb/datastore/io) for detailed documentation.

***

<h2 id="iteration">
  Iteration
</h2>

| Method | Description |
| - | - |
| `items()` | Iterate (column, Series) |
| `iterrows()` | Iterate (index, Series) |
| `itertuples()` | Iterate as named tuples |

***

<h2 id="differences">
  Key Differences from Pandas
</h2>

<h3 id="return-types">
  1. Return Types
</h3>

```python theme={null}
# Pandas returns Series
pdf['col']  # → pd.Series

# DataStore returns ColumnExpr (lazy)
ds['col']   # → ColumnExpr
```

<h3 id="lazy-execution">
  2. Lazy Execution
</h3>

```python theme={null}
# DataStore operations are lazy
result = ds.filter(ds['age'] > 25)  # Not executed yet
df = result.to_df()  # Executed here
```

<h3 id="no-inplace-parameter">
  3. No inplace Parameter
</h3>

```python theme={null}
# Pandas
df.drop(columns=['col'], inplace=True)

# DataStore (always returns new object)
ds = ds.drop(columns=['col'])
```

<h3 id="comparing-results">
  4. Comparing Results
</h3>

```python theme={null}
# Use to_pandas() for comparison
pd.testing.assert_frame_equal(
    ds.to_pandas(),
    expected_df
)
```

See [Key Differences](/chdb/guides/pandas-differences) for complete details.