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

# DataStore aggregation functions

> Aggregate functions, window functions, and the F namespace in DataStore

DataStore provides comprehensive aggregation and window function support, leveraging ClickHouse's powerful SQL aggregation capabilities.

<h2 id="basic">
  Basic Aggregations
</h2>

<h3 id="builtin">
  Built-in Methods
</h3>

| Method      | SQL Equivalent    | Description           |
| ----------- | ----------------- | --------------------- |
| `sum()`     | `SUM()`           | Sum of values         |
| `mean()`    | `AVG()`           | Average/mean          |
| `count()`   | `COUNT()`         | Count non-null values |
| `min()`     | `MIN()`           | Minimum value         |
| `max()`     | `MAX()`           | Maximum value         |
| `median()`  | `MEDIAN()`        | Median value          |
| `std()`     | `stddevPop()`     | Standard deviation    |
| `var()`     | `varPop()`        | Variance              |
| `nunique()` | `COUNT(DISTINCT)` | Count unique values   |

**Examples:**

```python theme={null}
from pathlib import Path
Path("sales.csv").write_text("""\
region,product,category,amount,quantity,price,date,order_id
East,Widget,Electronics,5200,10,120,2024-01-15,1001
West,Gadget,Electronics,800,5,160,2024-02-20,1002
East,Gizmo,Home,6500,3,100,2024-03-10,1003
North,Widget,Electronics,4500,6,150,2024-06-18,1004
West,Gadget,Electronics,2000,8,250,2024-09-14,1005
""")

from chdb import datastore as pd

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

# Single column aggregation
total = ds['amount'].sum()
average = ds['amount'].mean()
count = ds['amount'].count()

# All aggregations
print(ds['amount'].sum())    # Total
print(ds['amount'].mean())   # Average
print(ds['amount'].std())    # Standard deviation
print(ds['amount'].median()) # Median
print(ds['amount'].nunique()) # Unique count
```

***

<h2 id="groupby">
  GroupBy Aggregations
</h2>

<h3 id="single-agg">
  Single Aggregation
</h3>

```python theme={null}
# Group by and aggregate
result = ds.groupby('category')['amount'].sum()
result = ds.groupby('region')['sales'].mean()
```

<h3 id="multi-agg">
  Multiple Aggregations
</h3>

```python theme={null}
# Dictionary syntax
result = ds.groupby('category').agg({
    'amount': 'sum',
    'quantity': 'mean',
    'order_id': 'count'
})

# List of aggregations per column
result = ds.groupby('category').agg({
    'amount': ['sum', 'mean', 'max'],
    'quantity': ['sum', 'count']
})
```

<h3 id="named-agg">
  Named Aggregations
</h3>

```python theme={null}
# Named aggregation (pandas style)
result = ds.groupby('region').agg(
    total_amount=('amount', 'sum'),
    avg_quantity=('quantity', 'mean'),
    order_count=('order_id', 'count'),
    max_price=('price', 'max')
)
```

<h3 id="multi-groupby">
  Multiple GroupBy Keys
</h3>

```python theme={null}
# Group by multiple columns
result = ds.groupby(['region', 'category']).agg({
    'amount': 'sum',
    'quantity': 'sum'
})
```

***

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

| Method        | SQL Equivalent | Description            |
| ------------- | -------------- | ---------------------- |
| `quantile(q)` | `quantile(q)`  | q-th quantile (0-1)    |
| `skew()`      | `skewPop()`    | Skewness               |
| `kurt()`      | `kurtPop()`    | Kurtosis               |
| `corr()`      | `corr()`       | Correlation            |
| `cov()`       | `covar()`      | Covariance             |
| `sem()`       | -              | Standard error of mean |

**Examples:**

```python theme={null}
# Quantiles
q50 = ds['amount'].quantile(0.5)  # Median
q95 = ds['amount'].quantile(0.95) # 95th percentile

# Multiple quantiles
quantiles = ds['amount'].quantile([0.25, 0.5, 0.75])

# Correlation between columns
correlation = ds[['sales', 'marketing_spend']].corr()
```

***

<h2 id="conditional">
  Conditional Aggregations
</h2>

ClickHouse-specific conditional aggregation functions.

| Function         | ClickHouse  | Description             |
| ---------------- | ----------- | ----------------------- |
| `sum_if(cond)`   | `sumIf()`   | Sum where condition     |
| `count_if(cond)` | `countIf()` | Count where condition   |
| `avg_if(cond)`   | `avgIf()`   | Average where condition |
| `min_if(cond)`   | `minIf()`   | Min where condition     |
| `max_if(cond)`   | `maxIf()`   | Max where condition     |

**Examples:**

```python theme={null}
from chdb.datastore import F, Field

# Sum only high value orders
high_value_sum = F.sum_if(Field('amount'), Field('amount') > 1000)

# Count active users
active_count = F.count_if(Field('status') == 'active')

# In groupby context
result = ds.groupby('region').agg({
    'total': ('amount', 'sum'),
    'high_value': ('amount', F.sum_if(Field('amount') > 1000)),
})
```

***

<h2 id="collection">
  Collection Aggregations
</h2>

ClickHouse-specific functions that collect values.

| Function             | ClickHouse         | Description               |
| -------------------- | ------------------ | ------------------------- |
| `group_array()`      | `groupArray()`     | Collect into array        |
| `group_uniq_array()` | `groupUniqArray()` | Collect unique into array |
| `group_concat(sep)`  | `groupConcat()`    | Concatenate strings       |
| `top_k(n)`           | `topK(n)`          | Top K frequent values     |
| `any()`              | `any()`            | Any value                 |
| `any_last()`         | `anyLast()`        | Last value                |
| `first_value()`      | `first_value()`    | First value in order      |
| `last_value()`       | `last_value()`     | Last value in order       |

**Examples:**

```python theme={null}
from chdb.datastore import F, Field

# Collect all tags per category
result = ds.groupby('category').agg({
    'all_tags': ('tag', F.group_array()),
    'unique_tags': ('tag', F.group_uniq_array())
})

# Get top 5 products per region
result = ds.groupby('region').agg({
    'top_products': ('product_id', F.top_k(5))
})
```

***

<h2 id="window">
  Window Functions
</h2>

<h3 id="ranking">
  Ranking Functions
</h3>

| Function         | SQL              | Description             |
| ---------------- | ---------------- | ----------------------- |
| `row_number()`   | `ROW_NUMBER()`   | Sequential row number   |
| `rank()`         | `RANK()`         | Rank with gaps          |
| `dense_rank()`   | `DENSE_RANK()`   | Rank without gaps       |
| `ntile(n)`       | `NTILE(n)`       | Divide into n buckets   |
| `percent_rank()` | `PERCENT_RANK()` | Percentile rank (0-1)   |
| `cume_dist()`    | `CUME_DIST()`    | Cumulative distribution |

**Examples:**

```python theme={null}
from chdb.datastore import F, Field

# Add row number
ds['row_num'] = F.row_number().over(order_by='date')

# Rank within groups
ds['rank'] = F.rank().over(
    partition_by='category',
    order_by='sales'
)

# Dense rank (no gaps)
ds['dense_rank'] = F.dense_rank().over(
    partition_by='region',
    order_by=('revenue', 'desc')
)

# Divide into quartiles
ds['quartile'] = F.ntile(4).over(order_by='score')
```

<h3 id="value-functions">
  Value Functions
</h3>

| Function        | SQL                 | Description           |
| --------------- | ------------------- | --------------------- |
| `lag(n)`        | `LAG(col, n)`       | Previous row value    |
| `lead(n)`       | `LEAD(col, n)`      | Next row value        |
| `first_value()` | `FIRST_VALUE()`     | First value in window |
| `last_value()`  | `LAST_VALUE()`      | Last value in window  |
| `nth_value(n)`  | `NTH_VALUE(col, n)` | Nth value in window   |

**Examples:**

```python theme={null}
# Previous and next value
ds['prev_price'] = F.lag('price', 1).over(order_by='date')
ds['next_price'] = F.lead('price', 1).over(order_by='date')

# First and last in partition
ds['first_order'] = F.first_value('amount').over(
    partition_by='customer_id',
    order_by='date'
)
```

<h3 id="cumulative">
  Cumulative Functions
</h3>

| Method          | Description                     |
| --------------- | ------------------------------- |
| `cumsum()`      | Cumulative sum                  |
| `cummax()`      | Cumulative maximum              |
| `cummin()`      | Cumulative minimum              |
| `cumprod()`     | Cumulative product              |
| `diff(n)`       | Difference from n rows back     |
| `pct_change(n)` | Percent change from n rows back |

**Examples:**

```python theme={null}
# Cumulative calculations
ds['running_total'] = ds['amount'].cumsum()
ds['running_max'] = ds['amount'].cummax()

# With grouping
ds['group_cumsum'] = ds.groupby('category')['amount'].cumsum()

# Period over period
ds['daily_diff'] = ds['sales'].diff(1)
ds['pct_change'] = ds['sales'].pct_change(1)
```

<h3 id="rolling">
  Rolling Windows
</h3>

```python theme={null}
# Rolling window aggregations
ds['rolling_avg'] = ds['price'].rolling(window=7).mean()
ds['rolling_sum'] = ds['amount'].rolling(window=30).sum()
ds['rolling_std'] = ds['value'].rolling(window=10).std()

# Expanding windows
ds['expanding_max'] = ds['price'].expanding().max()
ds['expanding_sum'] = ds['amount'].expanding().sum()
```

***

<h2 id="f-namespace">
  F Namespace
</h2>

The `F` namespace provides access to ClickHouse functions.

<h3 id="f-import">
  Import
</h3>

```python theme={null}
from chdb.datastore import F, Field
```

<h3 id="f-usage">
  Using F Functions
</h3>

```python theme={null}
# Aggregations
F.sum(Field('amount'))
F.avg(Field('price'))
F.count(Field('id'))

# Statistical
F.quantile(Field('value'), 0.95)
F.stddev_pop(Field('score'))
F.corr(Field('x'), Field('y'))

# Conditional
F.sum_if(Field('amount'), Field('status') == 'completed')
F.count_if(Field('is_active'))

# String
F.length(Field('name'))
F.upper(Field('text'))

# Date/Time
F.to_year(Field('date'))
F.date_diff('day', Field('start'), Field('end'))

# Array
F.array_sum(Field('values'))
F.array_avg(Field('scores'))

# Math
F.abs(Field('delta'))
F.round(Field('price'), 2)
F.floor(Field('value'))
F.ceil(Field('value'))
```

<h3 id="f-window">
  F with Window Functions
</h3>

```python theme={null}
# Define window frame
window = F.window(
    partition_by='category',
    order_by='date',
    rows_between=(-7, 0)  # Current row and 7 preceding
)

ds['rolling_avg'] = F.avg(Field('price')).over(window)
```

***

<h2 id="patterns">
  Common Aggregation Patterns
</h2>

<h3 id="top-n">
  Top N per Group
</h3>

```python theme={null}
# Top 3 products per category by sales
result = (ds
    .assign(rank=F.row_number().over(
        partition_by='category',
        order_by=('sales', 'desc')
    ))
    .filter(ds['rank'] <= 3)
)
```

<h3 id="running-total">
  Running Total
</h3>

```python theme={null}
# Running total of sales
ds['running_total'] = F.sum('amount').over(
    order_by='date',
    rows_between=(None, 0)  # All rows up to current
)
```

<h3 id="moving-avg">
  Moving Average
</h3>

```python theme={null}
# 7-day moving average
ds['ma_7'] = F.avg('price').over(
    order_by='date',
    rows_between=(-6, 0)
)
```

<h3 id="yoy">
  Year-over-Year Comparison
</h3>

```python theme={null}
# YoY comparison
ds['prev_year_sales'] = F.lag('sales', 12).over(
    partition_by='product_id',
    order_by='month'
)
ds['yoy_growth'] = (ds['sales'] - ds['prev_year_sales']) / ds['prev_year_sales']
```

<h3 id="percentile">
  Percentile Ranking
</h3>

```python theme={null}
# Rank customers by total spend
ds['spend_percentile'] = F.percent_rank().over(order_by='total_spend')
```

***

<h2 id="summary">
  Aggregation Methods Summary
</h2>

| Category        | Methods                                                       |
| --------------- | ------------------------------------------------------------- |
| **Basic**       | `sum`, `mean`, `count`, `min`, `max`, `median`                |
| **Statistical** | `std`, `var`, `quantile`, `skew`, `kurt`, `corr`, `cov`       |
| **Conditional** | `sum_if`, `count_if`, `avg_if`, `min_if`, `max_if`            |
| **Collection**  | `group_array`, `group_uniq_array`, `group_concat`, `top_k`    |
| **Ranking**     | `row_number`, `rank`, `dense_rank`, `ntile`, `percent_rank`   |
| **Value**       | `lag`, `lead`, `first_value`, `last_value`, `nth_value`       |
| **Cumulative**  | `cumsum`, `cummax`, `cummin`, `cumprod`, `diff`, `pct_change` |
| **Rolling**     | `rolling().mean/sum/std/...`, `expanding().mean/sum/...`      |