### Install pyadomd

Source: https://github.com/s-c-o-u-t/pyadomd/blob/master/docs/getting_started.rst

Installs the pyadomd library using pip. This is the first step to start using the library for querying SSAS data models.

```bash
pip install pyadomd
```

--------------------------------

### Install pyadomd

Source: https://github.com/s-c-o-u-t/pyadomd/blob/master/README.md

Installs the pyadomd library using pip, the Python package installer.

```console
pip install pyadomd
```

--------------------------------

### pyadomd Module Documentation

Source: https://github.com/s-c-o-u-t/pyadomd/blob/master/docs/pyadomd.rst

Automates the documentation generation for the pyadomd module, including its members and undocumented members, and displays inheritance information.

```python
import pyadomd

# Accessing members of the pyadomd module
# Example: print(dir(pyadomd))

# Accessing members of the pyadomd.pyadomd submodule
# Example: import pyadomd.pyadomd
# print(dir(pyadomd.pyadomd))
```

--------------------------------

### Troubleshoot FileNotFoundException

Source: https://github.com/s-c-o-u-t/pyadomd/blob/master/README.md

Resolves a `FileNotFoundException` for 'Microsoft.AnalysisServices.AdomdClient' by ensuring the ADOMD.NET client DLL directory is added to the Python path before importing the pyadomd package.

```csharp
System.IO.FileNotFoundException: Unable to find assembly 'Microsoft.AnalysisServices.AdomdClient'.
   at Python.Runtime.CLRModule.AddReference(String name)
```

```python
from sys import path
#added to the path _before_ importing the pyadomd package
path.append('\\Program Files\\Microsoft.NET\\ADOMD.NET\\150')

from pyadomd import Pyadomd
```

--------------------------------

### Query SSAS Tabular Model with pyadomd

Source: https://github.com/s-c-o-u-t/pyadomd/blob/master/docs/getting_started.rst

Demonstrates how to connect to an SSAS tabular model using a connection string, execute a DAX query, and fetch all results. It requires the ADOMD.NET library to be in the system path.

```python
from sys import path
path.append('\Program Files\Microsoft.NET\ADOMD.NET\150')

from pyadomd import Pyadomd

conn_str = 'Provider=MSOLAP;Data Source=localhost;Catalog=AdventureWorks;'
query = """EVALUATE Product"""

with Pyadomd(conn_str) as conn:
    with conn.cursor().execute(query) as cur:
        print(cur.fetchall())
```

--------------------------------

### Integrate pyadomd Results with Pandas

Source: https://github.com/s-c-o-u-t/pyadomd/blob/master/docs/getting_started.rst

Shows how to execute a DAX query using pyadomd and then convert the fetched results into a pandas DataFrame. This facilitates data analysis and manipulation in Python.

```python
from pandas import DataFrame

conn_str = 'Provider=MSOLAP;Data Source=localhost;Catalog=AdventureWorks;'
query = """EVALUATE Product"""

with Pyadomd(conn_str) as conn:
    with conn.cursor().execute(query) as cur:
        df = DataFrame(cur.fetchone(), 
        columns=[i.name for i in cur.description])
```

--------------------------------

### Query SSAS Tabular Model

Source: https://github.com/s-c-o-u-t/pyadomd/blob/master/README.md

Connects to a SQL Server Analysis Services (SSAS) instance, executes an MDX query against a tabular model, and fetches all results. It requires setting the ADOMD.NET client path and uses a connection string to specify the data source and catalog.

```python
from sys import path
path.append('\\Program Files\\Microsoft.NET\\ADOMD.NET\\150')

from pyadomd import Pyadomd

conn_str = 'Provider=MSOLAP;Data Source=localhost;Catalog=AdventureWorks;'
query = """EVALUATE Product"""

with Pyadomd(conn_str) as conn:
    with conn.cursor().execute(query) as cur:
        print(cur.fetchall())
```

--------------------------------

### Integrate with Pandas DataFrame

Source: https://github.com/s-c-o-u-t/pyadomd/blob/master/README.md

Fetches a single row of data from an SSAS tabular model and integrates it into a pandas DataFrame. The DataFrame columns are named based on the cursor's description, providing a structured data representation.

```python
from pandas import DataFrame

with Pyadomd(conn_str) as conn:
    with conn.cursor().execute(query) as cur:
        df = DataFrame(cur.fetchone(), columns=[i.name for i in cur.description])
```

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