### Installation

Source: https://github.com/nlpub/pymystem3/blob/master/README.rst

Instructions on how to install the pymystem3 library using pip.

```APIDOC
## Installation

### Description

Install the stable version of pymystem3 using pip.

### Command

```bash
pip install pymystem3
```

### Description

Install the latest version of pymystem3 directly from GitHub.

### Command

```bash
pip install git+https://github.com/nlpub/pymystem3
```
```

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

### Install pymystem3 via pip

Source: https://github.com/nlpub/pymystem3/blob/master/README.rst

Instructions for installing the pymystem3 library from PyPI or directly from the GitHub repository.

```bash
pip install pymystem3
pip install git+https://github.com/nlpub/pymystem3
```

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

### Install pymystem3 via pip

Source: https://context7.com/nlpub/pymystem3/llms.txt

Installs the pymystem3 library using pip. This is the standard method for installing Python packages.

```bash
pip install pymystem3
```

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

### Automatic Mystem Binary Installation with autoinstall()

Source: https://context7.com/nlpub/pymystem3/llms.txt

The `autoinstall()` function automatically downloads and installs the Mystem binary if it's not already present on the system. It also shows how to check the current binary location and set a custom path via an environment variable.

```Python
from pymystem3 import autoinstall, MYSTEM_BIN, MYSTEM_DIR
import os

# Check current binary location
print(f"Mystem directory: {MYSTEM_DIR}")
print(f"Mystem binary path: {MYSTEM_BIN}")

# Manually trigger installation (normally done automatically)
autoinstall()

# Verify installation
if os.path.isfile(MYSTEM_BIN):
    print("Mystem binary is installed")
else:
    print("Installation required")

# Set custom binary path via environment variable
import os
os.environ["MYSTEM_BIN"] = "/custom/path/to/mystem"
m = Mystem()  # Will use custom path
```

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

### Install Latest pymystem3 from GitHub

Source: https://context7.com/nlpub/pymystem3/llms.txt

Installs the latest development version of pymystem3 directly from its GitHub repository using pip.

```bash
pip install git+https://github.com/nlpub/pymystem3
```

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

### Manage Mystem Process with start() and close()

Source: https://context7.com/nlpub/pymystem3/llms.txt

The `start()` method pre-loads the Mystem binary to reduce latency for subsequent operations. The `close()` method properly terminates the subprocess. The context manager (`with Mystem() as m:`) provides automatic cleanup.

```Python
from pymystem3 import Mystem

m = Mystem()

# Pre-start the binary to avoid first-call latency
m.start()

# Process multiple texts efficiently
texts = [
    "Мама мыла раму",
    "Красивая погода сегодня",
    "Программирование это интересно"
]

for text in texts:
    lemmas = m.lemmatize(text)
    print(''.join(lemmas))

# Explicitly close the process when done
m.close()

# Alternative: use context manager for automatic cleanup
with Mystem() as m:
    for text in texts:
        lemmas = m.lemmatize(text)
        print(''.join(lemmas))
```

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

### Analyze Text Example

Source: https://github.com/nlpub/pymystem3/blob/master/README.rst

Shows how to get detailed grammatical information and lemmas for each token in a text.

```APIDOC
## Analyze Text

### Description

This example demonstrates how to use the `analyze` method to obtain comprehensive morphological analysis, including lemmas and grammatical attributes, for each word in a text.

### Method

```python
import json
from pymystem3 import Mystem

text = "Красивая мама красиво мыла раму"
m = Mystem()
analysis = m.analyze(text)

print("lemmas:", ''.join([ana.get('analysis', [{}])[0].get('lex', ana['text']) for ana in analysis if ana.get('analysis')]))
print("full info:", json.dumps(analysis, ensure_ascii=False))
```

### Output

```
lemmas: красивый мама красиво мыть рама
full info: [{"text": "Красивая", "analysis": [{"lex": "красивый", "gr": "A=им,ед,полн,жен"}]}, {"text": " "}, {"text": "мама", "analysis": [{"lex": "мама", "gr": "S,жен,од=им,ед"}]}, {"text": " "}, {"text": "красиво", "analysis": [{"lex": "красиво", "gr": "ADV="}]}, {"text": " "}, {"text": "мыла", "analysis": [{"lex": "мыть", "gr": "V,несов,пе=прош,ед,изъяв,жен"}]}, {"text": " "}, {"text": "раму", "analysis": [{"lex": "рама", "gr": "S,жен,неод=вин,ед"}]}, {"text": "\n"}]
```
```

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

### Lemmatization Example

Source: https://github.com/nlpub/pymystem3/blob/master/README.rst

Demonstrates how to perform basic text lemmatization using the Mystem analyzer.

```APIDOC
## Lemmatization

### Description

This example shows how to use the `lemmatize` method to get the base form of words in a given text.

### Method

```python
from pymystem3 import Mystem

text = "Красивая мама красиво мыла раму"
m = Mystem()
lemmas = m.lemmatize(text)
print(''.join(lemmas))
```

### Output

```
красивый мама красиво мыть рама
```
```

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

### Initialize Mystem Class in Python

Source: https://context7.com/nlpub/pymystem3/llms.txt

Demonstrates how to initialize the Mystem class for morphological analysis. It shows both basic initialization with default settings and advanced initialization with various configuration options. The example also includes using the Mystem class as a context manager for automatic resource cleanup.

```python
from pymystem3 import Mystem

# Basic initialization with default settings
m = Mystem()

# Advanced initialization with custom options
m = Mystem(
    mystem_bin=None,           # Path to mystem binary (auto-downloads if None)
    grammar_info=True,         # Print grammatical information (-i)
    disambiguation=True,       # Apply POS disambiguation (-d)
    entire_input=True,         # Copy entire input including spaces (-c)
    glue_grammar_info=True,    # Glue grammatical info for same lemmas (-g)
    weight=False,              # Print context-independent lemma weight
    generate_all=False,        # Generate all hypotheses for non-dict words
    no_bastards=False,         # Print only dictionary words (-w)
    end_of_sentence=False,     # Print end of sentence marks (-s)
    fixlist=None,              # Path to custom dictionary file
    use_english_names=False    # Use English names for grammemes (--eng-gr)
)

# Using context manager for automatic resource cleanup
with Mystem() as m:
    result = m.lemmatize("Красивая мама красиво мыла раму")
    print(''.join(result))
```

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

### Lemmatize Text using lemmatize() in Python

Source: https://context7.com/nlpub/pymystem3/llms.txt

Shows how to use the `lemmatize()` method to reduce words to their base forms. Examples include basic lemmatization, lemmatization with and without whitespace preservation, and lemmatizing text from a file.

```python
from pymystem3 import Mystem

m = Mystem()

# Basic lemmatization
text = "Красивая мама красиво мыла раму"
lemmas = m.lemmatize(text)
print(''.join(lemmas))

# Lemmatization without whitespace preservation
m_compact = Mystem(entire_input=False)
lemmas_compact = m_compact.lemmatize("Мама мыла раму")
print(lemmas_compact)

# Lemmatization with whitespace preserved (default)
m_full = Mystem(entire_input=True)
lemmas_full = m_full.lemmatize("Мама мыла раму")
print(lemmas_full)

# Lemmatize from file
lemmas_from_file = m.lemmatize(file_path="/path/to/russian_text.txt")
print(''.join(lemmas_from_file))
```

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

### Apply Custom Dictionary (Fixlist) for Lemmatization

Source: https://context7.com/nlpub/pymystem3/llms.txt

This example shows how to use a custom dictionary file (fixlist) to override or supplement the default analysis for specific words. The `fixlist` parameter in the Mystem constructor takes the path to the custom dictionary file.

```Python
from pymystem3 import Mystem

# Create a custom dictionary file (fixlist format)
# Each line: word{lemma:grammar}
# Example fixlist.txt content:
# спб{санкт-петербург:S,муж,неод=им,ед}
# питер{санкт-петербург:S,муж,неод=им,ед}

# Initialize with custom dictionary
m = Mystem(fixlist="/path/to/fixlist.txt")

# Analyze with custom dictionary applied
analysis = m.analyze("Я живу в СПб")
# Custom lemmatization will be applied for 'СПб'
```

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

### Perform Full Morphological Analysis with analyze() in Python

Source: https://context7.com/nlpub/pymystem3/llms.txt

Demonstrates the `analyze()` method for detailed morphological analysis, returning structured data for each token. Includes examples of analyzing text, analyzing from a file, and accessing individual token analysis results.

```python
import json
from pymystem3 import Mystem

m = Mystem()

# Full morphological analysis
text = "Красивая мама красиво мыла раму"
analysis = m.analyze(text)
print(json.dumps(analysis, ensure_ascii=False, indent=2))

# Analyze from file
analysis_from_file = m.analyze(file_path="/path/to/russian_text.txt")

# Access individual token analysis
for token in analysis:
    if 'analysis' in token and token['analysis']:
        lemma = token['analysis'][0]['lex']
        grammar = token['analysis'][0]['gr']
        print(f"Word: {token['text']}, Lemma: {lemma}, Grammar: {grammar}")
```

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

### Get Human-Readable Token Representation with get_printable_repr()

Source: https://context7.com/nlpub/pymystem3/llms.txt

The static method `get_printable_repr()` formats token analysis results into a human-readable string. This is helpful for debugging and understanding the detailed analysis output, especially when dealing with ambiguous words.

```Python
from pymystem3 import Mystem

m = Mystem()

text = "мыла"
analysis = m.analyze(text)

# Get human-readable representation for each token
for token in analysis:
    print(Mystem.get_printable_repr(token))

# For text with multiple hypotheses (when disambiguation is off)
m_no_disambig = Mystem(disambiguation=False)
analysis_ambiguous = m_no_disambig.analyze("мыла")
for token in analysis_ambiguous:
    print(Mystem.get_printable_repr(token))
```

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

### Perform Lemmatization and Morphological Analysis

Source: https://github.com/nlpub/pymystem3/blob/master/README.rst

Demonstrates how to initialize the Mystem analyzer to lemmatize text and retrieve full morphological information including part-of-speech tags.

```python
import json
from pymystem3 import Mystem

text = "Красивая мама красиво мыла раму"
m = Mystem()

# Lemmatization
lemmas = m.lemmatize(text)
print(''.join(lemmas))

# Full morphological analysis
analysis = m.analyze(text)
print(json.dumps(analysis, ensure_ascii=False))
```

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

### analyze() - Full Morphological Analysis

Source: https://context7.com/nlpub/pymystem3/llms.txt

Returns detailed morphological information for each token, including lemma, part-of-speech tags, and grammatical attributes.

```APIDOC
## analyze(text, file_path=None)

### Description
Performs full morphological analysis, returning a structured dictionary containing the original token, its lemma, and grammatical tags.

### Method
Method call (Python)

### Parameters
#### Arguments
- **text** (string) - Optional - The raw Russian text to analyze.
- **file_path** (string) - Optional - Path to a file containing text to analyze.

### Request Example
```python
m = Mystem()
analysis = m.analyze("Мама")
```

### Response
#### Success Response (List)
- **list** (array) - A list of dictionaries containing 'text' and 'analysis' keys.

#### Response Example
[{"text": "Мама", "analysis": [{"lex": "мама", "gr": "S,жен,од=им,ед"}]}]
```

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

### Configure English Grammeme Names

Source: https://context7.com/nlpub/pymystem3/llms.txt

This snippet demonstrates how to configure the Mystem analyzer to use English names for grammatical categories instead of the default Russian abbreviations. This is achieved by setting the `use_english_names` parameter during Mystem initialization.

```Python
import json
from pymystem3 import Mystem

# Using Russian grammeme names (default)
m_rus = Mystem(use_english_names=False)
analysis_rus = m_rus.analyze("мама")
print(json.dumps(analysis_rus, ensure_ascii=False))

# Using English grammeme names
m_eng = Mystem(use_english_names=True)
analysis_eng = m_eng.analyze("мама")
print(json.dumps(analysis_eng, ensure_ascii=False))
```

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

### lemmatize() - Text Lemmatization

Source: https://context7.com/nlpub/pymystem3/llms.txt

Performs morphological analysis and returns a list of lemmas (base dictionary forms) for each token in the input text.

```APIDOC
## lemmatize(text, file_path=None)

### Description
Reduces words in the input string or file to their base dictionary form (lemmas).

### Method
Method call (Python)

### Parameters
#### Arguments
- **text** (string) - Optional - The raw Russian text to process.
- **file_path** (string) - Optional - Path to a file containing text to process.

### Request Example
```python
m = Mystem()
lemmas = m.lemmatize("Красивая мама")
```

### Response
#### Success Response (List)
- **list** (array) - A list of strings representing the lemmas and original whitespace/punctuation.

#### Response Example
["красивый", " ", "мама"]
```

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

### Extract Part-of-Speech Tags with get_pos()

Source: https://context7.com/nlpub/pymystem3/llms.txt

The static method `get_pos()` extracts the main part-of-speech tag from a token's analysis result. This is useful for filtering tokens by their grammatical category, such as identifying verbs or nouns.

```Python
from pymystem3 import Mystem

m = Mystem()

text = "Красивая мама красиво мыла раму"
analysis = m.analyze(text)

# Extract POS tags for each token
for token in analysis:
    pos = Mystem.get_pos(token)
    if pos:
        print(f"Word: {token['text']}, POS: {pos}")

# Filter only verbs
verbs = [token for token in analysis if Mystem.get_pos(token) == 'V']
print([v['text'] for v in verbs])

# Filter only nouns
nouns = [token for token in analysis if Mystem.get_pos(token) == 'S']
print([n['text'] for n in nouns])
```

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