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Polars Cheat Sheet

Installation

pip install polars

# Install Polars with all optional dependencies:
pip install 'polars[all]'

# You can also install a subset of all optional dependencies:
pip install 'polars[numpy,pandas,pyarrow]'

# We also have a conda package (however pip is the preferred way):
conda install -c conda-forge polars

Usage:

Importing Polars:

import polars as pl

2. Creating DataFrames:

# From dictionary
df = pl.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']})

# From list of dictionaries
df = pl.DataFrame([{'A': 1, 'B': 'a'}, {'A': 2, 'B': 'b'}])

# From CSV
df = pl.read_csv('file.csv')

# From Pandas DataFrame
pandas_df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']})
df = pl.from_pandas(pandas_df)

3. Basic DataFrame Operations:

# Display DataFrame
print(df)

# Get DataFrame info
df.schema

# Select columns
df.select(['A', 'B'])

# Filter rows
df.filter(pl.col('A') > 2)

# Sort DataFrame
df.sort('A', reverse=True)

# Add new column
df.with_columns(pl.lit('new_col').alias('C'))

# Rename columns
df.rename({'A': 'X', 'B': 'Y'})

# Drop columns
df.drop(['A', 'B'])

# Group by and aggregate
df.groupby('A').agg(pl.sum('B'))

4. Data Manipulation:

# Apply function to column
df.with_columns(pl.col('A').map(lambda x: x * 2).alias('A_doubled'))

# Fill null values
df.fill_null(strategy='forward')

# Replace values
df.with_columns(pl.col('A').replace({1: 10, 2: 20}))

# Melt DataFrame
df.melt(id_vars=['A'], value_vars=['B', 'C'])

# Pivot DataFrame
df.pivot(values='value', index='A', columns='variable')

5. String Operations:

# Convert to uppercase
df.with_columns(pl.col('B').str.to_uppercase())

# String contains
df.filter(pl.col('B').str.contains('pattern'))

# String replace
df.with_columns(pl.col('B').str.replace('old', 'new'))

# String length
df.with_columns(pl.col('B').str.lengths().alias('B_length'))

6. DateTime Operations:

# Parse strings to datetime
df.with_columns(pl.col('date').str.strptime(pl.Datetime, '%Y-%m-%d'))

# Extract components
df.with_columns(pl.col('date').dt.year().alias('year'))

# Date arithmetic
df.with_columns((pl.col('date') + pl.duration(days=1)).alias('next_day'))

7. Joining DataFrames:

# Inner join
df1.join(df2, on='key', how='inner')

# Left join
df1.join(df2, on='key', how='left')

# Outer join
df1.join(df2, on='key', how='outer')

8. Window Functions:

# Cumulative sum
df.with_columns(pl.col('A').cum_sum().over('B'))

# Rolling average
df.with_columns(pl.col('A').rolling_mean(window_size=3).over('B'))

# Rank
df.with_columns(pl.col('A').rank().over('B'))

9. IO Operations:

# Write to CSV
df.write_csv('output.csv')

# Write to Parquet
df.write_parquet('output.parquet')

# Read Parquet
pl.read_parquet('file.parquet')

10. Lazy Execution:

# Create lazy DataFrame
lazy_df = pl.scan_csv('large_file.csv')

# Define operations
result = lazy_df.filter(pl.col('A') > 0).groupby('B').agg(pl.sum('C'))

# Execute lazy computation
result.collect()

11. Advanced Filtering:

# Multiple conditions
df.filter((pl.col('A') > 5) & (pl.col('B') < 10))

# Is in list
df.filter(pl.col('A').is_in([1, 3, 5]))

# Is null
df.filter(pl.col('A').is_null())

# Is between
df.filter(pl.col('A').is_between(5, 10))

12. Sampling:

# Random sample
df.sample(n=10)

# Stratified sample
df.group_by('category').sample(n=5)

13. Set Operations:

# Union
df1.vstack(df2)

# Intersection
df1.join(df2, on='key', how='inner')

# Difference
df1.join(df2, on='key', how='anti')

14. Advanced Aggregations:

# Multiple aggregations
df.groupby('A').agg([
    pl.sum('B').alias('B_sum'),
    pl.mean('C').alias('C_mean'),
    pl.n_unique('D').alias('D_unique_count')
])

# Custom aggregation
df.groupby('A').agg(pl.col('B').agg_groups(lambda x: x.sum() / x.count()))

15. Reshaping Data:

# Explode a list column
df.with_columns(pl.col('list_col').explode())

# Concatenate string columns
df.with_columns(pl.concat_str(['A', 'B'], separator='-').alias('A_B'))

16. Time Series Operations:

# Resample time series
df.group_by_dynamic('timestamp', every='1h').agg(pl.sum('value'))

# Shift values
df.with_columns(pl.col('A').shift(1).alias('A_lagged'))

# Difference between consecutive rows
df.with_columns((pl.col('A') - pl.col('A').shift(1)).alias('A_diff'))

17. Missing Data Handling:

# Drop rows with any null values
df.drop_nulls()

# Drop rows where specific columns have null values
df.drop_nulls(subset=['A', 'B'])

# Interpolate missing values
df.with_columns(pl.col('A').interpolate())

18. Data Type Operations:

# Cast column to different type
df.with_columns(pl.col('A').cast(pl.Float64))

# Get unique values
df.select(pl.col('A').unique())

# Count unique values
df.select(pl.col('A').n_unique())

19. Advanced String Operations:

# Extract using regex
df.with_columns(pl.col('text').str.extract(r'(\d+)', group_index=1))

# Split string into multiple columns
df.with_columns([
    pl.col('full_name').str.split(' ').list.get(0).alias('first_name'),
    pl.col('full_name').str.split(' ').list.get(1).alias('last_name')
])

20. Window Functions with Custom Sorting:

# Cumulative sum with custom sorting
df.with_columns(
    pl.col('value')
    .cum_sum()
    .over(['category', 'subcategory'])
    .sort('date')
)

21. Conditional Expressions:

# When-Then-Otherwise
df.with_columns(
    pl.when(pl.col('A') > 5)
    .then(pl.lit('High'))
    .when(pl.col('A') < 2)
    .then(pl.lit('Low'))
    .otherwise(pl.lit('Medium'))
    .alias('A_category')
)

22. Advanced IO Operations:

# Read JSON
pl.read_json('file.json')

# Read from database
pl.read_database(query='SELECT * FROM table', connection_uri='postgresql://user:pass@host/db')

# Write to database
df.write_database(table_name='my_table', connection_uri='postgresql://user:pass@host/db')

23. Performance Optimization:

# Parallel execution
pl.Config.set_num_threads(4)

# Streaming mode for large CSV files
for batch in pl.read_csv('large_file.csv', batch_size=10000):
    process_batch(batch)

24. Expressions and Custom Functions:

# Custom function
def custom_func(x):
    return x * 2 + 1

# Apply custom function
df.with_columns(pl.col('A').map(custom_func).alias('A_custom'))

# Complex expressions
df.with_columns(
    ((pl.col('A') * 2 + pl.col('B')) / pl.col('C')).alias('complex_calc')
)

25. List Operations:

# Get list length
df.with_columns(pl.col('list_col').list.lengths().alias('list_length'))

# Get nth element from list
df.with_columns(pl.col('list_col').list.get(1).alias('second_element'))

# Join list elements
df.with_columns(pl.col('list_col').list.join(',').alias('joined_list'))

# Slice list
df.with_columns(pl.col('list_col').list.slice(0, 3).alias('first_three'))

26. Struct Operations:

# Create struct column
df.with_columns(pl.struct(['A', 'B']).alias('AB_struct'))

# Access struct field
df.with_columns(pl.col('AB_struct')['A'].alias('A_from_struct'))

# Unnest struct
df.unnest('AB_struct')

27. Advanced Groupby Operations:

# Rolling groupby
df.groupby_rolling('date', period='7d').agg(pl.sum('value'))

# Dynamic groupby
df.groupby_dynamic('timestamp', every='1h', offset='30m').agg(pl.mean('value'))

# Groupby with exclusions
df.groupby('category', maintain_order=True).agg(
    pl.all().exclude(['category', 'id'])
)

28. Vectorized User-Defined Functions (UDFs):

import numpy as np

# Numpy UDF
@pl.api.register_vectorized_udfs(input_type=[pl.Float64], return_type=pl.Float64)
def custom_log(x):
    return np.log(x)

df.with_columns(custom_log(pl.col('A')).alias('A_log'))

29. Meta Operations:

# Get column names
df.columns

# Get dtypes
df.dtypes

# Get shape
df.shape

# Memory usage
df.estimated_size()

30. Advanced Joining:

# Asof join
df1.join_asof(df2, left_on='date', right_on='date', by='id')

# Cross join
df1.join(df2, how='cross')

# Fuzzy join
df1.join(df2, left_on='name', right_on='name', how='left', algorithm='fuzzy', matcher='levenshtein', threshold=2)

31. Polars-specific Optimizations:

# Predicate pushdown
(df.lazy()
   .filter(pl.col('A') > 0)
   .groupby('B')
   .agg(pl.sum('C'))
   .collect())

# Projection pushdown
(df.lazy()
   .select(['A', 'B'])
   .filter(pl.col('A') > 0)
   .collect())

32. Working with Missing Data:

# Fill null with different values based on condition
df.with_columns(
    pl.when(pl.col('A').is_null())
    .then(pl.col('B'))
    .otherwise(pl.col('A'))
    .alias('A_filled')
)

# Fill null with forward fill and a limit
df.with_columns(pl.col('A').fill_null(strategy='forward', limit=2))

33. Advanced DateTime Operations:

# Truncate to specific time unit
df.with_columns(pl.col('datetime').dt.truncate('1d').alias('day_start'))

# Get day of week
df.with_columns(pl.col('date').dt.weekday().alias('weekday'))

# Date range
pl.date_range(start='2021-01-01', end='2021-12-31', interval='1d')

34. Statistical Functions:

# Covariance
df.select(pl.covariance('A', 'B'))

# Correlation
df.select(pl.corr('A', 'B'))

# Quantile
df.select(pl.col('A').quantile(0.75))

35. Advanced String Matching:

# Fuzzy matching
df.with_columns(
    pl.col('text').str.fuzzy_match('pattern', threshold=80).alias('fuzzy_match')
)

This cheat sheet covers many functions and operations in Polars, which has many more features and capabilities, including advanced filtering, reshaping, time series operations, struct operations, vectorized UDFs, meta operations, performance and Polars-specific optimizations. please refer to the official docs for the most up-to-date and more comprehensive information on available functions and best practices.

## Installation
```bash
pip install polars 

# Install Polars with all optional dependencies:
pip install 'polars[all]'

# You can also install a subset of all optional dependencies:
pip install 'polars[numpy,pandas,pyarrow]'

# We also have a conda package (however pip is the preferred way):
conda install -c conda-forge polars
```

## Usage:

1. Importing Polars:
```python
import polars as pl
```

2. Creating DataFrames:
```python
# From dictionary
df = pl.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']})

# From list of dictionaries
df = pl.DataFrame([{'A': 1, 'B': 'a'}, {'A': 2, 'B': 'b'}])

# From CSV
df = pl.read_csv('file.csv')

# From Pandas DataFrame
pandas_df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']})
df = pl.from_pandas(pandas_df)
```

3. Basic DataFrame Operations:
```python
# Display DataFrame
print(df)

# Get DataFrame info
df.schema

# Select columns
df.select(['A', 'B'])

# Filter rows
df.filter(pl.col('A') > 2)

# Sort DataFrame
df.sort('A', reverse=True)

# Add new column
df.with_columns(pl.lit('new_col').alias('C'))

# Rename columns
df.rename({'A': 'X', 'B': 'Y'})

# Drop columns
df.drop(['A', 'B'])

# Group by and aggregate
df.groupby('A').agg(pl.sum('B'))
```

4. Data Manipulation:
```python
# Apply function to column
df.with_columns(pl.col('A').map(lambda x: x * 2).alias('A_doubled'))

# Fill null values
df.fill_null(strategy='forward')

# Replace values
df.with_columns(pl.col('A').replace({1: 10, 2: 20}))

# Melt DataFrame
df.melt(id_vars=['A'], value_vars=['B', 'C'])

# Pivot DataFrame
df.pivot(values='value', index='A', columns='variable')
```

5. String Operations:
```python
# Convert to uppercase
df.with_columns(pl.col('B').str.to_uppercase())

# String contains
df.filter(pl.col('B').str.contains('pattern'))

# String replace
df.with_columns(pl.col('B').str.replace('old', 'new'))

# String length
df.with_columns(pl.col('B').str.lengths().alias('B_length'))
```

6. DateTime Operations:
```python
# Parse strings to datetime
df.with_columns(pl.col('date').str.strptime(pl.Datetime, '%Y-%m-%d'))

# Extract components
df.with_columns(pl.col('date').dt.year().alias('year'))

# Date arithmetic
df.with_columns((pl.col('date') + pl.duration(days=1)).alias('next_day'))
```

7. Joining DataFrames:
```python
# Inner join
df1.join(df2, on='key', how='inner')

# Left join
df1.join(df2, on='key', how='left')

# Outer join
df1.join(df2, on='key', how='outer')
```

8. Window Functions:
```python
# Cumulative sum
df.with_columns(pl.col('A').cum_sum().over('B'))

# Rolling average
df.with_columns(pl.col('A').rolling_mean(window_size=3).over('B'))

# Rank
df.with_columns(pl.col('A').rank().over('B'))
```

9. IO Operations:
```python
# Write to CSV
df.write_csv('output.csv')

# Write to Parquet
df.write_parquet('output.parquet')

# Read Parquet
pl.read_parquet('file.parquet')
```

10. Lazy Execution:
```python
# Create lazy DataFrame
lazy_df = pl.scan_csv('large_file.csv')

# Define operations
result = lazy_df.filter(pl.col('A') > 0).groupby('B').agg(pl.sum('C'))

# Execute lazy computation
result.collect()
```

11. Advanced Filtering:
```python
# Multiple conditions
df.filter((pl.col('A') > 5) & (pl.col('B') < 10))

# Is in list
df.filter(pl.col('A').is_in([1, 3, 5]))

# Is null
df.filter(pl.col('A').is_null())

# Is between
df.filter(pl.col('A').is_between(5, 10))
```

12. Sampling:
```python
# Random sample
df.sample(n=10)

# Stratified sample
df.group_by('category').sample(n=5)
```

13. Set Operations:
```python
# Union
df1.vstack(df2)

# Intersection
df1.join(df2, on='key', how='inner')

# Difference
df1.join(df2, on='key', how='anti')
```

14. Advanced Aggregations:
```python
# Multiple aggregations
df.groupby('A').agg([
    pl.sum('B').alias('B_sum'),
    pl.mean('C').alias('C_mean'),
    pl.n_unique('D').alias('D_unique_count')
])

# Custom aggregation
df.groupby('A').agg(pl.col('B').agg_groups(lambda x: x.sum() / x.count()))
```

15. Reshaping Data:
```python
# Explode a list column
df.with_columns(pl.col('list_col').explode())

# Concatenate string columns
df.with_columns(pl.concat_str(['A', 'B'], separator='-').alias('A_B'))
```

16. Time Series Operations:
```python
# Resample time series
df.group_by_dynamic('timestamp', every='1h').agg(pl.sum('value'))

# Shift values
df.with_columns(pl.col('A').shift(1).alias('A_lagged'))

# Difference between consecutive rows
df.with_columns((pl.col('A') - pl.col('A').shift(1)).alias('A_diff'))
```

17. Missing Data Handling:
```python
# Drop rows with any null values
df.drop_nulls()

# Drop rows where specific columns have null values
df.drop_nulls(subset=['A', 'B'])

# Interpolate missing values
df.with_columns(pl.col('A').interpolate())
```

18. Data Type Operations:
```python
# Cast column to different type
df.with_columns(pl.col('A').cast(pl.Float64))

# Get unique values
df.select(pl.col('A').unique())

# Count unique values
df.select(pl.col('A').n_unique())
```

19. Advanced String Operations:
```python
# Extract using regex
df.with_columns(pl.col('text').str.extract(r'(\d+)', group_index=1))

# Split string into multiple columns
df.with_columns([
    pl.col('full_name').str.split(' ').list.get(0).alias('first_name'),
    pl.col('full_name').str.split(' ').list.get(1).alias('last_name')
])
```

20. Window Functions with Custom Sorting:
```python
# Cumulative sum with custom sorting
df.with_columns(
    pl.col('value')
    .cum_sum()
    .over(['category', 'subcategory'])
    .sort('date')
)
```

21. Conditional Expressions:
```python
# When-Then-Otherwise
df.with_columns(
    pl.when(pl.col('A') > 5)
    .then(pl.lit('High'))
    .when(pl.col('A') < 2)
    .then(pl.lit('Low'))
    .otherwise(pl.lit('Medium'))
    .alias('A_category')
)
```

22. Advanced IO Operations:
```python
# Read JSON
pl.read_json('file.json')

# Read from database
pl.read_database(query='SELECT * FROM table', connection_uri='postgresql://user:pass@host/db')

# Write to database
df.write_database(table_name='my_table', connection_uri='postgresql://user:pass@host/db')
```

23. Performance Optimization:
```python
# Parallel execution
pl.Config.set_num_threads(4)

# Streaming mode for large CSV files
for batch in pl.read_csv('large_file.csv', batch_size=10000):
    process_batch(batch)
```

24. Expressions and Custom Functions:
```python
# Custom function
def custom_func(x):
    return x * 2 + 1

# Apply custom function
df.with_columns(pl.col('A').map(custom_func).alias('A_custom'))

# Complex expressions
df.with_columns(
    ((pl.col('A') * 2 + pl.col('B')) / pl.col('C')).alias('complex_calc')
)
```

25. List Operations:
```python
# Get list length
df.with_columns(pl.col('list_col').list.lengths().alias('list_length'))

# Get nth element from list
df.with_columns(pl.col('list_col').list.get(1).alias('second_element'))

# Join list elements
df.with_columns(pl.col('list_col').list.join(',').alias('joined_list'))

# Slice list
df.with_columns(pl.col('list_col').list.slice(0, 3).alias('first_three'))
```

26. Struct Operations:
```python
# Create struct column
df.with_columns(pl.struct(['A', 'B']).alias('AB_struct'))

# Access struct field
df.with_columns(pl.col('AB_struct')['A'].alias('A_from_struct'))

# Unnest struct
df.unnest('AB_struct')
```

27. Advanced Groupby Operations:
```python
# Rolling groupby
df.groupby_rolling('date', period='7d').agg(pl.sum('value'))

# Dynamic groupby
df.groupby_dynamic('timestamp', every='1h', offset='30m').agg(pl.mean('value'))

# Groupby with exclusions
df.groupby('category', maintain_order=True).agg(
    pl.all().exclude(['category', 'id'])
)
```

28. Vectorized User-Defined Functions (UDFs):
```python
import numpy as np

# Numpy UDF
@pl.api.register_vectorized_udfs(input_type=[pl.Float64], return_type=pl.Float64)
def custom_log(x):
    return np.log(x)

df.with_columns(custom_log(pl.col('A')).alias('A_log'))
```

29. Meta Operations:
```python
# Get column names
df.columns

# Get dtypes
df.dtypes

# Get shape
df.shape

# Memory usage
df.estimated_size()
```

30. Advanced Joining:
```python
# Asof join
df1.join_asof(df2, left_on='date', right_on='date', by='id')

# Cross join
df1.join(df2, how='cross')

# Fuzzy join
df1.join(df2, left_on='name', right_on='name', how='left', algorithm='fuzzy', matcher='levenshtein', threshold=2)
```

31. Polars-specific Optimizations:
```python
# Predicate pushdown
(df.lazy()
   .filter(pl.col('A') > 0)
   .groupby('B')
   .agg(pl.sum('C'))
   .collect())

# Projection pushdown
(df.lazy()
   .select(['A', 'B'])
   .filter(pl.col('A') > 0)
   .collect())
```

32. Working with Missing Data:
```python
# Fill null with different values based on condition
df.with_columns(
    pl.when(pl.col('A').is_null())
    .then(pl.col('B'))
    .otherwise(pl.col('A'))
    .alias('A_filled')
)

# Fill null with forward fill and a limit
df.with_columns(pl.col('A').fill_null(strategy='forward', limit=2))
```

33. Advanced DateTime Operations:
```python
# Truncate to specific time unit
df.with_columns(pl.col('datetime').dt.truncate('1d').alias('day_start'))

# Get day of week
df.with_columns(pl.col('date').dt.weekday().alias('weekday'))

# Date range
pl.date_range(start='2021-01-01', end='2021-12-31', interval='1d')
```

34. Statistical Functions:
```python
# Covariance
df.select(pl.covariance('A', 'B'))

# Correlation
df.select(pl.corr('A', 'B'))

# Quantile
df.select(pl.col('A').quantile(0.75))
```

35. Advanced String Matching:
```python
# Fuzzy matching
df.with_columns(
    pl.col('text').str.fuzzy_match('pattern', threshold=80).alias('fuzzy_match')
)
```

Source: https://gist.github.com

Clear formatting from selected text using keyboard shortcuts in Word

 To clear formatting from selected text using keyboard shortcuts:

• Press Ctrl + Spacebar to clear character formatting only (such as bold, font and font size) from selected text.
• Press Ctrl + Q to clear paragraph formatting only (such as indents and line spacing) from selected text.
• Press Ctrl + Shift + N to reapply the Normal style to selected text.

Sub ResetParagraphFormat()
'
' Reset Selection Paragraph Formatting
'
'
    With Selection.ParagraphFormat
        .Reset
        .LeftIndent = CentimetersToPoints(0)
        .RightIndent = CentimetersToPoints(0)
        .SpaceBefore = 0
        .SpaceBeforeAuto = False
        .SpaceAfter = 0
        .SpaceAfterAuto = False
        .LineSpacingRule = wdLineSpaceSingle
        .Alignment = wdAlignParagraphJustify
        .WidowControl = False
        .KeepWithNext = False
        .KeepTogether = False
        .PageBreakBefore = False
        .NoLineNumber = False
        .Hyphenation = True
        .FirstLineIndent = CentimetersToPoints(0)
        .OutlineLevel = wdOutlineLevelBodyText
        .CharacterUnitLeftIndent = 0
        .CharacterUnitRightIndent = 0
        .CharacterUnitFirstLineIndent = 0
        .LineUnitBefore = 0
        .LineUnitAfter = 0
        .MirrorIndents = False
        .TextboxTightWrap = wdTightNone
        .CollapsedByDefault = False
    End With
End Sub

Sub SelectionClearFormatting()
'
' Clear All Formatting
'
    With Selection
        .ClearFormatting
    End With
End Sub
 

Sub ResetParagraph()
' Removes manual paragraph formatting (formatting not applied using a style).
' If you manually right align a paragraph and the underlying style has a different alignment,
' the Reset method changes the alignment to match the formatting of the underlying style.
    Selection.Paragraphs.Reset
End Sub



 

Economic Terminology German - English - French - ihk.de

Deutsch	Amerikanisches Englisch	Britisches Englisch	Französisch
Aktiengesellschaft (AG)	Stock Corporation (Corp. oder Inc.)	Public Limited Company (Plc)	Société Anonyme (S.A.)
Mitglied des Vorstandes	Member of the Executive Board, Member of the Board of Management	Member of the Board of Management	Membre du Directoire
Stv. Mitglied des Vorstandes	Deputy Member of the Executive Board, Deputy Member of the Board of Management	Deputy Member of the Board of Management	Membre Suppléant du Directoire
Vorsitzender des Vorstandes	President and Chief Executive Officer, Chief Executive Officer, President, Chairman of the Executive Board, Chairman of the Board of Management	Managing Director, Chief Executive Officer, Chairman of the Board of Management	Président du Directoire
Stv. Vorsitzender des Vorstandes	Deputy Chairman of the Executive Board, Deputy Chairman of the Board of Management	Vice Chairman of the Board of Management	Vice-Président du Directoire
Generalbevollmächtigter	General Manager	General Manager	Directeur Général
Arbeitsdirektor	Executive for Labor Relations	Director of the Labour Relations	Directeur des Affaires Sociales
Prokurist	Authorized Officer	Authorised Officer	Fondé de Pouvoir
Handlungsbevoll- mächtigter	Assistant Manager	Assistant Manager	Mandataire, Fondé de Pouvoir
Aufsichtsrat	Supervisory Board	Supervisory Board	Conseil de Surveillance
Mitglied des Aufsichtsrates	Member of the Supervisory Board	Member of the Supervisory Board	Membre du Conseil de Surveillance
Vorsitzender des Aufsichtsrates	Chairman of the Supervisory Board	Chairman of the Supervisory Board	Président du Conseil de Surveillance
Stv. Vorsitzender des Aufsichtsrates	Deputy Chairman of the Supervisory Board	Vice Chairman of the Supervisory Board	Vice-Président du Conseil de Surveillance
Verwaltungsrat	Administrative Board	Administrative Board	Conseil d´Administration
Vorsitzender des Verwaltungsrates	Chairman of the Administrative Board	Chairman of the Administrative Board	Président Directeur Général
Beirat	Advisory Board	Advisory Board	Comité Consultatif
Aktionär	Stockholder, Shareholder	Shareholder	Actionaire
Hauptversammlung	Stockholders Meeting, Shareholders Meeting	Shareholders´ Meeting, General Meeting	Assemblée Générale des Actionaires
GmbH	Closed Corporation Privately-Held Corporation	Private Limited Company (Ltd.)	Société à responsabilité limitée (S.A.R.L.)
Geschäftsführer	General Manager, Managing Director	Director	Gérant
Vorsitzender der Geschäftsführung	Chief Executive Officer, President, Chairman of the Board of Management	Chairman of the Board of Directors	Président Directeur Général
Prokurist	Authorized Officer	Authorised Officer	Fondé de Pouvoir Supérieur
Handlungsbevollmächtigter	Assistant Manager	Assistant Manager	Fondé de Pouvoir, Mandataire
Deutsch	Amerikanisch	Englisch	Französisch
Aufsichtsrat	Supervisory Board	Supervisory Board	Conseil de Surveillance
Beirat	Advisory Board	Advisory Board	Comité Consultatif
Gesellschafter	Stockholder, Shareholder	Shareholder, Member	Associé
Gesellschafterversammlung	Stockholders Meeting, Shareholders Meeting	Shareholders`Meeting, General Meeting	Assemblée Général des Associés
OHG	Partnership	Partnership	Société en nom collectif
Gesellschafter	Partner	Partner	Associé
Geschäftsführender Gesellschafter	Managing Partner	Managing Partner	Associé Gérant
KG	Limited Partnership	Limited Partnership	Société en commandite simple
Komplementär	General Partner	General Partner	Commandité
Persönlich haftender Gesellschafter	General Partner	General Partner	Commandité
Kommanditist	Limited Partner	Limited Partner	Commanditaire
Geschäftsführender Gesellschafter	Managing Partner	Managing Partner	Associé Gérant
GmbH & Co. KG	Limited Partnership with Limited Company as General Partner	Limited Partnership with Limited Company as General Partner	Société à responsabilité limitée et Co., Société en commandite
Einzelunternehmen / Einzelkaufmann	Sole Proprietorship	Sole Proprietorship, Sole Trader	Entreprise individuelle / Etablissement
Geschäftsinhaber	Proprietor	Proprietor	Propriétaire exploitant
Geschäftsteilhaber	Co-owner	Co-owner, Co-Proprietor	Co-Proprétaire
Alleininhaber	Sole Proprietor	Sole Proprietor	Propriétaire
Prokurist	Authorized Officer	Authorised Officer	Fondé de Pouvoir
Verband	Association	Association	Association
Geschäftsführer	Managing Director	Director	Directeur
Hauptgeschäftsführer	General Executive Manager	Managing Director	Secrétaire Général
Präsident	President	President	Président
Vorstand / Präsidium	Board of Directors, Executive Board	Board of Directors, Executive Board	Conseil d`Administration
Ehrenvorsitzender	Honorary Chairman of the Board of Directors	Honorary Chairman of the Board of Directors	Président d`Honneur
Vorsitzender	Chairman of the Board of Directors	Chairman of the Executive Board, Chairman of the Board of Directors	Président du Conseil d`Administration
Hauptausschuss	Executive Committee	Executive Committee	Comité Executif
Sonstige Titel
Abteilungsdirektor	Division Manager	Division Manager	Chef de Division/Département
Handlungsbevollmächtigter	Assistant Manager	Assistant Manager	Fondé de Pouvoir
Bevollmächtigter	Authorized Representative	Authorised Representative	Mandataire

Deutsch	Amerikanisches Englisch	Britisches Englisch	Französisch
Leiter der Rechtsabteilung	Head of Legal Department, General Counsel	Head of the Legal Department, General Counsel	Chef du Département juridique
Leiter der Personalabteilung	Head of Personnel Department, Head of Human Resources Department	Head of the Personnel Department, Director of Personnel	Chef du Personnel, Directeur du Personnel
Betriebsdirektor	Production Manager	Production Manager	Directeur Technique
Werksleiter	Plant Manager	Works Manager	Directeur d`Usine
Hauptabteilungsleiter	Head of Division	Head of Division	Directeur de Division
Bereichsleiter	Head of Department	Head of Department	Directeur de Département
Betriebsleiter	Production Manager	Production Manager	Chef de Production

Romanian NLP

 Table of contents

• Unlabeled text Corpora
• Semantic Textual Similarity / Paraphrasing
• Natural Language Inference
• Summarization
• Dialect and regional speech identification
• Named Entity Recognition (NER)
• Autorship Attribution
• Sentiment Analysis
• Dependency Parsing
• Diacritics Restoration / Grammar Correction
• Fake News / Clickbait / Satirical News
• Offensive Language
• Questions and Answering
• Spelling, Dictionaries and Gramatical Errors

Unlabeled text Corpora

• ❄️FuLG dataset ❄️

The FuLG dataset is a comprehensive Romanian language corpus comprising
150 billion tokens, carefully extracted from Common Crawl. 

• 🌐 Oscar Common Crawl dataset 🌐

Part of a large multilanguage corpus originated from Common Crawl.
It's a raw, unannotated corpus. It has roughly 50 GB of Romanian text
in 4.5 million documnets. For details check its homepage 
and the paper

• 📚 CC-100 📚

 Similar to Oscar, part of a multilanguage corpus also based on Common Crawl
 from 2018. Romanian text is 16GB large

• 🌍 Wikipedia Corpus 🌍

  Romanian language wikipedia dump. 

• 📰⚖️ RoTex Collection 📰⚖️

  A collection of varoius unannotated corpora collected around 2018-2019.
  Includes books, scraped newspapers and juridical documents  

• 📖 Romanian Language Repository 📖

  A collection of written and spoken text from various
  sources: Articles, Fairy tales, Fiction, History, Theatre, News

• 🏛️ MARCELL Legislative Corpus 🏛️

 Romanian national legilation from  1881 to 2021. The corpus
 includes mainly: governmental decisions, ministerial orders,
 decisions, decrees and laws.
 Automatically annotated for Named Entities

• 🦠 COVID-19 Tweets 🐦

Mega-COV is a billion-scale dataset from Twitter for studying COVID-19. It is available in over 100+ languages, Romanian being one of them. Tweets need to be rehydrated

• COVIDSentiRO

A corpus of Romanian tweets related to COVID and vaccination against COVID, created and collected between January 2021 and February 2022. It contains 19319 tweets.

• 📜 Minutes of the Sittings of the Chamber of Deputies of Romania 📜

Minutes of the Sittings of the Chamber of Deputies of Romania (2016-2018)
Unannotated corpus

• 🔊 Minutes of the Sittings of the Romanian Parliament 🔊

contains 500k+ instances of speech from the parliament podium from
1996 to 2018. Sentence splitting and deduplication onm sentence level
have been applied as processing steps
Unannotated corpus

• 🗣️ Romanian Presidential Discourses 🗣️

Romanian presidential discouses (1990-2020) split in 4 files
one for each president. Unannotated corpus

• 🎭 Culture Domain Corpus 🎭

Monolingual Romanian corpus, including content from public websites related to culture

• Law Domain Corpus

Monolingual (ron) corpus, containing 38063991 tokens and 854096 lexical types in the law domain.

• Public Administration Domain Corpus

Monolingual Romanian corpus, containing 360833 sentences (9064764 words) in the public administration domain.

• New Civil Procedure Code

The New Civil Procedure Code in Romanian (monolingual) comprising 297888 words.

• New Criminal Code

The Romanian updated criminal code: text with law content.

• Romanian News Articles Dataset

news articles dataset from romanian newssites title, summary and article

• Old Newspapers

multi-language corpus from online available news sources. It contains also 43mil words in Romanian language from Twitter, Blogs and Newspapers

• ELTeC-Rom

The Romanian novel collection for ELTeC, the European Literary Text Collection Sources: Biblioteca Metropolitana din Bucuresti, Biblioteca Universitara "Mihai Eminescu" din Iasi, Biblioteca Judeteana din Botosani, personal micro-collections uploaded on Zenodo under the following labels: "Hajduks Library"; "RomanianNovel Library"; "CityMysteries Library"; "BibliotecaDHL_Iasi"

• RO Business Emails

Public dataset of 1447 manually annotated Romanian business-oriented emails. The corpus is annotated with 5 token-related labels, as well as 5 sequence-related classes

• 📖RO-Stories📖

The corpus consists of texts written by Romanian authors between 19th century and present, representing stories, short-stories, fairy tales and sketches. The current version contains 19 authors, 1263 full texts and 12516 paragraphs of around 200 words each, preserving paragraphs integrity.

• 📕ROST📕

A dataset containing 400 Romanian texts written by 10 authors The dataset contains stories, short stories, fairy tales, novels, articles, and sketches written by Ion Creangă, Barbu Ştefănescu Delavrancea, Mihai Eminescu, Nicolae Filimon, Emil Gârleanu, Petre Ispirescu, Mihai Oltean, Emilia Plugaru, Liviu Rebreanu, Ioan Slavici.

• 🍳Romanian Cooking Recipes🍳

891 Cooking Recipes in Romanian Language

Semantic Textual Similarity / Paraphrasing

• RO-STS

Semantic Textual Similarity dataset for the Romanian language RO-STS contains 8,628 sentence pairs with their similarity scores

• Romanian Bible Paraphrase Corpus

A paraphprase corpus created from 10 different Romanian language Bible versions. The final dataset contains 904,815 similar records and 218,977 non matching records, totaling 1,123,927

• Romanian paraphrase dataset

Around ~100k examples of paraphrases. No clear explanation on how the dataset was built

• TaPaCo

A multi-language paraphrase corpus for 73 languages extracted from the Tatoeba database. It has ~ 2000 romanian phrases totaling 941 paraphrase groups.

Natural Language Inference

• RONLI

We introduce the first Romanian NLI corpus (RoNLI) comprising 58K training sentence pairs, which are obtained via distant supervision, and 6K validation and test sentence pairs, which are manually annotated with the correct labels.

• RO-NLI

The repository seems to be just an attempt at starting to build the dataset

Summarization

• RO Text Summarization

Around ~72k Full texts and their summary. Source seems to be news websites. No description or explanation available

Dialect and regional speech identification

• RoDia

varied compilation of speech samples from five distinct regions of Romania, covering both urban and rural environments. Around 2800 records labeled with age, gender and type of dialect

• MOROCO

MOROCO: The Moldavian and Romanian Dialectal Corpus The MOROCO data set contains Moldavian and Romanian samples of text collected from the news domain. The samples belong to one of the following six topics: culture, finance, politics, science, sports, tech totaling over 32.000 labeled records

Named Entity Recognition (NER)

• LegalNERo
• RONEC
• WikiAnn
• SiMoNERo

Autorship Attribution

• ROST

Sentiment Analysis

• RO_Sent
• Senti_Lex
• LaROSeDa
• SART
• RED
• Romanian Categorized Web Dataset
• Romanian Sentiment Movie Reviews

Dependency Parsing

• CoNLL 2017 & 2018
• Deep Universal Dependencies
• Curlicat Romanian Corpus
• HamleDT
• RoWordNet
• RoRefTrees

Diacritics Restoration / Grammar Correction

• Corpus for training and evaluating diacritics restoration systems
• RONACC

Fake News / Clickbait / Satirical News

• Fakerom
• Clickbait dataset on Romanian SciTech News
• SaRoCo

Offensive Language

• RO-Offense

• News RO-Offense

manually annotated 4,052 comments on a Romanian local news website into one of the following classes: non-offensive, targeted insults, racist, homophobic, and sexist.

• FB RO-Offense

4455 organic generated comments from Facebook live broadcasts annotated not binary offensive language detection tasks and for fine-grained offensive language detection

• RO-Offense-Sequences

4800 Romanian comments annotated with offensive text spans Offensive span detection

• Hate Speech RO

3860 labeled hate speech records

• ROFF

Dataset consists of 5000 tweets, from which 924 were labeled as offensive (18.48 %) and 4076 tweets as non-offensive.

• CoRoSeOf

The corpus contains 39 245 tweets, annotated by multiple annotators, following the sexist label set of a recent study.

Questions and Answers

• 🧮 GSM8K RO 🧮

This dataset is just the translation of the gsm8k dataset. GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. There is no information on the quality of the translation

• 💻 ROCODE 💻

RoCode, a competitive programming dataset, consisting of 2,642 problems written in Romanian, 11k solutions in C, C++ and Python and comprehensive testing suites for each problem. The purpose of RoCode is to provide a benchmark for evaluating the code intelligence of language models trained on Romanian / multilingual text as well as a fine-tuning set for pretrained Romanian models.

Spelling, Dictionaries and Gramatical Errors

• Grammar-RO

Synthetic dataset with ~1.9M records. Altered and correct statement as columns

• RoAcReL

Romanian Archaisms Regionalisms Lexicon containing ~ 1940 Word definitions

• RoRuDi

Romanian Rules for Dialects - 1940 regionalisms, meanings and the region of provenience