scientific-skills/vaex/references/data_processing.md
This reference covers filtering, selections, virtual columns, expressions, aggregations, groupby operations, and data transformations in Vaex.
Vaex uses boolean expressions to filter data efficiently without copying:
# Simple filter
df_filtered = df[df.age > 25]
# Multiple conditions
df_filtered = df[(df.age > 25) & (df.salary > 50000)]
df_filtered = df[(df.category == 'A') | (df.category == 'B')]
# Negation
df_filtered = df[~(df.age < 18)]
Vaex can maintain multiple named selections simultaneously:
# Create named selection
df.select(df.age > 30, name='adults')
df.select(df.salary > 100000, name='high_earners')
# Use selection in operations
mean_age_adults = df.mean(df.age, selection='adults')
count_high_earners = df.count(selection='high_earners')
# Combine selections
df.select((df.age > 30) & (df.salary > 100000), name='adult_high_earners')
# List all selections
print(df.selection_names())
# Drop selection
df.select_drop('adults')
# String matching
df_filtered = df[df.name.str.contains('John')]
df_filtered = df[df.name.str.startswith('A')]
df_filtered = df[df.email.str.endswith('@gmail.com')]
# Null/missing value filtering
df_filtered = df[df.age.isna()] # Keep missing
df_filtered = df[df.age.notna()] # Remove missing
# Value membership
df_filtered = df[df.category.isin(['A', 'B', 'C'])]
# Range filtering
df_filtered = df[df.age.between(25, 65)]
Virtual columns are computed on-the-fly with zero memory overhead:
# Arithmetic operations
df['total'] = df.price * df.quantity
df['price_squared'] = df.price ** 2
# Mathematical functions
df['log_price'] = df.price.log()
df['sqrt_value'] = df.value.sqrt()
df['abs_diff'] = (df.x - df.y).abs()
# Conditional logic
df['is_adult'] = df.age >= 18
df['category'] = (df.score > 80).where('A', 'B') # If-then-else
# Mathematical
df.x.abs() # Absolute value
df.x.sqrt() # Square root
df.x.log() # Natural log
df.x.log10() # Base-10 log
df.x.exp() # Exponential
# Trigonometric
df.angle.sin()
df.angle.cos()
df.angle.tan()
df.angle.arcsin()
# Rounding
df.x.round(2) # Round to 2 decimals
df.x.floor() # Round down
df.x.ceil() # Round up
# Type conversion
df.x.astype('int64')
df.x.astype('float32')
df.x.astype('str')
# where() method: condition.where(true_value, false_value)
df['status'] = (df.age >= 18).where('adult', 'minor')
# Multiple conditions with nested where
df['grade'] = (df.score >= 90).where('A',
(df.score >= 80).where('B',
(df.score >= 70).where('C', 'F')))
# Using searchsorted for binning
bins = [0, 18, 65, 100]
labels = ['minor', 'adult', 'senior']
df['age_group'] = df.age.searchsorted(bins).where(...)
Access string methods via the .str accessor:
# Case conversion
df['upper_name'] = df.name.str.upper()
df['lower_name'] = df.name.str.lower()
df['title_name'] = df.name.str.title()
# Trimming
df['trimmed'] = df.text.str.strip()
df['ltrimmed'] = df.text.str.lstrip()
df['rtrimmed'] = df.text.str.rstrip()
# Searching
df['has_john'] = df.name.str.contains('John')
df['starts_with_a'] = df.name.str.startswith('A')
df['ends_with_com'] = df.email.str.endswith('.com')
# Slicing
df['first_char'] = df.name.str.slice(0, 1)
df['last_three'] = df.name.str.slice(-3, None)
# Length
df['name_length'] = df.name.str.len()
# Replacing
df['clean_text'] = df.text.str.replace('bad', 'good')
# Splitting (returns first part)
df['first_name'] = df.full_name.str.split(' ')[0]
# Concatenation
df['full_name'] = df.first_name + ' ' + df.last_name
# Padding
df['padded'] = df.code.str.pad(10, '0', 'left') # Zero-padding
Access datetime methods via the .dt accessor:
# Parsing strings to datetime
df['date_parsed'] = df.date_string.astype('datetime64')
# Extracting components
df['year'] = df.timestamp.dt.year
df['month'] = df.timestamp.dt.month
df['day'] = df.timestamp.dt.day
df['hour'] = df.timestamp.dt.hour
df['minute'] = df.timestamp.dt.minute
df['second'] = df.timestamp.dt.second
# Day of week
df['weekday'] = df.timestamp.dt.dayofweek # 0=Monday
df['day_name'] = df.timestamp.dt.day_name # 'Monday', 'Tuesday', ...
# Date arithmetic
df['tomorrow'] = df.date + pd.Timedelta(days=1)
df['next_week'] = df.date + pd.Timedelta(weeks=1)
Vaex performs aggregations efficiently across billions of rows:
# Single column
mean_age = df.age.mean()
std_age = df.age.std()
min_age = df.age.min()
max_age = df.age.max()
sum_sales = df.sales.sum()
count_rows = df.count()
# With selections
mean_adult_age = df.age.mean(selection='adults')
# Multiple at once with delay
mean = df.age.mean(delay=True)
std = df.age.std(delay=True)
results = vaex.execute([mean, std])
# Central tendency
df.x.mean()
df.x.median_approx() # Approximate median (fast)
# Dispersion
df.x.std() # Standard deviation
df.x.var() # Variance
df.x.min()
df.x.max()
df.x.minmax() # Both min and max
# Count
df.count() # Total rows
df.x.count() # Non-missing values
# Sum and product
df.x.sum()
df.x.prod()
# Percentiles
df.x.quantile(0.5) # Median
df.x.quantile([0.25, 0.75]) # Quartiles
# Correlation
df.correlation(df.x, df.y)
df.covar(df.x, df.y)
# Higher moments
df.x.kurtosis()
df.x.skew()
# Unique values
df.x.nunique() # Count unique
df.x.unique() # Get unique values (returns array)
Group data and compute aggregations per group:
# Single column groupby
grouped = df.groupby('category')
# Aggregation
result = grouped.agg({'sales': 'sum'})
result = grouped.agg({'sales': 'sum', 'quantity': 'mean'})
# Multiple aggregations on same column
result = grouped.agg({
'sales': ['sum', 'mean', 'std'],
'quantity': 'sum'
})
# Multiple grouping columns
result = df.groupby(['category', 'region']).agg({
'sales': 'sum',
'quantity': 'mean'
})
# Custom aggregation functions
result = df.groupby('category').agg({
'sales': lambda x: x.max() - x.min()
})
# Available aggregation functions
# 'sum', 'mean', 'std', 'min', 'max', 'count', 'first', 'last'
# Bin continuous variable and aggregate
result = df.groupby(vaex.vrange(0, 100, 10)).agg({
'sales': 'sum'
})
# Datetime binning
result = df.groupby(df.timestamp.dt.year).agg({
'sales': 'sum'
})
Create bins from continuous variables:
# Create bins
df['age_bin'] = df.age.digitize([18, 30, 50, 65, 100])
# Labeled bins
bins = [0, 18, 30, 50, 65, 100]
labels = ['child', 'young_adult', 'adult', 'middle_age', 'senior']
df['age_group'] = df.age.digitize(bins)
# Note: Apply labels using where() or mapping
# Equal-width bins
df['value_bin'] = df.value.digitize(
vaex.vrange(df.value.min(), df.value.max(), 10)
)
# Quantile-based bins
quantiles = df.value.quantile([0.25, 0.5, 0.75])
df['value_quartile'] = df.value.digitize(quantiles)
Compute statistics on grids:
# 2D histogram/heatmap data
counts = df.count(binby=[df.x, df.y], limits=[[0, 10], [0, 10]], shape=(100, 100))
# Mean on a grid
mean_z = df.mean(df.z, binby=[df.x, df.y], limits=[[0, 10], [0, 10]], shape=(50, 50))
# Multiple statistics on grid
stats = df.mean(df.z, binby=[df.x, df.y], shape=(50, 50), delay=True)
counts = df.count(binby=[df.x, df.y], shape=(50, 50), delay=True)
results = vaex.execute([stats, counts])
Work with missing, null, and NaN values:
# Check for missing
df['age_missing'] = df.age.isna()
df['age_present'] = df.age.notna()
# Count missing
missing_count = df.age.isna().sum()
missing_pct = df.age.isna().mean() * 100
# Filter out missing
df_clean = df[df.age.notna()]
# Fill missing with value
df['age_filled'] = df.age.fillna(0)
df['age_filled'] = df.age.fillna(df.age.mean())
# Forward/backward fill (for time series)
df['age_ffill'] = df.age.fillna(method='ffill')
df['age_bfill'] = df.age.fillna(method='bfill')
Vaex distinguishes between:
# Check which missing type
df.is_masked('column_name') # True if uses Arrow null (NA)
# Convert between types
df['col_masked'] = df.col.as_masked() # Convert to NA representation
# Sort by single column
df_sorted = df.sort('age')
df_sorted = df.sort('age', ascending=False)
# Sort by multiple columns
df_sorted = df.sort(['category', 'age'])
# Note: Sorting materializes a new column with indices
# For very large datasets, consider if sorting is necessary
Combine DataFrames based on keys:
# Inner join
df_joined = df1.join(df2, on='key_column')
# Left join
df_joined = df1.join(df2, on='key_column', how='left')
# Join on different column names
df_joined = df1.join(
df2,
left_on='id',
right_on='user_id',
how='left'
)
# Multiple key columns
df_joined = df1.join(df2, on=['key1', 'key2'])
# Virtual column (no memory)
df['new_col'] = df.x + df.y
# From external array (must match length)
import numpy as np
new_data = np.random.rand(len(df))
df['random'] = new_data
# Constant value
df['constant'] = 42
# Drop single column
df = df.drop('column_name')
# Drop multiple columns
df = df.drop(['col1', 'col2', 'col3'])
# Select specific columns (drop others)
df = df[['col1', 'col2', 'col3']]
# Rename single column
df = df.rename('old_name', 'new_name')
# Rename multiple columns
df = df.rename({
'old_name1': 'new_name1',
'old_name2': 'new_name2'
})
# Multiple derived features
df['log_price'] = df.price.log()
df['price_per_unit'] = df.price / df.quantity
df['is_discount'] = df.discount > 0
df['price_category'] = (df.price > 100).where('expensive', 'affordable')
df['revenue'] = df.price * df.quantity * (1 - df.discount)
# Clean and standardize text
df['email_clean'] = df.email.str.lower().str.strip()
df['has_valid_email'] = df.email_clean.str.contains('@')
df['domain'] = df.email_clean.str.split('@')[1]
# Extract temporal features
df['year'] = df.timestamp.dt.year
df['month'] = df.timestamp.dt.month
df['day_of_week'] = df.timestamp.dt.dayofweek
df['is_weekend'] = df.day_of_week >= 5
df['quarter'] = ((df.month - 1) // 3) + 1
# Compute statistics by group
monthly_sales = df.groupby(df.timestamp.dt.month).agg({
'revenue': ['sum', 'mean', 'count'],
'quantity': 'sum'
})
# Multiple grouping levels
category_region_sales = df.groupby(['category', 'region']).agg({
'sales': 'sum',
'profit': 'mean'
})
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