> ## Documentation Index
> Fetch the complete documentation index at: https://private-7c7dfe99-detect-table-modification.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Pandas cookbook
> Common pandas patterns and their DataStore equivalents
Common pandas patterns and their DataStore equivalents. Most code works unchanged!
Data Loading
Read CSV
```python theme={null}
# Pandas
import pandas as pd
df = pd.read_csv("data.csv")
# DataStore - same!
from chdb import datastore as pd
df = pd.read_csv("data.csv")
```
Read Multiple Files
```python theme={null}
# Pandas
import glob
dfs = [pd.read_csv(f) for f in glob.glob("data/*.csv")]
df = pd.concat(dfs)
# DataStore - more efficient with glob pattern
df = pd.read_csv("data/*.csv")
```
***
Filtering
Single Condition
```python theme={null}
# Pandas and DataStore - identical
df[df['age'] > 25]
df[df['city'] == 'NYC']
df[df['name'].str.contains('John')]
```
Multiple Conditions
```python theme={null}
# AND
df[(df['age'] > 25) & (df['city'] == 'NYC')]
# OR
df[(df['age'] < 18) | (df['age'] > 65)]
# NOT
df[~(df['status'] == 'inactive')]
```
Using query()
```python theme={null}
# Pandas and DataStore - identical
df.query('age > 25 and city == "NYC"')
df.query('salary > 50000')
```
isin()
```python theme={null}
# Pandas and DataStore - identical
df[df['city'].isin(['NYC', 'LA', 'SF'])]
```
between()
```python theme={null}
# Pandas and DataStore - identical
df[df['age'].between(18, 65)]
```
***
Selecting Columns
Single Column
```python theme={null}
# Pandas and DataStore - identical
df['name']
df.name # attribute access
```
Multiple Columns
```python theme={null}
# Pandas and DataStore - identical
df[['name', 'age', 'city']]
```
Select and Filter
```python theme={null}
# Pandas and DataStore - identical
df[df['age'] > 25][['name', 'salary']]
# DataStore also supports SQL-style
df.filter(df['age'] > 25).select('name', 'salary')
```
***
Sorting
Single Column
```python theme={null}
# Pandas and DataStore - identical
df.sort_values('salary')
df.sort_values('salary', ascending=False)
```
Multiple Columns
```python theme={null}
# Pandas and DataStore - identical
df.sort_values(['city', 'salary'], ascending=[True, False])
```
Get Top/Bottom N
```python theme={null}
# Pandas and DataStore - identical
df.nlargest(10, 'salary')
df.nsmallest(5, 'age')
```
***
GroupBy and Aggregation
Simple GroupBy
```python theme={null}
# Pandas and DataStore - identical
df.groupby('city')['salary'].mean()
df.groupby('city')['salary'].sum()
df.groupby('city').size() # count
```
Multiple Aggregations
```python theme={null}
# Pandas and DataStore - identical
df.groupby('city')['salary'].agg(['sum', 'mean', 'count'])
df.groupby('city').agg({
'salary': ['sum', 'mean'],
'age': ['min', 'max']
})
```
Named Aggregations
```python theme={null}
# Pandas and DataStore - identical
df.groupby('city').agg(
total_salary=('salary', 'sum'),
avg_salary=('salary', 'mean'),
employee_count=('id', 'count')
)
```
Multiple GroupBy Keys
```python theme={null}
# Pandas and DataStore - identical
df.groupby(['city', 'department'])['salary'].mean()
```
***
Joining Data
Inner Join
```python theme={null}
# Pandas
pd.merge(df1, df2, on='id')
# DataStore - same API
pd.merge(df1, df2, on='id')
# DataStore also supports
df1.join(df2, on='id')
```
Left Join
```python theme={null}
# Pandas and DataStore - identical
pd.merge(df1, df2, on='id', how='left')
```
Join on Different Columns
```python theme={null}
# Pandas and DataStore - identical
pd.merge(df1, df2, left_on='emp_id', right_on='id')
```
Concat
```python theme={null}
# Pandas and DataStore - identical
pd.concat([df1, df2, df3])
pd.concat([df1, df2], axis=1)
```
***
String Operations
Case Conversion
```python theme={null}
# Pandas and DataStore - identical
df['name'].str.upper()
df['name'].str.lower()
df['name'].str.title()
```
Substring
```python theme={null}
# Pandas and DataStore - identical
df['name'].str[:3] # First 3 characters
df['name'].str.slice(0, 3)
```
Search
```python theme={null}
# Pandas and DataStore - identical
df['name'].str.contains('John')
df['name'].str.startswith('A')
df['name'].str.endswith('son')
```
Replace
```python theme={null}
# Pandas and DataStore - identical
df['text'].str.replace('old', 'new')
df['text'].str.replace(r'\d+', '', regex=True) # Remove digits
```
Split
```python theme={null}
# Pandas and DataStore - identical
df['name'].str.split(' ')
df['name'].str.split(' ', expand=True)
```
Length
```python theme={null}
# Pandas and DataStore - identical
df['name'].str.len()
```
***
DateTime Operations
```python theme={null}
# Pandas and DataStore - identical
df['date'].dt.year
df['date'].dt.month
df['date'].dt.day
df['date'].dt.dayofweek
df['date'].dt.hour
```
```python theme={null}
# Pandas and DataStore - identical
df['date'].dt.strftime('%Y-%m-%d')
```
***
Missing Data
Check Missing
```python theme={null}
# Pandas and DataStore - identical
df['col'].isna()
df['col'].notna()
df.isna().sum()
```
Drop Missing
```python theme={null}
# Pandas and DataStore - identical
df.dropna()
df.dropna(subset=['col1', 'col2'])
```
Fill Missing
```python theme={null}
# Pandas and DataStore - identical
df.fillna(0)
df.fillna({'col1': 0, 'col2': 'Unknown'})
df.fillna(method='ffill')
```
***
Creating New Columns
Simple Assignment
```python theme={null}
# Pandas and DataStore - identical
df['total'] = df['price'] * df['quantity']
df['age_group'] = df['age'] // 10 * 10
```
Using assign()
```python theme={null}
# Pandas and DataStore - identical
df = df.assign(
total=df['price'] * df['quantity'],
is_adult=df['age'] >= 18
)
```
Conditional (where/mask)
```python theme={null}
# Pandas and DataStore - identical
df['status'] = df['age'].where(df['age'] >= 18, 'minor')
```
apply() for Custom Logic
```python theme={null}
# Works, but triggers pandas execution
df['category'] = df['amount'].apply(lambda x: 'high' if x > 1000 else 'low')
# DataStore alternative (stays lazy)
df['category'] = (
df.when(df['amount'] > 1000, 'high')
.otherwise('low')
)
```
***
Reshaping
Pivot Table
```python theme={null}
# Pandas and DataStore - identical
df.pivot_table(
values='amount',
index='region',
columns='product',
aggfunc='sum'
)
```
Melt (Unpivot)
```python theme={null}
# Pandas and DataStore - identical
df.melt(
id_vars=['name'],
value_vars=['score1', 'score2', 'score3'],
var_name='test',
value_name='score'
)
```
Explode
```python theme={null}
# Pandas and DataStore - identical
df.explode('tags') # Expand array column
```
***
Window Functions
Rolling
```python theme={null}
# Pandas and DataStore - identical
df['rolling_avg'] = df['price'].rolling(window=7).mean()
df['rolling_sum'] = df['amount'].rolling(window=30).sum()
```
Expanding
```python theme={null}
# Pandas and DataStore - identical
df['cumsum'] = df['amount'].expanding().sum()
df['cummax'] = df['amount'].expanding().max()
```
Shift
```python theme={null}
# Pandas and DataStore - identical
df['prev_value'] = df['value'].shift(1) # Lag
df['next_value'] = df['value'].shift(-1) # Lead
```
Diff
```python theme={null}
# Pandas and DataStore - identical
df['change'] = df['value'].diff()
df['pct_change'] = df['value'].pct_change()
```
***
Output
To CSV
```python theme={null}
# Pandas and DataStore - identical
df.to_csv("output.csv", index=False)
```
To Parquet
```python theme={null}
# Pandas and DataStore - identical
df.to_parquet("output.parquet")
```
To pandas DataFrame
```python theme={null}
# DataStore specific
pandas_df = ds.to_df()
pandas_df = ds.to_pandas()
```
***
View SQL
```python theme={null}
# DataStore only
print(ds.to_sql())
```
Explain Plan
```python theme={null}
# DataStore only
ds.explain()
```
ClickHouse Functions
```python theme={null}
# DataStore only - extra accessors
df['domain'] = df['url'].url.domain()
df['json_value'] = df['data'].json.get_string('key')
df['ip_valid'] = df['ip'].ip.is_ipv4_string()
```
Universal URI
```python theme={null}
# DataStore only - read from anywhere
ds = DataStore.uri("s3://bucket/data.parquet")
ds = DataStore.uri("mysql://user:pass@host/db/table")
```