chdb-datastore

chdb DataStore — It's Just Faster Pandas

The Key Insight

# Change this:
import pandas as pd
# To this:
import chdb.datastore as pd
# Everything else stays the same.

DataStore is a lazy, ClickHouse-backed pandas replacement. Your existing pandas code works unchanged — but operations compile to optimized SQL and execute only when results are needed (e.g., print(), len(), iteration).

pip install chdb

Decision Tree: Pick the Right Approach

1. "I have a file/database and want to analyze it with pandas"
   → DataStore.from_file() / from_mysql() / from_s3() etc.
   → See references/connectors.md

2. "I need to join data from different sources"
   → Create DataStores from each source, use .join()
   → See examples/examples.md #3-5

3. "My pandas code is too slow"
   → import chdb.datastore as pd — change one line, keep the rest

4. "I need raw SQL queries"
   → Use the chdb-sql skill instead

Connect to Any Data Source — One Pattern

from datastore import DataStore

# Local file (auto-detects .parquet, .csv, .json, .arrow, .orc, .avro, .tsv, .xml)
ds = DataStore.from_file("sales.parquet")

# Database
ds = DataStore.from_mysql(host="db:3306", database="shop", table="orders", user="root", password="pass")

# Cloud storage
ds = DataStore.from_s3("s3://bucket/data.parquet", nosign=True)

# URI shorthand — auto-detects source type
ds = DataStore.uri("mysql://root:pass@db:3306/shop/orders")

All 16+ sources and URI schemes → connectors.md

After Connecting — Full Pandas API

result = ds[ds["age"] > 25]                                          # filter
result = ds[["name", "city"]]                                        # select columns
result = ds.sort_values("revenue", ascending=False)                  # sort
result = ds.groupby("dept")["salary"].mean()                         # groupby
result = ds.assign(margin=lambda x: x["profit"] / x["revenue"])     # computed column
ds["name"].str.upper()                                               # string accessor
ds["date"].dt.year                                                   # datetime accessor
result = ds1.join(ds2, on="id")                                      # join
result = ds.head(10)                                                 # preview
print(ds.to_sql())                                                   # see generated SQL